increase across regions. Similar strong increases of between 12% and 34% emerge for antenatal and postnatal cases as well as the number of deliveries handled at the unit, something that was associated with a decrease in the number of deliveries handled by traditional birth assistants at least in some regions. However, more detailed exploration of the issue using household data is needed to explore whether the strong regional differences emerging from administrative data are just a reflection of differential rates of reporting or associated with conditions of ongoing fighting or shortages in technical equipment in the North, the extent to which they accrue to the poor, and whether they actually translate into an impact on individuals’ health status.

# **4.2 Rationing for those suffering from sickness**

Probit regressions to explore the extent to which the abolition of user fees helped to reduce rationing for people who were sick, i.e. them not being able to access health services because of high costs or long distances, are reported in table 6, separately for adults (columns 2 and 3) and children (columns 4 and 5). All the coefficients reported are marginal probabilities. As discussed earlier, the net effect of the policy change is the coefficient on the year dummy or its interaction with various socio-economic attributes. We also note that there is no gender bias as indicated by the lack of significance of the coefficient on the female dummy.

Holding constant for other characteristics, the elimination of user fees is estimated to have resulted in a reduction of 8% and 11.5% for adults and children respectively in the probability of being rationed. Not surprisingly, the probability of being rationed decreases in wealth, as approximated by the total value of assets held by the household, and also (for adults) in household size. It is lower by about 5% for cases of malaria and, in the case of children, also for intensive sickness, defined as including measles as well as cases of HIV/AIDS, hypertension and accidents. Distance from a hospital is not significant for adults but increases the probability of being rationed for children. This probability is also higher (by about 6% in rural areas) and increases with education as well as the households having a cement floor and a pit latrine, two attributes that reflect higher standards of hygiene as well as wealth and is convex in age for adults and concave for children. Regional differences are pronounced, with the probability of rationing being lowest in the West and highest in the East for adults as well as children.

In addition to the reduction in the probability of rationing over time which can be interpreted as a direct measure of the impact of the policy change, the coefficients on the year dummy’s interaction with other socio-economic variables can provide insights as to the extent to which the policy change favoured (or put at a disadvantage) some specific groups in the population. We first interpret the findings for adults and

> 12  Each consultation provided income of USh 500 to establishments (about 80% of the total) who collected such fees before the policy change. In each month 27% of individuals in the population got sick (of which 70% visited a private hospital and 30% public facilities). The exchange rate

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