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

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 Policy issues

Impoverishing too Policy issues

Out-of-pocket payments for health care pushed 2.6m Vietnamese into poverty in Increased headcount by 23% and poverty gap by 25% Impoverishing too Policy issues

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

How decision 139 will change coverage 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 YesD=0 No Y 1 outcome with treatment ? Y 0 outcome without treatment ? Difference = effect of treatment on treated Difference = bias Methodological issues

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

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 # casesMeanStd. Dev.MinMax Before matching Uninsured Insured After nearest neighbor matching Uninsured Insured After caliper matching with caliper Uninsured Insured Empirical results

Histograms of probabilities, before and after matching UninsuredInsured Empirical results

PSM results #1 (DD & SD) SampleEstimatorOutcomeEffectT-stat SampleDDOut-of-pocket payments InpatientsSDInpatient costs InpatientsSDOut-of-pocket payments SampleSDInpatient costs SampleSDNon-hospital costs SampleDDContact probability SampleDDWeight-for-age kids < SampleDDWeight-for-height kids < SampleDDNon-health consumption DD=double difference; SD=single difference Empirical results

PSM results #2 (SD) SamplePoorest quintile EffectT-statEffectT-stat Total visits Hospital visits Inpatient nights CHS visits Polyclinic visits Private visits Traditional healers Pharmacy visits 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