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bounding approaches

Invisible sample selection: Why you should care about those who leave when you are interested in those left behind: Guest post by Andreas Steinmayr

This is the eighth in our series of posts by students on the job market this year.
A key problem in the literature on the economics of migration is how emigration of an individual affects the welfare of households left behind (see Antman (2013) for a literature overview). The literature has worried a lot about the possibility that households that select into migration are different from those that don’t. A whole range of different IV approaches, along with a few migration lottery experiments have tried to address this form of selection.  However, the literature has worried less about (and been less successful dealing with) a second form of selection, namely that some households do not leave any member behind. I call this invisible sample selection since these all-move households are not observed at all in the standard household surveys in origin countries used in most studies (and also not in many other datasets). But failing to account for this problem leads to biased estimates, as explained below and shown in this graphical illustration.

Help for attrition is just a phone call away – a new bounding approach to help deal with non-response

David McKenzie's picture

Attrition is a bugbear for most impact evaluations, and can cause even the best designed experiments to be subject to potential bias. In a new paper, Luc Behaghel, Bruno Crépon, Marc Gurgand and Thomas Le Barbanchon describe a clever new way to deal with this problem using information on the number of attempts it takes to get someone to respond to a survey.