This study finds that although suspended sentences were introduced as an alternative to prison, in New South Wales they appear to have had the opposite effect.

Abstract

Aim: To see whether the introduction of suspended sentences reduced the number of offenders receiving a fulltime sentence of imprisonment.

Method: The number of persons receiving a prison sentence was regressed against the number receiving a suspended sentence while controlling for changes in the total number of proven offenders and the monthly variability using multiple linear regression with ARIMA errors. The data set used for the analysis consisted of the monthly number of persons imprisoned, persons given a suspended sentence and proven offenders from January 2002 to December 2013.

Results: Every 10 additional offenders given suspended sentences was associated with an extra 3-4 offenders sent to prison.

Conclusion: Although suspended sentences were introduced as an alternative to prison, they appear to have had the opposite effect

Don Weatherburn

Patricia Menendez

English

Analysis and Policy Observatory (APO)

1Issue paper no. 97 
August 2014
Bureau BriefNSW Bureau of CrimeStatistics and Research
The effect of suspended sentences on imprisonment
Patricia Menéndez and Don Weatherburn
Aim: To see whether the introduction of suspended sentences reduced the number of offenders receiving a full-
time sentence of imprisonment
Method: The number of persons receiving a prison sentence was regressed against the number receiving a 
suspended sentence while controlling for changes in the total number of proven offenders and the monthly 
variability using multiple linear regression with ARIMA errors. The data set used for the analysis consisted of the 
monthly number of persons imprisoned, persons given a suspended sentence and proven offenders from January 
2002 to December 2013. 
Results: Every 10 additional offenders given suspended sentences was associated with an extra 3-4 offenders 
sent to prison.
Conclusion: Although suspended sentences were introduced as an alternative to prison, they appear to have had 
the opposite effect. 
Introduction
Suspended sentences were introduced in NSW in April 2000. 
They proved extremely popular. Between 2000 and 2013, the 
number of suspended sentences imposed by adult courts in 
NSW rose by more than 180 per cent (from 1,849 to 5,224) (see 
Figure 1). Under s.12 of the Crimes (Sentencing Procedure) Act 
(1999), suspended sentences of imprisonment should only be 
imposed where a sentence of full-time imprisonment would 
normally be appropriate. Other things being equal, then, we 
would expect an increase in the use of suspended sentences 
to be accompanied by a reduction in the use of full-time 
imprisonment. 
In an earlier report in this series, McInnis and Jones (2010) found 
that the increase in the use of suspended sentences had not 
been accompanied by a reduction in the rate of imprisonment. 
Instead, it had been accompanied by a reduction in the use of 
other non-custodial penalties, most notably community service 
orders (CSOs). Over the period between 1999 and 2008, the 
percentage of CSOs imposed in the Local Courts fell from 20.4 
per cent to 11.5 per cent. In the year prior to the introduction 
of suspended sentences, CSOs accounted for 9.1 per cent of 
all penalties imposed by the Higher Criminal Courts. By 2008, 
Figure 1. Time series for the total number of proven 
offenders (PO), total number receiving a 
sentence of imprisonment (FTP) and total 
number given suspended sentence (SS)
70
00
90
00
11
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0
P
O
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2002 2004 2006 2008 2010 2012 2014
S
S
Time
2the use of CSOs by the NSW Higher Criminal Courts had all but 
ceased.   
In discussing their findings, McInnis and Jones (2010) raised the 
possibility that the increased use of suspended sentences, far 
from reducing the use of imprisonment, might actually increase 
it. As they put it: 
“This imposition of suspended custodial sanctions on 
offenders who would otherwise have received a non-
custodial sanction has potentially serious implications 
for imprisonment rates over the longer term. The risk 
of imprisonment is probably higher for breaching 
the conditions of a suspended sentence than it is 
for breaching a good behaviour bond or a CSO. One 
unintentional consequence of increasing the use of 
suspended sentences is that a greater number of 
offenders may be drawn into the prison population.” 
(McInnis & Jones 2010, p. 4)
The purpose of this brief is to see whether the concerns 
expressed by McInnis and Jones (2010) have been realised. 
Specifically, we report the results of a study designed to test the 
hypothesis that the introduction of suspended sentences has 
contributed to a growth in the number of convicted offenders 
receiving a sentence of imprisonment.   
Method
Data
Data for the study consisted of monthly counts between 2002 
and 2013 of (a) the total number of people convicted in NSW 
Local, District and Supreme (Higher Criminal) Courts, hereafter 
referred to as the number of proven offenders (b) the number 
receiving a suspended sentence and (c) the number receiving a 
full-time sentence of imprisonment. January 2002 was chosen 
as the start point for analysis because, although suspended 
sentences were introduced in April 2000, the monthly number 
imposed remained quite low through 2000 and 2001. Note that 
we count a person as a proven offender if one or more offences 
are proved against them, even if no conviction is recorded and 
the offender placed on a bond. 
Analysis
The question we seek to address is whether the number of 
sentences of full-time imprisonment increased in response 
to the growth in the number of suspended sentences. In 
examining this question we need to control for the total 
number of proven offenders because an increase in this 
number could drive up both the number of persons receiving 
suspended sentences and the number receiving a sentence of 
full-time imprisonment. We also need to control for seasonal 
variation in the number of proven offenders and the number 
of suspended sentences. To meet these concerns, we model 
the relationship between the monthly number of persons 
receiving a sentence of full-time imprisonment against the 
number receiving a suspended sentence, while controlling for 
the monthly number of proven offenders and seasonal variation 
in both the number of proven offenders and the number 
ing a suspended sentence. To deal with the problem of 
autocorrelation so often found in time series analysis, and to 
avoid problems of spurious regression relationships, we test 
our hypothesis using multiple linear regression with ARIMA 
errors (Box, Jenkins, Reinsel 2013). Our model of the number 
of offenders imprisoned is therefore given by:
yt = β0 + β1x1 + β2x2 + d1I1 + . . . d11I11 + ηt
ηt ∼ ARIMA(0, 1, 1)
1
where: 
x1 = the num
rece
x2 = the total n
ber of suspended sentences;
umber of persons sentenced; 
Ii = monthly dummies i=1, ... ,11; and
ηt = an ARIMA error term to capture autocorrelation in the series.
Our interest lies in the sign and magnitude of β1, which 
measures the effect of suspended sentences on the number 
of persons imprisoned. If increasing the number of suspended 
sentences has the effect of decreasing the imprisonment rate, 
we expect the sign to be negative. Although equations (1) and 
(2) identify the model being tested, because the series being 
analysed were not stationary, the model was estimated in 
differences. 
The contemporaneous relationship between the number of 
sentences of full-time imprisonment, the suspended sentences 
and the total number of proven offenders in the model was 
investigated via the cross-correlation function. Checking of 
model assumptions was carried out by looking at the residuals 
of the fitted m odel. The i ndependence o f t he residuals w as 
checked by examining their autocorrelation and partial 
autocorrelation functions, and by  using the Ljung-Box test 
(Ljung and Box, 1978) based on the first  24 autocorrelations 
(H0: Residuals are independent).  The normality assumption for 
the residuals was tested using the Shapiro-Wilk test (Shapiro 
and Wilk, 1965) (H0: Residuals are normally distributed). The 
significance level for all tests in this analysis was set at α = 0.05. 
Results
Before presenting the results of the analysis it is useful to 
examine trends in the variables examined in the study. 
Figure 1 shows time series plots of the number of persons 
receiving a full-time sentence of imprisonment, the number 
receiving a suspended sentence and the number of proven 
offenders. All three series show a rising trend between 2002 
and around 2009 and a falling trend until 2012. At this point, 
the total number sentenced continues to fall but the number 
given a prison sentence or a suspended sentence rises again. 
Figure 2 presents scatterplots of the relationship between 
first differenced series for the dependent and independent 
variables. The terms PO, FTP and SS refer, respectively, to the 
total number of proven offenders, the total number receiving 
a sentence of imprisonment and the total number given a 
suspended sentence. Each panel shows a scatterplot in which 
the y-axis is the change in the variable listed in the same row 
iv
3Figure 2.  Pairs plot exhibiting the relationships for the first differenced time series between PO, FTP and SS
PO
−200 −100 0 100 200
−2
00
0
−1
00
0
0
10
00
20
00
−2
00
−1
00
0
10
0
20
0
FTP
−2000 −1000 0 1000 2000 −150 −100 −50 0 50 100 150
−1
50
−1
00
−5
0
0
50
10
0
15
0
SS
and the x-axis is the change in the variable listed in the same 
column. The middle scatterplot on the top row, for example, 
plots the change in the total number of proven offenders 
against the change in the number given a prison sentence. The 
panel to the right plots the total number of proven offenders 
against the number given a suspended sentence. The panel 
on the right hand side of the middle row plots the number 
given a prison sentence against the number given a suspended 
sentence. 
There are two key points to note about the scatterplots.  The 
first is that, as the total number of proven offenders increases, 
so too does the number receiving a prison sentence. This 
underscores the need to control for the total number of proven 
offenders. The second is that, as the number given a suspended 
sentence increases, the number given prison sentence rises 
also. In order to further investigate the relationship between 
the total number receiving a sentence of imprisonment (FTP) 
with the total number of proven offenders (PO) and the total 
number given a suspended sentence (SS), we study the cross-
correlations between the series. To avoid spurious relationships 
due to the non-stationarity nature of the series, we calculated 
the cross-correlations (at different lags) between the pre-
whitened time series for FTP, PO and SS.  
The cross-correlations results confirm a very strong 
contemporaneous relationship between the variables. The 
contemporaneous (significant) cross-correlation values (i.e. the 
cross-correlation values at lag 0) were 0.55 for FTP and PO, and 
0.52 for FTP and SS. This provides prima facie evidence that the 
rise in suspended sentences has brought with it a rise in the 
number entering prison. 
Residual checks for the fitted model are shown in Figure 3. 
The residuals of the fitted model fulfilled the assumptions of 
independence, as shown by the ACF and PACF in the figure, and 
as confirmed by the Box-Ljung test p-value of 0.93. As noted 
earlier, the normality assumption was checked via the Shapiro-
Wilk test. The test gave a p-value of 0.2, indicating that the 
Table 1.  Results for the multiple linear regression with 
ARIMA errors for modelling FTP
Estimates Coefficients SD t-statistics
p-values  
(2 sided test)
SS 0.36 0.10 3.71 0.00
PO 0.05 0.01 6.71 0.00
Jan -184.13 16.87 -10.92 0.00
Feb -76.83 18.60 -4.13 0.00
Mar -39.49 19.65 -2.01 0.05
Apr -57.50 16.99 -3.38 0.00
May -30.67 21.53 -1.42 0.16
Jun -42.81 17.52 -2.44 0.02
Jul -92.42 17.15 -5.39 0.00
Aug -47.63 16.76 -2.84 0.01
Sep -55.55 16.57 -3.35 0.00
Oct -39.11 16.50 -2.37 0.02
Nov -12.59 16.61 -0.76 0.45
ma1 -0.72 0.06 -11.32 0.00
loglikelihood = 716.76
AIC = 1463.52
4Figure 3. ACF, PACF, histogram and Normal QQ-plots for the residuals of the fitted multivariate  
ARIMA regression model
assumption was met. The normality of the residuals can be also 
seen in the residuals histogram and Normal QQ-plots (which 
compare the theoretical quantiles of the Normal distribution 
with the quantiles of the residuals).  
Table 1 presents the results of our regression analysis which 
assesses the relationships between the total number receiving 
a sentence of imprisonment (FTP) with the total number of 
proven offenders (PO) and the total number given a suspended 
sentence (SS).
The coefficient measuring the effect of suspended sentences 
(SS) on imprisonments is positive, significant and large. It tells 
us that, for every 10 additional persons given a suspended 
sentence, approximately 3.6 receive a full-time sentence of 
imprisonment. The variable measuring the effect of the total 
number of proven offenders on the number going to prison is 
also (as one would expect) positive and significant. Its effects, 
however, are much weaker; with every 20 additional proven 
offenders resulting in just one additional person being sent 
to prison. The negative coefficients on the monthly dummies 
indicate that the number receiving a prison sentence is 
significantly higher in December than it is in any other month 
except November, where the difference does not reach 
statistical significance. 
Discussion
The suspended sentence has been described by some as a 
‘Sword of Damocles’ (Bartels 2007) that offers the promise 
of deterrence without the costs associated with actually 
imprisoning an offender. Research (Weatherburn & Bartels 
2008) has shown that suspended sentences are no more 
effective in reducing the risk of re-offending than good 
behaviour bonds. Many, however, still regard suspended 
sentences as a useful alternative to prison. Some even argue 
that their abolition would result in a dramatic increase in the 
rate of imprisonment.
Gelb (2013), for example, has argued that abolishing wholly 
suspended sentences in Victoria would add approximately 
5,500 people to the corrections population. This claim was 
based on the assumption that all offenders currently receiving 
suspended sentences in Victoria would, if the sanction were 
abolished, end up receiving prison sentences. This view is 
open to question because (in NSW at least), many offenders 
given suspended sentences would, prior to their introduction, 
not have gone to prison anyway. It also ignores the fact that 
breach of a suspended sentence carries with it a high risk of 
imprisonment. 
0.0 0.5 1.0 1.5
−0
.2
0.
0
0.
2
0.
4
0.
6
0.
8
1.
0
Lag
AC
F
Residuals ACF
0.5 1.0 1.5
−0
.1
5
−0
.0
5
0.
00
0.
05
0.
10
0.
15
Lag
Pa
rti
al
 A
C
F
Residuals PACF
Residuals histogram
Residuals
Fr
eq
ue
nc
y
−100 −50 0 50 100
0
5
10
15
20
25
30
−2 −1 0 1 2
−5
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Residuals QQ−plot
Theoretical Quantiles
S
am
pl
e 
Q
ua
nt
ile
s
Residuals ACF Residuals ACF
Residuals histogram Residuals QQ-plot
La L g
Residuals Theoretical uantiles
Fr
eq
ue
nc
y
AC
F
Sa
m
pl
e 
Q
ua
nt
ile
s
Pa
rt
ia
l A
CF
5The fact that suspended sentences are being imposed on 
offenders who would not otherwise have gone to prison 
was what led McInnis and Jones (2010) to express concern 
about their potential effects on the prison population. The 
present study lends weight to their concerns. Our findings 
suggest that, far from reducing the rate of imprisonment, 
suspended sentences have increased it. This suggests that 
one way of reducing the rate of imprisonment is to abolish or 
curtail the use of suspended sentences in favour of sanctions 
(e.g. community service orders) that, if breached, do not 
automatically result in imprisonment.  
As with all regression analyses, our conclusion regarding the 
effect of suspended sentences on the rate of imprisonment is 
subject to a number of caveats. The most important of these is 
the assumption that we have not omitted any factor that might 
have induced a correlation between suspended sentences 
and imprisonment. One factor that might appear to breach 
this assumption is a change in the seriousness of offending. A 
general increase in the seriousness of offending (even without 
any increase in the total number of proven offenders) would 
cause both the number of persons given suspended sentences 
and the number of persons given a prison sentence to increase. 
This would create the spurious impression that the former was 
causing the latter.
Fortunately, the current study is not vulnerable to this 
problem. It will be recalled that, rather than analyse the 
relationship between suspended sentences, proven offenders 
and imprisonment in levels (i.e. using the raw time series of 
each), we analysed the series in differences so that they were 
stationary. This had the effect of removing the trend from each 
series, leaving only the month to month variation for analysis. 
Removal of the trend in our dependent and independent 
variables would have removed the effect of any general 
increase in the seriousness of offending. 
References  
Bartels, L. (2007). The Use of Suspended Sentences in Australia: 
Unsheathing the Sword of Damocles, Criminal Law Journal, 31, 
113-132
Box, G. E., Jenkins, G. M., & Reinsel, G. C. (2013). Time series 
analysis: forecasting and control. Hoboken, New Jersey. John 
Wiley & Sons.
Gelb, K. (2013). The Perfect Storm? The Impacts of Abolishing 
Suspended Sentences in Victoria. Report prepared for Catholic 
Social Services in Victoria. Available at: http://www.css.
org.au/Portals/51/The%20Perfect%20Storm%20-%20
The%20Impacts%20of%20Abolishing%20Suspended%20
Sentences%20in%20Victoria,%2011%20December%202013.pdf
Ljung, G.M. & Box G. E. (1978). On a measure of lack of fit in time 
series models. Biometrika, 65(2), 297-303.
McInnis, L. & Jones, C. (2010). Trends in the use of suspended 
sentences in NSW. Bureau Brief, 47. Sydney: NSW Bureau of Crime 
Statistics and Research. 
Shapiro, S.S. and Wilk, M.B. (1965). An analysis of variance test 
for normality (complete samples). Biometrika, 52(3-4), 591-611.
Weatherburn, D. & Bartels, L. (2008). The recidivism of offenders 
given suspended sentences in New South Wales. Australia. 
British Journal of Criminology, 48(5), 667-683.
NSW Bureau of Crime Statistics and Research - Level 8, St James Centre, 111 Elizabeth Street, Sydney 2000 
bcsr@agd.nsw.gov.au  •  www.bocsar.nsw.gov.au  •  Ph: (02) 9231 9190  •  Fax: (02) 9231 9187   •   ISBN  978-1-921824-87-6
© State of New South Wales through the Department of Justice 2014. You may copy, distribute, display, download and otherwise freely deal with this work 
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wish to (a) charge others for access to the work (other than at cost), (b) include the work in advertising or a product for sale, or (c) modify the work.


An analysis of variance test for normality (complete samples).

On a measure of lack of fit in time series models.

The Perfect Storm? The Impacts of Abolishing Suspended Sentences in Victoria. Report prepared for Catholic Social Services in Victoria. Available at: http://www.css.

The recidivism of offenders given suspended sentences in New South Wales.

The Use of Suspended Sentences in Australia: Unsheathing the Sword of Damocles,

Trends in the use of suspended sentences in NSW.

The effect of suspended sentences on imprisonment

http://apo.org.au/sites/all/modules/pubdlcnt/pubdlcnt.php?nid=41477&file=http://apo.org.au/files/Resource/bb97.pdf

The effect of suspended sentences on imprisonment

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