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. Author manuscript; available in PMC: 2017 Oct 6.
Published in final edited form as: Economica. 2015 Nov 11;83(329):1–30. doi: 10.1111/ecca.12168

Table 1.

Girls’ Scholarship Programme Baseline Characteristics and Short-run Impacts (2001–2)

Dependent variable Comparison group variable mean (s.d.) Coefficient estimate (s.e.) on programme indicator
Panel A: Baseline characteristics (20012 surveys)
Student age (2001) 13.3 (1.44) −0.14 (0.15)
Iron roof ownership 0.82 (0.38) −0.048 (0.038)
Mother years of schooling 8.71 (4.18) 0.79* (0.40)
Father years of schooling 10.47 (3.99) 0.55 (0.49)
Proportion ethnic Luhya 0.79 (0.41) 0.067 (0.056)
Proportion ethnic Luo 0.104 (0.31) −0.054 (0.038)
Proportion ethnic Teso 0.055 (0.23) 0.018 (0.033)
Test score pre-programme, all subjects (normalized) 0.00 (1.00) 0.12 (0.20)
Panel B: Short-run impacts (20012)
Test score post-programme, all subjects (normalized) 0.00 (1.00) 0.34* (0.20)
Student school attendance 0.788 (0.36) 0.060* (0.032)
Teacher school attendance 0.822 (0.262) 0.069*** (0.025)

Notes

Each row is from a separate OLS regression. Significant at 90% (*), 95% (**), 99% (***) confidence.

The outcome variable is regressed on the GSP (treatment) indicator. Standard errors are clustered by school. The sample size in panel A ranges from 789 to 1387 observations depending on the dependent variable. The sample consists of female students in the GSP schools in Busia who were interviewed in the long-run follow-up and will be included in subsequent analysis. The academic subjects tested included English, geography/history/civics, mathematics, science and Swahili. The attendance data for both pupils and teachers were collected during unannounced visits to schools in 2001 and 2002. The sample size in panel B is 993 students in the test score regressions, and 1351 students and 666 teachers in the attendance regressions, respectively.