because the income and aspirations that drive households to choose private schooling also drive them to get tutoring for their children, or because private schooling, though often preferred to government schools, also suffers from important deficiencies, or both.

# **III. Private sector engagement and student achievement in South Asia**

18. In this section, we present evidence of student achievement, as measured in terms of test scores, associated with the different types of private sector engagement. The order of the subsections follows roughly the order of the types listed in Table 1, with some adjustments to facilitate discussion of underlying factors and policy implications. The subsection titles mention the countries for which evidence is available. For each type of private sector engagement, there may be countries that are discussed in the previous section with regards to the type’s extent, socio-economic patterns and over-time evolution, but not discussed in this section due to lack of evidence with regards to student achievement. For a richer and more complete discussion, we refer to examples and evidence from outside of South Asia where possible and relevant. Muralidharan and Sundararaman (2013) point out the importance of the choice of subjects for measuring and comparing test scores due to potentially higher-than-average performance in high-stakes subjects, or due to different schools focusing on different subjects. We are not able to address this issue in our paper, and acknowledge the limitation.

# **III.a. Unaided private schools in India, Nepal and Pakistan (Type (1) in Table 1)**

19. A number of studies, mostly on India (see, for example, Kingdon, 2007; Muralidharan and Kremer, 2007; Tooley and Dixon, 2007; Goyal, 2009; Desai et al., 2009; Tooley, 2009; French and Kingdon, 2010; Goyal and Pandey, 2011) and some on Pakistan and Nepal (for example, Andrabi et. al., 2007; Aslam, 2009; Thapa, 2011), find that unaided schools perform poorly but on average at least as well as government schools. Figure 6 presents the distributions of several test score datasets for the three countries. For India, the first dataset is criterionreferenced scores from the 2009 internationally standardized test<sup>6</sup> PISA administered to a sample of 15-year-olds at their schools in the states of Himachal Pradesh and Tamil Nadu. The second dataset is criterion-referenced scores from a short standardized test administered to 8–11 yearolds from a nationally representative sample of households in 2004/05. For Nepal, the scores are of a nationally representative sample of students who appeared for the 2004 nationwide SLC test at the end of grade 10. And the dataset for Pakistan is percentages of correct answers on standardized tests conducted at the school of Grade 4 students in rural areas in three Punjab districts in 2006 (Andrabi et al., 2007). The “aided” and “unaided” categories are defined for India only; the vast majority of private schools in Nepal and Pakistan are unaided, as mentioned in the previous section. Across the graphs, the score distribution is more to the right for students in unaided schools than for students in government schools, although both school types have academically strong and weak students.

> 6 For standardized tests, the questions, conditions for administering, scoring procedures and interpretations are consistent with a predetermined standard framework. There are two types of standardized test score interpretations: the norm-referenced interpretation compares a student’s test score to the scores of other students who have taken the test while the criterion-referenced interpretation compares a student’s test score to a pre-defined set of criteria.

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