Training Frontline Health Workers in Low- and Middle-Income Countries to Reduce Contraceptive Stock-Outs: Evidence from Indonesia

Frontline health workers are an indispensable asset in the healthcare ecosystem of low- and middle-income countries (LMICs). They not only provide clinical care to patients but also frequently shoulder nonclinical responsibilities such as inventory management. However, many lack the necessary skills to effectively manage inventories, leading to the pervasive problem of health commodity stock-outs. To address this problem, several LMICs have launched training programs aimed at enhancing the inventory management skills and capabilities of frontline health workers. Leveraging fine-grained data over a five-year period (2015–2019) from approximately 17,000 health facilities across Indonesia—where the public health supply chain operates as a hybrid push–pull model—we evaluate the impact of a large-scale inventory management training program on contraceptive stock-outs. Applying difference-in-differences estimations, we find that the training program is associated with a significant reduction in stock-outs (i.e., 6.52 percentage points, or approximately 30%). Our numerical calculations suggest that the training program prevents, on average, more than 800 unintended pregnancies and saves 4.25 maternal and newborn lives per 100,000 women of reproductive age within the catchment area of a treated health facility. A counterfactual benefit–cost analysis shows that on-site delivery of the training program (i.e., on the job within a health facility) yields operational and public health benefits that significantly exceed those achieved through off-site delivery (i.e., outside the health facility in a classroom). Our research highlights the practicality and scalability of human-capital-focused interventions in improving health commodity availability and public health outcomes in LMICs.

This paper was accepted by Stefan Scholtes, healthcare management.

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03696.