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dc.titlePartial Identification of Population Average and Quantile Treatment Effects in Observational Data under Sample Selection
dc.contributor.authorChristelis, Dimitris
dc.contributor.authorMessina, Julián
dc.contributor.orgunitDepartment of Research and Chief Economist
dc.coverageBrazil
dc.date.available2019-03-14T00:00:00
dc.date.issue2019-03-14T00:00:00
dc.description.abstractWe partially identify population treatment effects in observational data under sample selection, without the benefit of random treatment assignment. We provide bounds both for the average and the quantile population treatment effects, combining assumptions for the selected and the non-selected subsamples. We show how different assumptions help narrow identification regions, and illustrate our methods by partially identifying the effect of maternal education on the 2015 PISA math test scores in Brazil. We find that while sample selection increases considerably the uncertainty around the effect of maternal education, it is still possible to calculate informative identification regions.
dc.format.extent57
dc.identifier.doihttp://dx.doi.org/10.18235/0001596
dc.identifier.urlhttps://publications.iadb.org/publications/english/document/Partial_Identification_of_Population_Average_and_Quantile_Treatment_Effects_in_Observational_Data_under_Sample_Selection_en_en.pdf
dc.language.isoen
dc.mediumAdobe PDF
dc.publisherInter-American Development Bank
dc.subjectPISA Test
dc.subjectEducational Evaluation
dc.subjectTeaching of Mathematics
dc.subjectData Analytics
dc.subject.jelcodeC21 - Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions
dc.subject.jelcodeC24 - Truncated and Censored Models • Switching Regression Models • Threshold Regression Models
dc.subject.jelcodeI2 - Education and Research Institutions
dc.typeWorking Papers
idb.identifier.pubnumberIDB-WP-00985
idb.operationRG-K1415
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