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Assessing the performance of matching algorithms when selection into treatment is strong

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dc.creator Augurzky, Boris
dc.creator Kluve, Jochen
dc.date 2004
dc.date.accessioned 2013-10-16T07:00:36Z
dc.date.available 2013-10-16T07:00:36Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/18572
dc.identifier ppn:396464890
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/18572
dc.description 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.
dc.language eng
dc.publisher
dc.relation RWI Discussion Papers 21
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject C14
dc.subject C61
dc.subject ddc:330
dc.subject Matching algorithms
dc.subject optimal full matching
dc.subject selection into treatment
dc.subject Mikroökonometrie
dc.subject Matching
dc.subject Bildungsertrag
dc.subject Schätzung
dc.subject Theorie
dc.subject Vereinigte Staaten
dc.title Assessing the performance of matching algorithms when selection into treatment is strong
dc.type doc-type:workingPaper


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