We leverage this experimental design and original data from 5,080 households, 508 community leaders, and 254 clinics to estimate the causal effects of these interventions on both medium-run outcomes measured one year later, as well as their longer run effects on reporting during the Ebola crisis. The clinics in our study cover nearly 1 million people, just over 15 percent of Sierra Leone’s population in 2011.

In the medium-run, prior to the crisis, we find that both interventions improve the perceived quality of care and confidence in the health system, as reflected in individuals’ satisfaction with health workers and their increased utilization of clinics. Facing a health need, individuals living in treatment areas are 7 percent more likely to seek care in a government-run clinic; and satisfaction increases by 0.1 standard deviations. These effects are similar across both the CM and NFA arms. In CM alone, we find increases in maternal utilization — for example, the probability of delivery in a facility increases by 11 percent — as well as improvements in child health outcomes. The likelihood of under-5 death in the household declines by 38 percent, which we attribute to maternal utilization, as well as increases in vaccinations and improvements in child weight-for length. These effects are similar in magnitude to Björkman and Svensson (2009), who find a 33 percent reduction in under-5 mortality in clinics subject to community monitoring in Uganda. In our experimental context, neither intervention changes the quantity of services provided, nor leads to greater resources at the clinic level. This is not surprising, as the interventions do not attempt to increase inputs to clinics from higher levels of government.

To examine longer-run effects on outcomes related to the Ebola crisis, we use a de-identified database maintained by the GoSL and CDC. We count the number of reported cases (including individuals who test negative for Ebola) in a given week and section — a small administrative unit with a median area of 40 square kilometers.<sup>4</sup> Pooling the treatments, we estimate that the interventions increased total reported cases substantially, by just over 60 percent. While both treatments generate sizable increases, and we cannot reject the null that the treatments have equivalent effects, we see a larger increase in total reported cases in CM (above 70 percent). While most cases test negative for Ebola, the interventions also increase the number of infected patients reporting: a back-of-the-envelope calculation suggests that the effects on reporting by infected individuals reduces the disease’s reproduction rate ( _R_ 0) by around 19 percent (Pronyk et al. 2016). We find no evidence that geographic spillovers, i.e. the movement of patients from control to treatment sections, amplify these effects.

Analogous to our medium-run effects, improvements in health outcomes during the Ebola crisis concentrate in areas receiving the CM intervention. Among patients who report, we find that a smaller number die in treated sections: our results imply that 1 patient dies for every 7 that reported in treated sections in the current or last week; that worsens to 1 in every 4 in control. This difference is driven by sections with CM

> 4 The 254 clinics in the full sample fall in 205 sections. 160 sections contain a single clinic and, thus, have a unique treatment status and common dosage. We use this as our primary sample in analyzing Ebola-related outcomes; yet, we find similar effects with a dose-response model that employs the full sample of 205 sections (see Table E.11).

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