Evaluation of the Australian Wage Subsidy Special Youth ...

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92 However, the Richardson (1998) examination of non-response was less developed than the earlier SYETP analyses where non-response was considered. In common with all the SYETP analyses, survey data was used. Stretton (1984) and Baker (1984) modelled nonresponse in their observational data and found non-response was a potential problem. As both of these had the administrative sample frame to identify some variables for modelling non-response to the survey, they were at an advantage. Richardson (1998) could not benefit from the administrative sample frame as this was not available with the survey data deposited, and this precluded this type of analysis. However it was possible to choose to use the non-response weights provided with the data, which had been developed from the non-response analysis for the first survey using the administrative data. Further, the panel of data used had 3 subsequent surveys, to which non-response 59 could be modelled from the first survey. In common with the earlier analyses, no treatment for non-response was applied. In the case of Richardson (1998) this was in line with the conclusions maintained from the analysis of non-response. For earlier evaluations by Stretton (1984) and Baker (1984) this was in spite of the conclusions about the existence of a potential non-response problem for estimates as a result of useful models of survey non-response from the administrative data sample. For Rao and Jones (1986), which used data from Baker (1984) but with additional survey data, ignoring non-response was in conflict with the Baker (1984) analysis, and the aggravation of non-response problems due to further attrition to the later survey was also ignored. In review it is likely that the choice of models potentially available with computing resources at the time limited modelling in these earlier analyses of SYETP. A choice had to be made as to what was the most important modelling consideration, given the computational capabilities of the time. As a result, non-response was not further accounted for. This is an adequate modelling assumption, but further invokes the assumption that the non-response was ignorable for their modelling. In Stretton (1984) and Baker (1984), the examination of non-response indicated this was not generally tenable. This issue is dealt with in the study of non-response that follows. 59 Usually termed attrition in this case.
93 Controlling for differences in individual characteristics between the comparison and treatment groups was carried out in all of the SYETP evaluations using econometric modelling of the post-programme employment. This accounts for observed differences in individual characteristics between the comparison and treatment groups by including them in the model. The earlier modelling showed that controlling for individual characteristics was important, but the set of characteristics was limited. The key issue then is whether all the observable characteristics that might affect employment have been included. The Richardson (1998) analysis had a very broad set of characteristics and it showed that some of these, such as education, are indeed important for modelling employment. Leaving out important variables for modelling employment is likely to lead to specification bias. Controlling for differences in individual characteristics between the control and treatment groups is treated further in this study when different modelling methods are used. As the earlier review of wage subsidy evidence shows, matching techniques can also be used to resolve differences in individual characteristics between the comparison and treatment groups. Later Australian evaluation of the Jobstart wage subsidy used direct matching techniques. These methods however require a limited set of characteristics and large data sample in order to enable the analysis to proceed. The more characteristics it is desirable to match on, the fewer matches become available amongst the treatment and comparison groups. As the earlier modelling of SYETP showed, controlling for individual characteristics was important, and the Richardson (1998) evidence indicates the breadth of characteristics that might be useful. Matching, such as that of the Jobstart evaluations, which does not control for these other characteristics that affect both employment and participation would then be subject to bias. As such, it is then most useful to control for a fairly broad set of characteristics. Other recent overseas evidence has used propensity score matching (PSM) techniques, which allow a greater number of characteristics to be controlled for, for example Bonjour et al (2001). This method has not been used for any published Australian evaluation so far. The PSM method is applied in the following study.
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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
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31 limited applications, such as fo
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33 who participated. It was conclud
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35 term unemployed for over 12 of t
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37 importance of the subsidy second
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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 and 98: 81 Stretton attributed the success
Page 99 and 100: 83 included in the employment model
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: 91 2.3.5 General discussion Some ge
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
Page 149 and 150: 133 Table 4.5, that the variance of
Page 151 and 152: 135 Table 4.6 Matching results, Sin
Page 153 and 154: 137 Table 6.3 using Swedish data wi
Page 155 and 156: 139 matching is the ability to weed
Page 157 and 158: 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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