1. 2014 Annual Conference APIRA
Cognition and Private Medical Insurance Purchase among Older Adults in China
Chu-Shiu Li
National Kaohsiung First University of Science and Technology, Taiwan
Saruultuya Tsendsuren
Asia University, Taiwan
Yue-hua Zhang
Zhejiang University, China
Chwen-Chi Liu
Feng Chia University Taichung, Taiwan
 2014 Annual Conference APIRA
July 27-30, 2014, Moscow

2. Outlines Introduction Purpose Method Results Conclusion
Limitations of the study

3. Introduction_(1/2)
Much literature regarding the demand for medical (or health) insurance focuses on the effects of demographic factors such as age, gender, education, marital status, income, and family size (King and Mossialos, 2002; Kirigia et al., 2005; Strauss et al., 2012; Vellakkal, 2013)
Few studies analyze the factor of cognitive ability affecting the decision-making in purchasing medical (or health) insurance .
Elderly with higher cognitive ability are more likely to purchase Medigap (Fang, Keane, and Silverman, 2008), and consumers with higher cognitive ability and lower risk tolerance are more likely to buy health insurance ( Chatterjee and Nielsen, 2010) in the US.
One study examines the determinants of voluntary private health insurance in the 11 European countries among the over 50s. They find that education level and cognitive ability have a strong positive effect on possession of private health insurance in most countries (Paccagnella, Rebba, and Weber, 2012)

4. Introduction_(2/2)
In addition, cognitive ability may act as an important source of advantageous selection in the private health insurance in Australia (Buchmueller, Fiebig, Jones, and Savage , 2013).
In China, types of insurance programs available to people and the benefits provided by the seemingly uniform public and worker programs vary (Henderson et al. (1995).
Higher income, presence of chronic disease, inpatient treatment, higher coverage rates, and residence in urban areas were significantly associated with higher gross medical cost in China ( Fang, Shia, and Ma, 2012).
However, none of these studies examine whether the factor of cognitive ability affects the decision to purchase individual private medical insurance (IPMI) in China.

5. Purpose
The objective of this study is to explore the factors affecting the decision to purchase Individual Private Medical Insurance (IPMI) in China, specifically focusing on the factor of cognitive ability.
This study uses a national survey data, the China Health and Retirement Longitudinal Study (CHARLS) in

6. Methods_(1/4)
Total 17,392 elderly subjects drawn from a national data set “CHARLS”.
Dependent variable : whether participants buy IPMI in
Control variables: demographic, life styles, diseases, other insurance types in China, and functional status.
The major independent variable: cognitive ability.

7. Methods_(2/4) cognitive ability indicators (Lei et al., 2012)
mental intactness (0-11)
-- naming today’s date (month, day, year, and season); the day of the week; ability to redraw a picture shown to respondents; serial 7 subtraction from 100 (up to five times); and whether the respondent needed any explanation or used an aid such as paper and pencil
episodic memory (0-10)
-- a respondent’s ability to immediately repeat in any order ten Chinese nouns that were just read to them (immediate word recall) and to recall the same list of words four minutes later (delayed recall).
mental intactness + episodic memory (0-21)

8. Total sample in CHARLS, 2011-12
Methods_(3/4)
GrpT (N=17392)
Total sample in CHARLS,
Grp NMI (n=17074)
without IPMI
GrpNI (n=1188)
Without any types of insurance
GrpOI (n=15886)
With other types of medical health insurance
Grp MI (n=318)
with the IPMI

9. Methods_(4/4) Two Multiple Probit Regression models: Compare groups:
(1) with IPMI vs. without IPMI (Table 2)
(2) with IPMI vs. without any medical insurance (Table 3)

10. Results_Table 1: Descriptive statistics of total samples
N=17392
GrpMI
GrpOI
GrpNI
p-value&
n=318 (%)
n=15886 (%)
n=1188 (%)
Demographic variables
Gender
Female
165 (51.9)
8232 (51.8)
652 (54.9)
0.13
Male
153 (48.1)
7654 (48.2)
536 (45.1)
Age
<50
144 (45.3)
3382 (21.3)
281 (23.7)
<0.001***
50-59
123 (38.7)
4825 (30.4)
354 (29.8)
>60
51 (16.)
7679 (48.3)
553 (46.5)
Education
Primary & under
216 (67.9)
13926 (87.7)
1050 (88.4)
High School
69 (21.7)
1583 (10.0)
127 (10.7)
College & above
33 (10.4)
377 (2.4)
11 (0.9)
Marital status
Yes
318 (100.0)
15765 (99.2)
1153 (97.1)
<0.001*** 
Spouse
272 (85.5)
12862 (81.0)
842 (70.9)
Number of Children, mean (SD)
0.1 (0.4)
0.3 (1.0)
0.5 (1.3)
Log household PCE, mean (SD)
9.7 (1.0)
8.9 (1.2)
8.6 (1.3)

11. Results_Table 1_cont: Descriptive statistics of total samples
GrpMI
GrpOI
GrpNI
p-value
n=318 (%)
n=15886 (%)
n=1188 (%)
Lifestyle variables
Smoke
Yes
118 (37.1)
6306 (39.7)
455 (38.3)
0.43 
Drink
131 (41.2)
5246 (33.0)
348 (29.3)
<0.001*** 
Exercise (walking)
102 (32.1)
5023 (31.6)
333 (28.0)
 0.035**
Exercise (moderate)
71 (22.3)
3597 (22.6)
193 (16.2)
Exercise (vigorous)
37 (11.6)
2204 (13.9)
112 (9.4)
 Health Exam
65 (20.4)
3121 (19.6)
164 (13.8)

12. Results_Table 1_cont: Descriptive statistics of total samples
GrpMI
GrpOI
GrpNI
p-value&
n=318 (%)
n=15886 (%)
n=1188 (%)
Disease variables
Hypertension
Yes
65 (20.4)
3924 (24.7)
249 (21.0)
0.004*** 
Diabetes
28 (8.8)
899 (5.7)
56 (4.7)
0.019** 
Cardiovascular
38 (11.9)
1913 (12.0)
123 (10.4)
0.22 
Stroke
4 (1.3)
374 (2.4)
30 (2.5)
0.40 
Cancer
6 (1.9)
164 (1.0)
7 (0.6)
0.10 
Memory
3 (0.9)
253 (1.6)
19 (1.6)
0.65 
Insurance type
Urban Employee
56 (17.6)
1843 (11.6)
0 (0.0)
<0.001*** 
 Urban Resident
21 (6.6)
770 (4.8)
 New Cooperative
176 (55.3)
12666 (79.7)
 Urban Rural
216 (1.4)
 Government
9 (2.8)
358 (2.3)
 Private R union
2 (0.6)
117 (0.7)
0.012** 

13. Results_Table 1_cont: Descriptive statistics of total samples
GrpMI
GrpOI
GrpNI
p-value&
n=318 (%)
n=15886 (%)
n=1188 (%)
Cognitive ability variable
Mental intactness (0-11), mean (SD)
7.7 (3.6)
6.5 (3.6)
5.9 (3.8)
Episodic memory (0-10), mean (SD)
4.1 (2.4)
3.3 (2.2)
2.9 (2.2)
Cognitive 0-21
(Mental + Episodic), mean (SD)
11.8 (5.3)
9.9 (5.2)
8.8 (5.3)
Functional disability
ADL, mean (SD)
1.3 (1.6)
2.0 (2.2)
2.0 (2.4)
IADL, mean (SD)
0.1 (0.5)
0.3 (0.9)
0.4 (1.0)

14. Multiple Probit Regression (Table 2) dependent variable: with IPMI (=1) vs. without IPMI (=0)
Model 2-1
Model 2-2
Model 2-3
Model 2-4
Mental intactness
(0-11)
0.03 *
Episodic memory
(0-10)
Mental + Episodic
(0-21)
0.02 **
Functional disability
With ADL
0.01
With IADL
0.04
purchase IMPI
Mental intactness
(＋)

15. Multiple Probit Regression (Table 3) dependent variable: with IPMI (=1) vs. without any insurance (=0)
Variable
Model 3-1
Model 3-2
Model 3-3
Model 3-4
Mental intactness
(0-11)
0.02 **
Episodic memory
(0-10)
0.04
***
Mental + Episodic
(0-21)
Functional disability
With ADL
0.05
With IADL
-0.06
purchase IMPI
Mental intactness
Episodic memory
(＋)

16. Test for Endogeneity * OLS regression y1=Cogtotal+X1
* Durbin-Wu-Hausman test of Endogeneity
Cog total= X1
y1=Cogtotal+X1+Cogtotal_res
We observe that higher p-value, so it indicates that OLS is consistent (No Endogeneity)

17. Conclusion
Factors impacting on the decision of purchasing Individual Private Medical Insurance (IPMI ) are higher education level, healthy life style (including walking for exercise), and higher household expenditures.
Compared with the group without IPMI ( n=17,074), individuals with better cognitive status, especially “mental intactness”, are more likely to own IPMI by adjusting all the control variables.
Compared with the group without any insurance ( n=1,188), individuals with better cognitive status, for both “mental intactness” and “episodic memory”, are more likely to own IPMI by adjusting all the control variables.
We find that people with higher cognitive ability are more likely to buy IPMI. These findings are similar to those of previous studies in Western countries.

18. Limitations of the study
First, there should be more detail about the reasons for owning or not owning IPMI. However, it is hard to observe what kind of motivations might be encouraged and who really makes the decision to purchase IPMI from the variety of medical insurances in China.
Second, in this research we use a huge data set, although the main variable of IPMI has a very small number of observations.

19. Thank you very much!!