either take individuals or service providers as the unit of observation. We would argue that the only cost-effective option is to focus on service provider units. Obviously, this choice will to some extent restrict what type of data we can collect and indicators we can create.

More specifically, regarding the DSI index, we consider a service delivery production function, _f_ , which maps physical inputs, _x_ , the effort put in by the service provider _e_ , as well as his/her type (or knowledge),  ,<sup>3</sup> to deliver quality services into individual level outcomes, _y_ . The effort variable _e_ could be thought of as a vector and thus include effort (broadly defined) of other actors in the service delivery system. Of course, as noted above, individual outcomes are not just affected by the service delivery unit, but also by the actions and behaviors of households,  . We can therefore write To assess the quality of services provided one should therefore measure _f_  _x_ , _e_ ,  . Of course, the arguments in _f_ are notoriously difficult to measure, and would involve a huge data collection effort. A more feasible approach is therefore to focus instead on the arguments which, to a firstorder approximation, have the largest effects. The recent literature on the economics of education and health gives a fairly clear picture.

While there is little evidence that traditional input indicators (staff, material, infrastructure, etc.) are related to education and health outcomes in developed countries, there is some evidence that resource policies positively influence student performance in developing countries, hinting to diminishing returns for resources (Hanushek, 2003, p. 84). In a meta-analysis of 96 education studies during the 1980s in developing countries, several estimates indicate that increased infrastructure and material positively influence student performance (Hanushek, 2003, Table 6). More recent evidence from natural experiments and randomized evaluations indicate some potential positive effect of school resources on outcomes, but not uniformly positive (Duflo 2001; Glewwe and Kremer 2006).<sup>4</sup>

> 3 We can think of type as the characteristic (knowledge) of the individuals self-selected into a specific tasks (or job).

> 4 Case and Deaton (1999) show, using a natural experiment in South Africa, that increase in school resources (as measured by the student-teacher ratio) raises academic achievement among black students. They find that decreasing the student-teacher ratio by half raises students’ reading test scores by an amount equivalent to the impact of two additional years of schooling (Hanushek, 2003). Also using a natural experiment, Duflo (2001) finds that a school construction policy in Indonesia was effective in increasing the quantity of education. She estimates that each school built for every 1,000 children led to an average increase of 0.12 years of education. Banerjee et al (2000) find, using a randomized evaluation in India, that provision of additional teachers in nonformal education centers increases school participation by girls. Furthermore, randomized trials in Nicaragua show that increase number of books has significant impacts on pupils' math scores (Jamison et al 1981). Similarly, Tan et al (1997) show that provision of textbooks raises performance on academic tests in the Philippines. However, a series of randomized evaluations in Kenya indicate that the only effect of textbooks on outcomes was among the better students (Glewwe and Kremer, 2006; Glewwe, Kremer and Moulin, 2002).