@article{Maluccio:16389,
      recid = {16389},
      author = {Maluccio, John A.},
      title = {ATTRITION IN THE KWAZULU NATAL INCOME DYNAMICS STUDY,  1993-1998},
      address = {2000},
      number = {583-2016-39569},
      series = {FCND Discussion Paper 95},
      pages = {50},
      year = {2000},
      abstract = {Panel (or longitudinal) data often provide an  understanding of the dynamic
behavior of individual  households not possible with cross-sectional or  time-series
information alone. However, a disturbing  feature of this type of survey in both developed
and  developing countries is that there is often substantial,  nonrandom attrition. Therefore,
an important concern is the  extent to which attrition inhibits inferences made using  the
data. This note examines attrition in the KwaZulu-Natal  Income Dynamics Study (1993–
1998) and assesses the extent  of attrition bias for a specific empirical example.  The
analysis shows that 1993 first round nonresponse is  largely unrelated to observable
characteristics of the  communities other than indicators of migration  activity.
Multivariate regressions are then used to  describe the characteristics of the households
attriting in  1998, revealing the importance of distinguishing between  two types of
attriting households, those that moved and  those that apparently moved but left no trace.
For example,  increased household size reduced the probability of either  type of attrition,
whereas measures of higher quality of  fieldwork in the 1993 survey only reduced the
probability  that a household left no trace. While observable  differences between attritors
and non-attritors indicate  attrition is nonrandom, it does not necessarily follow  that
estimated relationships based on the non-attriting  sample suffer from attrition bias.
To more directly explore  attrition bias, which is by its nature model specific,  this
analysis estimates household-level expenditure  functions correcting for attrition bias
using standard  Heckman selection procedures and a quality of 1993  interview variables as identifying instruments. There is  positive selection, and although many of the  other
parameter estimates are quite similar, a Hausman test  rejects the equality of coefficients
between the corrected  and uncorrected models. Therefore, this study concludes, at  least
for this simple case, that attrition does appear to  bias the “behavioral” coefficients. These
results are in  contrast to other work using these data that suggests  little attrition bias for
different estimated models,  highlighting that attrition is indeed model specific.  Large
levels of attrition do not always lead to attrition  bias; however, sometimes they do. Since
it is typically  difficult to determine the bias for a particular analysis a  priori, it behooves
researchers using panel data not to  avoid using panel data when there is attrition, but  to
always evaluate the effect of such bias on the analysis  at hand.},
      url = {http://ageconsearch.umn.edu/record/16389},
      doi = {https://doi.org/10.22004/ag.econ.16389},
}