Page 97 - IJPS-11-4

International Journal of
Population Studies Health disparities and older adults well-being in China

For cognitive health, we used the MMSE score as the Table 1. Summary statistics
dependent variable in both models 5 and 6. Since model 5
does not consider the impact of physical and mental health Variables Mean SD
on cognitive health, we incorporated IADLs and CESD ADLs≥1 0.883 0.321
scores as explanatory variables in model 6. The difference IADLs≥1 0.759 0.428
between models 5 and 6 is the inclusion of these two CESD≥10 0.367 0.482
variables to represent the physical and mental health of the MMSE≤23 0.411 0.492
older ages in model 6. Finally, in model 7, we constructed Age 67.863 8.104
a dummy variable for the older ages who are physically, Male 0.487 0.499
mentally, and cognitively healthy. Using logit regression, Education
we verify if the socioeconomic variables have statistically
significant positive effects on the older people’s physical, Did not finish elementary school 0.216 0.412
mental, and cognitive health. Elementary school graduates 0.204 0.403
Junior high school graduates 0.188 0.391
In addition, Table 1 presents variables that serve as proxies
for socioeconomic status among the older adults, including Senior high school graduates 0.107 0.309
education level, residential location, household registration University graduates and above 0.015 0.123
type, and Communist Party of China membership. We Currently married 0.816 0.388
created five dummy variables to represent various education One-person household 0.201 0.401
levels. For residential location, we constructed an interaction Household location
of two dummy variables: one indicating urban residence Urban residence with rural household registration 0.099 0.299
(assigned a value of 1) and the other representing a rural Urban residence with urban household registration 0.172 0.378
household registration (assigned a value of 1). This creation Rural residence with rural household registration 0.676 0.468
of dummies resulted in four combinations reflecting
different household locations: urban residence with an urban Communist Party of China member 0.108 0.310
household registration (1,0), urban residence with a rural Having savings 0.332 0.471
household registration (1,1), rural residence with an urban Living in the Yangtze River Delta/Pearl River Delta 0.095 0.293
household registration (0,0), and rural residence with a districts
rural household registration (0,1). In addition, we employed Abbreviations: ADLs: Activities of daily living; CESD: Center for
Communist Party of China membership as a proxy for the Epidemiological Studies Depression Scale; IADLs: Instrumental
activities of daily living; MMSE: Mini-mental state examination;
socioeconomic status of individuals who were employed in SD: Standard deviation.
government-related institutions before retirement.
To control for potential confounding variables, we In summary, our study employs a robust array of
introduced control variables such as age, gender, marital variables to comprehensively explore the multifaceted
status, and household status into our models to mitigate relationship between health and socioeconomic status
estimation bias. Furthermore, we incorporated two among the older adults in China. This approach enables us
dummy variables into our models to account for the to gain valuable insights into the intricate dynamics at play
impact of location on interviewed households (those in this critical demographic segment.
in well-developed eastern districts) and whether the
households have savings. Both of these factors are known 3. Key findings
to influence the health of the older adults. Given China’s We employed the fourth wave of CHARLS data to conduct
vast territory, uneven economic development, and diverse a regression analysis of the health of the older adults from
population structures, we introduced dummy variables for three different perspectives: physical health (Models 1
specific regions, including Shanghai City, Jiangsu Province, and 2), mental health (models 3 and 4), and cognitive health
Zhejiang Province, and Guangdong Province, primarily (models 5 and 6). After analyzing these three different health
concentrated in the Yangtze River Delta and the Pearl
River Delta. These regions exhibit higher economic output perspectives of the older-aged, we attempted to combine the
older people who are physically, mentally, and cognitively
and greater demographic potential compared to others.
Furthermore, in the context of China’s imperfect social healthy into one index as an explanatory variable in model
insurance system, we posit that the presence or absence 7. The main results are summarized as follows:
of household savings can exert a positive influence on the First, in the evaluation of physical health (Table 2),
health of the older adults. we used ADLs and IADLs as explanatory variables in

Volume 11 Issue 4 (2025) 91 https://doi.org/10.36922/ijps.2035