Predicting School Dropout with Administrative Data

Predicting School Dropout with Administrative Data

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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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Bibliographic Details
Main Authors: Ham, Andres, Adelman, Melissa, Vazquez, Emmanuel, Haimovich, Francisco
Format: Working Paper biblioteca
Language:English
en_US
Published: World Bank, Washington, DC 2017-07
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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spelling 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
institution Banco Mundial
collection DSpace
country Estados Unidos
countrycode US
component Bibliográfico
access En linea
databasecode dig-okr
tag biblioteca
region America del Norte
libraryname Biblioteca del Banco Mundial
language English
en_US
topic DROPOUT RATES
SCHOOL ENROLLMENT
SECONDARY EDUCATION
PREDICTIVE MODEL
DROPOUT RATES
SCHOOL ENROLLMENT
SECONDARY EDUCATION
PREDICTIVE MODEL
spellingShingle 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
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