Predicting School Dropout with Administrative Data
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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Format: | Working Paper biblioteca |
Language: | English en_US |
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World Bank, Washington, DC
2017-07
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Subjects: | DROPOUT RATES, SCHOOL ENROLLMENT, SECONDARY EDUCATION, PREDICTIVE MODEL, |
Online Access: | http://documents.worldbank.org/curated/en/273541499700395624/Predicting-school-dropout-with-administrative-data-new-evidence-from-Guatemala-and-Honduras https://hdl.handle.net/10986/27645 |
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dig-okr-10986276452024-08-09T08:42:33Z Predicting School Dropout with Administrative Data New Evidence from Guatemala and Honduras Ham, Andres Adelman, Melissa Vazquez, Emmanuel Haimovich, Francisco DROPOUT RATES SCHOOL ENROLLMENT SECONDARY EDUCATION PREDICTIVE MODEL 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. 2017-07-19T18:08:36Z 2017-07-19T18:08:36Z 2017-07 Working Paper Document de travail Documento de trabajo http://documents.worldbank.org/curated/en/273541499700395624/Predicting-school-dropout-with-administrative-data-new-evidence-from-Guatemala-and-Honduras https://hdl.handle.net/10986/27645 English en_US Policy Research Working Paper;No. 8142 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank application/pdf text/plain World Bank, Washington, DC |
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DROPOUT RATES SCHOOL ENROLLMENT SECONDARY EDUCATION PREDICTIVE MODEL DROPOUT RATES SCHOOL ENROLLMENT SECONDARY EDUCATION PREDICTIVE MODEL |
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DROPOUT RATES SCHOOL ENROLLMENT SECONDARY EDUCATION PREDICTIVE MODEL DROPOUT RATES SCHOOL ENROLLMENT SECONDARY EDUCATION PREDICTIVE MODEL Ham, Andres Adelman, Melissa Vazquez, Emmanuel Haimovich, Francisco Predicting School Dropout with Administrative Data |
description |
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. |
format |
Working Paper |
topic_facet |
DROPOUT RATES SCHOOL ENROLLMENT SECONDARY EDUCATION PREDICTIVE MODEL |
author |
Ham, Andres Adelman, Melissa Vazquez, Emmanuel Haimovich, Francisco |
author_facet |
Ham, Andres Adelman, Melissa Vazquez, Emmanuel Haimovich, Francisco |
author_sort |
Ham, Andres |
title |
Predicting School Dropout with Administrative Data |
title_short |
Predicting School Dropout with Administrative Data |
title_full |
Predicting School Dropout with Administrative Data |
title_fullStr |
Predicting School Dropout with Administrative Data |
title_full_unstemmed |
Predicting School Dropout with Administrative Data |
title_sort |
predicting school dropout with administrative data |
publisher |
World Bank, Washington, DC |
publishDate |
2017-07 |
url |
http://documents.worldbank.org/curated/en/273541499700395624/Predicting-school-dropout-with-administrative-data-new-evidence-from-Guatemala-and-Honduras https://hdl.handle.net/10986/27645 |
work_keys_str_mv |
AT hamandres predictingschooldropoutwithadministrativedata AT adelmanmelissa predictingschooldropoutwithadministrativedata AT vazquezemmanuel predictingschooldropoutwithadministrativedata AT haimovichfrancisco predictingschooldropoutwithadministrativedata AT hamandres newevidencefromguatemalaandhonduras AT adelmanmelissa newevidencefromguatemalaandhonduras AT vazquezemmanuel newevidencefromguatemalaandhonduras AT haimovichfrancisco newevidencefromguatemalaandhonduras |
_version_ |
1807157436048474112 |