Predicting School Dropout with Administrative Data : New Evidence from Guatemala and Honduras
Across Latin America, school dropout is a growing concern, because of its negative social and economic consequences. Although a wide range of interventions hold potential to reduce dropout rates, policy makers in many countries must first address t...
| Main Authors: | , , , |
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| Format: | Working Paper |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2017
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/273541499700395624/Predicting-school-dropout-with-administrative-data-new-evidence-from-Guatemala-and-Honduras http://hdl.handle.net/10986/27645 |
| Summary: | Across Latin America, school dropout is
a growing concern, because of its negative social and
economic consequences. Although a wide range of
interventions hold potential to reduce dropout rates, policy
makers in many countries must first address the basic
question of how to target limited resources effectively for
such interventions. Identifying who is most likely to drop
out and, therefore, who should be prioritized for targeting,
is a prediction problem that has been addressed in a rich
set of research in countries with strong education system
data. This paper makes use of newly established
administrative data systems in Guatemala and Honduras, to
estimate some of the first dropout prediction models for
lower-middle-income countries. These models can correctly
identify 80 percent of sixth grade students who will drop
out in the transition to lower secondary school, performing
as well as models used in the United States and providing
more accurate results than other commonly used targeting approaches. |
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