Evaluation of the Australian Wage Subsidy Special Youth ...
Evaluation of the Australian Wage Subsidy Special Youth ... Evaluation of the Australian Wage Subsidy Special Youth ...
114 Table 4.1 Difference between treatment group and comparison group SYETP mean s.d. Non- SYETP mean Female 43.3 0.50 41.0 0.49 2.3* Average age 1984 19.0 1.97 20.1 2.42 1.1* Aboriginal/Torres Strait Islander 1.0 0.10 3.1 0.17 2.1* Other ethnic minority 7.7 0.27 7.9 0.27 0.2 Married 1984 2.9 0.17 12.5 0.33 9.6* Spouse employed 1984 1.9 0.14 6.3 0.24 4.4* Children 1984 1.9 0.14 5.8 0.23 3.9* Highest qualification in 1984 Degree/diploma 7.7 0.27 12.2 0.33 4.5* Apprenticeship 2.9 0.17 8.6 0.28 5.7* Other post-school qualification 6.7 0.25 7.1 0.26 0.4 Year 12 of school 23.1 0.42 13.9 0.35 9.2* Year 11 of school 17.3 0.38 13.6 0.34 3.7* Year 10 of school 31.7 0.47 31.5 0.46 0.2 Year 9 of school 10.6 0.31 12.6 0.33 2.0* Parental background when resp. aged 14 Father postschool qualification 26.0 0.44 34.6 0.48 8.6* Mother postschool qualification 20.2 0.40 18.2 0.39 2.0* Father manager, professional, para-professional 25.0 0.44 25.8 0.44 0.8 Father not employed 3.8 0.19 5.6 0.23 1.8* Father not present 19.2 0.40 15.4 0.36 3.8* Mother manager, professional, para-professional 6.7 0.25 9.8 0.30 3.1* Mother not employed 48.1 0.50 55.3 0.50 7.2* Mother not present 8.7 0.28 5.0 0.22 3.7* Longest job ever held by 1984 Never held a job 11.5 0.32 11.6 0.32 0.1 < 1 year 55.8 0.49 40.1 0.49 15.7* 1 year 13.5 0.34 13.5 0.34 0.0 2 years 13.5 0.34 14.1 0.35 0.6 3 years or more 5.8 0.23 19.8 0.40 14* Average pre-programme unemployment 69 19.0 1.97 20.1 2.42 7.5* Ever employed in 1986 70 86.5 0.34 72.9 0.44 13.6* Ever government programme 1986 71 14.4 0.35 10.7 0.31 3.7* Number of cases 104 1179 NOTE: Column 5 shows the t statistic for hypothesis that difference of mean for SYETP and comparison is zero, where * indicates is significant at the 1 percent level of significance. s.d. SYETP versus comparison absolute difference in means 68 68 The statistic for any variable is the absolute value of the difference in means for SYETP and the control groups. 69 Proportion of 1984 reference period to 3 June spent unemployed. 70 Ever held a non-subsidised, non-government program job in the 1986 reference period, after the first 17 weeks. 71 Ever go on a government program, including SYETP, in the 1986 reference period.
115 4.2 Propensity score matching methods The propensity score matching methods, expounded in Rosenbaum and Rubin (1983), Dehijia and Wahba (1998), Heckman, Ichimura and Todd (1997), Heckman, Ichimura and Todd (1998) and Imbens (2000) have more recently been applied in the evaluation literature. Lechner (2000, 2001) extended the propensity score matching methods to the multi-treatment case. Using propensity score matching moves the emphasis away from specifying the selection bias towards more careful construction of the comparison group. It has been suggested that the key enhancement for evaluation allowed by this method is the comparison of comparable people (Heckman, Lalonde and Smith (1999): 2083). In order to do this, irrelevant comparison cases, which are not similar to the treated, are removed from the analysis. Matching methods find for each individual in the treated, at least one comparison group member with very similar pre-treatment characteristics. The differences in outcomes after the treatment are then attributed to the programme. Recalling the evaluation problem discussed earlier, matching is subject initially to the same difficulty of all nonexperimental methods where assignment to treatment is non-random. However Rubin (1974) showed that matching balances the distributions of all pre-treatment characteristics that influence assignment to the treatment, and so gives an unbiased estimate of treatment on the treated, as long as all relevant similar pre-treatment characteristics (X) are controlled for, and the Conditional Independence Assumption (CIA) is invoked (further explained below). Propensity score matching uses the propensity score to provide a single measure of the set of characteristics (X) that influence the probability of participating and employment. Rosenbaum and Rubin (1983) established that if matching on a set of observed characteristics is valid, then matching on the probability of selection into the programme conditional on these characteristics, the propensity score, is also valid. Whereas matching on each characteristic leads to problems with dimensions, the propensity score reduces the problem to a single dimension.
- Page 79 and 80: 63 to the end of the 1980’s. An o
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- Page 87 and 88: 71 employer survey estimates were t
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- Page 93 and 94: 77 Table 2.17 State usage of progra
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- Page 101 and 102: 85 completers. Their argument was t
- Page 103 and 104: 87 was an issue for the data. Unlik
- Page 105 and 106: 89 Table 2.21 Richardson (1998) Est
- Page 107 and 108: 91 2.3.5 General discussion Some ge
- Page 109 and 110: 93 Controlling for differences in i
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- Page 115 and 116: 99 suitability of the underlying as
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- Page 119 and 120: 103 effect on employment relative t
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- Page 125 and 126: 109 duration of Pre-June 1984 unemp
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- Page 135 and 136: 119 (7) E(Y c | D=1) = E P(X) {E[Y
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- Page 151 and 152: 135 Table 4.6 Matching results, Sin
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115<br />
4.2 Propensity score matching methods<br />
The propensity score matching methods, expounded in Rosenbaum and Rubin (1983),<br />
Dehijia and Wahba (1998), Heckman, Ichimura and Todd (1997), Heckman, Ichimura<br />
and Todd (1998) and Imbens (2000) have more recently been applied in <strong>the</strong> evaluation<br />
literature. Lechner (2000, 2001) extended <strong>the</strong> propensity score matching methods to <strong>the</strong><br />
multi-treatment case. Using propensity score matching moves <strong>the</strong> emphasis away from<br />
specifying <strong>the</strong> selection bias towards more careful construction <strong>of</strong> <strong>the</strong> comparison group.<br />
It has been suggested that <strong>the</strong> key enhancement for evaluation allowed by this method is<br />
<strong>the</strong> comparison <strong>of</strong> comparable people (Heckman, Lalonde and Smith (1999): 2083). In<br />
order to do this, irrelevant comparison cases, which are not similar to <strong>the</strong> treated, are<br />
removed from <strong>the</strong> analysis.<br />
Matching methods find for each individual in <strong>the</strong> treated, at least one comparison group<br />
member with very similar pre-treatment characteristics. The differences in outcomes after<br />
<strong>the</strong> treatment are <strong>the</strong>n attributed to <strong>the</strong> programme. Recalling <strong>the</strong> evaluation problem<br />
discussed earlier, matching is subject initially to <strong>the</strong> same difficulty <strong>of</strong> all nonexperimental<br />
methods where assignment to treatment is non-random. However Rubin<br />
(1974) showed that matching balances <strong>the</strong> distributions <strong>of</strong> all pre-treatment<br />
characteristics that influence assignment to <strong>the</strong> treatment, and so gives an unbiased<br />
estimate <strong>of</strong> treatment on <strong>the</strong> treated, as long as all relevant similar pre-treatment<br />
characteristics (X) are controlled for, and <strong>the</strong> Conditional Independence Assumption<br />
(CIA) is invoked (fur<strong>the</strong>r explained below).<br />
Propensity score matching uses <strong>the</strong> propensity score to provide a single measure <strong>of</strong> <strong>the</strong><br />
set <strong>of</strong> characteristics (X) that influence <strong>the</strong> probability <strong>of</strong> participating and employment.<br />
Rosenbaum and Rubin (1983) established that if matching on a set <strong>of</strong> observed<br />
characteristics is valid, <strong>the</strong>n matching on <strong>the</strong> probability <strong>of</strong> selection into <strong>the</strong> programme<br />
conditional on <strong>the</strong>se characteristics, <strong>the</strong> propensity score, is also valid. Whereas matching<br />
on each characteristic leads to problems with dimensions, <strong>the</strong> propensity score reduces<br />
<strong>the</strong> problem to a single dimension.
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