wealth, education and demand for medical care ___ evidence from rural china feng jin qin bei yu...
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Wealth, Education and Demand for Medical Care
___Evidence from Rural China
Feng Jin
Qin Bei
Yu Yangyang
Background
• In many developing areas, health is much more important, since a person with poor health is more likely to be burdened with the tremendous medical expenditure
• Due to the collapse of health care system and the increasing of medical price, the sick people suffer heavy financial burden
• In rural China, disease has been cited as one of the top two reasons accounting for impoverishment
Wealth education and health(raw data)
22.
53
3.5
Hea
lth (S
RH
)
0 20 40 60 80 100age
Fitted values Fitted values
22.
53
3.5
Hea
lth (S
RH
)
0 20 40 60 80 100age
Fitted values Fitted values
Age-wealth-health Graph A Age-education-health Graph B
Research Objective
• To check the expected medical burden of people with certain wealth and education stocks
Who has the higher probability to be sick
Who has more expenditure after sick
Who has more heavy medical burden
• To test the hypothesis of use-related deprecation rate on health
Literature
• Grossman (1972): health capital and demand for health
• Muurinen (1982): three separated stocks (education, wealth and health) are substitutable
• Empirical tests :Muurinen and Le Grand (1985) , Van Doorslaer (1987) , Case and Deaton (2004)
Demand for medical expenditure
))(),(),(),(),(),(()( tXtEtagetptwtHftM
( ) ( ( ), ( ), ( ), ( ), ( ))H t g w t p t age t E t X t
( ) ( ( ), ( ), ( ), ( ), ( ))M t m w t p t age t E t X t
Two-part models
• The first part: decision for participation
• The second part: decision for expenditure
iccicic XI 1
iccicicic uXIMED 2)0|log(
)exp()()|( 1 icicicic XXXMEDE
Marginal effect
( ) (Pr( 0) ( | 0))
( | 0) Pr( 0)Pr( 0) ( | 0)
k k
k k
E y y E y y
x x
E y y yy E y y
x x
( | 1) ( | 0)
Pr( 0 | 1) Pr( 0 | 0) ( | 0 | 1)
Pr( 0 | 0) ( | 0 | 1) ( | 0 | 0)
k k
k k k
k k k
E y x E y x
y x y x E y y x
y x E y y x E y y x
Measurement issues
• Wealth: household income per capita
• Medical price: possible problem of self-selection. Using survey data of health providers. We use average price paying for a treatment of cold or flu in the community
• Insurance: public insurance, worker insurance, cooperative medical insurance and all kinds of insurance. whether the individual has medical insurance
Data
• CHNS (China Health and Nutrition Survey) 1991,1997 data of rural China
• collected by Carolina Population Center (CPC) at the University of North Carolina at Chapel Hill, the Institute of Nutrition on Food Hygiene, and the Chinese Academy of Preventive Medicine
Sample size
province community Household individual Illness samples
1991 8 128 2550 10374 884 (8.59%)
1997 8 128 2590 9583 504 (5.18%)
Medical expenditure and household income
Percentage of samples who have medical expenditure when ill (%)
Medical expenditure (mean) (yuan)
Household adjusted income (total sample)(mean) (yuan)
Household adjusted income (illness sample)(mean) (yuan)
1991 77.26 172.69 1494.27 1470.95
1997 84.13 312.96 1302.48 1298.22
Full samples Illness samples
Variable definition 1991 1997 1991 1997
edu Education (year) 5.78 5.04 4.73 4.02
age1 =1, 15 to 35 years old
0.46 0.44 0.25 0.17
age2 =1, 36 to 65 years old
0.42 0.43 0.58 0.59
age3 =1, older than 65
0.09 0.10 0.15 0.23
Education and age
Price and Insurance
Province Medical Price Health insurance coverage (%)
1991年 1997年 1991年 1997年
Heilongjiang - 22.35 - 9.2
Liaoning 4.31 - 18.0 -
Jiangsu 5.58 21.98 45.7 36.8
Shandong 2.75 18.63 18.1 39.3
Henan 2.61 8.85 14.0 18.1
Hubei 5.24 17.53 20.4 9.7
Guanxi 2.20 9.64 10.3 12.4
Guizhou 3.72 12.17 7.8 5.5
Hunan 3.46 21.21 9.1 3.8
Results
• Possibility to be sick (Xtprobit)
• Possibility to have expenditure (Xtprobit)
• Possibility to have expenditure after sick (budget constrain) (Xtprobit)
• Medical expenditure (random effect)
• Marginal effect on medical expenditure of two part model
Marginal effect on probability of illness and having medical expenditure
Dependent variable
ill(full sample)
haveexp(full sample)
haveexp(sick sample)
Ln(inc) -0.004( 0.03) 0.000( 0.03) 0.069***( 0.021)Ln(price) 0.02( 0.04) 0.003( 0.04) 0.027( 0.025)
Ln(cominc) 0.011( 0.008) 0.006( 0.007) -0.086*( 0.053)Age1 -0.058***( 0.001) -0.040***( 0.006) 0.058*( 0.039)Age2 -0.017***( 0.006) -0.008( 0.005) -0.060( 0.035)Edu -0.002***( 0.000) -0.001**( 0.001) 0.011***( 0.004)Job -0.021***( 0.006) -0.0141***( 0.005) 0.083**( 0.037)
Insurance 0.017***( 0.1161) 0.009*( 0.005) -0.026( 0.037)Year1997 -0.038*** (0.007) -0.022*** (0.006) 0.107***( 0.355)Sigma_u 0.389 (0.033) 0.409 (0.036) 0.450 (0.083)
LR test of rho=0
Prob>=chaibar2=0 Prob>=chaibar2=0 Prob>=chaibar2=0
Observation 14863 14881 1102
Medical Expenditure
Dependent variable: Ln(medical expenditure) Random effect model
Ln(inc) 0.104( 0.110)Ln(price) 0.223*( 0.133)
Ln(cominc) 0.072( 0.264)Age1 -0.078( 0.224)Age2 -0.088( 0.192)Edu -0.036**( 0.019)Job 0.010( 0.172)
Insurance 0.085( 0.178)Severe2 1.131***( 0.134)Severe3 1.957***( 0.190)
Year1997 0.217( 0.203)R-sq 0.150
Observation 884
Breusch and Pagan Lagrangian multiplier test for random effects:
Test: Var(u) = 0
chi2(1) = 11.58 Prob>chi2= 0.0007
Marginal effect of two-part model
variableMarginal effect (unconditional)
Marginal effect (conditional on illness)
income 0.006 0.331
price 0.020 0.280
Education -0.039 0.009
Age2 -0.146 0.114
Age3 -0.024 0.270
job -0.047 0.242
insurance 0.036 -0.038
Test the endogineity of medical insurance
• people with medical insurance might have some unobserved characteristics which influence their medical expenditure
• We use “if the village enterprises subsidize the insurance” as an Instrument Variable
*1 2i i i iy x z
* 0iy 1iy indicate the individual has medical insurance
The first stage regression of ivprobit (instrumented: insurance)
Dependent variable: having insurance
Participating equation (full sample)
Participating equation(illness sample)
Have subsidy or not 0.153*** (0.009) 0.082** (0.034)
Walt test for exogeneity Prob>Chi(2) =0.292 Prob>Chi(2) =0.783
observations 14881 1102
Medical burden by income group and education group
inc1 inc2 inc3 inc4 inc5 edu1 edu2
Probability of being ill
1991 0.089 0.088 0.089 0.097 0.102 0.133 0.050
1997 0.052 0.051 0.053 0.054 0.057 0.082 0.028
Medical burden (%)
full smaple
1991 1.03 0.53 0.41 0.34 0.24 0.86 0.21
1997 1.19 0.55 0.4 0.3 0.21 0.97 0.22
illness sample
1991 13.53 7.09 5.99 3.86 2.81 8.1 5.5
1997 37.63 12.01 9.64 7.68 5.01 18.61 10.6
conclusions
• the less educated people have higher probability to be sick and expend more on medical care after sick.
• the income elasticity of demand for medical care is low, so the lower income people have heavier medical burden .
• due to the lack of price elasticity, the medical burden of lower income people is growing more fast
Policy implications
• the inequality is much larger if taking account of the health inequality and the heavier medical burden imposed in poor people
• it is particularly emergency to establish appropriate and widely covered public health insurance to share the risk of illness and medical expenditure
• A proper insurance scheme will play a redistributive role, since the poor and the low educated people face higher risk
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