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ntreatreg: A Stata module for estimation of treatment effects in the presence of neighborhood interactions

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  • Giovanni Cerulli

    (CNR-CERIS National Research Council of Italy, Institute for Economic Research on Firms and Growth)

Abstract

This presentation presents a parametric counterfactual model identifying average treatment effects (ATEs) by conditional mean independence when externality (or neighborhood) effects are incorporated within the traditional Rubin potential-outcome model. As such, it tries to generalize the usual control-function regression, widely used in program evaluation and epidemiology, when the stable unit treatment value assumption (SUTVA) is relaxed. As a by-product, the paper also presents ntreatreg, a user-written Stata command for estimating ATEs when social interaction may be present. Finally, an instructional application of the model and of its Stata implementation (using ntreatreg) through two examples (the first on the effect of housing location on crime; the second on the effect of education on fertility) is shown and results compared with a no-interaction setting.

Suggested Citation

  • Giovanni Cerulli, 2014. "ntreatreg: A Stata module for estimation of treatment effects in the presence of neighborhood interactions," Italian Stata Users' Group Meetings 2014 06, Stata Users Group.
    • Handle: RePEc:boc:isug14:06
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    References listed on IDEAS

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    1. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
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    8. Wooldridge, Jeffrey M., 1997. "On two stage least squares estimation of the average treatment effect in a random coefficient model," Economics Letters, Elsevier, vol. 56(2), pages 129-133, October.
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