Evaluation of the Australian Wage Subsidy Special Youth ...

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142 Table 4.8 Employment effects of Heckman versus PSM Heckman PSM selection bivariate probit Employment effect 0.264 93 0.18 T statistic (2.85)** (2.74)** PSM: one-to-one nearest-neighbour within-caliper (0.001) matching with replacement. A number of factors are thought to be behind the difference in the two estimates. The PSM argues that it controls better for differences in individual characteristics that arise in the SYETP and comparison group. The differences between the groups were found to be large, and so the difference in part may be due to this. The participation equation was identical for both modelling methods. The importance of this is now examined. In the Heckman method, the participation equation is explicitly modelled to provide a variable that is then used to control for the part of unobserved variation of the employment equation that is correlated with the unobserved variation in the participation decision. There is some evidence to support the need to model accounting for selection bias in the SYETP process. For example, Stretton and Chapman (1990) p42 in discussing programme entry conclude that it “…does involve some selection bias as the CES selects those eligible clients who are judged to be ‘job ready with assistance´ for referral ... Employers then select their subsidised employee from among a number of referrals made by the CES.” Although Stretton and Chapman (1990) were describing the selection for the later Jobstart programme, a very similar process existed for SYETP, as can be seen described in the earlier Chapter 2. In the SYETP administrative data it was found that gaining referrals and gaining employment were closely linked in a selection process [Wielgosz (1984), Aungles and Stewart (1986)]. As there is no variable indicating the administrative value of whether individuals were ‘job- ready-with-assistance’, then this is 93 For SYETP in the employment equation: dy/dx =0.0325919 and mean for SYETP is 0.081060. The mean SYETP translates to 8.1 per cent. The marginal effect calculated at the mean is then 0.26418. This is interpreted as a 26 per cent increase in employment. Estimated using the mfx command in STATA7.0
143 the unobserved component. If the variables included in the propensity score model and participation equation of the bivariate probit were used by the caseworkers in SYETP to determine that an applicant is ‘job-ready-with-assistance’, then selection on unobservables should be negligible. For the exclusion restriction to identify the bivariate probit model, some variables included in the participation equation estimated are then excluded from the employment equation. An important aspect of this estimation approach is the identification of a credible instrument for the exclusion restriction, in this case age and referrals to CEP. There is some support for this role for these variables in the unweighted data, as later sensitivity analysis in Chapter 7 reports. Also, the results of estimation rest upon the suitability of the underlying assumption of the bivariate normal distribution for the errors in the participation and employment equations. Finally, the specification of the model is assumed correct, with no variables left out to cause mis-specification bias. For the semi-parametric method of PSM, CIA is the critical underlying assumption. However, again the specification of the model for the propensity to participate in SYETP is assumed correct. The variables in the model are central to the credibility of the CIA assumption. Whether the CIA is met cannot be accurately tested, however the plausibility of CIA can be assessed. In regard to labour market evaluation, pre-programme labour market history is considered the most important explanatory variable, and this is part of the model applied here (see Ham and Lalonde (1996)). There are a large number of variables generally and it is considered that at least with regard to the breadth of individual characteristics controlled for, the CIA has been satisfied. However, although a wide range of individual characteristics has been included, it could be argued that some are lacking that could affect both participation and employment. Gerfin and Lechner (2000) had the long-term unemployment at the regional placement office, which here would correspond to the CES. This sort of variable is not available in the data however. A variable in the data and which
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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
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41 Matching methods are theoretical
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43 2.2 SYETP implementation As SYET
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45 In July1978, the subsidy was cal
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47 In January 1979, variations were
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49 benefits were paid at a slightly
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51 2.2.3 SYETP operation Earlier re
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53 ceiling constraints’ applied t
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55 Award Conditions for employment
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57 Harris (2001) claims that during
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59 display boards listed details of
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61 restriction was used. If there w
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63 to the end of the 1980’s. An o
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65 for teens overall had risen, emp
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67 for Australia using data from th
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69 training, can provide a form of
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71 employer survey estimates were t
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73 provisions for SYETP and extende
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75 withdrawals occurred at similar
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77 Table 2.17 State usage of progra
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79 2.3.1 Stretton (1982, 1984) 53 S
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81 Stretton attributed the success
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83 included in the employment model
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85 completers. Their argument was t
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87 was an issue for the data. Unlik
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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
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: 141 Table 4.7 Matching results, All
Page 161 and 162: 145 5: Study 3 Attrition and non-re
Page 163 and 164: 147 occur by design, because the mi
Page 165 and 166: 149 (1990) extended and improved th
Page 167 and 168: 151 (10) A* = δ 0 + δ 1 x +δ 2 z
Page 169 and 170: 153 again from September to Novembe
Page 171 and 172: 155 5.5.2 Univariate examination of
Page 173 and 174: 157 lower, the job lengths are only
Page 175 and 176: 159 Work limited by health 1984 0.1
Page 177 and 178: 161 The characteristics of the SYET
Page 179 and 180: 163 para-professional Mother not em
Page 181 and 182: 165 comparison group where the shar
Page 183 and 184: 167 5.5.4 Attrition: natural attrit
Page 185 and 186: 169 both sources that impose change
Page 187 and 188: 171 para-professional Father not em
Page 189 and 190: 173 work in later sections, this su
Page 191 and 192: 175 Table 5.6: Effect of selection/
Page 193 and 194: 177 appropriate to discard these fr
Page 195 and 196: 179 Australia/Tasmania. Amongst tho
Page 197 and 198: 181 Table 5.5a Summary statistics b
Page 199 and 200: 183 5.6.1.2 Effects of the non-resp
Page 201 and 202: 185 3 years + -0.35 -0.47 -0.34 -0.
Page 203 and 204: 187 5.7 Multivariate analysis of ef
Page 205 and 206: 189 proportion of time spent unempl
Page 207 and 208: 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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