The correlation between motoring offences and other types of offence

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1 The correlation between motoring offences and other types of offence Prepared for Road Safety Strategy Division, Department for Transport J Broughton TRL Report TRL650

2 First Published 2006 ISSN ISBN Copyright TRL Limited This report has been produced by TRL Limited, under/as part of a contract placed by the Department for Transport. Any views expressed in it are not necessarily those of the Department. TRL is committed to optimising energy efficiency, reducing waste and promoting recycling and re-use. In support of these environmental goals, this report has been printed on recycled paper, comprising 100% post-consumer waste, manufactured using a TCF (totally chlorine free) process. ii

3 CONTENTS Page Executive Summary 1 1 Introduction 3 2 Data sources The TRL archive The Offenders Index Data from Scotland 4 3 Matching The sample of drivers Check of the matching 5 4 Analysis of linked data Preliminary analyses Disposals Statistical models DVLA motoring offences 12 5 Recidivism 14 6 Conclusions 16 7 Acknowledgements 18 8 References 18 Appendix A: Standard list offences 19 Abstract 21 Related publications 21 iii

4 iv

5 Executive Summary Broughton (2003) demonstrated that the number of motoring and of non-motoring offences that a driver commits are linked. This confirmed the hypothesis that a driver s willingness to commit motoring offences tends to be associated with their willingness to commit non-motoring offences what may broadly be called criminal offences. The result was achieved by linking data from two sources:! The archive of driving licence information maintained at TRL: this is based on licensing information supplied by the DVLA and provides details of the driving history of a sample of drivers including their convictions for motoring offences.! The Offenders Index: this is maintained by the Home Office and holds the criminal histories of all people convicted of standard list offences in courts in England and Wales, so provides details of convictions for non-motoring offences. The two sources were re-linked in 2004, about three years after the original study. This report presents the results of various analyses of the new data which have updated and expanded the results of the earlier study. A sample of over 52,000 drivers was selected from the TRL archive, stratified according to the motoring offences that they committed in or after Details of these drivers were sent to the Home Office, where they were matched with the Offenders Index using a standard computer program. When a driver could be matched with an offender, details of their offence history were returned to TRL and linked to details of any motoring offences in the archive. The DVLA data include drivers from England, Wales and Scotland while the Offenders Index includes only people who were convicted in English and Welsh courts. A detailed study of drink/drive offences showed that drivers who lived south of the border committed very few offences north of the border and vice versa. Consequently, the sample was restricted to drivers who lived in England and Wales to compensate for the different coverage of the two data sets. The matching process is statistical, relying on the degree of agreement of offenders details such as surname and date of birth in the two sources. The Home Office s standard list of offences was expanded in 1996 to include 3 types of motoring offence: Dangerous driving, Drink/ driving and Driving whilst disqualified. These offences are also recorded in the DVLA data, and this allows the accuracy of the matching process to be assessed. The proportion of successful matches has risen progressively from 75% in 1996 to 82% in 1999 and 87% in The new study focuses on the period The earlier study used data from , and the increased proportion of successful matches in the latter period means that the numerical results of this study should be more reliable than those of the earlier study. The Offenders Index groups together all offences that led to sentencing on a single day; the primary offence is the one which incurred the greatest sentence while all others are secondary. Primary offences are on the standard list and should be fully recorded, but there is an element of chance as to whether secondary offences are recorded, so the study has focused on primary offences. Nonetheless, two-thirds of secondary motoring offences are found to be associated with primary motoring offences: a quarter of the remainder were associated with primary Other theft and handling offences and a fifth with primary Theft of/from a vehicle offences. Only 22% of secondary offences associated with primary motoring offences were not motoring offences. Preliminary analyses confirm that the numbers of motoring and of non-motoring offences committed by individual drivers are strongly correlated. For example, 2.5% of male drivers committed at least one primary nonmotoring offence between 1999 and 2003 but this group included 30.6% of the men who committed at least one serious motoring offence (i.e. one of the 3 standard list offences) in this period. The correlation is even stronger among women: the corresponding proportions were 0.5% and 16.4%. The numerous offence codes in the Offenders Index are combined into 11 groups for this study. About 29% of primary offences committed by the sample of drivers are standard list motoring offences, almost 19% are offences of violence against the person and 18% are other theft and handling offences. By comparison with other types of offence that were committed by this sample of drivers, a high proportion of motoring offences resulted in fines rather than other forms of punishment, and in addition most of the offenders would have been disqualified from driving (although this is not recorded in the Offenders Index). 11% of motoring offences resulted in a custodial sentence. The average fine for motoring offences was rather higher than for other offences, but the average length of custodial sentences for motoring offences was comparable with the average for offences such as theft and handling. Statistical models are then fitted to the linked data to identify the separate influences of various factors upon the number of motoring offences, with particular interest in the number of non-motoring offences committed. Motoring offences are grouped into serious offences (the 3 standard list offences) and other offences. It is found, for example, that men who committed between 4 and 8 non-motoring offences committed on average 21 times as many serious motoring offences as men who committed no nonmotoring offences, but only 3.9 times as many other motoring offences. The effect is even stronger for women. The region where a driver lives also influences the number of motoring offences that he or she commits. It is not possible, however, to determine from the linked data whether this is the result of the greater willingness of drivers living in some regions to commit offences, or of the greater effectiveness of the local police and judicial procedures to catch and convict offending drivers, or some combination of the two. 1

6 A wide range of relationships is found when the modelling is repeated for specific types of motoring offence. The most extreme case is Driving while disqualified: on average, men who committed at least 9 non-motoring offences committed more than 100 times as many of these offences as men who committed no nonmotoring offences. The relationship between motoring offences and specific types of non-motoring offence can also be compared. The relationship is strongest for vehicle theft; for example, a driver with 2 convictions for vehicle theft committed on average about 25 times as many serious motoring offences as one with no vehicle theft convictions. The corresponding figure for non-vehicular theft was 10, and it was lower for other offence types. The offence details in both data sets are complete over many years. It is thus possible to study recidivism, specifically the extent to which the number of motoring and non-motoring offences committed by a driver in may be related to the number of offences committed in The number of offences committed by a driver in is shown to be approximately proportional to the number in On average, drivers committed fewer offences in than in , the main reason probably being that each was 5 years older in the latter period. The degree of recidivism is found to vary regionally, with the variation having a similar pattern to the variation of the regional offence rates. A study of the incidence of other motoring offences suggests that the regional variation of the offence rate cannot be explained in terms of the existence of a group of recidivists which is proportionately large in regions with relatively high offence rates. Reference Broughton J (2003). The number of motoring and nonmotoring offences. TRL Report TRL562. Wokingham: TRL Limited. 2

7 1 Introduction Broughton (2003) explored the hypothesis that a driver s willingness to commit motoring offences tends to be associated with a willingness to commit other types of offence what may broadly be called criminal offences. That report analysed the offence histories of almost 50 thousand British drivers to investigate the relationship between the number of motoring and non-motoring offences committed by these drivers, taking account of factors such as age and sex that may influence offending behaviour. That study has now been updated, taking advantage of the larger set of matched data that the passage of three years has provided and exploring a wider range of issues than the earlier study. The data sources are described in Section 2. The starting point for the study is the archive of driving licence information that has been maintained at TRL since 1986 (Broughton, 1999), based on licensing information supplied three times per year by the Driving and Vehicle Licensing Agency (DVLA) at Swansea. This includes details of convictions for motoring offences, but the DVLA file contains no information about nonmotoring offences. This is, however, available from the Offenders Index that is maintained by the Home Office, one of the largest criminal databases in Europe. A criminal history is the cumulative record of court appearances of an individual offender and the Index currently holds over 6 million criminal histories. A stratified sample of drivers was selected from the TRL archive, and Section 3 describes how these were matched with the Offenders Index. Where a driver was matched with an offender, details of non-motoring offences were combined with any details of motoring offences from the archive. If a driver cannot be matched then by implication they had not been convicted of standard list offences, so the reliability of the matching process is crucial. Section 3.2 describes the checks that were possible. Section 4 describes the analyses that were made of the linked sets of data, focusing on the extent of the correlation between the number of motoring and nonmotoring offences that a driver commits. Section 5 examines recidivism, the extent the number of offences committed in one period is related to the number committed in a prior period. Section 6 discusses the conclusions that can be drawn from these analyses. Neither the driving licence file nor the Offenders Index contains information relating to social background. The earlier study added this type of information using a commercial system widely employed in market research to summarise socio-economic factors that is based on postcode, and found that this scale was able to explain a part of the variation in offence rates. It was decided not to repeat this aspect of the analysis because of the expense of renewing the software licence and the low likelihood of achieving significant new results about the influence of social background upon offending behaviour. The available records do not show the type of vehicle being driven when a motoring offence was committed. Consequently, in this report the term driver will include any rider of a motorised two-wheeler as well as the driver of a motor vehicle of any size. 2 Data sources Broughton (2003) provides full details of the TRL archive and Home Office Offenders Index. These are summarised below, together with a number of changes in the procedure adopted for this study. 2.1 The TRL archive The DVLA licensing information includes details of convictions for motoring offences, but these can be removed after a relatively short period in accordance with the relevant legislation. This severely restricts the period of time over which a driver s offence history can be studied (Broughton, 1986), so the TRL archive was established to overcome this loss of information. It covers about 1 per cent of licence records, is maintained purely for statistical and scientific purposes, and has been registered in accordance with the Data Protection Act. The DVLA Driver Licence file is ordered by the driver number (which appears on any driving licence); this is begins with the first five letters of the driver s surname and a six digit code derived from the date of birth. The effect is that the file consists of a sequence of groups of records, with one group containing the records of drivers whose surnames share the first five letters. Within each group, drivers born in the same decade are brought together, with male drivers preceding female drivers. The file naturally contains details of all drivers with licences, as its main function is to administer the driver licensing system. It also contains details of any unlicensed driver convicted of a motoring offence, in case that person were to apply subsequently for a licence. Hence, details of all people convicted of motoring offences should be held on the file. The sampling strategy chosen for the TRL archive when it was established in 1986 was to include all drivers with surnames in 2 ranges. The ranges CHAME-CHEND and SWEET-TAYLL were chosen with the aim of avoiding bias due to regional surnames although Broughton (1986) found that there was some regional bias, in particular a shortage of Scottish drivers. Section 2.3 will show that in fact drivers resident in Scotland need to be excluded from the study, so this does not impose any limitation on the present investigation. Each record in the TRL archive contains a selected subset of the information held in the corresponding DVLA record. The following details were used for this study: i Personal details such as surname, initials, date of birth and postcode. ii Date of issue of first licence. iii Details of driving test passes. iv Details of convictions for motoring offences. Courts supply details of motoring convictions to DVLA after the cases have been tried, so records are sometimes updated several months after the offence was committed. 3

8 The date of each offence and conviction are recorded, together with a four character offence code taken from the DVLA system of Endorsement offence codes. The first two characters of an offence code indicate the general type of offence, while the remaining digits are more specific: for example, AC denotes Accident offences while AC10 denotes Failing to stop after an accident. Offences that never involve penalty points are not endorsable and so do not appear in the DVLA records, so all analyses reported below relate to endorsable motoring offences. Lesser offences such as parking and obstruction are excluded and do not contribute to a driver s offence history. The analysis uses the driver details that were in the TRL Archive in December In view of the delays involved in data reaching the DVLA file, details should be largely complete for convictions before September The Offenders Index The data in the Offenders Index are derived from the Court Appearance system and record details of each offender s offences and the sentences imposed. All offences that led to sentencing on a single day are grouped together into a court appearance ; the offence which incurred the greatest sentence is the primary offence while the others are secondary. An offender only appears in the Index if he or she has committed at least one standard list offence. This list includes all indictable offences, all triable either way offences and a few of the more serious summary offences. The standard list of offences is revised from time to time. Convictions for non-standard list summary offences (i.e. those normally tried at a magistrate s court or where fixed penalties are given) appear in the Offenders Index only when they are dealt with at a court appearance for a standard list offence, so they are not fully recorded in the Offenders Index. Primary offences are on the standard list so should be fully recorded but there is an element of chance as to whether secondary offences are recorded, depending upon the nature of the primary offence. Consequently, the study will focus on primary offences. The standard list was expanded from 1 January 1996 to include three motoring offences: i Dangerous driving. ii Driving or attempting to drive a motor vehicle while having a breath, blood or urine alcohol concentration in excess of the prescribed limit (but excluding several of the DVLA drink/driving offence categories). iii Driving whilst disqualified from holding or obtaining a licence. The Index should provide a complete count of these motoring offences since 1996, but previous offences only appear if they were dealt with at a court appearance for a (pre-1996) standard list offence. The three groups of offence correspond to separate DVLA offence codes, so they can be identified in the TRL archive and Section 3.2 will show how this can be used to check the reliability of the matching process. Offence details are never removed from the Index in the way that they are from the DVLA file. The matching of the TRL sample and the Offenders Index was carried in July 2004, and the time taken for offence data to reach the Index means that the Index will be incomplete for several months previously. Analysis of the drink/drive offences in the Offenders Index supplied to TRL suggest that these data are effectively complete up to the end of October More information about the Index can be found at: Each offence in the Offenders Index is classified according to an elaborate coding system that has developed over many years. The system is highly detailed, and a simplified version is generally used for research purposes. The system that has been used in this project uses the following 11 groups, which are listed in Appendix A. Each offence is accompanied by up to 4 disposal codes that record the sentences imposed. 1 Violence against the person 2 Sexual offences 3 Burglary 4 Robbery 5 Theft of/from a vehicle 6 Other theft and handling 7 Fraud and forgery 8 Criminal damage 9 Drug offences 10 Motoring offences 11 Other offences 2.3 Data from Scotland The Offenders Index only holds data from courts in England and Wales, so there is no information about convictions in Scottish courts. On the other hand, the DVLA data cover the whole of Great Britain, and hence include convictions in Scottish courts. This mismatch must be considered when designing the study. Excluding Scottish drivers (i.e. drivers resident in Scotland, as identified by the postcode in the DVLA data) would compensate fully for the absence of Scottish data in the Offenders Index if Scottish drivers only offended north of the border and English and Welsh drivers only offended to the south. Neither condition is likely to apply fully in practice, so that:! Some English and Welsh drivers will have been convicted of standard list motoring offences in Scottish courts; the convictions will appear in the DVLA data but not the Offenders Index and lead to a false impression of the incompleteness of the Index.! Some English and Welsh drivers will be convicted of criminal offences in Scottish courts, so their criminal histories will be incomplete or missing. Broughton (2003) was able to assess the extent to which these occur using the court code from the DVLA offence record to determine whether or not the conviction had occurred in Scotland. This showed that relatively few of these motoring offences were cross-border. The proportion of motoring convictions that occurred in Scottish courts was broadly linked to a region s distance 4

9 from Scotland, ranging from 0.3% of convictions of Welsh drivers to 1.8% of convictions of drivers from North East England; 92.9% of convictions of Scottish drivers were in Scottish courts. This gives confidence in the accuracy of the allocation of drivers to regions. Drink/drive offences form the great majority of standard list motoring offences and will be used to assess the accuracy of the matching process. The level of crossborder convictions was even lower for these offences. These results apply to motoring offences, but the conclusion that the level of cross-border offending is very low is also likely to apply to non-motoring offences. Motoring offences are linked with mobility and can potentially be committed many miles from a driver s home. Many types of criminal offence, however, such as violence, theft and criminal damage, tend to be committed relatively close to the offender s home. Thus, it is likely that relatively few drivers living in England and Wales have a criminal record in Scotland. These results suggest that restricting analyses to drivers with English or Welsh postcodes should be an effective solution to the difficulty caused by the fact that the Offenders Index only holds data from England and Wales while DVLA data cover the whole of Great Britain. 3 Matching A sample of drivers was extracted from the TRL archive according to a design that is described in Section 3.1. This section also describes the process by which this sample was matched with the Offenders Index, while Section 3.2 assesses the accuracy of the matching as far as possible. 3.1 The sample of drivers The design chosen for drawing the sample of drivers was based on the motoring offences committed by drivers in or after The reason is that the Offenders Index should contain details of all standard list motoring offences that were sentenced from 1 January Consequently, each of these offences that is recorded in the TRL archive should correspond to a unique offence record in the Offenders Index, which provides a good opportunity to test the reliability of the matching process. Only drivers with a non-scottish postcode were included in the sample. It was agreed with the Home Office that the overall sample size would be about 50,000 drivers, which should provide sufficient data for a rigorous analysis while not imposing an unreasonable burden on the Home Office computing system. The actual sample sent to the Home Office was stratified using four sampling groups based on the number of motoring offences committed in or after 1996: 1 All drivers who had committed at least 1 standard list motoring offence. 2 All drivers who had committed more than 5 motoring offences. 3 A 1:N 1 sample of drivers who had committed between 1 and 5 motoring offences. 4 A 1:N 2 sample of drivers with had committed no motoring offences. Drivers who appeared in groups 1 and 2 were allocated to group 1, as were any who appeared in groups 1 and 3. N 1 and N 2 were set to 3 and 16 respectively, so that groups 3 and 4 would be of roughly equal size. The size of each group was: Group 1 6,176 Group Group 3 25,679 Group 4 19,608 Total 52,261 If both sets of files contained a common unique identifier then it would be possible to link the two directly. For example, the driver number is the unique identifier for the TRL archive, so if the Offender Index had also included the driver number then the two files could be linked directly via the driver number. There is no common unique identifier, however, so it is necessary to match the sample of drivers to the Offender Index. This consists of checking each driver in turn to see whether their details match the details of any single Offender with sufficient confidence to accept that the two are actually the same person. The Home Office uses a standard computer program for this. The matching depends on: Full surname; First two initials; Date of birth; Sex A file was sent to the Home Office that contained these items for each of the 52,261 drivers. The matching program then compared each driver with all offenders, and calculated a percentage match rate for each based on the similarity of these data. This was done in July 2004, and the file that was returned to TRL consisted of: a Name details followed by information from the Offenders Index if the driver had a true match and hence a criminal record. b Name details followed by a row of zeros if the driver could not be matched and so had no identifiable criminal record. 38 drivers were missing from the returned file, 0.07 per cent of the number sent, so relatively few drivers had proved to be questionable matches. 75 records subsequently had to be omitted because of incomplete data in the archive, leaving a total of 52,148 for analysis. 3.2 Check of the matching It is important to check the accuracy of the matching, and the fact that standard list motoring offences from January 1996 recorded in the TRL archive should also appear in the Offenders Index provides a good opportunity to do this. Broughton (2003) describes the checks that can be made in some detail, and this section presents the results of checking the current matching. Table 3.1 analyses the convictions of Group 1 drivers recorded in the TRL archive for the standard list motoring offences (there is no way of checking the validity of the matches for the remainder of the TRL sample since there is 5

10 Table 3.1 Matching rate for standard list offences, Group 1 drivers Offences that were: No OI Not Percentage Matched offender 1 matched matched % % % % % % % % 1 The driver could not be matched in the Offenders Index. no reason to expect other motoring offences in the archive to have their equivalents in the Index). With many drivers, standard list offences in the TRL archive corresponded exactly to offences in the Offenders Index, so the matching program had worked satisfactorily in these cases. In other cases, however, offences in the archive had no corresponding offence in the Offenders Index despite allowing some latitude in the dates recorded. There was no possibility of finding a corresponding offence in cases where the matching program had not identified an OI offender who corresponded to the driver in the TRL archive. The matching rate has tended to rise year by year, and the number as well as the rate of successful matches were highest in 2003 in spite of the incompleteness of the data in the two sources for the end of The percentage of matched offences exceeded 80% from 1999, so the analyses reported in Section 4 will be based on the period The proposed matching of a driver in the TRL sample with an offender from the Offenders Index will be accepted when at least one standard list conviction matched (but possibly not others), and Table 3.2 shows the results. The DVLA court codes have been analysed as a final check on the possibility that failures to match could be explained by drivers in the TRL sample being convicted in Scottish courts (which would not be recorded in the Offenders Index). The court code is available for 98.1% of their drink/ drive convictions: 0.5% of these convictions were in Scottish courts. 12 of the 288 drivers who could not be matched to the Offenders Index had drink/drive convictions in Scottish courts but not in English or Welsh courts. Hence, while the restriction of the sample to English and Welsh drivers has been largely effective in compensating for the different coverage of the DVLA and Home Office datasets, the difference still leads to a few failures to match. Table 3.2 Outcome of attempt to match Group 1 drivers, sentences in Number Percentage Outcome % Were matched successfully % Could not be matched to an offender in the Offenders Index % Of proposed matches were not acceptable % The previous research matched data from , and Table 3.1 shows that the percentage of offences matched was lower between 1996 and 1999 than between 1999 and Consequently, the matching has been more successful on this occasion, which should mean that the results achieved will be more reliable. Broughton (2003) demonstrated that mismatching diminishes the apparent differences between groups of drivers and causes the strength of the relationship between the number of motoring and of non-motoring offences to be underestimated. The improvement in matching implied by Table 3.2 suggests that the diminution should be reduced by about two-fifths. In addition, using offence data from a 5-year period in this study will increase the number of offences analysed and thereby improve the statistical precision of the results. 4 Analysis of linked data This section presents various analyses of the linked data sets. The main aim is to examine the extent to which drivers convictions for non-motoring offences can explain variations in the number of convictions for motoring offences, based on the records of motoring and nonmotoring offences between 1999 and The only date recorded in the Offenders Index is the date of sentence, so strictly the non-motoring offences included are those where sentencing occurred during these five years. The TRL archive records the dates of offence and of conviction but not of sentence, and the motoring offences included are those where the conviction occurred during this period. This minor discrepancy should not affect the results materially. 4.1 Preliminary analyses Table 4.1 presents the primary and secondary offences recorded in the Offenders Index in for the offenders who were matched to the drivers in the TRL sample. As there is one primary offence per court appearance, the number of primary offences is equal to the number of court appearances. The predominance of motoring offences is exaggerated to some extent by the deliberate over-sampling of drivers who were known from Table 4.1 Offences recorded in the Offenders Index, Offence Primary Secondary group Offence type offences offences 1 Violence against the person % % 2 Sexual offences % % 3 Burglary % % 4 Robbery % % 5 Theft of/from a vehicle % % 6 Other theft and handling % % 7 Fraud and forgery % % 8 Criminal damage % % 9 Drug offences % % 10 Motoring offences % % 11 Other offences % % All % % 6

11 the DVLA data to have committed motoring offences. There is an average of 1.32 secondary offences per primary offence, and Figure 4.1 shows that certain types of secondary offences tend to be associated with specific primary offences. Most secondary offences associated with primary motoring offences are motoring offences, and the primary offence of theft of/from a vehicle also tends to be associated with secondary motoring offences. As explained in Section 2.2, only three types of motoring offence should appear as primary offences in the Offenders Index: Dangerous driving, Drink/driving and Driving whilst disqualified. The primary offences in the linked data set have been checked, and indeed no other motoring offence does appear as a primary offence. One or more offences of each of the 3 offence types can be sentenced at a single court appearance. The incidence of the seven possible combinations is as follows: Drink/driving alone 56.5% Driving whilst disqualified alone 26.4% Dangerous driving alone 2.9% Drink/driving + Driving whilst disqualified 12.4% Driving whilst disqualified + Dangerous driving 1.2% Drink/driving + Dangerous driving 0.3% Drink/driving + Driving whilst disqualified + Dangerous driving 0.3% As Figure 4.1 shows, secondary motoring offences (which include non-standard list offences as well as the 3 standard list offences) were frequently associated with various types of primary offence as well as with primary motoring offences. 64% of secondary motoring offences were associated with primary motoring offences: 24% of the remainder were associated with primary Other theft and handling offences and 19% with primary Theft of/ from a vehicle offences. 78% of secondary offences associated with primary motoring offences were lesser motoring offences matched offenders appeared in court over these 5 years, an average of 2.16 appearances per person. 62% appeared once while a few appeared many times: the distribution of offenders by the number of appearances is: % 15% 8% 5% 3.2% 2.3% >12 1.5% 1.0% 0.8% 0.6% 0.7% 0.2% 0.9% In order to relate these offences to the TRL sample, Table 4.2 shows the number of primary non-motoring offences committed by these drivers as matched in the Offenders Index. By design, the sample contains relatively many motoring offenders, so the table also contains Table 4.2 Number of primary non-motoring offences per driver in Number of Men Women primary nonmotoring Population Population offences Number % estimate Number % estimate % 97.5% % 99.5% % 1.5% % 0.32% % 0.4% % 0.05% % 0.19% % 0.04% % 0.26% % 0.06% % 0.05% 3 0.0% 0.00% Any % 100.0% % 100.0% Type of primary offence 1 Codes for offence types 1 Violence against the person 5 Theft of/from a vehicle 9 Drug offences 2 Sexual offences 6 Other theft and handling 10 Motoring offences 3 Burglary 7 Fraud and forgery 11 Other offences 4 Robbery 8 Criminal damage Figure 4.1 Mean number of secondary offences per primary offence 7

12 population estimates that compensate for the sample design. These are estimates for the full population of drivers, based on the results for the four groups within the TRL sample. Thus, it is estimated that 2.5% of all male drivers and 0.5% of all female drivers were convicted of non-motoring offences between 1999 and Table 4.3 presents the motoring offences recorded in for the drivers in the TRL sample. The first three are standard list offences and Group 1 of the sample consisted of drivers who had committed these offences so there are many more of these offences than would be found with a random sample of drivers. The table includes population estimates that make allowance for the sample design. Table 4.3 DVLA motoring offences, Offence type Population estimate Driving while disqualified % 1.7% Reckless or Dangerous Driving % 0.2% Drink or drugs offences % 4.3% Careless driving % 1.7% Construction and Use offences % 1.4% Insurance offences % 10.9% Licence offences % 5.7% Speed limit offences % 65.8% Traffic direction and Signs % 5.3% Other % 2.8% All % 100.0% The most immediate way to see whether drivers who committed motoring offences also tended to commit nonmotoring offences is to compare the mean number of nonmotoring offences per driver in the four groups of the TRL sample between 1999 and Table 4.4 presents the results for men and women. The column headed All drivers estimates the mean for all drivers, taking account of the sampling fraction of each group. The relative offence rate is the offence rate for a particular combination of Group and sex divided by the All drivers rate so, for example, a male driver in Group 1 committed 15 times as many non-motoring offences during this period as the average male driver. Table 4.4 Mean number of primary non-motoring offences per driver, Group 1 (at least 1 standard Group 2 Group 3 Group 4 list (more than (1-5 (no motoring 5 motoring motoring motoring offence offences offences offences since since since since All 1996) 1996) 1996) 1996) drivers Offences per driver Men Women Both Relative offence rate Men Women Both It is clear that drivers with a poor record of motoring offences tended to commit many more non-motoring offences than those who committed no motoring offences. The differences may be influenced by factors such as the ages of the different groups of driver, and statistical models will be fitted in later sections to take account of these. The matched data can also be examined to see whether drivers with a poor record of non-motoring offences tend to commit motoring offences. Table 4.5 compares the mean number of motoring offences per driver in the four groups of the TRL sample. Serious motoring offences are taken as those on the Home Office standard list, namely Dangerous driving, Drink/driving and Driving whilst disqualified. The relative number of offences in this case is: Mean number of motoring offences by drivers who committed N primary non-motoring offences Mean number of motoring offences by drivers who committed none which emphasises the relation between the numbers of motoring and of non-motoring offences. It can be seen that the number of non-motoring offences is related more closely to the number of serious motoring offences than to the number of other motoring offences. Table 4.5 Mean number of motoring offences per driver in by number of primary non-motoring offences Serious motoring offences Other motoring offences Number of primary Relative Relative non- Mean number Mean number motoring offences Men Women Men Women Men Women Men Women When the same analysis was performed during the original study, the relative numbers found for serious motoring offences were rather lower than those in this table while those found for other offences were similar. This is likely to be a result of the more successful matching, as discussed in Section 3.2. Figure 4.2 illustrates the relative numbers, with quadratic functions fitted to show the general trends. These functions represent the data for men well, but there is more scatter in the data for women. Table 4.6 examines the distribution of the number of primary non-motoring offences among three groups: all drivers, those who committed serious motoring offences and those who committed other motoring offences but no serious offence. As before, only offences committed between 1999 and 2003 are considered, and the results have been adjusted to compensate for the sampling procedure. One important point to emerge is that, in spite of the strong relationship shown in Table 4.5 between convictions for serious motoring offences and non- 8

13 Relative number Number of non-motoring offences Serious motoring offences, men Other motoring offences, men Serious motoring offences, women Other motoring offences, women Figure 4.2 Relative numbers from Table 4.5 Table 4.6 Distribution of drivers and motoring offenders, by number of primary non-motoring offences Number of primary Drivers Serious motoring offenders 1 Other motoring offenders 2 non-motoring offences Men Women Either Men Women Either Men Women Either % 99.5% 98.3% 69.4% 83.6% 70.5% 94.3% 98.3% 95.2% 1 1.5% 0.3% 1.1% 11.5% 6.3% 11.1% 3.0% 0.9% 2.5% % 0.05% 0.29% 6.0% 3.1% 5.8% 1.0% 0.3% 0.8% % 0.04% 0.13% 4.0% 2.4% 3.9% 0.6% 0.2% 0.5% % 0.06% 0.18% 7.2% 4.2% 6.9% 0.9% 0.3% 0.8% % 0.00% 0.03% 1.8% 0.3% 1.7% 0.2% 0.02% 0.15% 1 Committed at least 1 serious motoring offence in Committed at least 1 other motoring offence in but no serious offence. motoring offences, almost 70% of males convicted of serious motoring offences in the period studied had no conviction for a non-motoring offence in that period. The TRL archive records the date of any change to the status of a driving licence. Some drivers will have acquired a new licence between 1999 and 2003, while others will have lost theirs as a result of disqualification. It is not feasible to include these details in the analysis, and to simplify matters drivers are allocated to one of the following licence groups: Full driver had passed a driving test by the end of Provisional driver had received a provisional licences by the end of 1999 but had not passed a driving test. Unlicensed driver had not received any licence by the end of Disqualified driver was disqualified from driving for some period between 1999 and the end of 2003 (irrespective of whether they had held a full or provisional licence). Most drivers convicted of serious motoring offences (Dangerous driving, Drink/driving and Driving whilst disqualified) are disqualified, while others who are convicted of several other offences accumulate penalty points and may be disqualified under the totting up process. DVLA files hold details of any unlicensed driver who is convicted of a motoring offence in case they subsequently apply for a licence so all unlicensed drivers in the linked data set had committed at least one motoring offence, although not necessarily between 1999 and Figure 4.3 examines the relationship between the number of primary non-motoring offences committed in and the driver licence group among men and women. The relationship for men is clear: as the number of non-motoring offences increases, so the proportion of drivers who are disqualified or unlicensed rises and the proportion who are fully licensed falls. Three quarters of drivers who had committed at least 9 non-motoring offences were disqualified at some time between 1999 and 2003, while one ninth had received no licence by the end of Conversely, only 1.5% of drivers who committed no non-motoring offence were disqualified and 0.8% were unlicensed. The relationship is less clear among women, probably because of the much lower number of women who had 9

14 100% Men 100% Women 80% 60% 40% Full Provisional None Disqualified 80% 60% 40% 20% 20% 0% Number of non-motoring offences 0% Number of non-motoring offences Figure 4.3 Distribution of driver licence group by number of primary non-motoring offences committed non-motoring offences. Disqualification tends to increase with the number of non-motoring offences, although less rapidly than among men, while the proportion of unlicensed drivers increases in a similar way to men. 4.2 Disposals The Offenders Index uses the term disposal to refer to the penalty awarded for each conviction; up to 4 can be recorded per conviction and the quantity of each disposal is recorded. An extensive list of numerical disposal codes is used, ranging from 15 [Detained Under Powers of Criminal Courts (Sentencing) Act 2000 SS90 and 92(1)] to 690 [Otherwise dealt with on conviction]. Disqualification from driving does not appear on the list of disposal codes. The disposal codes have been grouped for this study as follows: Custody Custody or detention. Punishment Community service orders, community rehabilitation orders, punishment orders, curfew orders. Supervision Supervision or probation orders. Fine Fine. Discharge Conditional or absolute discharge. Other Other, e.g. bound over. These disposal data for primary offences will be analysed to see how the severity of the penalties imposed upon this sample of drivers between 1999 and 2003 for motoring offences compares with the severity of the penalties that they received for other types of offence committed over the same period. The results presented are population estimates which take account of the sampling strategy. Although up to 4 disposal codes can be recorded per conviction, the mean number was only 1.24 (varying between 1.05 for motoring offences and 1.41 for drug offences). Consequently, it is sufficient to base the comparison upon the principal disposals. It should be remembered that only standard list motoring offences appear as primary offences in the Offenders Index: drink/ driving, driving whilst disqualified and dangerous driving. Hence the motoring offences that contribute to the following tables are not typical motoring offences but are amongst the most serious. Table 4.7 shows the joint distribution of disposal and offence types. For example, 20% of those convicted of violence against the person received a custodial sentence as their principal punishment, while 27% were fined. The table also includes the distribution of offence types. This differs from the distribution shown for primary offences in Table 4.1 since the calculation has taken account of the Table 4.7 Distribution of principal disposal by offence type Type of disposal Proportion Offence type Custody Discharge Fine Punishment Supervision Other of offences Violence against the person 20% 15% 27% 20% 15% 2% 18.6% Sexual offences 36% 3% 29% 6% 26% 0% 0.9% Burglary 46% 3% 7% 23% 19% 3% 3.9% Robbery 91% 0% 1% 4% 4% 1% 1.0% Theft of/from a vehicle 18% 9% 21% 33% 17% 3% 3.5% Other theft and handling 18% 19% 29% 18% 16% 1% 18.3% Fraud and forgery 19% 16% 18% 26% 21% 0% 3.3% Criminal damage 2% 34% 41% 13% 8% 1% 5.5% Drug offences 14% 17% 52% 9% 6% 2% 9.4% Motoring offences 11% 2% 62% 14% 7% 3% 29.2% Other offences 22% 10% 40% 17% 5% 6% 6.3% All offences 17% 12% 40% 17% 12% 2% 100.0% 10

15 sampling strategy, so should represent the distribution in the population of drivers much more reliably. By comparison with other offences, a high proportion of motoring offences were punished by fines (most of these offences would also have involved disqualification from driving, although this is not recorded in the Offenders Index). 11% of motoring offences received a custodial sentence. Table 4.8 compares the mean sentence imposed on those drivers who received custodial sentences or fines as their principal punishment (note that the mean is only calculated for those who received the particular type of punishment). Some combinations of offence and disposal type in Table 4.7 involve relatively few drivers in the linked data set, and to minimise the effects of chance mean values are only shown for those combinations with at least 100 offences. The table shows that average fines are rather higher for motoring offences than for other offences, but that average sentences are comparable with sentences for offences such as theft and handling. Table 4.8 Mean custodial sentence and fine by offence type Custodial Offence type sentence (years) Fine ( ) Violence against the person Burglary 1.28 Theft of/from a vehicle Other theft and handling Criminal damage 110 Drug offences Motoring offences Other offences All offences Statistical models This section reports on the fitting of statistical models to the linked data to examine the relationship between the numbers of motoring offences and of non-motoring offences in more detail. Factors such as driver age might influence the results, and a statistical model is needed to identify these influences separately. The data from the previous section will be modelled first, then disaggregate data will be used. The driver licence group is included in the modelling, but with one modification. As mentioned above, drivers convicted of serious motoring offences tend to be disqualified, as are those who are convicted of multiple other offences, so the disqualified category cannot be used to explain the number of motoring offences. Consequently, any disqualification is overlooked when deriving a driver s licence group. Analyses of the non-motoring offence data from the Offenders Index such as Table 4.5 have counted the number of primary offences, and hence the number of court appearances. The DVLA motoring offence data are treated consistently by counting only one motoring offence per court appearance, which is either serious or other if there was no conviction for a serious motoring offence. The following Generalised Linear Model has been fitted using the GLIM program: Log(Motor ijkr ) = Age i + Non-motor j + Licence k + Region r (1) where: Motor ijkr Age i, Non-motor j, Licence k,region r is the number of motoring offences committed by a driver of age group i, the number of non-motoring offences being in the j-th range, the licence being of type k and the driver living in region r (based on the postcode). are factors estimated to represent the effects of age, number of primary nonmotoring offences and type of licence Separate models are fitted for men and women. Drivers grew older during this period, and their ages are calculated at the end of the period, 31 December The offence data come from the 5 years , with separate models fitted for serious and other motoring offences. The coefficients from the fitted models will be presented as the relative effect of these factors on the number of motoring offences. The base-line levels for calculating the relative effects are: Age group Drivers with no convictions for non-motoring offences. Drivers with full licences. Drivers living in London. The relative effect for the number of non-motoring offences corresponds exactly to the relative number presented in Table 4.5. The models fit the data well, especially for the serious offences, and Table 4.9 presents the resulting coefficients, except for the regional results which are presented later. The table shows, for example, that a male driver with 1 conviction for a non-motoring offence committed about 7 times as many serious motoring offences as a driver with no convictions, on average, irrespective of age or type of licence. Some estimated coefficients are relatively imprecise because of the number of cases involved, and the standard errors calculated by GLIM should identify these. In the table, indicates values that do not differ significantly from the corresponding base-line value at the 95% level as indicated by the standard errors (some differences within the sets of values may not be significant). The relative effects of the number of non-motoring offences are illustrated by Figure 4.4, with bars to show the 95% confidence intervals for the results. For example, between 1999 and 2003 the mean number of serious motoring offences committed by men who committed 3 non-motoring offences was 19.4 times the mean for men who committed no non-motoring offence, the 95% confidence interval for this estimate being (16.8, 22.4). The intervals are calculated from the standard errors calculated by GLIM, and it can be seen that the estimates are far more precise for men than for women largely because more men committed non-motoring offences than women. These are nominal standard errors, since they take no account of the mis-matching that was identified in 11

16 Table 4.9 Relative effect of three factors on the number of motoring offences in Serious motoring offences Other motoring offences Men Women Men Women Number of non-motoring offences 0 * Age group * Type of licence Full* Provisional Unlicensed * Denotes the baseline level. Indicates that coefficient does not differ significantly from 1.00 (95% level). Section 3.2. This means that the actual effects are somewhat greater than those estimated from the matched data, although the width of the confidence intervals should be largely unaffected. The inclusion of the variable region improves the model fit significantly, especially the models of other motoring offences. The regional effects are shown in Figure 4.5. The level in London was chosen as the baseline because this region has the greatest number of drivers and the lowest offence rates overall. The results show the number of motoring offences per driver in each region, taking account of any variations in age distribution and the recorded number of non-motoring offences. Results are not included for serious motoring offences committed by women because they do not differ significantly in most regions, perhaps because of the relatively small number of these offenders in the linked data. It should be noted that two factors influence these relative offence rates:! The willingness of drivers living in a region to commit offences.! The effectiveness of local police and judicial procedures in catching and convicting drivers who commit offences. The figure shows, for example, that drivers living in London were convicted less frequently than drivers living in Wales. This could mean that drivers in London were less willing to break the law than drivers in Wales, or that offenders in London were less likely to be detected and prosecuted successfully than offenders in Wales, or some combination of the two. There is no way of using these data to separate the two factors, and indeed they could well be linked. In particular, an effective process for catching and convicting offenders in a region is likely to reduce the willingness of local drivers to commit offences. These analyses have grouped together all types of nonmotoring offence, but it is possible that the association with the number of motoring offences is stronger for certain types of offence than for others. This is done by applying model (1) to each of the following groups in turn: Offenders Index groups (as defined in Section 2) Violence against the person 1 Theft of/from a vehicle 5 Non-vehicular theft 3, 4, 6 Drug offences 9 In order that the results for a particular group of offence are not biased by the effects of other groups, the analysis for each group excludes all drivers who committed offences in other groups. Applying this restriction reduces the number of cases for analysis but should provide more reliable estimates of the effects. Figure 4.6 shows the estimated relative effects for the four groups of offence together with the overall effects; the 95% confidence interval of each estimate is also shown. It is clear that the number of motoring offences is especially associated with offences of Theft of/from a vehicle, particularly when multiple offences were committed. For example, a man who committed 2 non-motoring offences between 1999 and 2003 committed on average 28 times as many serious motoring offences during this period as a man who committed none, the 95% confidence interval for this estimate being (11, 68) DVLA motoring offences The analyses reported in the previous section have grouped the DVLA motoring offences as either serious or other. The incidence of specific DVLA motoring offence codes will now be analysed, using the same general approach. A variant of the Generalised Linear Model (1) has been fitted, with Motor now being the number of motoring offences of a specific type. Women constituted 28% of the drivers in the DVLA sample but only 14% of the drivers convicted of motoring offences. Percentages were even lower for the more serious types of offence: 8% for drink driving and 4% for dangerous driving. Consequently, results from the modelling are relatively imprecise for women and detailed results will only be presented for men. Figure 4.7 compares the relative effects on the total number of DVLA motoring offences for men and women, including 95% confidence intervals. The general pattern of Figure 4.4 is seen again, of a regular increase which is more rapid for women than for men. The confidence intervals are much wider for women than for men. 12

17 North West North East Yorkshire and Humberside East Midlands East West Midlands South East South West London * Wales Men, serious motoring offences Men, other motoring offences Women, other motoring offences Figure 4.5 Relative regional effect on the number of motoring offences in Serious motoring offences 10 Other motoring offences 50 8 Relative number Relative number Number of non-motoring offences Number of non-motoring offences 4- Violence against the person Non vehicular theft All non-motoring offences Theft of/from a vehicle Drug offences Figure 4.6 Relative effect of number of non-motoring offences on number of motoring offences 40 Men Women 30 Relative number Number of non-motoring offences Figure 4.7 Relative effect of number of non-motoring offences on total number of motoring offences 13

18 Figure 4.8 presents corresponding results for specific types of offence, in 2 groups because of the much greater effects found for Dangerous driving and Driving whilst disqualified. These 2 offences, and to a lesser extent Drink driving, also differ from other offences in respect to the effect of age: relatively many offences are committed by young drivers (up to 24 years old) and relatively few by drivers at least 40 years old. These results are comparable to those found in the earlier study, with one exception. Figure 4.8 shows the relative number of speeding offences declining as the number of non-motoring offences increases beyond 2, while the earlier study found that it remained slightly above 1.0. The 2 studies agree that the results for speeding differ qualitatively from the results for other offences, but the reason for this difference in detail is not clear. It seems unlikely that drivers who commit multiple non-motoring offences have become more willing to observe speed limits since the earlier study. Perhaps they frequently drive stolen cars so cannot be traced from speed camera photographs, or they may use their experience of the judicial process to find other ways to avoid being convicted of speeding by this type of evidence. 5 Recidivism In principal, the Offenders Index is a complete record of primary standard list offences over many years and the TRL Archive has a complete record of endorsable motoring offences from 1986 for a sample of drivers. This section will report an investigation of the extent to which the number of offences in a before period is related to the number of offences in an after period. The before and after periods chosen are the calendar years and For each driver in the linked data set, the number of primary non-motoring offences in the Offenders Index in either period is calculated, also the number of serious and other motoring offences in either period in the TRL Archive. The standard list of offences did not change significantly over these years, apart from the addition of the 3 motoring offences in 10 Relative number Number of non-motoring offences 9- All offences Careless driving Speeding Drink driving Relative number Number of non-motoring offences All offences Dangerous driving Driving whilst disqualified Figure 4.8 Relative effect of number of non-motoring offences on number of specific motoring offences (men only) 14

19 1996, so the basis for calculating the number of primary non-motoring offences is consistent throughout this decade. The extent to which drivers re-offend is likely to vary with age so various age groups have been analysed separately. Results are clearer for men than for women because of the larger number of offenders (Table 4.2) so only results for men will be presented. As in previous sections, the age used in the analysis is the driver s age at the end of 2003, so drivers were on average 5 years older in the after period than in the before. Broughton (1999) showed that the average annual number of motoring offences per driver falls with age, so the greater age in the after period is likely to lead to fewer motoring offences overall. First, Figure 5.1 examines the relationship between the number of primary non-motoring offences in the before period ( ) and the number in the after period ( ), also the relationship with the number of serious motoring offences in the after period. The results presented are population estimates which take account of the sampling strategy. On average, men committed about half as many non-motoring offences in the second period as in the first, and this fraction tends to fall with age. The number of serious motoring offences in the after period also increases with the number of primary non-motoring offences in the before period, although the relationship with age of driver is less regular. It had previously been shown that the number of primary non-motoring and serious motoring offences committed in were related, these analyses have shown that the cross-sectional relationship extends through time as well. The number of other motoring offences in also has an increasing relationship with the number of primary non-motoring offences, but it is much weaker and is not included in the figure. The relationship between the number of serious motoring offences in and in is examined in Figure 5.2. Drivers on average committed less than one quarter as many offences in the after period as in the before, but the number in the before period clearly influenced the number in the after. Drivers who committed no serious motoring offence in the before period were unlikely to commit a serious motoring offences in the after period: the mean for this group was 0.01 offences. On the other hand, drivers who committed 4-8 serious motoring offences in the before period committed 0.6 offences on average in the after period. The figure also presents the corresponding results for other offences, and shows that the relationship was much weaker in this case. Drivers who committed no other motoring offence in the before period committed on average 0.16 other offences in the after period. The average was 0.89 for those who had committed 4-8 other motoring offences in the before period. The statistical analysis shows clear differences between regions: drivers from some parts of the country are more willing to re-offend than drivers from other parts. The results are summarised in Figure 5.3, which again uses the level in London as the baseline. It shows, for example, that a male driver living in Wales who had committed a serious motoring offence in was over 40% more likely to commit another in than a similar driver living in London. The regional variations in recidivism shown in Figure 5.3 are similar in many respects to the variations in motoring offence levels for men that were shown in Figure 4.5. This suggests that the two processes are linked, although the nature of the link is unclear. It could be that drivers in certain regions are more likely to re-offend because offence rates are generally higher in those regions. On the other hand, there could be relatively many recidivists in these regions and that their propensity to re-offend raises the regional offence rates. These possibilities can be examined using the matched data, although this can only be done with data for other motoring offences because there are insufficient nonmotoring and serious motoring offences per region in the data set. Drivers are grouped according to the number of other motoring offences (0 or 1, 2 or more) in the before period and in the after period. Drivers with 2 or more offences in both periods can be regarded as recidivists. The threshold of 2 may not be ideal but ensures that the 6 Non-motoring offences 0.6 Serious motoring offences Offences in after period Offences in after period Non-motoring offences in before period Non-motoring offences in before period Figure 5.1 Relationship between number of non-motoring offences in and number of offences in , by age of driver 15

20 2.0 Serious motoring offences 2.0 Other motoring offences Offences in after period Offences in after period Offences in before period Offences in before period Figure 5.2 Relationship between motoring offences in and in , by age of driver North West North East Yorkshire and Humberside East Midlands East West Midlands South East South West London * Wales Serious motoring offences Other motoring offences Non-motoring offences Figure 5.3 Relative regional variation in willingness to re-offend 2 or more bands include substantial numbers of drivers so that the results should not be unduly influenced by chance. Figure 5.4 shows the results, with the regions sorted by the regional index for other motoring offences from Figure 4.5; the regional proportions of drivers use the left hand scale and the index uses the right hand scale. The proportion of recidivists rises reasonably steadily with the regional index (r 2 =0.51), but so does the proportion of drivers with 0 or 1 offence in the before period and 2 or more in the after period (r 2 =0.54). This suggests that the regional variation of the offence rate has a general cause, it is not confined to a relatively small group of recidivists in each region. 6 Conclusions An earlier report (Broughton, 2003) demonstrated that the number of motoring offences committed by a driver is linked to the number of non-motoring offences that he or she commits. This report has presented the results of a study which has updated and expanded the earlier study, using data from the same two sources:! The archive of driving licence information maintained at TRL: this is based on licensing information supplied by the DVLA and provides details of the driving history of a sample of drivers including convictions for motoring offences.! The Offenders Index: this is maintained by the Home Office and holds the criminal histories of all people convicted of standard list offences in courts in England and Wales, so provides details of convictions for nonmotoring offences. A sample of over 52,000 drivers was selected from the TRL archive, stratified according to the motoring offences that they had committed in or after Details of these drivers were sent to the Home Office, where they were matched with the Offenders Index using a standard computer program. When a driver could be matched with 16

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