Evaluation of the Australian Wage Subsidy Special Youth ...

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evaluations, as shown in the review, although most of the results can only be considered
with strong caveats, due to the inadequate methods employed. At least partially, due to
the varying timeframes for the analyses, this gives evidence that SYETP gave
employment gains in a dynamic context.
In all variations, the research here found positive impacts of SYETP, even if some were
not statistically significant, a difficulty influenced by the small sample and efficiency
losses from some of the methods. Regardless of whether selection was based on the
assumption of observables or unobservables, the impact remained positive. This
contributes evidence of the robustness of the positive effect on employment for SYETP
participants.
The sensitivity of the specifications of both the bivariate probit and the propensity score
used for the PSM were explored. In the bivariate probit this centered around the
exclusion restriction, and in the propensity score matching the implications of the
exclusion restriction for the comparative analysis were investigated. In the case of the
bivariate probit, comparisons of the weighted and unweighted results found that the
specification has great bearing on the results. An interpretation advanced here is that
because sample attrition was confounded with the SYETP program treatment, the
bivariate probit accounted for this with the modelling of the selection into the program.
Hence, the strong differences in the outcomes of the weighted and unweighted analyses.
There were serious difficulties in estimating alternative specifications in the unweighted
data, indicating that the search for a plausible specification could be problematic. In the
PSM, in line with recent contributions to the matching literature, the variants examined
the restriction of the propensity to those variables expected on empirical or theoretical
grounds to have significant relations with both SYETP participation and employment.
The substantial changes to the results provide a useful illustration of the importance of
the modelling assumptions, and indicate that the assumptions involved in matching are
not simple. The results show that the statistical modelling uncertainty can be difficult to
resolve without clear evidence of a strong basis for favouring one model over another,
unless the goodness of fit assessment undertaken provides obvious resolution. It is