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Identification and Estimation of Treatment Effects in the Presence of Neighbourhood Interactions

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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.

Suggested Citation

  • Giovanni Cerulli, 2014. "Identification and Estimation of Treatment Effects in the Presence of Neighbourhood Interactions," CERIS Working Paper 201404, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    • Handle: RePEc:csc:cerisp:201404
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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.
    2. Sobel, Michael E., 2006. "What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1398-1407, December.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    5. Hudgens, Michael G. & Halloran, M. Elizabeth, 2008. "Toward Causal Inference With Interference," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 832-842, June.
    6. Donald B. Rubin, 1977. "Assignment to Treatment Group on the Basis of a Covariate," Journal of Educational and Behavioral Statistics, , vol. 2(1), pages 1-26, March.
    7. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    8. Rosenbaum, Paul R., 2007. "Interference Between Units in Randomized Experiments," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 191-200, March.
    9. 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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    Cited by:

    1. Roberto Gabriele & Enrico Tundis, 2015. "Contesto regionale, struttura economica e impatto delle politiche regionali: il caso degli alberghi in Trentino," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2015(3 Suppl.), pages 37-59.
    2. Daniele Di Gennaro & Guido Pellegrini, 2016. "Policy Evaluation In Presence Of Interferences: A Spatial Multilevel Did Approach," Working Papers 0416, CREI Università degli Studi Roma Tre, revised 2016.
    3. Atuesta, Laura H. & Hewings, Geoffrey J.D., 2019. "Housing appreciation patterns in low-income neighborhoods: Exploring gentrification in Chicago," Journal of Housing Economics, Elsevier, vol. 44(C), pages 35-47.
    4. Roberto Gabriele & Enrico Tundis, 2015. "the effect of longitudinal multiple subsidies on firm performance in the presence of neighbour interactions," ERSA conference papers ersa15p1368, European Regional Science Association.

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    More about this item

    Keywords

    ATEs; Rubin’s causal model; SUTVA; neighbourhood effects; Stata command. JEL Codes: C21; C31; C87;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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