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Enhancing Consistency in Sentencing: Exploring the Effects of Guidelines in England and Wales

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Abstract

Objectives

The development and application of methods to assess consistency in sentencing before and after the 2011 England and Wales assault guideline came into force.

Methods

We use the Crown Court Sentencing Survey to compare the goodness of fit of two regression analyses of sentence length on a set of legal factors before and after the assault guideline came into force. We then monitor the dispersion of residuals from these regressions models across time. Finally, we compare the variance in sentence length of equivalent types of offences using exact matching.

Results

We find that legal factors can explain a greater portion of variability in sentencing after the guideline was implemented. Furthermore, we detect that the unexplained variability in sentencing decreases steadily during 2011, while results from exact matching point to a statistically significant average reduction in the variance of sentence length amongst same types of offences.

Conclusions

We demonstrate the relevance of two new methods that can be used to produce more robust assessments regarding the evolution of consistency in sentencing, even in situations when only observational non-hierarchical data is available. The application of these methods showed an improvement in consistency during 2011 in England and Wales, although this positive effect cannot be conclusively ascribed to the implementation of the new assault guideline.

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Notes

  1. The new assault guidelines can be downloaded from: http://sentencingcouncil.judiciary.gov.uk/docs/Assault_definitive_guideline_-_Crown_Court.pdf.

  2. The professional consultation can be downloaded from: http://sentencingcouncil.judiciary.gov.uk/docs/ASSAULT_Professional_web.pdf.

  3. The old assault guideline can be downloaded from: http://webarchive.nationalarchives.gov.uk/20100305172947/http://www.sentencing-guidelines.gov.uk/guidelines/council/final.html.

  4. The newsletter can be downloaded from this link: http://www.bbc.co.uk/news/uk-12681250.

  5. Students of sentencing reform have recognised the need for more and better research to evaluate how well these reforms have reduced unwarranted disparity (Hofer et al. 1999, p. 262).

  6. […] such research has been rife with methodological limitations not least of which is the failure to quantify or appropriately define disparity. This calls into question the true level of disparity within the system (Casey and Wilson 1998, p. 237).

  7. See Pina-Sánchez and Linacre (2013) for a description of how measures of inconsistency from random slopes are less prone to problems of omitted relevant variables.

  8. See Roberts (2013a), and the Guide to CCSS Statistics for more information on the CCSS. The latter can be found here, http://sentencingcouncil.judiciary.gov.uk/docs/Guide_to_CCSS_Statistics.pdf.

  9. E.g. In 2012 the British Attitudes Survey and Labour Force Survey achieved a response rate of 54 and 48 %, respectively.

  10. See Rubin (1987) for a classification of the implications and possible adjustments for the different missing data mechanisms.

  11. This was due to administrative difficulties in ensuring that introduction of new forms into courts coincided with the date the new guideline came into effect.

  12. The CCSS questionnaire also considers a category for ten or more previous convictions, but the sample used here does not capture subjects with that value. These are more common in more recidivistic offences such as theft.

  13. The statistical modelling presented in this paper was carried out in R. For robust SEs we used the sandwich estimator, from the sandwich package.

  14. This correlation could also be exploited to obtain some first insights into disparities between courts in the sentence length imposed. This method is further explored in Anderson and Spohn (2011) and Pina-Sánchez and Linacre (2013).

  15. This change of R2 in addition to the previous changes for the coefficients of GBH, intent, and previous convictions, were found to be statistically significant using a Chow test, with a p value >.001.

  16. See also Keele (2008) for an excellent review of semi and non-parametric regression methods.

  17. Under the dispersion of residuals methodology a linear regression model has to be formulated to specify the mathematical relationship between legal factors and sentence length. Although this model allows considerable flexibility in specifying functional form, it can only ever provide an approximation to the true empirical relationship.

  18. The number of matched groups and their sample size are shown in Appendix 2.

  19. The characteristics used to define the 10 matches, and their before and after variances are presented in Appendix 3.

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Author information

Authors and Affiliations

  1. Social Statistics, University of Manchester, Manchester, UK

    J. Pina-Sánchez

  2. Sentencing Council for England and Wales, London, UK

    R. Linacre

  3. G-45, CCSR, School of Social Sciences, Humanities Bridgeford St Building, University of Manchester, Manchester, M13 9PL, UK

    J. Pina-Sánchez

Authors
  1. J. Pina-Sánchez
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  2. R. Linacre
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Corresponding author

Correspondence to J. Pina-Sánchez.

Additional information

Disclaimer

The views expressed in this paper are those of the authors and do not necessarily represent the views of the Sentencing Council for England and Wales.

Appendices

Appendix 1: Descriptive Statistics of the Variables Used

Variable

Mean: before

Mean: after

SD: before

SD: after

Log sentence length

6.11

6.25

.78

.85

Previous convictions

2.04

1.55

.71

.50

First opportunity

.22

.30

.42

.46

Remorse

.34

.29

.47

.45

Carer

.05

.02

.22

.14

Gang

.20

.08

.40

.27

Vulnerable

.15

.10

.36

.30

Public officer

.04

.04

.21

.20

Sustained

.30

.32

.46

.47

Drugs

.35

.35

.48

.48

ABH

.53

.48

.50

.50

GBH

.31

.31

.46

.46

GBH with intent

.15

.21

.36

.41

Appendix 2: Matched Groups Ordered by Sample Size

figure a

Appendix 3: Top Ten Largest Matched Groups

Type of offence

Previous convictions

Aggravating/mitigating

Group size: before

Group size: after

Variance: before

Variance: after

Variance difference

ABH

0

–

112

112

.37

.42

−.05

ABH

1–3

–

141

89

.36

.30

.05

GBH

1–3

–

78

74

.21

.24

−.03

GBH

0

–

60

59

.32

.24

.08

ABH

1–3

Sustained

40

51

.50

.34

.16

GBH

1–3

Drugs

48

37

.23

.32

−.08

ABH

1–3

Drugs

62

35

.28

.20

.08

Intent

1–3

–

33

33

.30

.14

.16

ABH

1–3

First op.

34

28

.55

.32

.23

GBH

1–3

Remorse

28

28

.13

.24

−.11

Appendix 4: Monte Carlo Simulation

Our approach to determine whether the change in the groups weighted variance is statistically significant involved four steps: (1) the simulation of a new dataset of sentence lengths with the number of groups and cases per group corresponding to our CCSS data. Simulations are drawn from different normal distributions for each group with mean of zero and variance determined by s 2 B,k ; (2) the statistic presented in Eq. (3) is calculated using the simulated dataset; (3) steps 1 and 2 are iterated 10,000 times so a sampling distribution of test statistics can be constructed; (4) the estimate of from Eq. (3) using the real data is compared to the 5th percentile of the simulated sampling distribution, and if the former is smaller we can say that the reduction in the variance after the new guideline came into force is statistically significant.

In Fig. 4 we show the simulated sampling distribution together with two vertical lines: a continuous red line signalling the 5th percentile at .927 and a black dashed line indicating the value of our test statistics at .923. Since the latter is smaller than the former we can claim that the observed reduction of the aggregated group variance after the new guideline came into force is statistically significant.

Fig. 4
figure 4

Simulated sampling distribution

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Pina-Sánchez, J., Linacre, R. Enhancing Consistency in Sentencing: Exploring the Effects of Guidelines in England and Wales. J Quant Criminol 30, 731–748 (2014). https://doi.org/10.1007/s10940-014-9221-x

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