Evaluation of the Australian Wage Subsidy Special Youth ...

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222 7: Study 5 Sensitivity analysis In this chapter, the specifications modelled so far are further investigated. Firstly, specification of the Heckman bivariate probit is examined. As a part of this, a potential source of confounding for the results is initially considered. Then the reliance of the exclusion restriction on the variable CEP referrals is explored. These analyses test some of the limitations of the Heckman bivariate probit modelling. The robustness of the propensity score matching (PSM) is then looked at by varying the specification of the probit used to estimate the propensity. This explores the importance of the parameterisation of the propensity to the measured employment outcome. 7.1 Sensitivity of Heckman specification The Heckman modelling in Chapter 6, where weighting for attrition was applied, showed a big fall in the size of the SYETP coefficient and a dramatic loss of it’s statistical significance. Although the size of the probit coefficient is not directly interpretable, it does impact on the marginal effect calculable from the coefficient as a measure of the employment impact of SYETP. Mostly the impact of weighting was not found to affect other coefficients, and where other coefficients were affected, the change was not so dramatic. The first sensitivity check examines a possible source of this effect. 7.1.1 Potential heteroskedasticity It is of interest to briefly consider the possibility of one of the modelling assumptions being violated. One way of allowing for this is through considering the Huber-White variance estimation for the equation. The Huber-White estimates of variance 157 provide a more conservative estimate that can allow for potential heteroskedasticity of undefined form amongst the residuals. Homoskedasticity is the standard assumption in modelling, such that the disturbances have the same variance. The variance of each disturbance term conditional for the chosen values of the explanatory variables is constant. 157 Also known as the ‘sandwich estimator of variance’, or ‘robust estimator of variance’, key recent development papers are White (1980) and White (1982).
223 Heteroskedasticity is a violation of this assumption. Heteroskedasticity can introduce a form of mis-specification if the modelling assumes homoskedasticity. The worth of allowing the heteroskedasticity to be of undefined form is that the form it takes is usually unknown. Furthermore, as Gujurati (1988) p326 points out, due to data problems the investigation of heteroskedasticity is often sheer speculation. The possibility of heteroskedasticity in cross-sectional or longitudinal data, involving heterogeneous units is very high however. The Huber-White variance estimates can be interpreted as trading off efficient estimation of the variance-covariance matrix for estimates that accommodate a degree of slack and so potentially allow for the issue of minor mis-specification. The point estimates of coefficients are unaffected. The replication results applying the Huber-White variance estimation are shown in Column 3 of Table 7.1. The first two columns show the Richardson (1998) results and the earlier replication estimation to better enable comparison. The t-statistics for some variables are quite different, smaller in size, reflecting more cautious estimates of error. The impact is not even across variables, which suggests some variables might be more affected by heteroskedasticity than others. The SYETP treatment variable loses statistical significance under the Huber-White variance estimates, with the significance level falling to 14 per cent from 1 per cent. The conventional levels of statistical significance are not now met, and the 95 per cent confidence interval for the SYETP coefficient now ranges from –0.50 to 3.69 whereas before it was 0.50 to 2.69. Other variables generally were not affected strongly in this way. The results above indicate that the assumption of homoskedasticity may be violated in the data. Given that the survey design used a clustered sample, one potential source of heteroskedasticity may be due to spatial correlation. 158 Maddala (1983) points out that little work studies the effects of violations of the different assumptions for this type of model. For the general class of limited dependent variable models, he reports that findings show the estimates are neither consistent nor efficient when there is 158 As the clusters are not known in the data, this cannot be examined.
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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
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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 and 236: 219 6.3 Discussion The comparison o
Page 237: 221 the selection into SYETP and th
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) (
Page 287 and 288: 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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