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Working Paper 120 - Community Based Health Insurance Schemes in Africa: The Case of Rwanda
15/01/2014 18:52
Working Paper 120 - Community Based Health Insurance Schemes in Africa: The Case of Rwanda
Every year, around 100 million people are driven into poverty due to burden of health expenditure. Most of these newly poor reside in resource poor countries such as Sub Saharan Africa (SSA) where health care systems significantly lag behind developed country counterparts and are characterized by either dysfunctional or non-existent health insurance schemes. The result has been a high disease burden propagating a sickly, unproductive labor force. In Sub-Saharan Africa, formal and well functioning health insurance schemes generally exist for the very few who are employed in the formal sector. Households in poorer countries generally tend to spend as much as those living in relatively richer countries. This paper evaluates the impact of the Community-based health insurance schemes (Mutuelles) in Rwanda on demand for modern health care, mitigation of out-of-pocket catastrophic health expenditure and social inclusiveness based on a nationally representative household survey using traditional regression approach and matching estimator popular in the evaluation literature. The data used in this study was collected in 2005/06 covering around 6,900 households with about 35,000 individual histories. The data is a typical living standard survey where information on household demographics, educational attainment, health, consumption, income sources, migration, agriculture, labor market condition, household assets, living conditions and other variables were collected. The preferred method of estimation is the matching estimator that uses data organized along the dichotomy: “treated” vs. “control” conditional on observed covariates. Such a dichotomy allows estimation of three statistics relevant for evaluation. The Average Treatment Effect (ATE) compares outcomes between “treated” vs. “control” group by taking randomly selected individuals from both samples so that impact of a program is evaluated directly. The results based on simple probit model suggest that membership into CBHISs had a potential of increasing health care utilization by about 15% following an illness episode. The effect is slightly higher for poor households than the non-poor. With regard to catastrophic expenditure, there is significant effect returned by the probit model where insured households had a much lower probability of experiencing catastrophic expenditure compared to the uninsured and more so among the poor than the non-poor. Similarly, the results from the matching estimator indicate that households that were members of the CBHISs had a 15 percentage point higher utilization of health care facilities than uninsured ones following an illness episode. According to our preferred method, higher utilization of health care services was found among the insured non-poor than insured poor households, with comparable effect in reducing health-related expenditure shocks. Utilization of modern health care services among the insured did not have statistically significant effect on health utilization, particularly among the poor. The non-poor did show 21 percentage point increase in the use of health services. The CBHISs succeeded however in reducing significantly health related consumption shocks in all households, more among the poor than the non-poor. This result is very encouraging since health related shocks have the potential of persisting for a long time in typical poor households. The study also shows that if the insurance scheme was extended to non-members, heath utilization would increase by 18 percentage points. This figure is close to 30 percentage points for non-poor households and about 10 percentage points among poor households. With respect to income protection, the potential of CBHIs is still very high. It could reduce catastrophic expenditure by 17 percentage points and much more significantly among the poor than the non-poor households. Overall the matching estimator indicates stronger evidence of better utilization of health care facilities and income protection due to CBHISs in Rwanda.Lire la suite
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