EconPapers    
Economics at your fingertips  
 

Identifying Treatment Effects with Data Combination and Unobserved Heterogeneity

Pablo Lavado () and Gonzalo Rivera
Additional contact information
Gonzalo Rivera: World Bank

No 2016-79, Working Papers from Peruvian Economic Association

Abstract: This paper considers identification of treatment effects when the outcome variables and covariates are not observed in the same data sets. Ecological inference models, where aggregate outcome information is combined with individual demographic information, are a common example of these situations. In this context, the counterfactual distributions and the treatment effects are not point identified. However, recent results provide bounds to partially identify causal effects. Unlike previous works, this paper adopts the selection on unobservables assumption, which means that randomization of treatment assignments is not achieved until time fixed unobserved heterogeneity is controlled for. Panel data models linear in the unobserved components are considered to achieve identification. To assess the performance of these bounds, this paper provides a simulation exercise.

New Economics Papers: this item is included in nep-ecm
Date: 2016-12
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://perueconomics.org/wp-content/uploads/2016/12/WP-79.pdf Application/pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:apc:wpaper:2016-079

Access Statistics for this paper

More papers in Working Papers from Peruvian Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Nelson Ramírez-Rondán ().

 
Page updated 2019-08-20
Handle: RePEc:apc:wpaper:2016-079