There are few significant differences between the sample of patients in all facilities and in the facilities where we also completed the household survey, although there _are_ differences between patients interviewed at home and those interviewed through exit surveys at the facility. The former tend to live closer to the facility, are older and more likely to be female. These differences are partially a result of the distance-restriction implicit in the survey design; the first because we only interviewed those who lived close to the facility, and the second because older patients are less likely to come from further away. Once we restrict ourselves to patients who come from similar distances, the differences between the two samples diminish, but patients interviewed at home are 20 percent more likely to be female and there are small but significant differences in the health status, with those at home reporting slightly worse health (Appendix Table 1). In sharp contrast to these small differences, there is a large and significant drop of 23 percentage points in reported satisfaction in the household survey.

Table 2 presents summary statistics for the observed interactions between doctors and patients. For every interaction, information volunteered by the patient (number of days sick and presenting symptoms) and the tasks that the doctor completed were noted. The latter included the time spent with the patient, the number of questions asked and the number of type of examinations performed. In addition, we also noted some aspects of the information that the doctor gave to the patient. These characteristics of the interaction—the time spent, the number of questions asked, and the number of physical examinations performed—were combined into a single “effort-index” using principal components analysis. The effort index is standardized with a mean of zero and a variance of one. On average, the effort exerted by the doctors is higher in Paraguay than in lower income countries, and is comparable to that observed in European countries (Table 2). Moreover, there are large differences in the nature of the interaction between doctors at different levels of the effort index. Doctors in the lowest tercile of the index spend around a third time, ask half as many questions, and perform a third as many examinations as those in the highest tercile. An increase of one standard-deviation in effort corresponds roughly to four extra minutes, three extra questions asked and 0.2 extra examinations performed by the doctor.

A second basic characterization of the effort-index and satisfaction variable is a decomposition of the total variation in these two variables into variation _within_ and variation _across_ doctors. That is, we ask whether most variation in effort and satisfaction is due to differences “across” doctors, so that some doctors always put in more effort or “within” doctors so that most doctors put in less effort with some patients and more with others. If some doctors consistently put in more effort and others less, variation _across_ doctors will account for most of the overall variation in the data. ANOVA (Analysis of Variance) decomposition suggests that the variation is roughly equivalent across the two categories—58 percent of the variation in effort and 28 percent of the variation in satisfaction is across doctors while 42 percent (effort) and 72 percent (satisfaction) is “within” doctors. The substantial variation “within” doctors is reassuring and presages a promising empirical strategy—by focusing on the variation in effort and satisfaction “ _within_ ” doctors, we can eliminate potential estimation biases arising from systematic variation in effort and satisfaction _across_ doctors. We turn to this next.

values into two allows us to present simpler probit specifications that can then be compared to the results from the linear probability model. This comparison is useful since in later specifications, we estimate fixed effect models only in the linear form.

5