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Remedying Education: Evidence from Two Randomised Experiments in India

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Author Info
Abhijit Banerjee ()
Abstract

Many efforts to improve school quality by adding school resources have proven to be ineffective. This paper presents the results of two experiments conducted in Mumbai and Vadodara, India, designed to evaluate ways to improve the quality of education in urban slums. A remedial education programme hired young women from the community to teach basic literacy and numeracy skills to children lagging behind in government schools. We find the programme to be very effective: it increased average test scores of all children in treatment schools by 0.14 standard deviations in the first year, and 0.28 in the second year, relative to comparison schools. A computer-assisted learning programme provided each child in the fourth grade with two hours of shared computer time per week, in which students played educational games that reinforced mathematics skills. The programme was also very effective, increasing math scores by 0.35 standard deviations the first year, and 0.47 the second year. These results were not limited to the period in which students received assistance, but persisted for at least one year after leaving the programme. Two instrumental variable strategies suggest that while remedial education benefited the children who attended the remedial classes, their classmates, who did not attend the remedial courses but did experience smaller classes, did not post gains, confirming that resources alone may not be sufficient to improve outcomes.

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Paper provided by esocialsciences.com in its series Working Papers with number id:360.

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Date of creation: 2006
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Handle: RePEc:ess:wpaper:id:360

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Related research
Keywords: education; school quality; basic literacy; numeracy; computer-assisted learning programme; Sociology; Education;

References listed on IDEAS
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  1. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March. [Downloadable!] (restricted)
  2. Michael Kremer & Nazmul Chaudhury & F. Halsey Rogers & Karthik Muralidharan & Jeffrey Hammer, 2005. "Teacher Absence in India: A Snapshot," Journal of the European Economic Association, MIT Press, vol. 3(2-3), pages 658-667, 04/05. [Downloadable!] (restricted)
  3. Cecilia E. Rouse & Alan B. Krueger, 2004. "Putting Computerized Instruction to the Test: A Randomized Evaluation of a "Scientifically-based" Reading Program," NBER Working Papers 10315, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  4. Hanushek, Eric A, 1995. "Interpreting Recent Research on Schooling in Developing Countries," World Bank Research Observer, Oxford University Press, vol. 10(2), pages 227-46, August.
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  5. Paul Glewwe & Michael Kremer & Sylvie Moulin & Eric Zitzewitz, 2000. "Retrospective vs. Prospective Analyses of School Inputs: The Case of Flip Charts in Kenya," NBER Working Papers 8018, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  6. Hanushek, Eric A, 1986. "The Economics of Schooling: Production and Efficiency in Public Schools," Journal of Economic Literature, American Economic Association, vol. 24(3), pages 1141-77, September. [Downloadable!] (restricted)
  7. Joshua Angrist & Victor Lavy, 2002. "New Evidence on Classroom Computers and Pupil Learning," Economic Journal, Royal Economic Society, vol. 112(482), pages 735-765, October. [Downloadable!] (restricted)
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  8. Michael Kremer & Edward Miguel & Rebecca Thornton, 2004. "Incentives to Learn," NBER Working Papers 10971, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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