1. The Impact of the Social Insurance on Long-term Care Insurance Demand
Dongmei Chen Jenny Shi
Insurance Dept., School of Economics, Fudan Univ.

2. Motivation:
According to the "Status of urban and rural areas disabled elderly research," in 2015, the number of disabled elderly will increase to 40 million nationwide.
For the disabled elderly, our traditional nursing are borne by family, but the decreasing of the family size, deepening of the "low birth rate", making the continuation of this approach difficult, leading to future direction of relying on long-term care nursing facility.
But our long-term care insurance is currently showing a contradiction of huge potential demand and lack of effective demand, and the status quo is the result of many factors likely including the social insurance.

3. Motivation
Majority of the foreign study shows that social insurance system or social security system have a huge crowding-out effect, ie substitution on long-term care insurance.
 In the domestic market, June 15, 2006, China PICC Health launched the "Worry-Free Long-Term Care Individual Health Insurance", which is China's first national long-term care insurance product.

4. Methodology Variables Selection
Variable is the long-term care insurance demand, but because of the late carry out of the insurance in China, there is no statistical data of long term care insurance premiums. This paper selects health care spending as the dependent variable.

5. Variables Selection The core explanatory variable is social insurance
Variables Selection The core explanatory variable is social insurance. We selected the urban basic pension insurance fund spending as the measurement of pension insurance, basic medical insurance for urban workers fund spending to measure the health insurance, and the sum of the two fund spending to measure the social insurance. The proportion of these two types of insurance fund is the largest in the five pillars of social insurance, and therefore the sum of the two can well reflect the current overall situation of China's social insurance.

6. Income, savings, education and quality of life
Variables Selection
In addition,including
Income, savings, education and quality of life
Degree of aging and family size
Inflation and unemployment

7. Data sources and sample descriptive analysis Select panel data of China's 31 provinces. Some years of Tibet data are missing, so Tibet had been removed. Use a balanced panel data of 30 provinces of 180 sample observations. All operations was completed in STATA12.0. The data are from every year’s "China Health Statistics Yearbook".

8. Table 1 Descriptive Statistical Analysis of the Variables
Unit
Symbol
Mean
Standard Deviation
Min
Max
Health Care Spending
Yuan
demand
790.74
237.68
329.80
Pension Insurance Fund Spending
100M Yuan
pen
280.05
209.32
20.30
993.50
Health Insurance Fund Spending
med
81.91
75.24
5.10
374.46
Social Insurance Fund Spending
soc
361.96
278.73
25.40
Per Capita disposable Income
income
5257.5
8871.3
Savings Balance of Residents
save
8057.2
6775.9
406.3
Education %
edu
8.39
5.32
2.59
32.62
Quality of Life
engel
36.91
3.51
30.10
47.10
Aging Degree
old
9.11
1.71
5.50
14.40
Family Size
Number of People
home
3.2
0.3
2.3
3.9
Inflation
Last Year = 100
cpi
112.3
7.5
100.8
136.9
Unemployment Rate
unem
3.68
0.60
1.37

9. Table 2 Descriptive Statistical Analysis of the Natural Logarithm of Variables
Symbol
Mean
Standard Deviation
Min
Max
Lndemand
Lnpen
Lnmed
Lnsoc
lnincome
lnsave
lnedu
lnengel
lnold
Lnhome
Lncpi
Lnunem

10. Model specification and empirical analysis
Use social pension insurance, social health insurance and social insurance as the core explanatory variables to establish three equations, other explanatory variables are control variables.

11. Model specification and empirical analysis
All the independent variables in the above three equations were conducted related test, the independent variables with high correlation were removed to prevent biased results due to multicollinearity. We used the Spearman correlation coefficient to test the correlation between the variables, the results are shown below.
Table 3 Equation（1）-（3）Variable’s Spearman correlation coefficient
lnpen
Lnmed
lnsoc
lnincome
lnsave
lnhome
lnedu
lnunem
lncpi
lnengel
lnold
Lnpen
1.00
0.94
Lnsoc
0.97
0.65
0.76
0.68
Lnsave
0.93
0.92
0.66
Lnhome
-0.66
-0.63
-0.56
-0.55
Lnedu
0.42
0.48
0.44
0.62
0.31
-0.57
Lnunem
-0.19
-0.25
-0.20
-0.39
-0.31
-0.02
Lncpi
0.19
0.22
0.20
0.11
0.04
0.27
-0.12
Lnengel
-0.29
-0.24
-0.17
-0.28
0.38
-0.33
0.18
0.15
Lnold
0.46
0.39
0.45
0.40
-0.61
0.12
0.13
-0.21

12. Empirical results and description
The core explanatory variables of the three equations and personal income, savings, family size and degree of aging are highly correlated, so the four variables including personal income, savings, family size and degree of aging are deleted to form the final equation (4) - (6) below.
Table 4 Preliminary Regression Results of Equation（4）
lnpen
lnedu
lnengel
lncpi
lnunem
_cons
Fixed Effects
Coef.
0.2945
0.9805
0.7639 T
4.74
-0.38
-0.36
3.1
-0.23
0.7
Random Effects
0.1515
0.0803
1.4488
0.5296 z
4.57
2.07
-2.29
7.37
-1.31
0.65
Hausman Test
chi2(5)=47.60，Prob>chi2=0.0000
Table 5 Preliminary Regression Results of Equation（5）
lnmed
lnedu
lnengel
lncpi
lnunem
_cons
Fixed Effects
Coef.
0.1793
1.3506
0.0743 T
4.07
-0.18
-0.65
4.85
-0.21
0.07
Random Effects
0.1295
0.0718
1.4526
0.8947 z
4.48
1.81
-2.47
7.36
-0.97
1.06
Hausman Test
chi2(5)=36.97，Prob>chi2=0.0000

13. Table 6 Preliminary Regression Results of Equation（6）
lnsoc
lnedu
lnengel
lncpi
lnunem
_cons
Fixed Effects
Coef.
0.2666
1.0635
0.4882 t
4.45
-0.39
-0.41
3.36
-0.16
0.45
Random Effects
0.1478
0.0774
1.4444
0.5551 z
4.51
1.98
-2.33
7.28
-1.19
0.68
Hausman Test
chi2(5)=42.71，Prob>chi2=0.0000
All three equations reject the null hypothesis of random effects, and thus we chose the fixed effects model. We further tested the heteroscedasticity and serial correlation in the fixed effects model (null hypothesis were no heteroscedasticity and no serial correlation), the results are shown in Table 7.
Table 7 Heteroscedasticity and Serial Correlation Test in Fixed Effects
Serial Correlation Test
Heteroscedasticity Test
Equation 4
F（1，29）= 4.666
Prob>F =
chi2（30）=
Prob>chi2 =
Equation 5
F（1，29）= 4.154
Prob>F =
chi2（30）=
Equation 6
F（1，29）= 4.652
Prob>F =
chi2（30）=

14. Model specification and empirical analysis
Test results showed that all three equations do not reject the null hypothesis that there is no serial correlation and heteroscedasticity, so we used the generalized linear regression equation estimates. The final result are as formula (7) - (9) below.

15. The regression results in the three equations are basically the same, social pension insurance expenses, social health insurance expenses and the sum of the two have basically the same impact on the dependent variable. In addition, the excluded individual income, savings, family size and degree of aging individuals were examined, and the individual impact of these factors on the dependent variable were as followed. Where the income, savings and degree of aging have a positive impact on long-term care insurance, family structure has a negative impact. P value shows the overall fit of each of the five equations are good.

16. Model specification and empirical analysis
The results show that each additional one percentage increase in the social insurance fund expenditure will result in 0.08 percentage increase in long-term care insurance demand.
So the major effect of social insurance for long-term insurance demand is a catalytic role.

17. Model specification and empirical analysis
Other factors:
a) Income
Household disposable income and long-term care insurance demand had a significant positive correlation. In recent years, the high cost of nursing makes long-term care insurance prices rise, only the population with stronger purchasing power have the ability to consume, so income is a very important factor.
b) Savings
Savings and long-term care insurance demand had a significant positive correlation.

18. c) Education background A significant positive correlation between level of education and long-term care insurance demand had been examined d) Life of quality Empirical results show that the Engel coefficient and long-term care insurance demand had a significant negative correlation. We can conclude that the quality of life and long-term care insurance demand had a significant positive correlation.

19. e) Aging degree and family size The degree of aging and long-term care insurance demand of our country had a significant positive correlation, and between family size and demand of long-term care insurance there was a significantly negative correlation. f) Inflation and unemployment rate There was a significant positive correlation between inflation and long-term care insurance demand and a significant negative correlation between the unemployment rate and the long-term care insurance demand.

20. Conclusions
The results showed that the impact of social insurance on long term care insurance demand are overall a promoting role, in addition, personal income, savings, education and quality of life are positively correlated, demographic aging, smaller family size and economic growth have contributed to increase the long-term care insurance demand.

21. Thanks！