We consider identification of peer effects under peer group miss-specification. Our model of group miss-specification allows for missing data and peer group uncertainty. Missing data can take the form of some individuals being completely absent from the data, and the researcher need not have any information on these individuals and may not even know that they are missing. We show that peer effects are nevertheless identifiable if these individualsare missing completely at random , and propose a GMM estimator which jointly estimates the sampling probability and peer effects. In practice this means that the researcher need only have access to an individual/household level sample with group identifiers. The researcher may also be uncertain as to what is the relevant peer group for the outcome under study. We show that peer effects are nevertheless identifiable provided that the candidate peer groups are nested within one another (e.g. classroom, grade, school) and propose a non-linear least squares estimator. We conduct a Monte-Carlo experiment to demonstrate our identification results and the performance of the proposed estimators in a setting tailored to real data (the Dartmouth room-mate data).
We consider identification of peer effects under peer group miss-specification. Our model of group miss-specification allows for missing data and peer group uncertainty. Missing data can take the form of some individuals being entirely absent from the data, and the researcher need not have any information on these individuals and may not even know that they are missing. We show that peer effects are nevertheless identifiable under mild restrictions on the probabilities of observing individuals , and propose a GMM estimator to estimate the peer effects. In practice this means that the researcher need only have access to an individual/household level sample with group identifiers. The researcher may also be uncertain as to what is the relevant peer group for the outcome under study. We show that peer effects are nevertheless identifiable provided that the candidate peer groups are nested within one another (e.g. classroom, grade, school) and propose a non-linear least squares estimator. We conduct a Monte-Carlo experiment to demonstrate our identification results and the performance of the proposed estimators in a setting tailored to real data (the Dartmouth room-mate data).
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if these individualsare missing completely at random
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