therefore scores 50. However, from the patient point of view, the average quality of the system should be calculated accounting for each doctor’s share of total patients: If 1/4 of patients see Doctor A and 3/4 see Doctor B, the average interaction quality would be 35. If the doctors were to switch places, the average interaction quality would be 65 instead, with no change at all in the medical competence of either Doctor A or B, or the physical location of the patients.

Following this logic, we provide an estimate of how much the quality of the average patient consultation could improve in each country if providers were to be “optimally” reallocated. Specifically, we compute the competence of the provider at the average outpatient consultation in the observed data. Then, we calculate the same quality metric, but with the most knowledgeable health care providers hypothetically matched to the largest caseloads in their country and sector, holding constant the number of patients at each facility. We emphasize that this reallocation is not intended as a policy recommendation, but as a sufficient statistic to quantify the loss in quality due to the fact that some high-competence providers are underutilized, even while some low-competence providers manage very large patient loads.

For the average patient interaction, our hypothetical provider reallocations increased quality (defined as having the competence to correctly manage a basic case) by 4% to 30% relative to current performance. Two observations are noteworthy. First, even with the near-zero correlations between caseloads and competence in current systems, improved allocation offered substantial gains in only half of the countries. Second, in those countries where gains from reallocation were low, improvements were just as low in simulations of an alternate policy that “upskilled” every non-doctor health care provider to the level of a doctor from the same country, a major investment in training resources. In these countries, even though the health care provider population still has ample excess capacity, there is an insufficient number of high-competence providers to reach patients in any configuration of human resources. Systemic quality improvements in such cases may require a complete overhaul of training infrastructures.

Our paper makes two fundamental contributions to the literature. First and foremost, we contribute to the literature on human resources for health, particularly in Sub-Saharan Africa. In contrast to a common view that there is a general human resource shortage, we show that published aggregate statistics and the SDI facility survey data are both consistent with substantial excess capacity for the median provider. Our result rationalizes and explains why recent studies<sup>10–12</sup> find that the causal impact of patient load on the quality of medical care is close to zero in multiple SSA countries: Most providers have plenty of capacity to handle additional patients. We further contribute to this literature by using variation across the facility surveys to show that excess capacity among providers is _also_ consistent with long queues and waits for the typical patient, because a small share of providers handles a large share of the total caseload.

Second, we show how data on individual provider competence can be combined with data on caseloads to quantify lost productivity, by estimating the potential gains from improved allocation. While such allocative gains have been shown to be quite important for quality improvement in high-income countries,<sup>13, 14</sup> our approach to estimating potential

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