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The Missing Transfers: Estimating Misreporting in Dyadic Data

Author

Listed:
  • Margherita Comola
  • Marcel Fafchamps

Abstract

Many studies have used self-reported dyadic data without exploiting the pattern of discordant answers. In this article we propose a maximum likelihood estimator that deals with misreporting in a systematic way. We illustrate the methodology using dyadic data on interhousehold transfers from the village of Nyakatoke in Tanzania. We show that not taking reporting bias into account leads to serious underestimation of the total amount of transfers between villagers. We also provide suggestive evidence that reporting bias can affect inference about estimated coefficients. The method introduced here is applicable whenever the researcher has two discordant measurements of the same dependent variable.

Suggested Citation

  • Margherita Comola & Marcel Fafchamps, 2017. "The Missing Transfers: Estimating Misreporting in Dyadic Data," Economic Development and Cultural Change, University of Chicago Press, vol. 65(3), pages 549-582.
  • Handle: RePEc:ucp:ecdecc:doi:10.1086/690810
    DOI: 10.1086/690810
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    Cited by:

    1. Yong Cai, 2022. "Linear Regression with Centrality Measures," Papers 2210.10024, arXiv.org.
    2. Grimm, Michael & Hartwig, Renate & Reitmann, Ann-Kristin & Bocoum, Fadima Yaya, 2021. "Inter-household transfers: An empirical investigation of the income-transfer relationship with novel data from Burkina Faso," World Development, Elsevier, vol. 144(C).
    3. Ethan Ligon & Laura Schechter, 2020. "Structural Experimentation to Distinguish between Models of Risk Sharing with Frictions in Rural Paraguay," Economic Development and Cultural Change, University of Chicago Press, vol. 69(1), pages 1-50.
    4. Comola, Margherita & Prina, Silvia, 2023. "The Interplay among Savings Accounts and Network-Based Financial Arrangements: Evidence from a Field Experiment," IZA Discussion Papers 16303, Institute of Labor Economics (IZA).
    5. Emla Fitzsimons & Bansi Malde & Marcos Vera‐Hernández, 2018. "Group Size and the Efficiency of Informal Risk Sharing," Economic Journal, Royal Economic Society, vol. 128(612), pages 575-608, July.
    6. Comola, Margherita & Inguaggiato, Carla & Mendola, Mariapia, 2024. "Social networks and economic transformation: Evidence from a resettled village in Brazil," Journal of Economic Behavior & Organization, Elsevier, vol. 221(C), pages 17-34.
    7. Timothy G. Conley & Nirav Mehta & Ralph Stinebrickner & Todd Stinebrickner, 2024. "Social Interactions, Mechanisms, and Equilibrium: Evidence from a Model of Study Time and Academic Achievement," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 824-866.
    8. Heath Henderson & Arnob Alam, 2022. "The structure of risk-sharing networks," Empirical Economics, Springer, vol. 62(2), pages 853-886, February.
    9. Lina Zhang, 2020. "Spillovers of Program Benefits with Missing Network Links," Papers 2009.09614, arXiv.org, revised Aug 2024.
    10. Janzen, Sarah A. & Magnan, Nicholas & Sharma, Sudhindra & Thompson, William M., 2017. "Aspirations failure and formation in rural Nepal," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 1-25.
    11. Arun Advani & Bansi Malde, 2018. "Credibly Identifying Social Effects: Accounting For Network Formation And Measurement Error," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1016-1044, September.
    12. Strupat, Christoph & Klohn, Florian, 2018. "Crowding out of solidarity? Public health insurance versus informal transfer networks in Ghana," World Development, Elsevier, vol. 104(C), pages 212-221.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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