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Assessing the performance of matching algorithms when selection into treatment is strong
Author(s) -
Augurzky Boris,
Kluve Jochen
Publication year - 2007
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.919
Subject(s) - matching (statistics) , selection (genetic algorithm) , earnings , computer science , measure (data warehouse) , inference , blossom algorithm , econometrics , algorithm , task (project management) , statistical inference , mathematical optimization , machine learning , data mining , mathematics , artificial intelligence , statistics , economics , finance , management
This paper investigates the method of matching regarding two crucial implementation choices: the distance measure and the type of algorithm. We implement optimal full matching—a fully efficient algorithm—and present a framework for statistical inference. The implementation uses data from the NLSY79 to study the effect of college education on earnings. We find that decisions regarding the matching algorithm depend on the structure of the data: In the case of strong selection into treatment and treatment effect heterogeneity a full matching seems preferable. If heterogeneity is weak, pair matching suffices. Copyright © 2007 John Wiley & Sons, Ltd.