Treatment Effects With Heterogeneous Externalities
Tiziano Arduini,
Eleonora Patacchini and
Edoardo Rainone
Journal of Business & Economic Statistics, 2020, vol. 38, issue 4, 826-838
Abstract:
This article proposes a new method for estimating heterogeneous externalities in policy analysis when social interactions take the linear-in-means form. We establish that the parameters of interest can be identified and consistently estimated using specific functions of the share of the eligible population. We also study the finite sample performance of the proposed estimators using Monte Carlo simulations. The method is illustrated using data on the PROGRESA program. We find that more than 50% of the effects of the program on schooling attendance are due to externalities, which are heterogeneous within and between poor and nonpoor households.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2019.1592755 (text/html)
Access to full text is restricted to subscribers.
Related works:
Working Paper: Treatment Effects with Heterogeneous Externalities (2019) 
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:taf:jnlbes:v:38:y:2020:i:4:p:826-838
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20
DOI: 10.1080/07350015.2019.1592755
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan
More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().