Evaluation of the Australian Wage Subsidy Special Youth ...

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220 As both the Heckman bivariate probit and PSM are not statistically significant when weighted, there would seem to be a substantial loss of efficiency in the application of the weights. Table 6.9 Employment effects of Heckman versus PSM unweighted Weighted for attrition Heckman PSM Heckman PSM selection bivariate probit selection bivariate probit Employment effect 0.26 153 0.18 0.10 154 0.08 t statistic (2.85)** (2.74)** (0.79) (1.50) 155 PSM: one-to-one nearest-neighbour within-caliper (0.001) matching with replacement. The key assumption where weighting is applied is that the non-response has a missing at random [MAR] process. Violation of this assumption would invalidate the results. It should be noted that not treating for non-response, as in the unweighted results, adopts the stricter missing completely at random [MCAR] assumption. Thus, weighting the data is a relaxation of this assumption. Part of the MAR assumption for the weighting treatment of attrition and non-response is that the observed variables used for modelling can accurately represent the non-response and there are no unobserved variables involved. 156 This assumption is added to the modelling assumptions applicable to each of the Heckman and PSM models, as discussed earlier. In the Heckman bivariate probit model, the unobservable information is assumed to be correlated suitably with the observed variables used, in order to solve the selection problem. For the PSM, CIA assumes that all observable variables that jointly influence employment and participation are both in the data and in the model. Unobserved factors, such as lack of job search effort, could affect 153 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. 154 For SYETP in the employment equation: dy/dx =0.0126509 and mean for SYETP is 0.080747. The mean SYETP translates to 8.1 per cent. The marginal effect calculated at the mean is then 0.10215. This is interpreted as a 10 per cent increase in employment. Estimated using the mfx command in STATA7.0. 155 Probability of (0.22). 156 In other words, MAR implies the absence of ‘attrition on unobservables’.
221 the selection into SYETP and the employment outcome jointly, invalidating the CIA (Frölich et al. (2000): 29). A key change to the Heckman bivariate probit results was that the test of the rho correlation factor in the selection modelling failed to reject that it was zero whereas in the former analysis this hypothesis was rejected. Furthermore, in the application of both methods, and for both the weighted and unweighted analyses, casewise deletion was used for missing data (when there was item non-response) on modelled variables. This may not be the most suitable treatment for missing data, as was discussed in the previous chapter. All of these considerations limit the validity of the results. Sensitivity of some of these modelling assumptions is addressed in the next chapter.
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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
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91 2.3.5 General discussion Some ge
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93 Controlling for differences in i
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95 taken by a previous researcher a
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97 If employability is assumed to b
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99 suitability of the underlying as
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101 Heckman, Lalonde and Smith (199
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103 effect on employment relative t
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105 Table 3.1, Part A Employment eq
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107 (-1.80) (-1.80) Tradesperson -0
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109 duration of Pre-June 1984 unemp
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111 4: Study 2 Propensity score mat
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113 Propensity score matching provi
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115 4.2 Propensity score matching m
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117 covariates that influence the a
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119 (7) E(Y c | D=1) = E P(X) {E[Y
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121 For CIA to be plausible, a ‘r
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123 employment and programme partic
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Highest qualification in 1984 (1.56
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127 4.6 Distribution of the propens
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129 Figure 4.3 Histograms of estima
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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
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
Page 209 and 210: 193 Generally, those variables foun
Page 211 and 212: 195 longj0 Longest job by 1984 < 1
Page 213 and 214: 197 adopted in order to maintain co
Page 215 and 216: 199 6: Study 4 Weighting to counter
Page 217 and 218: 201 Table 6.1, part A Employment eq
Page 219 and 220: 203 Methodist 0.133 0.261 (0.77) (1
Page 221 and 222: 205 CEP referrals 1984 0.143* 0.128
Page 223 and 224: 207 6.2 Results of weighting the PS
Page 225 and 226: 209 The distribution of the propens
Page 227 and 228: 211 Table 6.3 Weighted probit used
Page 229 and 230: 213 (0.76) Tradesperson mtrad 0.20
Page 231 and 232: 215 Table 6.5 Summary statistics fo
Page 233 and 234: 217 Table 6.7 Matching results, sin
Page 235: 219 6.3 Discussion The comparison o
Page 239 and 240: 223 Heteroskedasticity is a violati
Page 241 and 242: 225 Table 7.1, Part A Employment eq
Page 243 and 244: 227 (1.26) (1.28) (1.16) Mothers oc
Page 245 and 246: 229 Other Post-School qualification
Page 247 and 248: 231 7.1.2 Exclusion restriction in
Page 249 and 250: 233 Finally, the third panel of new
Page 251 and 252: 235 Table 7.2 summary of changes to
Page 253 and 254: 237 schooling that was statisticall
Page 255 and 256: 239 (0.25) (0.21) 3 years + -0.50 -
Page 257 and 258: 241 Table 7.4 Summary of distributi
Page 259 and 260: 243 7.2.2 Propensity score matching
Page 261 and 262: 245 original model, but with the pe
Page 263 and 264: 247 maintained, then the Heckman bi
Page 265 and 266: 249 Table 7.6 Weighted Probit used
Page 267 and 268: 251 Table 7.7 Summary of distributi
Page 269 and 270: 253 8: Summary and Conclusions The
Page 271 and 272: 255 over which it ran, the review m
Page 273 and 274: 257 the Heckman and PSM methods wer
Page 275 and 276: 259 posited that that such sensitiv
Page 277 and 278: 261 Description Derivation and deta
Page 279 and 280: Appendix 2 Tables 263
Page 281 and 282: 2 years 0.28 0.07 (1.70) (1.70) 3 y
Page 283 and 284: 267 Table A2.0b Univariate Probit o
Page 285 and 286: 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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