Evaluation of the Australian Wage Subsidy Special Youth ...

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208 6.2.2 Effects of weighting PSM The first effect of the weighting was to produce slightly different propensity score distributions for the SYETP and comparison than the unweighted estimations. The weight used is the combined weight developed in Chapter 5. The results for the estimation of the weighted probit used to create the propensity scores are shown in Table 6.3. The application of the weight produces no real change to the fit of the model. The model is estimated to have fit 147 of 92 per cent, which indicates that by this measure it has the same predictive power as the unweighted model. The Wald chi squared and pseudo R squared indicate no problems with the fit of the model and are only slightly large than for the unweighted model. The Akaike Information Criterion is very similar to that of the unweighted model. More coefficients are statistically significant in the weighted model than was the case for the unweighted model of the propensity. A number of variables formerly statistically insignificant, are statistically significant in the weighted equation; these include: marital status, partner in employment, the location of Western Australia/Tasmania, private schooling, past job held for more than 3 years, and background living in a non-Capital city. The CEP referral variable which was significant in the unweighted propensity, is not statistically significant in the weighted propensity model. The direction of the coefficients remains plausible in the weighted propensity. It is reasonable to expect that for SYETP age has a negative influence, additionally negative influences on SYETP participation are from being married, or having private schooling, past job held for more than 3 years, poor health, and background living in a non-Capital city or country town. Longer spells of unemployment retain a positive impact on participation, with further positive influences from partner in employment, school-leavers with highest qualification of year 12, living in Western Australia (which evidence in chapter 2 shows is the state had the highest usage of SYETP). 147 A fitted probability exceeding 0.5 is taken to indicate a predicted response to the survey; these predicted responses are compared to the actual participants/non-participants in SYETP to check which cases the model correctly predicted.
209 The distribution of the propensity scores is shown using the histogram in Figure 6.4; kernel density plots of the propensities before matching are shown in Figure 6.6. The statistics summarizing the distribution of the new propensity scores are also shown in Table 6.5 for both the comparison group and SYETP participants. The weighted propensity distributions, when compared to the unweighted shown previously in Figures 4.3, 4.4 and Table 4.5, do not appear very much altered by the application of the weights. However, the distributions have had slight changes. The SYETP propensities have grown slightly at the upper end, with the largest cases greater in size than when unweighted, while at the lower end of the distribution the smallest cases have shrunk in size. The median is roughly the same as when unweighted, but the mean propensity is now slightly larger. For the comparison group of nonparticipants, it is also true that the tails have been slightly altered, with the largest cases greater in size than when unweighted, and the smallest cases smaller than when unweighted. However, the mean and median of the comparisons are largely unaffected by the introduction of weighting. Comparison with the former unweighted propensity scores shows only very small change. The propensities still have good properties, in that the distributions overlap, and the range of the distributions gives a probability roughly from zero up to 0.5 for both treated and comparisons. The kernel density mapping doesn’t look too different to that of the unweighted propensity model. A result of the new weighted propensity scores is that now the application of the common support condition leads to one case being dropped from the SYETP group, the SYETP case with the largest propensity score value. The implication is that for that SYETP case, there was no comparison case which had a propensity score this high. Indeed the distributions shown in the histogram and kernel density plot indicate most comparison cases had near-zero propensity values. The matching results are shown in the Table 6.7, where the PSM implemented was single nearest –neighbour –with replacement, within-caliper, with the additional protocol of weighting the propensity with survey weights for attrition. Comparison with the former results shows that the weighting has led to much smaller results for the employment effect. The statistical significance of the employment effects is also low. Although
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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
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
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: 207 6.2 Results of weighting the PS
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 and 236: 219 6.3 Discussion The comparison o
Page 237 and 238: 221 the selection into SYETP and th
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
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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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