Evaluation of the Australian Wage Subsidy Special Youth ...

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144 might give an approximation measure of the functioning of the administration is CES referrals, and also CEP referrals which have been used in analysis. In this analysis, age and referrals to CEP might be the key variables under suspicion for violating the CIA rule for PSM. This is because their use for the exclusion in Richardson (1998) 94 was argued as due to their plausibly not affecting employment independently once SYETP participation was accounted for. Indeed, in later checks carried out in this study it was not possible to estimate the Heckman model if these variables were included in the employment equation which might indicate misspecification. In later chapter 7, the sensitivity of the PSM specification to CEP referrals is checked. Furthermore, in the application of both methods, casewise deletion was used for missing data (item non-response) on modelled variables. This may not be the most suitable treatment for missing data, as briefly discussed in the next chapter. However, as later discussion shows, casewise deletion is preferable because it assumes the correct standard error. As well, in light of the replication aims, it was necessary to repeat the modelling assumptions of Richardson (1998). However, at least one strong confounding factor to discussion comparing the results is the issue of non-response to the surveys. The issue of attrition can introduce selection bias which would confound both Heckman and PSM methods unless it was treated. As a result, further comparison of the results is deferred. This comparison is then again treated in later chapters after consideration of the data problems and repairing the data to treat them. 94 This was checked by modelling the univariate probit for employment, where these variables were not found to be statistically significant when included.
145 5: Study 3 Attrition and non-response in the ALS In this chapter, data loss to the sample is examined for the Australian Longitudinal Survey data. The theory relating to survey attrition is broadly summarized. Literature on the empirical aspects of attrition and effects on estimation are then reviewed. Next, a practical means of investigating and treating attrition in the data is presented. Following this, attrition in the ALS is investigated in depth and the extent of sample reduction is considered. A univariate analysis of the scale of attrition effects is conducted. The different sources of sample loss are recognized separately. Initially the final sample and those lost from the sample are compared. Then, sample loss is examined within the SYETP treatment and comparison groups. The non-response to the first survey and sampling design effects are examined to check their effects on analysis. These are accounted for with a weight that was supplied with the data. Multivariate analysis is then conducted of the effects survey attrition on the participation model. Attrition is concluded to be a problem for the modelling. Weights are then constructed to repair the data. These attrition weights are then united with the sample design/non-response weights. These combined weights are used in subsequent chapters. 5.1 Examining sample reduction in the ALS A particular concern for the analysis of longitudinal data is sample attrition. Attrition and survey non-response are a form of non-response usually termed ‘unit non-response’ by statisticians. The key worry is that attrition may lead to selective samples, which in turn may make interpretation of resulting estimates problematic. One potential problem for the analysis in the earlier studies is the possibility of attrition bias due to sample reduction between the first sample survey in 1984 and the 1986 survey. An important reason for properly accounting for attrition is that ignoring attrition can lead to biased parameter estimates if the attrition is related to the behaviour being modelled. The issue of attrition was briefly examined in Richardson (1998) by comparing the summary statistics of the final analysis sample (referred to as the ‘whole sample’), those
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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
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 and 158: 141 Table 4.7 Matching results, All
Page 159: 143 the unobserved component. If th
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
Page 209 and 210: 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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