2007年-世界发展银行全球_Health_Insurance_for_the_Poor___Initial_Impacts_of_Vietnams_Health_Care_Fund_for_the_Poor_33页_337kb

Summary of "Health Insurance for the Poor: Initial Impacts of Vietnam's Health Care Fund for the Poor"

Core Content

This paper evaluates the initial impact of Vietnam's Health Care Fund for the Poor (HCFP), a program introduced in 2003 to provide health care coverage to the poor, ethnic minorities in designated mountainous provinces, and households in highly disadvantaged communes. The evaluation is based on secondary data from the 2004 Vietnam Household and Living Standard Survey (VHLSS), and uses a single-difference approach due to the lack of suitable baseline data for double-differencing.

Main Points

• Program Overview:
  HCFP was established to reduce financial barriers to health care for the poor. It replaced the earlier Free Health Care Cards for the Poor program, which had limited success due to inadequate funding and poor implementation. HCFP involves central and provincial government funding and aims to provide free health care through the social health insurance (SHI) system.

• Target Groups:
  The program targets:

  • Households officially classified as poor
  • Households in communes designated as highly disadvantaged
  • Ethnic minorities in selected mountainous provinces

• Coverage and Funding:

  • Approximately 14% of the sample were covered by HCFP.
  • The program has not yet achieved its full target, with only about 15% of the population enrolled.
  • Coverage is primarily through SHI, with some provinces using direct reimbursement.

• Impact Findings:

  • Service Utilization: HCFP has significantly increased the use of health services, particularly inpatient care.
  • Catastrophic Spending: It has reduced the risk of catastrophic out-of-pocket health expenditures.
  • Out-of-Pocket Spending: It has not reduced average out-of-pocket spending.
  • Poorest Decile: The program has negligible impact on service utilization among the poorest 10% of the population.

Key Information

• Methodology:

  • The study uses propensity score matching (PSM) to estimate the impact of HCFP.
  • It compares outcomes of HCFP beneficiaries with those of non-beneficiaries who are eligible for the program.
  • The propensity score is calculated using a probit model, which estimates the probability of being covered by HCFP based on observable characteristics.

• Limitations of the Study:

  • The use of single-difference rather than double-difference methods due to the lack of appropriate baseline data.
  • The evaluation covers only the first one-to-two years of the program's implementation.
  • Changes to the program in 2006 are not reflected in the analysis.
  • The study provides quantitative evidence but lacks qualitative insights from officials and households.

• Propensity Score Results:

  • Being classified as officially poor significantly increases the likelihood of HCFP coverage.
  • Ethnic minorities in disadvantaged mountainous provinces and beneficiaries of Decision 135 (the disadvantaged commune program) also have higher chances of coverage.
  • Other variables, such as household income, literacy, and location, influence the probability of being covered, even after controlling for official eligibility criteria.

• Descriptive Statistics:

  • The poorest 10% of the population account for over 30% of HCFP beneficiaries.
  • The poorest 20% account for just over 50% of beneficiaries.
  • Non-prescribed medicines account for nearly 25% of out-of-pocket health expenditures.
  • Inpatient care accounts for almost 60% of total HCFP budget.

Conclusion

• The HCFP program has had a positive impact on service utilization and reduced the risk of catastrophic health spending.
• However, it has not reduced average out-of-pocket payments.
• The poorest households have not seen significant improvements in health service access or utilization.
• The study highlights the importance of targeting and the need for improvements in program design and implementation.
• While the evaluation is limited in scope, it provides valuable insights that can inform future, more comprehensive assessments of the program.

Additional Notes

• The propensity score matching method is used to control for observable differences between beneficiaries and non-beneficiaries.
• The sample is trimmed to minimize variance in the estimated average treatment effects.
• The regression approach with propensity score weights allows for estimating differential impacts across income groups.
• The study acknowledges potential biases from unobservable factors but suggests they may be smaller than in other similar programs.