This paper starts from a review of recently published RCT studies in development economics,
and documents many studies largely ignore attrition once attrition rates are found balanced between
treatment arms. The paper then analyzes the implications of attrition for the internal and
external validity of the results of a randomized experiment with balanced attrition rates. We rely
on a 10-years longitudinal data set with a final attrition rate of 10%, obtained after intensive
tracking of migrants, and document the sensitivity of ITT estimates for schooling gains for a social
program in Nicaragua. We find that not including those found during the intensive tracking
leads to an overestimate of the ITT effects for the target population by more than 30%, and that
selection into attrition is driven by observable baseline characteristics. We propose a new method
to correct for attrition using inverse probability weighting with estimates of weights that exploit
the similarities between missing individuals and those found during an intensive tracking phase.
We compare these estimates with alternative strategies using bounds or proxy information.