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Regional Need Analysis of The Viatical Settlements Market

保险贴现市场的区域性需求分析GAO Xiaoting, LIU Chenzhe,

ZHAO Zan高筱婷 刘晨哲 赵赞

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Introduction

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Our purpose

• The purpose of our paper is to analyze the regional demand of the viatical (as well as life) settlements market in China.

• Since the development of the primary market of life insurance has not reached the identical level among different areas in China, the demand of secondary market can hardly be the same. Thus, in order to find out the best experimental unit, it is necessary to know the difference among different cities and provinces.

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Methodology• The paper first outlines the research status of

viatical and life settlements market in China.• Then we use cluster analysis to classify China’s

cities and provinces into four groups according to their insurance penetration and density.

• We select two typical places from each group (except the second group), and then establish the regression equation for each place using the data of the aged population, medical expenses per capita and the sum of surrender value and policy loan.

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Methodology

• Since we have the regression equations for each place, then we use these equations to predict the demand of the viatical and life settlements market in the next 5 years.

• With the prediction, we can decide which city or province is the most suitable experimental unit.

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Findings• After the cluster analysis considering the

insurance penetration and density from 2008 to 2012, we classify cities and provinces into four groups:

分类 地区 保险密度均值（元 /人）

保险深度均值 (%)

1 类 北京、上海 3197.162 3.785

2 类 深圳 1736.256 1.46%

3 类 天津、青岛、宁波、江苏、浙江、广东、厦门、大连

983.291 1.7615

4 类 贵州、广西等 25 个省市自治区

428.392 1.89

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Findings

• We select Tianjin and Jiangsu as the representatives of the third group, and Guizhou and Guangxi the fourth group.

• Due to the lack of data, the regression equation of Shenzhen (the second group) is not available.

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Findings

• We use the OLS method to make the equation. The model of our equation is

• denotes the sum of surrender value and policy loan

• denotes the population beyond 65• denotes medical expenses in general hospital

of inpatient per capital

1 20 1 2( ) ( ) ( )t t t tf Y f X f X U

tY

1tX2tX

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Equation for Beijing

• Using the data from 2002 to 2011,we have time series , and . The result of their ADF unit root test is :

• From the table, we can conclude that three series are all integrated of order one.

lnY 1ln X 2ln X

ordervariable

level Integrated of order one

Two integrated of order

t p t p t p

-2.1116 0.2442 -2.1168 0.0401 -3.0388 0.0095

-3.7842 0.0737 -4.7985 0.0078 -5.6878 0.0061

-3.5440 0.0969 -4.4253 0.0121 -4.4502 0.0147

lnY1ln X2ln X

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Equation for Beijing

• Then we will use EG two-step procedure to testify whether they have cointegration.

• The estimated equation for Beijing is

• (0.1739) (2.0495) (-0.7032)

• Adjusted =0.660, DW=1.388• The residual series is level. Therefore these

three series are co integrated and the equation is valid.

1 2ln 1.0258 ln 4.3785 ln 47.6555t t tY X X t

2Rte

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Equation for Beijing

• The adjusted is 0.660,which indicates that the fitting effect is acceptable.

• We cannot tell whether there is self-correlation problem from the value of DW. Thus we use Breusch-Godfrey Serial Correlation LM Test. The result is

• Since , we reach the conclusion that there is no self-correlation problem.

2R

2 0.5542nR 2 (10) 0.758

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Equation for Beijing

• The equation

Means that when remain unchanged, if increase by 1%, will increase by 1.03%; while remain unchanged, when increase by 1%, will increase by 4.38%.

1 2ln 1.0258 ln 4.3785 ln 47.6555t t tY X X

2X 1XY 1X

2X Y

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Equation for Shanghai

• Using the same method, we have the equation for Shanghai:

• t (2.0872) (6.4687) (-3.343)• Adjusted =0.8158 DW=1.512• Since adjusted is 0.8158, the fitting effect is

fair. And Breusch-Godfrey Serial Correlation LM Test indicates that there is no self-correlation problem.

1 2ln 1.8444 ln 3.5386 ln 51.0379t t tY X X

2R2R

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Equation for Shanghai

• The equation shows that when the medical expenses per capita remain unchanged, if the population of people beyond 65 increase by 1%,

will increase by 1.85%; while remain unchanged, if increase by 1%, will increase by 3.54%.

2X

1XY 1X

2X Y

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Equation for Jiangsu

• Using the same method, we have the equation for Jiangsu:

• t (1.4796) (5.4804) (-2.4334)• Adjusted =0.9124 DW=1.459• Since adjusted is0.9124,the fitting effect is fair

enough. And Breusch-Godfrey Serial Correlation LM Test indicates that there is no self-correlation problem.

1 2ln 2.6745 ln 3.1392 ln 61.6839t t tY X X

2R2R

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Equation for Jiangsu

• The equation indicates that when remain unchanged, if increase by 1%, then will increase by 2.67% ; while remain unchanged, if

increase by 1%, then Y will increase by 3.14%.

2X1X Y

1X2X

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Equation for Tianjin

• Using the same method, we have the equation for Tianjin:

• t (-0.09589) (2.6755) (-2.4848)• Adjusted =0.6982 DW=1.756• Since adjusted is0.9124,the fitting effect is fair

enough. And Breusch-Godfrey Serial Correlation LM Test indicates that there is no self-correlation problem.

1 20.0083 0.0955 7978.1945t t tY X X

2R2R

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Equation for Jiangsu

• The equation indicates that when remain unchanged, when increase by 1, will increase by0.0083; while remain unchanged, when increase by1, will decrease by0.0955.

1X Y1X

Y2X

2X

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Results for Guizhou and Guangxi

• There is no co integration in time series , and as well as , and until three

order differential. But that will impair equations economic meaning.Therefore, we assume that there is no link between the demand for life settlement market, population of the aged and medical expenses. The reason may lie in the fact that the primary market of life insurance is not well developed to establish the secondary market.

tY1tX

2tX ln tY

1ln tX2ln tX

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Prediction

• Since we have the equations, we can use them for prediction for the next five years. The result is (million) :

Beijing Shanghai Tianjin Jiangsu

2012 22318.9592 11346.2258 3178.4184 22158.76

2013 29559.6915 14726.1105 3413.4862 29745.39

2014 39212.5942 19161.6598 3610.0432 40015.05

2015 52101.9148 24997.4692 3745.7229 53946.59

2016 69340.9696 32695.37435 3787.601663 72886.9

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Analysis

• The table indicates that the demand for viatical(life) settlement market will be growing in increasing speed. Thus establishing viatical(life) settlement market in China is necessary.

• From the table, we can see that in 2016 the demand of Jiangsu has the largest scale. The second is Beijing. Then Shanghai.

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Analysis

• Since Jiangsu has the largest demand, it is reasonable that we choose Jiangsu as the best experimental unit.

• However, considering other factors, Beijing may be the ideal choice.

• In 2016, the demand of Beijing is 69.3 billion, while the demand of Jiangsu is 72.8 billion. The demand of Jiangsu is 5.05% larger than Beijing’s. So there is no big difference between these two places.

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Analysis

• Despite that two places almost have the same scale of demand, Beijing is municipality directly under the Central Government with the population of 20.19 million in 2011, while Jiangsu is a province with the population of 78.66 million. Thus the cost to establish viatical settlement market will be much lower than than in Jiangsu.

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Analysis

• Furthermore, Beijing has a much more mature insurance market with abundant insurance companies, agents and brokers. Till 2011, the total number of head quarters of insurance companies in Beijing is 95. And the total numbers of agents and brokers in Beijing is 357. In contrast, there are only 3 head quarters of insurance companies and 148 agents and brokers in Jiangsu.

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Analysis

• Therefore, viatical or life settlement product can be sold more easily in Beijing rather than Jiangsu.

• So we reach the conclusion that Beijing is the best experimental unit.