outpatients times 20 expected working days per month for each provider.<sup>12</sup> We winsorized the final number of daily outpatients per provider for the top 2.5% of facilities within each country.

SDI surveys measured absenteeism in health facilities via unannounced visits where enumerators compared the actual presence of providers to the roster of listed providers. We provide an upper-bound estimate of the per-provider caseload by counting only those providers who were present at the facility on the day of the unannounced provider survey and we assumed that this count was representative across an estimated 20 working days at the facility. This implicitly assumed that providers who were observed on the survey day across the sample of facilities were representative of the availability of providers at each facility on any given day and representative of the workload of others not observed. If, for instance, the providers who were not observed were at home and not seeing patients, we would over-estimate the average patient load in the system. We were not able to match the identity of providers who worked at multiple facilities, either inside or outside the sample. For our purposes, the representative “provider-day” workload at the sampled facilities is accurately estimated and is the quantity of interest: It is the number of patients a typical provider sees during a typical working day.

We caution that the differences under other assumptions can be large, as the mean facility reported 4.3 providers on staff, of whom only 2.6 were present during the unannounced provider survey visit. If we spread patients across all providers on the roster (include those not present), or assumed that each facility would be open more days each month, the number of patients per average provider-day would decrease proportionally. For the 5% of facilities with over 10 staff on the roster, we did not have a true sample of provider availability from the vignette survey. In these cases, we estimated the number of providers present. To do so, we calculated the average share of providers present across all facilities with 2–10 providers (60%), and multiplied that by the total number of providers listed on the roster. To have the providers observed in the vignettes represent the present providers not observed in the vignettes, we weighted those observations by the factor appropriate to each facility’s staff roster count. The **Supplemental Appendix** describes these computations and the robustness of our results to the exclusion of those large facilities where we estimated the true number of present staff.

Following Daniels _et al._ (2024),<sup>9</sup> we used item response theory (IRT) to estimate a continuously-distributed provider competence index based on adherence to the predetermined diagnostic checklists in the clinical vignettes. We used a regression model to predict the share of vignette cases each provider would have correctly managed, which increases from 14% at the 10th percentile to 55% at the 90th percentile. We report either the predicted or actual correct management shares as our measure of quality as statistically appropriate.

# **2.3 Simulation: Potential Gains from Provider Reallocation**

We characterize the potential quality gains from improving the allocation of providers across facilities in the following way. We define a “posting” as the number of daily outpatients seen by each provider as calculated above. We then define an “optimal

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