Evaluation of the Australian Wage Subsidy Special Youth ...

200
reduced the need to account for selection into SYETP, with subsequent impacts on the
treatment effect measured. This potential problem, where attrition seemed related to
SYETP participation, was hinted at in earlier analysis of the sample reduction effects on
the univariate probit of SYETP participation. 143
The Wald test of significance of the selection correction also gives a value which in the
chi square test that correlation is zero, leads to the conclusion of failing to reject the null
hypothesis. This might suggest that a simple probit of employment might be an
acceptable modelling format, when the attrition has been accounted for with weighting.
The basic univariate probit results, similarly weighted and specified, are shown in the
Appendix Tables A2.5a and A2.5b. In the basic probit analysis of employment, the
employment effect of SYETP is positive i.e. marginal effect of 13 percentage points, and
statistically significant. This is slightly higher than the marginal effect of SYETP in the
bivariate probit, which gives a 10 percentage point gain in employment (see Table 6.9
later).
Other differences also occur in comparison of the weighted to the former unweighted
Heckman bivariate probit results. A number of variables formerly statistically
insignificant, are statistically significant in the weighted equation; these include: being of
other ethnic minority, holding highest qualification of up to year 9 schooling, having
good English, no religion and Presbyterian religion. Other variables lose statistical
significance once weighting is applied: holding highest qualification of apprentice,
mother’s occupation of plant operative, and negative attitude to women in work. Similar
effects are also evident in the participation equation, but are not detailed. Following
Fitzgerald et al. (1998a), the interpretation of these changes with the introduction of
attrition weighting is that sample reduction lead to bias in the estimates. Accounting for
the sample reduction with weights reduces the bias.
143 See section 5.8 on attrition and the discussion of the Probit Model of SYETP and effects of sample
reduction.