more than the lower quantiles within each market size group, and particularly so for markets with greater competition, as measured by the number of facilities. Interestingly, the differences between the lowest and highest quantiles are larger and more precisely estimated for private facilities.<sup>30</sup> Table 7: Treatment Effects on JHIC Score, OOP, Outpatients, and Entry by Treatment Groups

||Unwei|ghted|Weig|hted|||
|---|---|---|---|---|---|---|
||JHIC Score<br>(pp of max)<br>(1)|OOP<br>(USD PPP)<br>(2)|JHIC Score<br>(pp of max)<br>(3)|OOP<br>(USD PPP)<br>(4)|Daily<br>Outpatients<br>(5)|New<br>(6)|
|Inspection Only (T1)|5.435***|0.917**|4.193***|0.174|1.421|-0.024|
||(1.112)|(0.438)|(1.582)|(0.607)|(2.180)|(0.025)|
||[0.000]|[0.037]|[0.009]|[0.775]|[0.515]|[0.328]|
|Inspections plus Information (T2)|4.924***|1.020**|3.686***|0.106|1.537|0.032|
||(0.858)|(0.491)|(1.245)|(0.556)|(1.886)|(0.025)|
||[0.000]|[0.039]|[0.003]|[0.849]|[0.416]|[0.200]|
|Observations<br>|1285|1285|1285|1285|1285|1319|
|R<sup>2</sup>|0.317|0.127|0.517|0.178|0.247|0.054|
|Control Mean|35.493|4.069|42.526|3.136|20.793|0.133|
|T1 Impact: _{_%; SD_}_|_{_15%; 0.51_}_|_{_23%; 0.19_}_|_{_10%; 0.35_}_|_{_6%; 0.04_}_|_{_7%; 0.05_}_|_{_-18%; -0.07_}_|
|T2 Impact: _{_%; SD_}_<br>|_{_14%; 0.46_}_|_{_25%; 0.21_}_|_{_9%; 0.31_}_|_{_3%; 0.02_}_|_{_7%; 0.05_}_|_{_24%; 0.09_}_|
|Test (T1)=(T2) (p-value)|0.629|0.805|0.633|0.849|0.955|0.021|

_Notes_ . Robust standard errors are reported in parentheses and clustered at the market level. P-values are reported in brackets. *** (**) (*) denotes significance at 1% (5%) (10%) level. Missing values for OOP in 5.8% of observations are imputed using means defined by level, ownership, treatment, license status at randomization, and daily outpatients. Regressions include randomization strata controls (by county and market size) and health facility level controls. JHIC = Joint Health Inspection Checklist; OOP = out-of-pocket payments; PPP = purchasing power parity.

The quantile treatment effects indicate improvements at the higher end of the quality distribution, but because they do not tell us which facilities improved, we cannot link facility improvements to regulatory incentives. Our second exercise therefore assesses the importance of the threat of government closures as a channel for our results. The idea is that if facilities have those characteristics that make them more likely to be closed by the government, a regulatory channel would suggest that they should also have more incentive to improve.

To assess this possibility, we use a logit model to predict a facility’s likelihood of closure by the government as a function of pre-treatment or fixed characteristics for all private facilities at randomization (see Supplemental Material Section 6 for variable construction details). We then classify facilities into three groups based on their predicted probability of closure: (i) 57% (536 facilities) are classified as “Low” with closure probability equal or less than 0.4; (ii) 20% (187 facilities) as “Mid” with closure probability greater than 0.4 and less 0.6 and; (iii) 23% (219 facilities) as “High” with closure probability equal or greater than 0.6. The mean predicted probability of

> 30Table S16 in the Supplemental Material shows similar analyses using unconditional quantile treatment effects, with similar qualitative results. Table S8 in the Supplemental Material also shows that there is no statistically significant correlation between the market size, and the average JHIC score at the market level at baseline, and, for treatment markets, there is no significant correlation between market size at randomization and the month of first inspection visit in the market, or the average number of inspections per facility in the market.

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