Can Public Rankings Improve School Performance?

Notes: Each column represents a separate regression. All specifications include district-by-year fixed effects, flexible controls for lagged test scores, and indicators for prereform associations between district-rank deciles and subsequent outcomes. Reported coefficients correspond to the differential effect of being ranked in the associated decile of within-district performance in the post- (vs. pre-) reform period, compared to the middle six deciles. In even columns, the specification is augmented with school fixed effects. In Columns 1 and 2 the outcome is the average PSLE score (ranging from 0–250), in Columns 3 and 4 it is the pass rate, and in Columns 5 and 6 it is the number of pupils who passed. 300 singleton schools are dropped when results are estimated using school fixed effects. Standard errors are clustered at the district level.

Notes: Each column represents a separate regression. All specifications include flexible controls for lagged test scores and school and district-by-year fixed effects. Column 1 is estimated on outcomes from 2012–2016, including indicators for prereform associations between district-rank deciles and subsequent outcomes. Reported coefficients in that column correspond to the differential effect of being ranked in the associated decile of within-district performance in the post- (vs. pre-) reform period, compared to the middle six deciles. In Columns 2–6, data are restricted to postreform years 2015–2016 in which EMIS data are available; this does not allow for a difference-in-difference specification. In Columns 1 and 2 the outcome variable is the number students sitting the exam, using PSLE data. In Columns 3–6 outcomes are different constructions of enrollment, based on EMIS data. The outcome in Columns 3–5 is the number of students enrolled in the corresponding grade(s). In Column 6 the outcome indicator is Grade 7 enrollment in 2016 divided by Grade 6 enrollment in 2015. Standard errors are clustered at the district level.

Notes: Each column represents a separate regression, estimated using SDI data, with flexible controls for lagged test scores and district-by-year and school fixed effects. Coefficients correspond to the effect of being ranked in the associated decile of within-district performance in the postreform period, compared to the middle six deciles. The SDI data were collected in 2014 and 2016 (the postreform period), but some variables were collected using the previous year (2013 or 2015) as the reference period. For each column, only two years of data are available: Columns 1, 4, and 5 use outcomes for the years of 2014 and 2016, and Columns 2 and 3 use outcomes for the years 2013 and 2015. The dependent variables in Columns 2 and 3 are inverse hyperbolic sine transformations (an approximation for the natural logarithm) and calculated at a per-student level, using enrollment data from 2014. Data from Column 3 are reported in Tanzanian shillings. The mean values reported in the penultimate row is of the untransformed outcome. We adjust for outliers in the following way: (i) we adjust downwards the per-student capitation grant to the maximum that a school can receive, 10,000 Tanzanian shillings; (ii) we set as missing one school that reported receiving 600 textbooks per student. Since the specifications include school fixed effects, schools with only one observation are dropped. Standard errors are clustered at the district level.