across the countries in our sample, the median outpatient caseload per facility is 25.9 patients per day. As many facilities have multiple providers, if we assume that the patient load in the facility is evenly distributed across available providers, the mean daily caseload per _provider_ was 15.7 and the median was 10.9 across our full sample, ranging from 1.2 in Nigeria to 20.7 in Malawi. This mean is remarkably close to the 15.5 patients per day that emerged from our crude calculation above, and it indicates that the caseloads of PHC providers are consistent with one hour and forty minutes of daily consultations across the countries in our sample.

To understand why patients still report long queues even though providers have substantial excess capacity, we then examine the _variation_ in caseloads across facilities. Across the countries in our sample, the 20% busiest providers saw between 40%–67% of all outpatients, which implies that most patients (by definition) visited facilities with relatively busy providers. The median _patient_ in our sample visited a provider who saw 25.7 patients each day, ranging from 6.6 in Nigeria to 64.6 in Malawi. These are substantially higher caseloads, and simulations using stochastic queuing theory show that even mild “clumping” of patients at any time of the day – or day of the week – would lead to long queues and waits at these higher caseloads. We conclude that official statistics and facility survey data are correct in indicating concurring patterns of excess capacity for providers. Simultaneously, the variation across facilities in the survey data implies that the characteristic patient experience is instead one of crowds and queues.

With this new perspective in hand, we turn to a second issue of human resource management for these PHC systems. We consider, as a starting point, that the unequal caseloads we observe could reflect efficient allocation mechanisms if the busier providers were also those of higher quality. Our second aim in this article is then to investigate whether caseloads were strongly positively correlated with provider competence.

In order to do so, we used measures of clinical competence for 14,367 health care providers that we linked to our estimates of utilization at the provider level,<sup>9</sup> again under the assumption that patients were equally divided among available providers in the facility. We uncover the striking result that the association between competence and caseloads is close to zero for most countries in our sample. It is negative so that more competent providers see _fewer_ patients in Madagascar and Mozambique, and positive but small in all other countries. Some examples are stunning: Nigeria has some of the most knowledgeable providers in the entire sample and is one of the countries with a statistically significant positive association between caseloads and competence. Even so, the correlation is so small that the average caseload among the 100 most knowledgeable Nigerian providers in the sample was only 5.2 patients per day.

The lack of a strong systematic association between caseloads and competence then motivates the third part of our study. We investigate the implications of this (mis)allocation, whereby highly competent providers are located in less busy facilities, by asking if overall quality could be improved by hypothetically matching underutilized high-competence providers to larger caseloads. An example demonstrates the reasoning behind this approach. Consider a health system with two providers: Doctor A, who scores 80 (out of 100) on a measure of competence and Doctor B, who scores 20. The average doctor

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