ABSTRACT: This paper presents a parametric counter-factual model
identifying Average Treatment Effects (ATEs) by Conditional Mean Independence
when externality (or neighbourhood) effects are incorporated within the
traditional Rubin’s potential outcome model.
As such, it tries to generalize the usual control-function regression,
widely used in program evaluation and epidemiology, when SUTVA (i.e. Stable
Unit Treatment Value Assumption) is relaxed. As by-product, the paper presents
also ntreatreg, an author-written Stata routine for estimating ATEs when social
interaction may be present. Finally, an instructional application of the model
and of its Stata implementation through two examples (the first on the effect
of housing location on crime; the second on the effect of education on
fertility), are showed and results compared with a no-interaction setting.
Keywords: ATEs, Rubin’s causal model, SUTVA, neighbourhood effects, Stata
command.
JEL Codes: C21, C31, C87
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An early version of this paper was presented at CEMMAP (Centre for
Microdata Methods and Practice), University College London, on March 27th 2013.
The author wishes to thank all the participants to the seminar and in
particular Richard Blundell, Andrew Chesher, Charles Manski, Adam Rosen and
Barbara Sianesi for the useful discussion. This version of the paper has been
presented at the Department of Economics, Boston College, on November 12th
2013.
The author wishes to thank all the participants to the seminar and in
particular Kit Baum, Andrew Beauchamp, Rossella Calvi, Federico Mantovanelli,
Scott Fulford and Mathis Wagner for their participation and suggestions.