treated facility, we estimated the operational cost per facility for the pilot to be USD 495, which could be reduced to USD 285 for the scaled-up model.<sup>24</sup> Supplemental Material Section 8 presents a snapshot of the costs with further details in Bedoya et al. (2020). To ensure the validity of our estimates, we also provided data to an independent team to complete a third-party costing of our intervention. Including the fixed cost, they computed a per-visit estimate of USD 103 or USD 309 for a full cycle in the scaled-up model, which is only slightly higher than our estimates (Chege et al., 2022). This compares to a cost of $8,000 per facility reported by King et al. (2021) for a similar standards-based approach intervention for private facilities, and much higher than costs of between $8,900 and $108,000 for results-based financing interventions, which have become one important mechanism for quality improvement in this region.<sup>25</sup> What about benefits? Although we do not have data on health outcomes, we can interpret the increase in quality as an equivalent decline in price and use the (back-of-the) envelope theorem via Roy’s identity to compute the gain in consumer surplus as the decline in price multiplied by the total number of patients.<sup>26</sup> Based on Table 2 (column 2) we estimate that patients are willing to pay USD 0.15 PPP (USD 0.075 nominal) for one additional percentage point JHIC score in a facility at baseline, after controlling for patient-level characteristics. Facilities in treatment markets receive 8.1 million outpatient visits each year and the impact of the intervention on the JHIC score, weighted by patient load, is 3.93 percentage points. This yields annual estimated gains in consumer surplus of USD 2.4 million in nominal exchange rates, compared to an operational cost of USD 242,000 for scaled-up program per year. This gain in consumer surplus is 10 times the cost of the program but it may still be underestimated, both because we have assumed it accrues for only one year and because we have excluded inpatients, who may value quality even more, from this computation.

# **IV.5 Additional Results**

Having demonstrated the impacts of the regulation on the JHIC score and the market structure, we now present three additional results before turning to potential mechanisms. Specifically, we assess

> 24Costs were higher in the pilot because of a single office in the county headquarters, which increased travel costs and the fact that inspectors were seconded from different government institutions and transferred from other regions, resulting in a salary supplement. In a scaled-up version, the number and location of inspectors can be flexibly determined to minimize costs.

> 25See examples cited in Chege et al. (2022) such as De Allegri et al. (2019), Zeng et al. (2018), and Borghi et al. (2015).

> 26There are no systems for measuring nosocomial infections, vital statistics are incomplete and are not linked either to facilities or to geographical areas at a sufficiently granular level. Even if mortality data were available, the sample size requirements for sufficient power are exceedingly large. If we use a value of a statistical life of USD 50,000, which may be relevant for very poor populations, the intervention would have to save an additional 9 lives over 8 million outpatient visits for benefits to exceed costs (Li, 2020). To be well powered, this requires samples of more than 1 billion patients in each treatment arm, assuming mortality rates common to the literature (National Academies of Sciences, 2018; World Bank, 2020). Using a VSL of USD 200,000, or 100 times the Kenyan nominal per capita GDP (based on the World Bank’s Indicators), would imply that the intervention is cost effective even if it saves two additional lives which requires even larger sample sizes to detect (World Bank, 2020).

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