Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/208173 
Year of Publication: 
2019
Series/Report no.: 
IDB Working Paper Series No. IDB-WP-985
Publisher: 
Inter-American Development Bank (IDB), Washington, DC
Abstract: 
This paper partially identifies population treatment effects in observational data under sample selection, without the benefit of random treatment assignment. Bounds are provided for both average and quantile population treatment effects, combining assumptions for the selected and the non-selected subsamples. We show how different assumptions help narrow identification regions, and we 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.
Subjects: 
Sample selection
Population treatment effects
Partial identification
Bounds
Observational data
PISA
Brazil
JEL: 
C21
C24
I2
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper

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