By: |
Emre Ekinci (Department of Business Administration, Universidad Carlos III de Madrid);
Insan Tunah (Department of Economics, Koc University);
Berk Yavuzoglu (Department of Economics, Nazarbayev University) |
Abstract: |
We augment the Additively Non-ignorable (AN) model of Hirano et. al. (2001) so
that it is suitable for data collection efforts that have a short panel
component. Our modification yields a convenient semi-parametric bias
correction framework for handling selective non-response that can emerge when
multiple visits to the same unit are planned. Selective non-response can be
due to attrition, when initial response is followed by nonresponse (the
commonly studied case), as well as a phenomenon we term reverse attrition,
when initial nonresponse is followed by response. Accounting for reverse
attrition creates an additional identification problem, which we circumvent by
rescaling. We apply our methodology to data from the Household Labor Force
Survey (HLFS) in Turkey, which shares a key design feature (namely a rotating
sample frame) with popular surveys such as the Current Population Survey and
the European Union Labor Force Survey. The correction amounts to adjusting the
observed joint distribution over the state space (inactive, employed,
unemployed in our example) using reflation factors expressed as parametric
functions of the states occupied in the initial and subsequent rounds. Our
method produces a unique set of corrected joint probabilities that are
consistent with externally obtained marginal distributions (in our case
published official statistics). The linear additive version has a closed form
solution, a feature which renders our method computationally attractive. Our
empirical results show that selective attrition/reverse attrition in
HLFS-Turkey is a statistically and substantially important concern. |
Keywords: |
attrition, reverse attrition, selective nonresponse, selection on observables, selection on unobservables, short panel, rotating sample frame, rotating panel, address-based sampling, labor force survey, ML estimation |
Date: |
2017–03 |
URL: |
http://d.repec.org/n?u=RePEc:naz:wpaper:1702&r=ara |