Evaluation of the Australian Wage Subsidy Special Youth ...

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5.7 Multivariate analysis of effects of sample reduction
The univariate evidence on the sample reduction effects treated earlier suggested that
SYETP participants and non-participants were differentially affected. Accordingly, the
model of SYETP is also examined in light of the further sample reduction after the 1984
survey. The estimation is repeated on both samples, all available in 1984 and those cases
available after sample reduction. This has the benefit of revealing whether the estimation
would have looked different using the entire sample available for the 1984 survey.
5.7.1 Accounting for item non-response
As previously pointed out, there are some cases who respond at the 1984 survey for
whom certain regressor variables have missing information. To best regain the lost
information of the incomplete observations, the modified zero order regression is usually
adopted (Greene (1991): 288). The missing cases are filled with zeros, and the regression
includes for each instance a dummy variable which takes the value of 1 for missing
observations and 0 for complete cases.
Recent work by King et al. (2001) examines the role of missing data in the explanatory
variables. King et al. (2001) examines the validity of this form of imputation (mean
substitution), as compared to deleting the observations where missing data occurs, or an
alternative form of multiple imputation algorithm proposed. The algorithm outperforms
both more commonly used alternatives. In particular it is pointed out that mean
substitution gives standard errors that are too small because it assumes in estimation that
the substituted values are known with the same certainty as the observed values (King et
al. (2001): 66). On the other hand, dropping those cases with missing covariates (or
listwise deletion) gives the correct standard error, although estimates do suffer the
problems of bias/inefficiency as recounted in our discussion. It is commented that for this
reason, deletion can be a preferable approach.
In light of this, Appendix Table A2.2 shows the effect of applying each of the mean
substitution and casewise deletion approaches. In order to detract from the issue of the