Evaluation of the Australian Wage Subsidy Special Youth ...

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82 SYETP at 53.6 per cent, 45.6 per cent for repeat SYETP and 52.4 per cent for extended SYETP. A small share between 5-6 per cent was in part time employment. Fulltimeemployment prior to SYETP was lower for SYETP Private at 52 per cent, so before controlling for individual characteristics they were more likely to hold a job after than before the programme (Baker (1984) p16, Tables A4 and 5.1). For other SYETP, post programme employment was lower or the same. A probit model of response to the survey was found to indicate response bias might be present. 55 The various programme dummies were significant in modelling response, where response was a full response with no missing items. The post-programme full-time employment outcome was modelled using a variable with 3 categories: continuous employment, including those retained in the job and those who left after the subsidy but got a job within a month; non-continuous employment, including those retained in the job for only 1 month after subsidy expiry but who later lost it by the survey date, and those who left after the subsidy and took longer than one month to find a job; no employment, including unemployed, education or other states of not in the labour force. This variable combined holding a job since the programme and the durability of the jobs held since programme, as well as the job search success, to perhaps to reflect employability. Table 2.18 shows the distribution of this variable for the various SYETP. It can be seen for example, that the continuous full-time work category for SYETP private is much lower at 41.1 per cent than the share in employment at interview (60.6 per cent). However, about 80 per cent of SYETP private participants had held some full-time employment. The employment variable with three categories (no employment, continuous and noncontinuous employment) was then modelled as an ordered probit, as if it were count data. The selection process onto programme participation was not modelled. However, the ordered employment probit was adjusted for non-response using the Heckman model. To adjust for non-response, the non-response model was used “…to estimate a correction factor LAMBDA…included as an additional independent variable”, and this variable was 55 Baker (1984) p49 Table A11 Variables included age, sex, unemployment (weeks in 52 week year before program entry), multiple program participation, dismissal, voluntary withdrawal, 10 program dummies with SYETP private as Base, state dummies. State dummies were not statistically significant.
83 included in the employment model. This is the Heckman selection correction model where the lambda is the inverse mills ratio. The employment model had the variables age, sex, year left school (but not qualifications), post-school formal training (any post-school qualifications), a post-school job (pre-programme), weeks unemployment (weeks in 52 week year before programme entry), multiple programme participation, dismissal, voluntary withdrawal, 10 programme dummies with SYETP private as base, state dummies and the LAMBDA correction factor. The response model used a subset of these variables, so that the exclusion restriction variables were year left school, post school formal training and post school job (pre-programme). No additional results were presented showing the sensitivity of the employment model to whether or not nonresponse was accounted for, so it is not clear whether the additional Heckman selection model performed better than a simpler model without nonresponse. Baker estimated predicted probabilities for employment for various subgroups using the model outcomes. Predicted probabilities were estimated for the eligibility period of 17 weeks unemployment in the past 52 weeks, for a participant who left school in year 10/11, did post-school training, took part in no other programme and completed the programme of participation. They found that SYETP private generally performed better than education based programmes, but did no better than other SYETP, and was significantly worse than extended SYETP. The results are shown below for SYETP in Table 2.19. It can be seen that the ‘out of work’ outcome had a very low probability for all types of SYETP. Table 2.18 Baker (1984) Post-programme full-time employment outcome Continuous fulltime work Non- Continuous fulltime work Retained Not-retained Total 21.1 14.3 35.5 33.7 30.8 SYETP Commonwealth SYETP private 34.9 6.3 41.1 38.4 20.4 2nd SYETP 24.3 7.3 31.6 41.2 27.2 Extended SYETP 37.1 5.5 42.6 30.6 26.8 Source: Baker (1984) p19 Table 5.2 May 1982 post-programme Survey of participants Out of work
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i Evaluation of the Australian Wage
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v Declaration I hereby declare that
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in method and assumptions. The orth
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3.3 Data and variables used for est
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List of Tables and Figures Figure 1
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Table A2.0b Univariate Probit of pa
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xv Abstract The job subsidy Special
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1 1: Wage subsidy theory and evalua
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3 programs of providing benefits an
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5 The empirical methods used for th
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7 how the outcomes from these metho
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9 employment effect could be mainly
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11 Real Wage Employment Figure 1.1:
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13 are two types of unemployment, s
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15 Real wages are predetermined whe
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17 Vella (1998), Heckman, Lalonde a
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19 Each individual has two potentia
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21 this case, if there was no data
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23 correlation for the error of the
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25 Table 1.3 collects together some
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27 Table 1.3 Brief overview of rece
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29 necessarily brief. Then, the evi
Page 47 and 48: 31 limited applications, such as fo
Page 49 and 50: 33 who participated. It was conclud
Page 51 and 52: 35 term unemployed for over 12 of t
Page 53 and 54: 37 importance of the subsidy second
Page 55 and 56: 39 differences in characteristics t
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
Page 69 and 70: 53 ceiling constraints’ applied t
Page 71 and 72: 55 Award Conditions for employment
Page 73 and 74: 57 Harris (2001) claims that during
Page 75 and 76: 59 display boards listed details of
Page 77 and 78: 61 restriction was used. If there w
Page 79 and 80: 63 to the end of the 1980’s. An o
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
Page 89 and 90: 73 provisions for SYETP and extende
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: 81 Stretton attributed the success
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
Page 111 and 112: 95 taken by a previous researcher a
Page 113 and 114: 97 If employability is assumed to b
Page 115 and 116: 99 suitability of the underlying as
Page 117 and 118: 101 Heckman, Lalonde and Smith (199
Page 119 and 120: 103 effect on employment relative t
Page 121 and 122: 105 Table 3.1, Part A Employment eq
Page 123 and 124: 107 (-1.80) (-1.80) Tradesperson -0
Page 125 and 126: 109 duration of Pre-June 1984 unemp
Page 127 and 128: 111 4: Study 2 Propensity score mat
Page 129 and 130: 113 Propensity score matching provi
Page 131 and 132: 115 4.2 Propensity score matching m
Page 133 and 134: 117 covariates that influence the a
Page 135 and 136: 119 (7) E(Y c | D=1) = E P(X) {E[Y
Page 137 and 138: 121 For CIA to be plausible, a ‘r
Page 139 and 140: 123 employment and programme partic
Page 141 and 142: Highest qualification in 1984 (1.56
Page 143 and 144: 127 4.6 Distribution of the propens
Page 145 and 146: 129 Figure 4.3 Histograms of estima
Page 147 and 148: 131 Table 4.5 Summary statistics fo
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133 Table 4.5, that the variance of
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135 Table 4.6 Matching results, Sin
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137 Table 6.3 using Swedish data wi
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139 matching is the ability to weed
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141 Table 4.7 Matching results, All
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143 the unobserved component. If th
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145 5: Study 3 Attrition and non-re
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147 occur by design, because the mi
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149 (1990) extended and improved th
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151 (10) A* = δ 0 + δ 1 x +δ 2 z
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153 again from September to Novembe
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155 5.5.2 Univariate examination of
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157 lower, the job lengths are only
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159 Work limited by health 1984 0.1
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161 The characteristics of the SYET
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163 para-professional Mother not em
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165 comparison group where the shar
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167 5.5.4 Attrition: natural attrit
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169 both sources that impose change
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171 para-professional Father not em
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173 work in later sections, this su
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175 Table 5.6: Effect of selection/
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177 appropriate to discard these fr
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179 Australia/Tasmania. Amongst tho
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181 Table 5.5a Summary statistics b
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183 5.6.1.2 Effects of the non-resp
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185 3 years + -0.35 -0.47 -0.34 -0.
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187 5.7 Multivariate analysis of ef
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189 proportion of time spent unempl
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191 post-school qualification, and
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193 Generally, those variables foun
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195 longj0 Longest job by 1984 < 1
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197 adopted in order to maintain co
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199 6: Study 4 Weighting to counter
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201 Table 6.1, part A Employment eq
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203 Methodist 0.133 0.261 (0.77) (1
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205 CEP referrals 1984 0.143* 0.128
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207 6.2 Results of weighting the PS
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209 The distribution of the propens
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211 Table 6.3 Weighted probit used
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213 (0.76) Tradesperson mtrad 0.20
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215 Table 6.5 Summary statistics fo
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217 Table 6.7 Matching results, sin
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219 6.3 Discussion The comparison o
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221 the selection into SYETP and th
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223 Heteroskedasticity is a violati
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225 Table 7.1, Part A Employment eq
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227 (1.26) (1.28) (1.16) Mothers oc
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229 Other Post-School qualification
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231 7.1.2 Exclusion restriction in
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233 Finally, the third panel of new
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235 Table 7.2 summary of changes to
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237 schooling that was statisticall
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239 (0.25) (0.21) 3 years + -0.50 -
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241 Table 7.4 Summary of distributi
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243 7.2.2 Propensity score matching
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245 original model, but with the pe
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247 maintained, then the Heckman bi
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249 Table 7.6 Weighted Probit used
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251 Table 7.7 Summary of distributi
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253 8: Summary and Conclusions The
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255 over which it ran, the review m
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257 the Heckman and PSM methods wer
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259 posited that that such sensitiv
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261 Description Derivation and deta
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Appendix 2 Tables 263
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2 years 0.28 0.07 (1.70) (1.70) 3 y
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267 Table A2.0b Univariate Probit o
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269 Religion brought up in (1.52) (
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271 hq9_84 Year 9 of school or less
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273 Table A2.1 Continued Means and
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275 mtrad Tradesperson 0.04 0.04 0.
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277 Year 9 of school or less -0.19
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279 Table A2.3 Probit of SYETP part
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281 (0.22) (0.44) (0.67) (0.89) Tra
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283 Table A2.5a Univariate probit f
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285 Manager/professional/para-profe
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3 years + -0.04 (2.13)* CEP referra
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289 Table A2.6 Part A Employment eq
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291 Table A2.6 Part B Selection/par
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301
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BLMR Bureau of Labour Market Resear
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Fay, R.G. (1996) "Enhancing the eff
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Heckman J.J. and Smith, J. A. (1998
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Lewis, H.G. (1963) "Unionism and re
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Quandt, R. (1972) "A new approach t
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Stromback, M. and Dockery, A. (2000
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