1. Adam Wagstaff Development Research Group & East Asia HD, The World Bank Health insurance for the poor in Vietnam An impact evaluation of Vietnam’s health insurance program Photos from Hans Kemp

2. 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

3. 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

4. Costly care, high spending Policy issues

5. Impoverishing too Policy issues

6. 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 too Policy issues

7. VHI before decision 139 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

8. How decision 139 will change coverage Policy issues

9. 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

10. Evaluation with non- experimental data Participation in program Outcome D=1 YesD=0 No Y 1 outcome with treatment ? Y 0 outcome without treatment ? Difference = effect of treatment on treated Difference = bias Methodological issues

11. Propensity score matching as approach to reducing bias Component of biasStrategy to reduce bias Participants 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

12. 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

13. 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

14. Descriptives of probability, before & after matching Predicted probability of coverage # casesMeanStd. Dev.MinMax Before matching Uninsured145370.127360.136650.000110.98705 Insured30150.381920.253350.004770.99989 After nearest neighbor matching Uninsured30150.381890.253260.004770.98705 Insured30150.381920.253350.004770.99989 After caliper matching with 0.001 caliper Uninsured27750.343300.221510.004770.98705 Insured27750.343300.221500.004770.98768 Empirical results

15. Histograms of probabilities, before and after matching UninsuredInsured Empirical results

16. PSM results #1 (DD & SD) SampleEstimatorOutcomeEffectT-stat SampleDDOut-of-pocket payments4.5820.19 InpatientsSDInpatient costs-738.18-1.69 InpatientsSDOut-of-pocket payments-1102.73-2.42 SampleSDInpatient costs10.091.04 SampleSDNon-hospital costs15.500.89 SampleDDContact probability0.0402.26 SampleDDWeight-for-age kids < 100.2031.98 SampleDDWeight-for-height kids <100.2151.90 SampleDDNon-health consumption387.535.37 DD=double difference; SD=single difference Empirical results

17. PSM results #2 (SD) SamplePoorest quintile EffectT-statEffectT-stat Total visits0.0170.220.0950.57 Hospital visits0.0513.850.0301.72 Inpatient nights0.9733.550.2160.78 CHS visits0.0251.850.0691.21 Polyclinic visits0.0000.000.0090.28 Private visits-0.001-0.03-0.052-1.67 Traditional healers-0.010-0.75-0.004-0.13 Pharmacy visits-0.056-0.940.0430.30 Empirical results

18. 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