IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2009.09614.html
   My bibliography  Save this paper

Spillovers of Program Benefits with Mismeasured Networks

Author

Listed:
  • Lina Zhang

Abstract

In studies of program evaluation under network interference, correctly measuring spillovers of the intervention is crucial for making appropriate policy recommendations. However, increasing empirical evidence has shown that network links are often measured with errors. This paper explores the identification and estimation of treatment and spillover effects when the network is mismeasured. I propose a novel method to nonparametrically point-identify the treatment and spillover effects, when two network observations are available. The method can deal with a large network with missing or misreported links and possesses several attractive features: (i) it allows heterogeneous treatment and spillover effects; (ii) it does not rely on modelling network formation or its misclassification probabilities; and (iii) it accommodates samples that are correlated in overlapping ways. A semiparametric estimation approach is proposed, and the analysis is applied to study the spillover effects of an insurance information program on the insurance adoption decisions.

Suggested Citation

  • Lina Zhang, 2020. "Spillovers of Program Benefits with Mismeasured Networks," Papers 2009.09614, arXiv.org.
  • Handle: RePEc:arx:papers:2009.09614
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2009.09614
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rossella Calvi & Arthur Lewbel & Denni Tommasi, 2018. "LATE with Missing or Mismeasured Treatment," Boston College Working Papers in Economics 959, Boston College Department of Economics, revised 15 Mar 2021.
    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. De Paula, Áureo & Rasul, Imran & Souza, Pedro, 2018. "Recovering Social Networks from Panel Data: Identification, Simulations and an Application," CEPR Discussion Papers 12792, C.E.P.R. Discussion Papers.
    4. Emily Breza & Arun G. Chandrasekhar & Tyler H. McCormick & Mengjie Pan, 2020. "Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data," American Economic Review, American Economic Association, vol. 110(8), pages 2454-2484, August.
    5. Yosef Rinott & Vladimir Rotar, 2000. "Normal approximations by Stein's method," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 23(1), pages 15-29.
    6. Edward Miguel & Michael Kremer, 2004. "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities," Econometrica, Econometric Society, vol. 72(1), pages 159-217, January.
    7. Guillaume Basse & Avi Feller, 2018. "Analyzing Two-Stage Experiments in the Presence of Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 41-55, January.
    8. Sergio Currarini & Matthew O. Jackson & Paolo Pin, 2009. "An Economic Model of Friendship: Homophily, Minorities, and Segregation," Econometrica, Econometric Society, vol. 77(4), pages 1003-1045, July.
    9. Hu, Yingyao, 2008. "Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution," Journal of Econometrics, Elsevier, vol. 144(1), pages 27-61, May.
    Full references (including those not matched with items on IDEAS)

    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. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2019. "Peer Effects in Networks: a Survey," CEPR Discussion Papers 14260, C.E.P.R. Discussion Papers.
    2. Chih-Sheng Hsieh & Stanley I. M. Ko & Jaromír Kovářík & Trevon Logan, 2018. "Non-Randomly Sampled Networks: Biases and Corrections," NBER Working Papers 25270, National Bureau of Economic Research, Inc.
    3. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    4. Francesco Drago & Friederike Mengel & Christian Traxler, 2020. "Compliance Behavior in Networks: Evidence from a Field Experiment," American Economic Journal: Applied Economics, American Economic Association, vol. 12(2), pages 96-133, April.
    5. Giacomo De Giorgi & Anders Frederiksen & Luigi Pistaferri, 2020. "Consumption Network Effects," Review of Economic Studies, Oxford University Press, vol. 87(1), pages 130-163.
    6. Boucher, Vincent, 2020. "Equilibrium homophily in networks," European Economic Review, Elsevier, vol. 123(C).
    7. Clarke, Damian, 2017. "Estimating Difference-in-Differences in the Presence of Spillovers," MPRA Paper 81604, University Library of Munich, Germany.
    8. 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.
    9. Koen Jochmans, 2020. "Peer effects and endogenous social interactions," Papers 2008.07886, arXiv.org.
    10. Davide Viviano, 2020. "Policy choice in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised Jan 2021.
    11. David M. Cutler & Adriana Lleras-Muney & Tom Vogl, 2008. "Socioeconomic Status and Health: Dimensions and Mechanisms," NBER Working Papers 14333, National Bureau of Economic Research, Inc.
    12. Michael Alexeev & Yao-Yu Chih, 2015. "Social network structure and status competition," Canadian Journal of Economics, Canadian Economics Association, vol. 48(1), pages 64-82, February.
    13. John Parman, "undated". "Childhood Health and Sibling Outcomes: The Shared Burden of the 1918 Influenza Pandemic," Working Papers 121, Department of Economics, College of William and Mary.
    14. S Anukriti & Catalina Herrera‐Almanza & Praveen K. Pathak & Mahesh Karra, 2020. "Curse of the Mummy‐ji: The Influence of Mothers‐in‐Law on Women in India†," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(5), pages 1328-1351, October.
    15. Gustavo J. Bobonis & Paul Gertler & Marco Gonzalez-Navarro & Simeon Nichter, 2017. "Vulnerability and Clientelism," NBER Working Papers 23589, National Bureau of Economic Research, Inc.
    16. Marco Battaglini & Eleonora Patacchini & Edoardo Rainone, 2019. "Endogenous Social Connections in Legislatures," NBER Working Papers 25988, National Bureau of Economic Research, Inc.
    17. David K. Evans & Arkadipta Ghosh, 2008. "Prioritizing Educational Investments in Children in the Developing World," Working Papers WR-587, RAND Corporation.
    18. Michael A. Clemens, 2017. "The Meaning Of Failed Replications: A Review And Proposal," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 326-342, February.
    19. Tiziano Arduini & Eleonora Patacchini & Edoardo Rainone, 2020. "Treatment Effects With Heterogeneous Externalities," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 826-838, October.
    20. Blanco, M. & Dalton, P.S. & Vargas, J.F., 2013. "Does the Unemployement Benefit Institution Affect the Productivity of Workers? Evidence from a Field Experiment," Other publications TiSEM ba37e033-06ab-4fc3-b56e-9, Tilburg University, School of Economics and Management.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2009.09614. 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: (arXiv administrators). General contact details of provider: http://arxiv.org/ .

    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.