excluding individuals who do not have common support with the sample of patients observed in the facility.<sup>7</sup> There are three basic messages. First, the relationship between satisfaction and individual characteristics is similar to that in the exit-survey: Most individual characteristics are insignificant but, as in Table 4, older patients and those who report excellent health status report higher satisfaction. In addition, those who travel farther report lower satisfaction. Second, being interviewed at the clinic increases reported satisfaction by _22 to 27_ percentage points, a result that is highly significant at the 1 percent level of confidence and stable across all specifications. Being interviewed at home, in terms of satisfaction, is similar to the effect of a doctor putting in _7 standard-deviations_ less effort in the clinic (this is the difference between the doctor spending 1 minute and 30 minutes with the patient). Third, there are no significant interaction effects between doctor and patient characteristics, so that the drop in satisfaction is similar across all patient and doctor characteristics; it is not the case that richer or healthier patients report differential drops in reported satisfaction across the household and exit surveys.

# **Section III.1. Further Results and Some Speculation**

The large differences between the reported satisfaction of patients interviewed at home and those interviewed in the clinic raises the possibility that the difference was induced by the nature of the study—since we observed doctor-patient interactions for patients interviewed at the clinic, perhaps doctors put in higher effort levels than they normally would have. In this case, the difference reflects intrinsic differences in doctor’s effort. Leonard’s (2006) discussion of the Hawthorne effect in a parallel study in Tanzania points to this possibility; his study also suggests that the Hawthorne effect dissipates over time, so that interactions observed later on may be more similar to the “actual” effort put in by a doctor when s/he is not being observed.

Figure 1 indicates that patient order _is_ important, suggesting either that doctors get used to the observers over time or that (independent of whether they are observed or not), effort naturally declines over time. Here, we plot the non-parametric relationship between the doctor’s effort and the interaction order, where the 1<sup>st</sup> interaction we observed is coded as order = 1 and the 20<sup>th</sup> as order = 20. Since non-parametric plots are sensitive to the number of observations, we also overlay the total number of observations we have for every patient order; the percentages are labeled on the righthand axis.

Doctor effort is higher for the first interaction and drops off for later patients, steeply at first and gradually later on. A regression of doctor effort on patient order is highly significant in the linear and quadratic form, with and without additional patient controls and with and without patient fixedeffects (Columns 1-3, Table 6). However, the absolute magnitude of the drop-off is too small to potentially explain the difference between household and exit surveys. The estimated drop-off from the 1<sup>st</sup> to the 20<sup>th</sup> patient is around 0.25 standard-deviations in effort, whereas to account for the 22 percentage point difference requires a difference of 7 standard-deviations or 28 times what is actually observed in the data.

Equally troubling is that there is _no reported decline in patient satisfaction with the order of the interaction_ in the exit survey (Columns 3-6, Table 6). Thus, although doctor-effort declines significantly with patientorder, patient-satisfaction itself remains unaffected. Returning to Equation 2, the estimated 7 The trimming is done by first fitting the likelihood of participation in either sample as a function of observed patient characteristics using a logistic regression model. Individuals with greater than a 90 percent or lower than a 10 percent probability of participation in the household sample (and who therefore do not have counterparts in the facility sample) are then excluded from the estimation. These results are not sensitive to alternative cutoffs for trimming the sample.

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