low population density or a good overall health status in the catchment area. Insofar as variation in output across facilities is driven by these types of factors, service capacity would appear a conceptually appealing measure of output, at least for the purpose of efficiency analysis. For example, one might measure _potential_ inpatient days rather than actual days, just as one might measure school _places_ rather than school _enrolment_ or attendance.1<sup>9</sup> However, in many cases, a divergence between service capacity and rendered services is due to factors that are within the discretion of the facility. Here, of course, there is some scope for disagreement as to precisely what _is_ under the control of providers.<sup>20</sup> In general, both "readiness-to-serve" capability and rendered services are problematic as measures of output. Most surveys collect data on the number of units of services delivered, largely because these type of data are more readily available. However, for some types of analysis it is clearly important to try to remove the confounding effects of influences that are genuinely beyond the control of providers.

A second issue to consider in the measurement of outputs is the heterogeneity of service outputs. There are different sources of this heterogeneity. First, quality may differ considerably over time, across health care facilities, and even between individual clients at a given facility. For example, a thorough outpatient consultation with a doctor is a very different service than a rushed consultation with a poorly trained nursing aide. While this is more important for some types of services than others (e.g., immunizations may be considered reasonably homogenous in terms of quality), this issue can create serious problems of comparability across time and space. Although it is in principle possible to control for quality differences in analysis, this has proved difficult in practice. Second, within a particular service category, there can be a notable variation in case mix and case complexity. Again, this causes problems in terms of comparability.21 For example, inpatient days can range from cases requiring simple interventions and limited monitoring, to highly complex cases where a broad range of material and human resources are required. Differences in case-mix can arise from the differences in the socio-economic characteristics of the population in provider catchment areas, or because more complex cases may seek out providers with particular characteristics. The problem can be at least partly overcome. For example, output can be disaggregated into more detailed diagnostic categories. Alternatively, we can stratify providers by "servicecapability groups" and assume homogeneity in terms of case mix and severity. 22 Finally, in addition to problems relating to the comparability of output measures in specific categories, most health care providers offer a wide range of services (service mix). Even with a small number of aggregated categories of interventions/services-e.g., inpatient days and outpatient visits-the issue arises of how to compare the quantity of outputs across facilities with different service mixes. A standard technique for dealing with multi-output production is to construct an output index using market prices as weights. In general, this approach is not appropriate in the health sector, where output prices do not exist or do not

> 19 See Prennushi, Rubio, and Subbarao (2001).

> 20 For example, if households fail to take advantage of a free child imnmunization program, providers presumably can be blamed for a part of the non-utilization if they fail to publicize it and fail to employ delivery mechanisms that respond to the reasons for non-utilization (e.g., outreach rather than clinic-based services).

> 21 This was pointed out by Feldstein (1967), who found that differences in case mix was an important explanation for variation in ward costs per case among hospitals.

> 22 See Thatchell (1983) for a detailed discussion.

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