IDEAS home Printed from https://ideas.repec.org/p/csc/cerisp/201404.html
   My bibliography  Save this paper

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

Author

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, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.
    • Handle: RePEc:csc:cerisp:201404
    as

    Download full text from publisher

    File URL: http://www.digibess.it/fedora/repository/object_download/openbess:TO094-00386/PDF/openbess_TO094-00386.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, September.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giovanni Cerulli, 2014. "ntreatreg: a Stata module for estimation of treatment effects in the presence of neighborhood interactions," United Kingdom Stata Users' Group Meetings 2014 15, Stata Users Group.
    2. Giovanni Cerulli & Roberto Gabriele & Enrico Tundis, 2014. "Evaluating locally-based policies in the presence of neighbourhood effects: The case of touristic accommodation in the Garda district of Trentino," ERSA conference papers ersa14p715, European Regional Science Association.
    3. Sourafel Girma & Yundan Gong & Holger Görg & Sandra Lancheros, 2016. "Estimating direct and indirect effects of foreign direct investment on firm productivity in the presence of interactions between firms," World Scientific Book Chapters, in: MULTINATIONAL ENTERPRISES AND HOST COUNTRY DEVELOPMENT, chapter 12, pages 227-239, World Scientific Publishing Co. Pte. Ltd..
    4. Kosuke Imai & Zhichao Jiang, 2020. "Identification and sensitivity analysis of contagion effects in randomized placebo‐controlled trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1637-1657, October.
    5. Clarke, Damian, 2017. "Estimating Difference-in-Differences in the Presence of Spillovers," MPRA Paper 81604, University Library of Munich, Germany.
    6. C. Tort`u & I. Crimaldi & F. Mealli & L. Forastiere, 2020. "Modelling Network Interference with Multi-valued Treatments: the Causal Effect of Immigration Policy on Crime Rates," Papers 2003.10525, arXiv.org, revised Jun 2020.
    7. Dionissi Aliprantis, 2011. "Assessing the evidence on neighborhood effects from moving to opportunity," Working Papers (Old Series) 1101, Federal Reserve Bank of Cleveland.
    8. Laura Forastiere & Patrizia Lattarulo & Marco Mariani & Fabrizia Mealli & Laura Razzolini, 2021. "Exploring Encouragement, Treatment, and Spillover Effects Using Principal Stratification, With Application to a Field Experiment on Teens’ Museum Attendance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 244-258, January.
    9. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org, revised Jun 2020.
    10. Chiba, Yasutaka, 2012. "A note on bounds for the causal infectiousness effect in vaccine trials," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1422-1429.
    11. Mathias Lundin & Maria Karlsson, 2014. "Estimation of causal effects in observational studies with interference between units," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 417-433, August.
    12. Arpino, Bruno & Mattei, Alessandra, 2013. "Assessing the Impact of Financial Aids to Firms: Causal Inference in the presence of Interference," MPRA Paper 51795, University Library of Munich, Germany.
    13. Laura Forastiere & Fabrizia Mealli & Tyler J. VanderWeele, 2016. "Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 510-525, April.
    14. Francis J. DiTraglia & Camilo Garcia-Jimeno & Rossa O'Keeffe-O'Donovan & Alejandro Sanchez-Becerra, 2020. "Identifying Causal Effects in Experiments with Spillovers and Non-compliance," Papers 2011.07051, arXiv.org, revised Feb 2021.
    15. Guillaume W Basse & Edoardo M Airoldi, 2018. "Model-assisted design of experiments in the presence of network-correlated outcomes," Biometrika, Biometrika Trust, vol. 105(4), pages 849-858.
    16. Michael P. Leung, 2020. "Treatment and Spillover Effects Under Network Interference," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 368-380, May.
    17. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    18. Alan Andre Borges da Costa & Sergio Pinheiro Firpo, 2018. "An analysis of the distributive effects of public policies and their spillovers," Working Papers, Department of Economics 2018_06, University of São Paulo (FEA-USP).
    19. Christopher Baum & Giovanni Cerulli, CNR-IRCrES, 2016. "Estimating a dose-response function with heterogeneous response to confounders when treatment is continuous and endogenous," EcoMod2016 9388, EcoMod.
    20. Rigdon, Joseph & Hudgens, Michael G., 2015. "Exact confidence intervals in the presence of interference," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 130-135.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:csc:cerisp:201404. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anna Perin) or (Giancarlo Birello). General contact details of provider: https://edirc.repec.org/data/cerisit.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.