12 cases can be seen here: <u>http://www.rbfhealth.org/resource/video-vignettes-lab-field-experimentburkina-faso).</u> Health worker behavior under different incentive schemes also depends on how they internalize the welfare effects of their decisions on patients. To capture those effects in the experiment, we implemented a link between effort on behalf of poor patients and the provision of benefits to actual poor people; and effort on behalf of non-poor patients and the provision of benefits to actual non-poor people. Accurate diagnoses and treatment of poor patients generated benefits for the poor: donations to a poor school. Similarly, accurate diagnoses and treatment of non-poor patients generated benefits for the non-poor: donations to a non-poor school.<sup>7</sup> To reduce noise in the pro-poor measure, we sought to hold constant the context and demographic characteristics of the poor and non-poor beneficiaries of subject effort, varying only their income. The instructions informed subjects that correct responses to the questions in the medical cases would generate donations to two schools in the capital city, Ouagadougou, the same two schools as above. This measure of pro-poor preferences holds constant demographics (beneficiaries were all children, and all located in Ouagadougou) and context (all the children are in primary school). A non-poor school was the beneficiary of accuracy in non-poor cases and a poor school was the beneficiary of accuracy in poor cases. To make the poor and non-poor differences more salient to subjects, we showed them pictures of classrooms in the poor and non-poor schools (figures 4a and 4b). Subjects saw pictures of the poor and non-poor classrooms, both either taken from the front or the rear of the classroom (randomly assigned and from the same angle), and always capturing a similar number of children.

Besides the photographs, subjects were given some additional information about the two schools: school fees (2,000 CFA per year versus 300,000 CFA per year); class size (57 students versus 25); percentage of students who passed the primary school-leaving exam (CEP) in 2013 (92 versus 100 percent); and what the schools indicated they would do with the additional funds (health; rehabilitating and equipping classrooms; and rehabilitation of the water well versus increasing modernizing classroom equipment - introducing more technology - and improving sports facilities and equipment).

In sum, then, poor and non-poor cases differed in four ways. Poor cases were more complex; they were more difficult to understand; they took more time; and they generated actual donations to members of poor groups. Note that, although subjects received ample information that allowed them to distinguish poor and non-poor patients, and poor and non-poor schools, at no time were the labels “poor” and “non-poor” utilized. The schools were given neutral labels (A and B), as were the health cases (X and Y).

> 7 Ideally, we would have assigned poor-patient donations to a health clinic that served the poor and non-poor-patient donations to a health clinic that served the non-poor.  This turned out not to be possible since we could not find a health clinic that specifically served the non-poor.  However, the mismatch between actual beneficiaries (schoolchildren) and experimental beneficiaries (patients) is only problematic if school beneficiaries exaggerate the effects of “propoorness” compared to health clinic beneficiaries.  This might happen, for example, if health care professionals had exhibited little variation in the share of donations that they might have given to poor clinics (as opposed to poor schools), preventing any identification of the effects of pro-poor preferences on effort.  There is no behavioral or empirical reason to think that primary school beneficiaries would yield such a bias, however.