Guidance on methods for analyzing PEPFAR prime and subpartner expenditures and results

To achieve this, USAID asked Data.FI to analyze project expenditures and results under different partnership models using two data sources: PEPFAR expenditure reporting (ER) data and PEPFAR monitoring, evaluation, and reporting (MER) data. The analysis sampled two projects: Strengthening Integrated Delivery of HIV/AIDS Services (SIDHAS) in Nigeria and the Boresha Afya Southern Zone (BASZ) in Tanzania. For each project, standardized PEPFAR ER data were used to obtain project expenditures, and PEPFAR MER indicator data were used to represent program results. Both data sets were for FY 2019. The ER data are organized by subrecipient, while the MER data are organized by facility, including the country and subregion (priority subnational unit, or PSNU) where the facility is located. The analysis aimed to link the subrecipients included in the ER data to the facilities that have attributed results in the MER data to facilitate a comparison of expenditures and results. The analysis identified the types of partners used by these projects, including international prime partners, primary subpartners, and local subrecipients. The prime partners report all the program management expenditures in the ER data, but do not have attributed results in the MER data. The primary subpartners provide technical assistance and management services to the projects, but do not have attributed results in the MER data. The analysis used information about the location of local subrecipients in the subrecipient name to map 54 of the 58 local subrecipients to states in the SIDHAS project. This provides an example of a mapping by geography, which may be relevant where there is a clear relationship between subrecipients and regions. In analyzing the BASZ data, the analysis used additional information provided by the project, in the form of a table that linked each facility to one of the primary subpartner organizations. This mapping allowed the analysis to summarize the results data by the supporting primary subpartner. The analysis further described some of the visualizations used in the related analysis brief, including figures that display a breakdown of SIDHAS project expenditures by partner type and expenditure class, and a comparison of shares of expenditures and results for HTS and C&T by state among the SIDHAS local subrecipients. The analysis demonstrated that standardized ER and MER datasets can be used to help understand the contribution of partners within a consortium to project results. However, the analysis was limited by the lack of information that allows a direct linkage of facilities to subrecipients.