Evaluation of the Australian Wage Subsidy Special Youth ...
Evaluation of the Australian Wage Subsidy Special Youth ... Evaluation of the Australian Wage Subsidy Special Youth ...
60 the CES. In support of this they cited statistics 37 reflecting that about 30 percent of all vacancies were registered with the CES. In addition to the volume constraint, they noted that the majority of vacancies were concentrated in low-skilled occupations. In contrast to the low vacancy attraction, the majority of jobseekers made use of the CES. It was noted that 80 to 90 per cent of jobseekers looking for full-time work used the CES. 38 However, it was also made clear that although the majority registered with the CES, as registration was a condition for receipt of unemployment benefits, more than 90 per cent additionally used other methods, so it was not the sole method and likely not the predominant method for job search. Referral and placement 39 data for the Brisbane area, regarded as a good example of average CES activities, was analysed by Wielgosz (1984). In examining referral to notified vacancies the underlying assumption about CES functioning in referral to vacancies was clarified as follows: where “…employers have certain preferences and expectations about the type of labour they require, that the CES is aware of these preferences and in it’s desire to fill vacancies and fill them as quickly as possible, will refer those unemployed applications who are most likely to be placed easily and quickly” (Wielgosz (1984): 12). Using a probit econometric analysis, Wielgosz (1984) found referrals to have several statistically significant factors amongst applicant characteristics, being negatively related to age, educational attainment (years of schooling) and duration of unemployment (current spell in weeks) of applicants, with positive effects for applicants in clerical/administrative and skill trade occupations (relative to a base of all other occupations except semi-skilled and unskilled). Wielgosz (1984) separately modelled successful placement in a job, with a variable for the number of referrals received and found this significantly increased the probability of placement using the Heckman method to control for sample selection (Wielgosz (1984) p19 Table 7). No details of the Heckman sample selection model are presented, and so it is not clear what exclusion 37 Wielgosz (1984) p4 footnote 4 citing ABS Cat.6231.0 ‘Job vacancies, Australia’ various issued to 1984. 38 Wielgosz (1984) p4 citing ABS Cat.6222.0 ‘Persons looking for work, Australia’ various issued to 1984. 39 Note that placement here is in subsidised or unsubsidised jobs.
61 restriction was used. If there was no exclusion restriction applied, and identification was based solely on functional form, then Monte Carlo evidence in the literature indicates that the modelling suffers from poor performance. The estimation procedure also lacks further detail, and so it is not clear whether the two equations needed for the Heckman sample selection model were estimated simultaneously, but it appears that a two step procedure was applied. If both equations are estimated as probits, then this is inappropriate due to the nonlinearity of the probit. It was found that the factors affecting referrals did not have an independent affect on placement. Duration of the current unemployment spell also had an additional negative effect on placement if selection bias was not controlled for. Wielgosz found that tests for significance of the selection correction factor indicated no selection bias. But it was concluded that the very high correlation between the selection correction factor and the duration of unemployment, coupled with the significance of this variable when selection bias was not controlled for, suggested that that the sample selection bias was “…closely and solely related to duration of unemployment” (Wielgosz (1984): 19). It was further noted that this was because to be referred, and so included in the sub-sample, was almost completely dominated by the length of unemployment spell. As a result, due to their correlation the coefficients for selection correction and unemployment duration were both insignificant in the placement equation because they reflected the same phenomenon. Aungles and Stewart (1986) used the same referral and placement data as Wielgosz (1984) to examine referrals and the duration of unemployment, together with exits from unemployment. Aungles and Stewart (1986) modelled durations of unemployment, and also found that the number of referrals jobseekers receive is related to the probability of leaving unemployment. They found that jobseekers receipt of CES referrals were most likely to occur early in their unemployment spell, but once referred by the CES, differences did not exist in the probability of leaving unemployment. The hazard function for leaving unemployment initially rose to peak at 11 days, less than two weeks, after which it fell.
- Page 25 and 26: 9 employment effect could be mainly
- Page 27 and 28: 11 Real Wage Employment Figure 1.1:
- Page 29 and 30: 13 are two types of unemployment, s
- Page 31 and 32: 15 Real wages are predetermined whe
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- Page 35 and 36: 19 Each individual has two potentia
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- Page 43 and 44: 27 Table 1.3 Brief overview of rece
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- Page 49 and 50: 33 who participated. It was conclud
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- Page 53 and 54: 37 importance of the subsidy second
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- Page 57 and 58: 41 Matching methods are theoretical
- Page 59 and 60: 43 2.2 SYETP implementation As SYET
- Page 61 and 62: 45 In July1978, the subsidy was cal
- Page 63 and 64: 47 In January 1979, variations were
- Page 65 and 66: 49 benefits were paid at a slightly
- Page 67 and 68: 51 2.2.3 SYETP operation Earlier re
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- Page 71 and 72: 55 Award Conditions for employment
- Page 73 and 74: 57 Harris (2001) claims that during
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- Page 81 and 82: 65 for teens overall had risen, emp
- Page 83 and 84: 67 for Australia using data from th
- Page 85 and 86: 69 training, can provide a form of
- Page 87 and 88: 71 employer survey estimates were t
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- Page 91 and 92: 75 withdrawals occurred at similar
- Page 93 and 94: 77 Table 2.17 State usage of progra
- Page 95 and 96: 79 2.3.1 Stretton (1982, 1984) 53 S
- Page 97 and 98: 81 Stretton attributed the success
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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
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- Page 123 and 124: 107 (-1.80) (-1.80) Tradesperson -0
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60<br />
<strong>the</strong> CES. In support <strong>of</strong> this <strong>the</strong>y cited statistics 37 reflecting that about 30 percent <strong>of</strong> all<br />
vacancies were registered with <strong>the</strong> CES. In addition to <strong>the</strong> volume constraint, <strong>the</strong>y noted<br />
that <strong>the</strong> majority <strong>of</strong> vacancies were concentrated in low-skilled occupations. In contrast to<br />
<strong>the</strong> low vacancy attraction, <strong>the</strong> majority <strong>of</strong> jobseekers made use <strong>of</strong> <strong>the</strong> CES. It was noted<br />
that 80 to 90 per cent <strong>of</strong> jobseekers looking for full-time work used <strong>the</strong> CES. 38 However,<br />
it was also made clear that although <strong>the</strong> majority registered with <strong>the</strong> CES, as registration<br />
was a condition for receipt <strong>of</strong> unemployment benefits, more than 90 per cent additionally<br />
used o<strong>the</strong>r methods, so it was not <strong>the</strong> sole method and likely not <strong>the</strong> predominant method<br />
for job search.<br />
Referral and placement 39 data for <strong>the</strong> Brisbane area, regarded as a good example <strong>of</strong><br />
average CES activities, was analysed by Wielgosz (1984). In examining referral to<br />
notified vacancies <strong>the</strong> underlying assumption about CES functioning in referral to<br />
vacancies was clarified as follows: where “…employers have certain preferences and<br />
expectations about <strong>the</strong> type <strong>of</strong> labour <strong>the</strong>y require, that <strong>the</strong> CES is aware <strong>of</strong> <strong>the</strong>se<br />
preferences and in it’s desire to fill vacancies and fill <strong>the</strong>m as quickly as possible, will<br />
refer those unemployed applications who are most likely to be placed easily and quickly”<br />
(Wielgosz (1984): 12).<br />
Using a probit econometric analysis, Wielgosz (1984) found referrals to have several<br />
statistically significant factors amongst applicant characteristics, being negatively related<br />
to age, educational attainment (years <strong>of</strong> schooling) and duration <strong>of</strong> unemployment<br />
(current spell in weeks) <strong>of</strong> applicants, with positive effects for applicants in<br />
clerical/administrative and skill trade occupations (relative to a base <strong>of</strong> all o<strong>the</strong>r<br />
occupations except semi-skilled and unskilled). Wielgosz (1984) separately modelled<br />
successful placement in a job, with a variable for <strong>the</strong> number <strong>of</strong> referrals received and<br />
found this significantly increased <strong>the</strong> probability <strong>of</strong> placement using <strong>the</strong> Heckman<br />
method to control for sample selection (Wielgosz (1984) p19 Table 7). No details <strong>of</strong> <strong>the</strong><br />
Heckman sample selection model are presented, and so it is not clear what exclusion<br />
37 Wielgosz (1984) p4 footnote 4 citing ABS Cat.6231.0 ‘Job vacancies, Australia’ various issued to 1984.<br />
38 Wielgosz (1984) p4 citing ABS Cat.6222.0 ‘Persons looking for work, Australia’ various issued to 1984.<br />
39 Note that placement here is in subsidised or unsubsidised jobs.