Predicting School Dropout with Administrative Data (Record no. 30348)

000 -LEADER
fixed length control field 02176cam a22003134a 4500
001 - CONTROL NUMBER
control field 8142
003 - CONTROL NUMBER IDENTIFIER
control field The World Bank
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20181114095513.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 160311s2017 dcu o i00 0 eng
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1596/1813-9450-8142
035 ## - SYSTEM CONTROL NUMBER
System control number (The World Bank)8142
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Adelman, Melissa.
245 10 - TITLE STATEMENT
Title Predicting School Dropout with Administrative Data
Medium [electronic resource] :
Remainder of title New Evidence from Guatemala and Honduras /
Statement of responsibility, etc Melissa Adelman.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Washington, D.C. :
Name of publisher, distributor, etc The World Bank,
Date of publication, distribution, etc 2017.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (42 p.)
520 3# - SUMMARY, ETC.
Summary, etc 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.
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Dropout
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Early Warning
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Prediction
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Adelman, Melissa.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Haimovich, Francisco.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ham, Andres.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Vazquez, Emmanuel.
776 18 - ADDITIONAL PHYSICAL FORM ENTRY
Main entry heading Print Version:
Display text Adelman, Melissa
Title Predicting School Dropout with Administrative Data: New Evidence from Guatemala and Honduras
Place, publisher, and date of publication Washington, D.C. : The World Bank, 2017
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Policy research working papers.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title World Bank e-Library.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-8142">http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-8142</a>

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