We estimate equation (2) separately for those above and below the age of 14. For the latter, the parents’ rather than own education will be relevant and, in addition, we can test for significant differences between orphans, “other relatives”, i.e. children who are not direct offsprings of the household head, and own children on access to health services that might warrant attention by policy makers.

_Incidence of sickness:_ As discussed earlier, the economic impact of the policy change will depend on how it affects the incidence of sickness in the overall population. To quantify this, we estimate a probit model _Sit =_ α _0 +_ α _1_ **_X_** _it +_ α _2_ **_H_** _it +_ α _3T +_ α _4_ **_H_** _itT +_ α _5_ **_X_** _itT +_ μ _it_ (3) where _Sit_ takes the value of 1 if an individual reported being sick 30 days prior to the survey and 0 otherwise and most other variables are as defined before. A key difference to equation (2) is that this regression includes the whole population, not only the subset of individuals who reported to have suffered from sickness during the period. We expect asset ownership, adequate dwelling characteristics, and education to reduce the incidence of sickness. Again, the coefficient of key interest is α _3_ , the net change in the propensity of sickness over time which we can interpret as the impact of the policy change, as well as the interaction of this variable with regional dummies and socio-economic characteristics. Also, as in the case of (2), we estimate separate regressions for adults and children aged 14 years and below. In the equation for children, we include a dummy that takes the value 1 if the child is an orphan and 0 otherwise in order to capture the impact of abolition of cost sharing on this group.

# **3.3 Data and descriptive evidence**

To empirically implement the above regressions, we rely on two cross-sectional surveys, undertaken before and after the policy change, respectively. The first is the 1999/2000 Uganda National Household Survey (UNHS I), a representative survey of 10,696 households, fielded between August 1999 and September 2000. It is complemented by a sample of 9,711 households from the 2002/3 UNHS II who were interviewed between May 2002 and April 2003. Both surveys provide a rich source of information on individual and household characteristics, economic activity, asset endowments, and demand for (incidence of sickness in the previous 30 days) as well as utilization of health services, including some indication of potential constraints faced in doing so. At the facility level, we use the community module of the UNHS II which includes information on a range of indicators for quality of service provision - including availability and prices of malarial drugs and antibiotics, the level of consultation fees and the number of opening hours- in public and private facilities<sup>8</sup> both at the time of the survey and for

> 8 For purposes of this paper, we define public hospitals as any health centre/facility/hospital financed out of public resources and managed by the government, either at the central or local government level. Private hospitals are defined as those health centres/facilities/hospitals that are financed by non-governmental organisations, private individuals and companies, including traditional health care establishments. In the discussion, we use the words ‘hospital’, ‘health care centres’ and ‘health care institutions’ inter-changeably to mean a health care facility, no matter its size. Our major concern is the type of ownership, which is distinguished as stated above. We also use the terms ‘government hospital’ and ‘public hospital’ interchangeably.

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