Differential treatment in the youth justice system - Equality and ...

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DIFFERENTIAL TREATMENT IN THE YOUTH JUSTICE SYSTEMTable C14Per cent of individual sample with Asset informationDisposalPercentage ofsample with AssetavailableTotalPolice Reprimand 3 3,213Final Warning 29 1,473Fixed Penalty Notice 50 6Discontinued/Withdrawn 38 526No Case ToAnswer/Acquitted 34 395Referral Order 76 1,493First Tier Penalties 39 1,083Community Sentences AllAges 80 907Community Sentences 16+ 76 320Custody 85 426Other 78 444Result Unknown 32 580Total 39 10,866Table C15Ethnic profile of Asset sampleMale Female TotalEthnicity (%) N (%) N (%) NAsian 13 471 5 37 12 508Black 18 628 14 105 17 733Chinese/Other 0 11 0 3 0 14Mixed 7 252 9 70 8 322Unknown 0 15 1 7 1 22White 61 2,144 70 526 63 2,670Total 100 3,521 100 748 100 4,269123
Appendix D:Multivariate logistic regressionAPPENDICESThe following tables present the results of regression analysis that tested whether thereare differences in outcomes for young people of different ethnic groups controlling for arange of case characteristics. The model is based on a complete year (2006) of youngpeople who have been charged with an offence. This means that a young person canappear in the dataset more than once, and that individual cases were not independentof each other. This violates an assumption for logistic regression which often leads tounderestimated standard errors.To adjust for the dependent data, the statistical package STATA logistic regression withrandom effect was used, and the intra-class correlation (which is produced as part of themodel) was examined to see what proportion of the variance was explained by therepeated occurrence of the individual only. If this proportion was not significantly differentto zero, then it can be assumed that case characteristics have explained away allindividual effects and that results are similar to a logistic regression without controlling forthe repeated occurrence (produced using the statistical package SPSS). The Hausmantest was conducted on the logistic random effect model to test whether the model wasconsistent. In cases where the random effect model was shown to be inconsistent, but theintraclass correlation indicated that a proportion of the variance was explained by theindividual, then a logistic regression was run with clustered robust standard errors.Pre-court decisionsTo model pre-court decisions, we needed to identify all those young people who wereavailable for a pre-court disposal. We selected all young people who had scored less thanthree on the variable counting the number of previous convictions, provided that they hadpleaded guilty to the current offence. 61,62 This resulted in a sample of 5,325 boys and1,931 girls. The sub-sample did not precisely identify all those who received a pre-courtdisposal, identifying 87% and 89% of reprimands of boys and girls, and 76% and 78%of final warning for boys and girls. The variable ‘no previous convictions’ may provideunderestimates of young people with no previous convictions. For all cases involvingboys, 15% had no previous convictions, which is smaller than the 54.8% found by Feilzer6162The ‘guilty plea’ variable includes admissions of guilt made at police stations.We are aware that offenders with two previous convictions are ineligible for pre-court disposals, butour database shows that many such offenders received final warnings. We were unable to establishwhether the variable actually counts the number of contacts with the police and youth justice systemincluding the present contact – which would make sense of the pattern of findings that we haveseen.124
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Equality and Human Rights Commissio
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© Equality and Human Rights Commis
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Appendix A: Area profiles 97Appendi
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Tables and figuresPageTables3.1 Pop
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GlossaryBCUBasic Command UnitCJSCri
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EXECUTIVE SUMMARYAssessing the reas
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EXECUTIVE SUMMARYPolice training•
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INTRODUCTIONover-represented in rob
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INTRODUCTIONthe increase in prosecu
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INTRODUCTIONIn contrast, a MORI sel
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INTRODUCTIONexplanation is that you
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INTRODUCTIONin itself, if the socia
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RESEARCH METHODS2. Research methods
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RESEARCH METHODS29% was white; 6% b
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RESEARCH METHODSfor several differe
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YOUNG PEOPLE’S ENTRY INTO THE YOU
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UNDERSTANDING THE POLICING OF YOUNG
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PASSING THROUGH THE YOUTH JUSTICE S
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Page 105 and 106: DISCUSSION AND CONCLUSIONS6. Discus
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Page 119 and 120: APPENDICESCustody record dataTable
Page 121 and 122: APPENDICESTable B7Area C custody re
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Page 125 and 126: APPENDICESTable C3ONS estimated 10-
Page 127 and 128: APPENDICESTable C5Profile of crimes
Page 129 and 130: APPENDICESTable C6 Profile of crime
Page 131 and 132: APPENDICESTable C7aCase profile% Ma
Page 133 and 134: APPENDICESTable C8Proportion of off
Page 135 and 136: Table C10Proportion of disposals gi
Page 137: Table C12Proportion of sentenced di
Page 141 and 142: APPENDICESTable D3Model of prosecut
Page 143 and 144: APPENDICESTable D6Model of dismissa
Page 145 and 146: APPENDICESTable D8: Model of Remand
Page 147 and 148: APPENDICESTable D10Asset sample, mo
Page 149 and 150: APPENDICESTable D11Asset sample, mo
Page 151 and 152: Table D13Model of community sentenc
Page 153 and 154: APPENDICESTable D15 Model of custod
Page 155 and 156: APPENDICESTable D17Length of custod
Page 157 and 158: REFERENCESReferencesAudit Commissio
Page 159 and 160: REFERENCESMirrlees-Black, C., Budd,
Page 161 and 162: ContactsEnglandEquality and Human R
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