Adam WagstaffDevelopment Research Group & East Asia HD, The World Bank

Health insurance for the poor in VietnamAn impact evaluation of

Vietnam’s health insurance program

Photos from Hans Kemp

Introduction Policy and program issues:

– Lack of health insurance in China and Vietnam following de-collectivization of agriculture

– New policy of public finance of free health care for the poor by enrolling them in social insurance

Substantive issues:– Health insurance literature focuses on negative– Paper looks at risk-reduction associated with HI, and

positive consequences from it Methodological issues:

– Paper uses propensity score matching (PSM) with pre- and post-intervention data to estimate impact of health insurance

Empirical findings & policy implications

Policy/institutional issues In China and Vietnam, cooperative health

insurance collapsed after de-collectivization of agriculture

In both countries concern over affordability of health care, esp. among rural poor

People encouraged to enroll in Vietnam’s health insurance (VHI) program—compulsory for certain groups

Decision 139 mandates and supports provinces to enroll poor in VHI (or make alternative arrangements for them)

What will impact of enrollment among 139 beneficiaries be on key outcomes?

Policy issues

Costly care, high spending

Health spending as % total and non-food expenditure,

Vietnam 1998

05

101520253035

Poor

est

2nd

Mid

dle

4th

Top

Quintiles

Totalexp

Nonfood exp

Cost per hospital visit as % annual per capita non-food

consumption, Vietnam

05

1015202530354045

Poor

est

2nd

Mid

dle

4th

Top

Quintiles

1993

1998

Policy issues

0123456789

10

1 500 999 1498 1997 2496 2995 3494 3993 4492 4991 5490 5989Households ranked by expend w/out hc payments

HH e

xpen

ditur

e as m

ultipl

e of P

L

Pov line = VND 1.8m/year Expend w/out hc payments

Impoverishing tooPolicy issues

0123456789

10

1 500 999 1498 1997 2496 2995 3494 3993 4492 4991 5490 5989Households ranked by expend w/out hc payments

HH e

xpen

ditur

e as m

ultipl

e of P

L

Pov line = VND 1.8m/year Expend w/out hc payments

0123456789

10

1 500 999 1498 1997 2496 2995 3494 3993 4492 4991 5490 5989Households ranked by expend w/out hc payments

HH e

xpen

ditur

e as m

ultipl

e of P

L

Pov line = VND 1.8m/year Expend w/out hc paymentsHC payments

Out-of-pocket payments for health care pushed 2.6m Vietnamese into poverty in 1998.Increased headcount by 23% and poverty gap by 25%

Impoverishing tooPolicy issues

VHI before decision 139Social insurance coverage,

Vietnam 1998

0%

10%

20%

30%

Botto

m

2nd

3rd

4th

Top

Quintiles

Set up in 1993, reformed in 1999

Compulsory scheme for formal sector workers, civil servants, etc.

Voluntary scheme—currently attracts mostly school kids & students

By 1998, 15% enrolled; 60% compulsorily

Coverage against inpatient costs, & fees incurred in outpatient care; less generous coverage for voluntary members

Policy issues

How decision 139 will change coverage

0%20%40%60%80%

100%Ko

n Tu

mGi

a La

iDa

c La

cLa

m D

ong

Cao

Bang

Lai C

hau

Bac

Kan

Tuye

n Qu

ang

Quan

g Na

mSo

n La

Dong

Tha

pQu

ang

Ninh

Can

Tho

An G

iang

Binh

Duo

ngKi

en G

iang

Dong

Nai

Ba R

ia V

ung

Tau

Ha N

oiHo

Chi

Min

h Ci

ty

provinces ranked by per capita income

VHI c

over

age coverage before decision 139

coverage after decision 139

Policy issues

Health insurance issues

Much of the health insurance literature emphasizes the negative:– Moral hazard– Adverse selection

Recent work emphasizes:– Risk-reduction benefits of insurance, and

positive consequences of this• Lower precautionary savings • Better health outcomes

– Difficulty of measuring true moral hazard

Substantive issues

Evaluation with non-experimental data

Participation in program

Outcome D=1 Yes D=0 No

Y1 outcome with treatment

?

Y0 outcome without treatment

?

Difference = effect of treatment on treated

Difference = bias

Methodological issues

Propensity score matching as approach to reducing bias

Component of bias Strategy to reduce biasParticipants and non-participants differ in relevant respects—i.e. have different X’s

Compute probability of participation as function of X’s, P(X). Match participants and non-participants on P(X). Compute mean difference in outcomes between matches (“single difference” or SD)

For some participants, there are no comparable non-participants

Confine comparisons to region of common support of P(X)

Outcome differences not attributable to treatment might remain even after conditioning on X’s and confining attention to common support—problem of selection bias

In cross-section, nothing can be done. With pre- and post-intervention data, compute difference between mean change among participants and mean change among non-participants (“double difference” or DD). This allows for time-invariant “selection on unobservables” effect

Methodological issues

Data & variables Data from Vietnam Living Standards Survey

– High proportion of HHs interviewed in 1993 were re-interviewed in 1998

Outcomes variables– Contact probability– Volume of services used (1998 data only, so can do

only single difference PSM)– Out-of-pocket payments– Non-medical HH spending– Child health, measured through anthropometrics

(underweight, etc.)

Empirical results

Probit model for participation VHI enrollment depends on

– Whether in school (+)– Employed:

• Communist party, government, army, social organization, state-owned company (+)

• Private company (-)– Income (+)– Education (+)– Urban (+)– Commune fixed effects

Empirical results

Descriptives of probability, before & after matching

Predicted probability of coverage

# cases Mean Std. Dev. Min Max

Before matching

Uninsured 14537 0.12736 0.13665 0.00011 0.98705

Insured 3015 0.38192 0.25335 0.00477 0.99989

After nearest neighbor matching

Uninsured 3015 0.38189 0.25326 0.00477 0.98705

Insured 3015 0.38192 0.25335 0.00477 0.99989

After caliper matching with 0.001 caliper

Uninsured

2775 0.34330 0.22151 0.00477 0.98705

Insured 2775 0.34330 0.22150 0.00477 0.98768

Empirical results

Histograms of probabilities, before and after matching

Frac

tion

Pr(insur_yes)0 .25 .5 .75 1

0

.05

.1

.15

Frac

tion

Pr(insur_yes)0 .25 .5 .75 1

0

.05

.1

.15

Frac

tion

Pr(insur_yes)0 .25 .5 .75 1

0

.05

.1

.15

Frac

tion

Pr(insur_yes)0 .25 .5 .75 1

0

.05

.1

.15

Uninsured Insured

Empirical results

PSM results #1 (DD & SD)Sample Estimator Outcome Effect T-statSample DD Out-of-pocket payments 4.582 0.19Inpatients SD Inpatient costs -738.18 -1.69Inpatients SD Out-of-pocket payments -1102.73 -2.42Sample SD Inpatient costs 10.09 1.04Sample SD Non-hospital costs 15.50 0.89Sample DD Contact probability 0.040 2.26Sample DD Weight-for-age kids < 10 0.203 1.98Sample DD Weight-for-height kids <10 0.215 1.90Sample DD Non-health consumption 387.53 5.37

DD=double difference; SD=single difference

Empirical results

PSM results #2 (SD)Sample Poorest quintile

Effect T-stat Effect T-statTotal visits 0.017 0.22 0.095 0.57Hospital visits 0.051 3.85 0.030 1.72Inpatient nights 0.973 3.55 0.216 0.78CHS visits 0.025 1.85 0.069 1.21Polyclinic visits 0.000 0.00 0.009 0.28Private visits -0.001 -0.03 -0.052 -1.67Traditional healers -0.010 -0.75 -0.004 -0.13Pharmacy visits -0.056 -0.94 0.043 0.30

Empirical results

Conclusions PSM useful for program

evaluation—use panel data and diffs-in-diffs estimator if possible

VHI increases contact probability, volume of use

No impact on out-of-pocket payments

Effect on non-medical consumption—reflects risk reduction?

For hospital care, smallest impact of VHI among the poor

Extrapolation to “139” difficult—poorest quintile estimates most relevant; but NB no copayments