Evaluation of the Australian Wage Subsidy Special Youth ...

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Table 6.3 using Swedish data with up to 2500 cases in matched samples, reported
standardized bias ranging to 10 for individual variables, and reported a summary median,
rather than the mean, of up to 6.5. Gerfin and Lechner(2000) p25 Table 7 in multiple
treatment matching, using Swiss data for matched treatment samples of 395 to 6000
amongst 16533 cases, found mean standardised bias ranging to 18.6 for some programme
comparisons. They commented that their use of a greater number of variables to form the
propensity estimate led the quality of the match to fall, but still considered these biases to
be quite small. Frölich et al. (2000) p40 Table 6.4 in weighted Swedish data with 6287
cases found mean standardised bias measures ranging to 8.5 and 15, but most were less
than 10. It was advised that some caution be applied in interpreting matching results
where the standardized bias was greater than 10. In this respect, the measure balance in
this study is approaching the need for caution but remains less than 10, and compares
reasonably well with other studies. Our data are of a much smaller size, and a fairly large
number of variables are used in the propensity estimation, so the constraints to matching
balance are greater. In light of this, the balance on the covariates represented by the
standardized bias indicates the matching has performed reasonably.
Examination of standardized mean bias in detail can be found by referring to Appendix
Table A2.1, which is deemed too large to be shown here. Rosenbaum and Rubin (1983)
p51 point out that one great advantage for propensity score matching is that even
variables that represent quite rare events can be accounted for where it would not have
been possible to find a match using individual matching. An example of such a variable
in our case is children, where none of the comparison matched were observed with
children. The average standardized bias helpfully summarises into one figure the
variation between the treatment and comparison, but using only this measure may detract
from quite poor individual bias measures for some of the individual variables.
It is most useful to examine some of the variables that were found influential in the probit
used to estimate the propensity score. Age has featured in the literature for SYETP as an
important aspect of participation, and age had a strong statistically significant influence
on SYETP participation in our propensity score probit estimation. The standardized bias