Evaluation of the Australian Wage Subsidy Special Youth ...

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weighting, both the weighted (columns 2 and 4) and unweighted (columns 1 and 3)
results are shown for each missing data approach. Of main interest is the comparison of
the estimates using each missing data approach, thus column 1 versus column 3, or
column 2 versus column 4. Discussion of Table A2.2 generalises the results to
comparison between the first panel (using mean substitution) and the second panel
(deletion of cases with missing data – this gives the smaller base of 2150 cases where
1984 survey information has no information missing on any explanatory variables) by
discussing the weighted results. Moving from mean substitution to the deletion approach
for the weighted data does change which variables are statistically significant, as the t-
statistic size changes – for example ’other city before aged 14’ becomes statistically
significant, and ‘age in 1984’ becomes insignificant. It also leads to a change in the size
of coefficients for statistically significant variables – for example the coefficient for
‘proportion of pre-June unemployment’ falls, and the t-statistic falls. In a probit, the
coefficient size is not clearly interpretable, so the interpretation of a positive influence of
this variable on participation in SYETP is not changed by the various missing data
approaches, however the calculated marginal effect would be affected. When the variable
is not statistically significant, changing the missing data approach can also lead to change
in the sign, such as for ‘children 1984’. Although not discussed in detail here, it can be
seen that substantively important changes in the estimates arise depending on each
approach used. The chief variation is in which variables are statistically significant, so
that choice of treatment of missing data affects which coefficients are interpreted as
statistically significant. The tradeoff between bias and efficiency and using more
information affects the interpretation of results. In light of this, it may be worth pursuing
the application of the imputation algorithm of King et al. (2001) in future research, which
is argued to outperform the mean substitution and deletion methods. However, only the
more commonly accepted approaches are dealt with here.
The effects of a set of dummy variables for mean imputation is shown in the first 2
columns of Table 5.8. Missing information on the parental occupation predicts failure
perfectly, the problem of collinearity, and these variables must be dropped from the
regression to enable estimation. The missing information for parental qualifications,