Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/263446 
Year of Publication: 
2022
Series/Report no.: 
IZA Discussion Papers No. 15230
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
We consider the identification of and inference on a partially linear model, when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using recent tools from optimal transport theory, we derive a constructive characterization of the sharp identified set. We then build on this result and develop a novel inference method that exploits the specific geometric properties of the identified set. Our method exhibits good performances in finite samples, while remaining very tractable. Finally, we apply our methodology to study intergenerational income mobility over the period 1850-1930 in the United States. Our method allows to relax the exclusion restrictions used in earlier work while delivering confidence regions that are informative.
Subjects: 
partially linear model
data combination
partial identification
intergenerational mobility
JEL: 
C14
C21
J62
Document Type: 
Working Paper

Files in This Item:
File
Size
994.89 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.