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# Part II. Accountability, Good Goveranance and School Performance

## **II.a. Are There Recipies for School Success? Lessons from Prior Research**

43. A large body of research is focused on identifying the determinants of student achievement in schools and school performance. The most commonly used approach for the study of educational outcomes is the so-called _production function approach_ (Hanushek, 1996), which relates students‟ and schools‟ performance to a number of measurable educational inputs, some of which (for example, physical and human resources available in schools) can be controlled by policy-makers while others (such as children‟s innate ability and socioeconomic background) cannot (although they can be indirectly affected by certain specific policies). The school (physical) resources usually investigated within the _production function framework_ are: the quality of building and infrastructures; the teacher-pupil ratio; and, when available, expenditure per pupil, and teachers‟ salaries. The most commonly used proxies for human resources are teachers‟ characteristics, such as years of schooling and teaching experience.

44. The empirical evidence on the effect of physical and human resources on students‟ achievement is mixed. Harbison and Hanushek (1992) provide a comprehensive review of 96 studies based on _retrospective data_ , i.e. based on ordinary (i.e. non-experimental data collected as responses to surveys) variation across schools and households in developing and transition countries, and illustrate that none of the physical and human resources listed above has been shown to be consistently and significantly related to educational outputs. In other words, “there seems to be no clear and systematic relationship between key inputs and educational outputs” (Hanushek, 1996). Further investigations in Ghana (Glewwe and Jacoby, 1994), Jamaica (Glewwe et al, 1995), India (Kingdon, 1996) and the Philippines (Tan et al. 1997) confirm that the quest for a causal link between teacher characteristics and school resources on the one hand, and educational attainment on the other, is still far from complete. Indeed, in the studies cited most variables proved statistically insignificant, and while each investigation found some teacher and school characteristics to be statistically significant, the specific resources tested or found significant varied greatly across the different studies.

45. Krueger (1999) highlights that “much of the uncertainty in the literature derives from the fact that the appropriate specification of the _education production function_ is uncertain”. Along the same line, Glewwe and Kremer (2005) stress that omitted variables at either the student, class, school, or local level may bias the estimates based on retrospective data either upward or downward. For example, unobservables such as children‟s innate ability and parents‟ motivation may be positively correlated with school overall performance, if parents enroll high-ability children in better performing schools. Similarly, teachers‟ unobservable characteristics, such as motivation, are likely to bias the estimates upward, if they are correlated with the observable characteristics included in the empirical specification.<sup>8</sup> 46. Researchers have tried to overcome these limitations by employing data generated by “natural experiments” or by conducting randomized control trials (RCTs). The resulting evidence on the impact of school resources on education outputs is still mixed. While studies exploiting natural experiments suggest, for instance, that reducing the pupil-teacher ratio increases test scores (Krueger, 1999; Angrist and Levy, 1999, among the others), RCTs conducted by Duflo et al. (2009)

> 8 The differences in the results obtained by different studies may therefore be the result of the different specifications used and, consequently, different omitted variables. Possible multicollinearity between the many school and teacher variables used with the attempt to reduce omitted variable bias, may also be responsible for the discrepancy in estimates across studies.  See Glewwe and Kremer (2005) for a comprehensive discussion of all possible limitations of estimates of education production functions based on retrospective data. Section 2 will also provide further discussion on this.