Under CM, women use clinics more during pregnancy and childbirth, and their children’s health and survival improves substantially. By contrast, the NFA intervention does not appear to significantly affect either maternal utilization or health outcomes. The scorecards used in the CM intervention focus attention on child and maternal health — these are the only metrics discussed in clinic meeting and a majority of metrics presented at community meetings. It is possible that this emphasis leads to greater improvements for these families in CM, as compared to NFA.

The final row of Table 1 presents impacts on patient satisfaction. Across both treatment arms, patient satisfaction increases by about 0.10 standard deviations, largely driven by increases in respondents’ satisfaction with their own health and health workers (see Table D.4). These effects are consistent with the idea that the interventions led to improvements in the quality of care provided by health workers in ways that are difficult to capture in survey measures.<sup>26</sup> For example, improvements in quality could include the extent to which health providers listen to patients describing symptoms, or the effort they expend on diagnosis and selecting and explaining an appropriate plan of care, which would translate into greater satisfaction with their performance.

The results in Table 1 show that these interventions, which were designed to make frontline health staff more accountable to users, can generate substantial improvements in the quality of care that patients anticipate when visiting a clinic. Both interventions increase general utilization of clinics, a behavior that reveals would-be patients’ increased confidence in clinics, as well as patients’ reported satisfaction with their health and with health workers.

We now turn to other families of outcomes that might help explain increased utilization and, in CM, improved health outcomes.<sup>27</sup> Specifically, in Table 2, we examine effects on inputs and resources available at clinics (clinic quality), as well as the types and quantity of services provided (health service delivery). We also examine community support for the clinic and community development and political engagement (CDPE). A supply-side response — more resources at clinics or a larger menu of services — or groundswell of community support could also draw would-be patients into clinics and improve their outcomes.

Yet, we do not observe effects on the resources or services available at the clinic: the clinic quality and health service delivery families do not change significantly in response to either treatment (or jointly). This is perhaps unsurprising given that the CM and NFA interventions do not facilitate clinics’ access to supplies or training; the Government’s interest in the evaluation is understanding how to extract greater effort from health workers under existing budget and logistical constraints. None of the sub-components of clinic quality significantly improve (see Table D.5). And the coefficients are essentially zero or negative for those sub-

> 26 Patient-provider interactions are rarely directly observed; for a rare exception see (Das et al. 2016).

> 27 Spillovers do not provide a plausible explanation: our utilization measure is based on household surveys, and not clinic registers. If our endline respondents traveled to treated clinics for care this would attenuate our medium-run results, as it would appear to increase utilization among households living in the catchment areas of control clinics.

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