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The Missing Transfers: Estimating Misreporting in Dyadic Data

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Margherita Comola, Marcel Fafchamps
Economic Development and Cultural Change
Issue number: 
Volume 65, Number 3 | April 2017
The University of Chicago Press Journals
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.
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