Predicting School Dropout with Administrative Data
School dropout is a growing concern across Latin America because of its negative social and economic consequences. Identifying who is likely to drop out, and therefore could be targeted for interventions, is a well-studied prediction problem in countries with strong administrative data. In this paper, we use new data in Guatemala and Honduras to estimate some of the first dropout prediction models for lower-middle income countries. These models correctly identify 80% of sixth grade students who will drop out within the next year, performing better than other commonly used targeting approaches and as well as models used in the United States.
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| Main Authors: | , , , |
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| Format: | Journal Article biblioteca |
| Published: |
Taylor and Francis
2018
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| Subjects: | DROPOUT RATE, SCHOOL ADMINISTRATION, PRIMARY EDUCATION, SECONDARY EDUCATION, PREDICTION, EARLY WARNING SYSTEM, ENROLLMENT, BASIC EDUCATION, |
| Online Access: | http://hdl.handle.net/10986/30146 |
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