Community Based Health Insurance Scheme (Mutuelles) in Rwanda: an evaluative note using household surveys Abebe Shimeles Development Research Department African Development Bank October 2009

1. Introduction According to WHO (2005), every year 100 million people are driven into poverty due to catastrophic expenditure on health related needs. Certainly the problem is more pervasive in Africa where there are little risk-mitigating mechanisms against health-related negative shocks. Out of pocket household health expenditure is generally high and non-monotonic across the income divide: poor income countries spend as much as middle income economies as a share of household income (see Figure 1) with considerable variation on health outcomes.

Figure 1: Share of household out of pocket expenditure on health in 47 African countries

2. CBIs in Rwanda In recent years Community-based health insurance schemes (CBHIs) emerged in Africa in response to failures by the state and the market to provide health insurance (e.g. Ghana, Senegal & Rwanda) CBHIs in Rwanda however are perhaps the largest (close to 85% coverage in 2008) and linked to national health policy.

2. CBIs in Rwanda (contd) Some of the features of CBIs in Rwanda include: Premiums are flat (earlier it used to be different across CBIs) Members have access to basic health care services and medication at a discount rate.

2. CBIs in Rwanda (contd) Why is CBHIs in Rwanda interesting ?. Scale up took place in the midst of controversy. The pros and cons are as follows: Rwanda being a poor country and basic health services are unaffordable to the majority (despite government subsidy), CBHIs are the only alternative to increase demand for modern health care and reduce illness related consumption risk

2. CBIs in Rwanda (contd) Others argue that flat rate is inherently discriminatory. The insurance premium is high for the extreme poor (about 6% of total income) and in fact could reduce health service utilization due to other layers of expenses. Since subscription to the program is not voluntary, there is no guarantee that the poor are protected from health related income shocks.

3. Objectives of the paper Do CBIs increase demand for modern health care services? Are insured households protected from catastrophic out of pocket health-related expenditure? Do the poor fare well compared to the non-poor since they contribute proportionately more to the system than the non-poor?

4. Data Nationally representative household survey conducted in 2005/06 covering 6900 households. The data is standard Living Standard Measurement Survey complete with information on household demographics, consumption, income, labor market conditions, education and health, etc. According to the survey 34% of households were members of CPIs (39% rural and 22% urban areas).

4. Data (contd..) 21% of households reported as having fallen sick in the previous two weeks of the survey. Of these only 30% sought medical care. Overall, 20% of households reported positive health related expenditure

5. Variable definition Dependent variables Dummy if a household sought treatment from health providers after reporting sick Dummy if a household experienced catastrophic expenditure which is defined as top decile of the share of health expenditure to total expenditure.

5. Variable definition (contd..) Covariates Dummy if a household was enrolled in community based health insurance scheme (key variable of interest) Age, size of household, sex of head, level of education, real consumption expenditure in adult equivalent, district dummies, urban dummy, disability status, etc..

6. Estimation issues Membership in CBHIs is very likely not random so that there is a real possibility of households self-selecting into the system which introduces biases into its effect on the dependent variables. One example is sick people self-selecting into the insurance system Or the flat premium provides built-in incentives to well-off households Well-run districts get far more members than weaker districts, etc..

6. Estimation issues(continued) Generally membership to CBIs was driven by the following factors: Household consumption quintile (richer households tend to enroll) Demographic factors are important: Male headed households, large families and older family heads tend to enroll into CBIs. Some districts have significantly higher enrollment than others But, there is also substantial pressure from local administrators that may be correlated with the above variables (the higher the stake, the higher the rate of compliance- richer and educated households tend to comply more than poor ones, etc. )

7. Empirical method Broadly speaking, the empirical literature uses two approaches to deal with the above research questions: econometric models (regression approach) and the matching estimator commonly used in the evaluation literature though conceptually the two are related

7. Empirical approach (contd..) The general specification of the econometric model follows the latent variable approach with endogenous dummy regressor (Heckman, 1974 and others) (see equation below)

7.1 regression approach (a bi- variate discrete choice model)

7.1 regression approach (contd..) It is safe to assume that membership to the CBIs is endogenous in the econometric model for a number of reasons (σ 12 #0):

7.2. Matching estimator This is a popular method used extensively in the evaluation literature. Some focus on before and after a program and often most focus on with and without a program The idea is to create a treated versus control group that would be matched on the basis of specified household and community characteristics. Works well when the bias introduced by unobserved factors are minimum.

8. Discussion of results Finding instruments that affect health utilization and health related risk only through membership to insurance is not easy. We identified two potential instruments. One is cluster level enrollment rate (to isolate some of the confounding factors in individual decision) and the other is a dummy whether or not a household owns title deeds for land ownership (proxy for well-run districts)

Table 1: marginal effects of membership to Mutuelles on selected variables: simple probit (with Blundell-Smith, 1986 test for Weak- exogenity)

Table 2: Average treatment effect of being insured on selected variables using Matching Estimator

6. Discussion of results (contd..) Conditional on other household and community level covariates (age, sex of head of the household, educational attainment, dummies for district, dummies for serious illness, occupation, etc) we find that membership to CBHIs have significant and positive impact on: Utilization of modern health care and protection of households from catastrophic health related expenditure, which is indeed reassuring.

6. Discussion of results (contd..) Generally the poor do not seem to have come out badly, though the non-poor seem to have better access. Catastrophic expenditure is not any different between insured and uninsured among households that reported sick. Generally the results with the Matching Estimator are very comparable and consistent (see Table 2)

6. Discussion of results (contd..) Despite the weak result on the effect of CBIs on health related expenditure, it is possible to see that generally insured households have less health related expenditure risk than uninsured households (see Figure 2 and Figure 3 below)

Figure 2: Health expenditure profile for the uninsured

Figure 3: Health expenditure profile for the insured

Summary and conclusions CBIs in Rwanda play an important role in increasing demand for modern health care controlling for other factors. In general, membership increases health service utilization by about 17% more among the non-poor than the poor. When illness strikes, the CBIs seem to protect member households from catastrophic expenditure.

Summary and conclusions The potential of CBIS seem to be very high among the non-poor than the poor in both cases that may reinforce the inequity inherent in the system.