Strategic Development Projectsin the Yorkshire and theHumber, East Midlands and Eastern regions

Home Office Online Report 41/04

Tim Hope, Jane Bryan Elaine Crawley, Peter Crawley Noel Russell, Alan Trickett

The views expressed in this report are those of the authors, not necessarily those of the Home Office (nor do theyreflect Government policy).

Strategic Development Projects in the Yorkshire and the Humber, East Midlands and Eastern regions

Tim Hope Jane Bryan Elaine Crawley Peter Crawley Noel Russell Alan Trickett

Online Report 41/04

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Acknowledgements

In addition to the authors, this paper draws upon the valued contribution of a number of people involved in the Midlands Consortium, including: Len Gill, Stephen Farrall, Polly Seaman, Angela Spriggs, Jenny Ewels, Stuart Lister, Charlotte Bilby, Ross Little, Martin Gill, Rolland Munro, David Knights and Chris Hale.

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Contents Summary iv 1. Introduction 1 2. Anticipated components of the SDP intervention 2 3. Evaluation research design 6 4. The impact of the projects on local burglary 14 5. Net effect – crime displacement or diffusion of benefit? 16 6. Anticipatory benefit 19 7. Did targeted (repeat victimisation) prevention work? 21

Summary and conclusions 30 Appendix 1 32 Table A.1 Average cost per crime and burglary equivalents 32 Table A.2 Proportion of crimes 33 Table A.3 Example of calculation for project C3 33 Appendix 2 34 References 36 List of figures Figure 3.1 Areas of investigation 10 Figure 7.1 Project A3 - monthly burglaries in SDP area and project progression 26 Figure 7.2 Project C7 - monthly burglaries in SDP area and project progression 26 List of tables Table 4.1 Impact of projects on local burglary 14 Table 5.1 Impact of the projects on burglary in the project area, displacement and 17

diffusion of benefit (measured in burglary-equivalent terms) – burglaries per month

Table 7.1 Measured change in rates of repeat victimisation by estimated effect of 21 project on burglary

Table 7.2 Project delivery of target-hardening to burglary victims (RV) 22

Table 7.3 Average total and percentage expenditure on burglary reduction 24 measures by project outcome group

Table 7.4 Number of interventions planned and implemented, leverage of 25 funds and total project expenditure (averages)

Table 7.5 Project management by outcome group 27

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Summary This report summarises the findings of the Midlands Consortium (comprising Keele University, and the Universities of Manchester and Leicester), who evaluated projects in the East Midlands, East, and Yorkshire and the Humber Government Office regions. As with the other consortia they evaluated 21 Strategic Development Projects (SDPs) across these areas.

The Consortium used a time-series evaluation design, which is a new method for such crime reduction work, and provides an informative new tool, that can be developed further, to do such evaluations in the future.

Overall, there was variability in the level of success of projects, with only a minority (6) associated with significant burglary reductions in their target areas. Further, there was little support for a prevention strategy focused on preventing repeat victimisation, or for the ‘anticipatory benefits’ effects of projects. Nevertheless, a substantial number of projects were associated with a diffusion of crime reduction benefits, and a net reduction of crime in their targeted and surrounding areas taken together.

Indications of successful projects included: those which were not isolated from other area programmes, or the crime trends in the area; those where interventions mutually supported each other, where a problem-solving approach to implementation was adopted; and those where partnership involvement was maximised, making use of relevant skills already employed by the partners. This final point regarding partnerships also saw those run between local authorities and regeneration contractors as being the most successful, in contrast to those managed directly from within the police service.

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1. Introduction The Strategic Development Projects were comprehensive strategies for burglary reduction in two senses: first, as part of the Reducing Burglary Initiative (RBI) they aimed for a comprehensive approach to preventing burglary and, second, they were also community initiatives intended to reduce the burglary rate of the target area, thereby benefiting all residents. The Home Office has actively sponsored comprehensive burglary reduction strategies (in both senses) over many years (Mawby, 2001), including burglary prevention projects such as the Scotswood Road (Allatt, 1984) and Kirkholt (Forrester et al., 1988; Forrester et al., 1990) projects, and community initiatives such as the Safer Cities Programme (Ekblom et al., 1996). The Reducing Burglary Initiative aimed to build upon the lessons from both types of prevention activity. This chapter assesses the development of 20 SDPs, supported by the RBI, in the Yorkshire and the Humber, East Midlands and Eastern regions of England.1

1 Initially there were 21 separate projects. However, two were adjacent to each other, within the same city, and increasingly shared the same management and approach. In this analysis, these projects have been combined into one SDP.

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2. Anticipated components of the SDP intervention It was expected that the SDPs would share a number of operational and organisational characteristics and would set about the task of tackling local burglary problems in particular ways (Home Office, 1998). These ‘ideal’ characteristics were taken from previous experience of burglary prevention projects and have been discussed in reports of the early stages of the RBI prepared by the Home Office team of programme ‘developers’ (see Tilley et al., 1999; Curtin et al., 2001).

The anticipated characteristics of SDPs included:

Strategic planning

Each SDP was encouraged to adopt a strategic planning model, consisting of “…identifying and analysing the [burglary] problem, devising solutions, assessing the likely impact of solutions, reviewing progress, refining approaches and evaluating success” (Tilley et al., 1999; p28). Via a detailed statistical analysis of local burglary data, knowledge of the local burglary problem was to be ascertained in advance of trying to implement action. The selection of what to do was to be tailored to the key features of local burglary in the specific target area. Particular emphasis was to be placed on identifying and addressing the ‘local chemistry of burglary’ (Tilley et al., 1999).

Projects as packages of measures

The developers report that in almost every case the SDPs proposed packages of measures (Tilley et al., 1999). Indeed, the prospectus issued by the Home Office invited the inclusion in a bid of “a mix of ‘tried and tested’ and innovative tactics” (Home Office, 1998). These were classified into three kinds:

• interactive packages – interventions designed to work in complementary and cumulative ways;

• combined packages – interventions that work independently of one another;

• contradictory packages – interventions that work against each other so that the success of one intervention results in the failure of another;

During their visits to the projects, the developers encouraged the projects to remove contradictions and improve the mix of measures to ensure a systematic approach that would have a good chance of maximising effectiveness (Tilley et al., 1999).

Crime prevention techniques and measures

Specific prevention measures making up each package were devised by the local projects. While innovation and relevance to the local burglary problem were encouraged (Home Office, 1998), the projects drew upon the available knowledge base and tool kit of good practice, provided and disseminated by the Home Office (see www.crimereduction.gov.uk). These resulted in a variety of measures, which were classified into a number of different types (Tilley et al., 1999). Subsequently, as part of the cost-effectiveness exercise agreed with the Home Office (see OLR 39/04), these were classified further into the following generic categories:

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area-wide situational crime prevention (SCP) – including, alley-gating, environmental improvements and CCTV schemes;

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• enforcement – including, gathering, analysing and using intelligence, disrupting offending behaviour, high visibility policing and witness protection;

• location-specific SCP – including, the surveillance and target hardening of individual dwellings;

• tackling offender behaviour – including youth diversion and drug abuse prevention and treatment schemes;

• property – primarily property marking schemes; and

• stakeholding – including education, public awareness and publicity campaigns, and resident involvement schemes.

Crime prevention tactics

Not only are there now available a range of crime prevention measures but there are also different tactics for applying these measures to ensure maximum cost-effectiveness. Perhaps the greatest contrast in tactics is between a targeted approach – based on the idea of identifying ‘at-risk’ or ‘vulnerable’ domestic burglary targets and thence ensuring that prevention measures are delivered accurately and promptly to them so as to forestall their subsequent risk – and what might be called a coverage approach – where the idea is to ensure that all possible targets are covered so that no one remains vulnerable.

Since the principle aim of coverage programmes is to ensure maximum coverage of targets across an area, they tend to rely on maximising the awareness and availability of prevention measures amongst residents – often through publicity or similar mass campaigns – and thence relying on residents themselves to take up and/or adopt the measures or advice offered. In contrast, targeted approaches more often rely on being able to identify specific vulnerable targets – often through available data such as crime reports and statistics – and then delivering crime prevention measures directly to the target, often accompanied with specific advice or help with installation.

The area-coverage approach has been used in previous Home Office prevention projects, most recently in the Safer Cities Programme. And there are some indications that it may have an impact. The evaluators found, for instance, that the mere presence of burglary action in an area seemed to reduce the burglary risk for all residents, suggesting that ‘area’ processes were operating rather than those which acted to defend individual homes (Ekblom et al., 1996). While it is not clear whether such blanket area effects are the result of all potential targets being covered with specific prevention measures (such as better locks for example) or whether they are due to a general improvement in the community’s attitudes towards burglary and security – what has sometimes been called ‘natural’ security and surveillance – the coverage approach does aim towards an area-wide, holistic conception of change.

Principal difficulties with the coverage approach are that it may prove costly – if many targets need to be covered to ensure maximum effectiveness – and inefficient, if some or many targets are covered that would not have needed to have been covered because they were actually at minimal risk. Greater efficiency and cost-effectiveness might be had if it were possible to identify potential targets reliably and then to concentrate prevention efforts upon them. This has been the main crime reduction rationale of the strategy of preventing repeat victimisation (RV): if victims are disproportionately likely to be victimised again over a short time-period, delivering prevention to them accurately and promptly is not only likely to deliver protection to the most vulnerable but also to bring down the area crime rate more rapidly (Pease, 1998). In recent years, the arguments made for targeted approaches have been compelling, resulting in the adoption of RV as one of the Home Secretary’s police performance indicators for crime reduction. The evidence

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suggests that the impact of the RV performance regime on police services has been considerable (Farrell et al., 2000).

In sum, both ‘coverage’ and ‘targeting’ tactics have been attempted in previous burglary reduction projects; while both share the same ends, they use contrasting means – coverage tactics aim to reduce the overall crime rate by covering as many potential targets as possible; targeting strategies seek to do the same by identifying and targeting those potential targets predicted to contribute disproportionately to the overall rate.

Action planning

If success for the projects would come about through strategic and tactical planning, then the more effort that was put in advance into devising strategies and subsequent action plans, the more confident were the Home Office consultants that success could be attained (see Curtin et al., 2001). The more that problems could be identified in advance, the more likely that implementation would go smoothly, so long as projects stuck to their action plans; implicitly, problems arise with deviations from agreed plans.

Problem-solving

Perhaps regrettably, nearly all crime prevention projects are likely to encounter a variety of problems in practice. Yet, while there has been much emphasis placed on the idea of ‘problem-solving’ in crime prevention programming, there is again a potential divergence as to what kind of problem-solving may be required. On the one hand, there is the view that problems are best solved by being anticipated and taken care of in advance, primarily through detailed action planning (Curtin et al., 2001) – what might be thought of as a ‘programmed’ approach to implementation. On the other hand, it may be that the range, diversity and complexity of practical and operational problems that might emerge during implementation cannot be anticipated easily and that solutions are more likely to arise out of the practical and immediate experience of overcoming problems at first hand – what might be called an ‘adaptive’ approach, which sees implementation as a complex process of change, adjustment and continuing innovation (for more discussion see Hope and Murphy, 1983).

Managing delivery

Unlike earlier Home Office prevention projects, bids for the Reducing Burglary Initiative were made, for the first time, by the multi-agency Crime and Disorder Reduction Partnerships (CDRPs) recently established by the Crime and Disorder Act, 1998 (Tilley et al., 1999). The statutory nature of the CDRPs and guidance provided by the Home Office tended to assume that there would be relative uniformity in the way in which SDPs were constituted and managed, primarily as a partnership between local authorities and the police, though involving other local partners within the CDRP. There seemed likely to be three levels of organisation of the SDPs.

• The steering-level – this would usually consist of an overall project steering group, with representatives from the key project stakeholders, primarily the CDRP partners, that would be accountable overall for the project and would steer its general direction. This would be the level at which strategic planning would take place.

• The management-level – this would be where day-to-day project management would be carried out, usually by particular designated personnel who would have responsibility for executing plans, accountable to the steering group. This would be the level responsible for action planning, including detailed targeting, specification and negotiation with relevant stakeholders to the respective prevention measures.

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• The implementation-level – this is the level at which the various prevention measures would be delivered to the community, whether in the form of publicity and marketing, contacting residents, organising meetings, patrols, etc. and the siting and installation of crime prevention measures.

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3. Evaluation research design The primary aim of the research was to relate a project’s activities – i.e. its outputs – to its outcomes – i.e. the reduction of burglary. This requires the use of reliable measurements of outputs and outcomes, and a research design that is capable of supporting valid inferences concerning the impact of outputs on outcomes. Both the course of implementation of most of the Strategic Development Projects and the nature of local burglary trends in their target areas presented particular challenges of measurement and analysis.

For most of the SDPs, their actual implementation showed considerable departures from plans made at the outset2. Reasons for this included: delay in implementation; variation in the ‘intensity’ of resources applied and of measures implemented; simultaneous implementation of different kinds of measure producing different mixes of measures applied at any one time; abandonment or only partial implementation of measures; modification during implementation, either altering the ‘context’, target group or the ‘mechanism’ of measures; introduction of new measures not appearing in initial plans; and interventions without clear aims or objectives. This meant that a simple measure of project output – for example, ‘before’ (0) versus ‘after’ (1) – would not be a reliable measurement of project impact, since the tempo, duration, resources and mix of measures were likely to vary for each project over time – giving a prolonged period of ‘during’ between ‘before’ and ‘after’ – and to differ between projects – making inter-project comparisons difficult. What was required, then, was to develop an indicator of a project’s Intensity of Output that measured its outputs reliably.

The nature of the major outcome variable – the trend in the number of burglaries to dwellings recorded by the police in the SDP target areas – also presented a range of measurement difficulties, particularly considerable month-on-month volatility in the number of burglaries, reflecting the wide range of possible influences on local burglary rates. This variation would make it even harder to detect the effect of the project amongst the ‘noise’ of other influences, especially using conventional techniques of data analysis. Many methods of analysing change over time – including ‘smoothing’ the data by aggregating monthly data into quarters risks ignoring important temporal information that would help in understanding more accurately the precise impact of the projects, especially if their intensity of output varied over time. However, the downside of using finer-grained figures such as monthly burglaries is that the trend in these appears to be more volatile, especially for small areas, and more difficult to describe validly. There is a danger, then, that seemingly ‘simple’ statistical methods of analysing the impact of the projects on outcomes could be misleading.

The measurement of project outputs

Two requirements underpinned the process of measuring project implementation: classifying interventions – what they are, what they are intended to do, and how it is expected they will do it; and measuring outputs – the tangible form taken by interventions during the course of implementation. The researchers undertook to pilot two recording instruments proposed by Home Office researchers measuring, respectively, the type of intervention and its associated outputs. As research tools for evaluation, the Home Office forms proved problematic in a number of ways. Our experience of using the Classification of Interventions Form made the researchers aware of the difficulties SDP personnel have in describing simply what they are doing. Difficulties were encountered in completing three elements of the form: classifying interventions, attributing crime reduction theory, and determining the status of implementation. Besides the accurate classification of interventions, it was also felt necessary to assess how far projects had succeeded in carrying out what was intended. For this purpose, the Home Office developed an Intervention Output Measures Form. Essentially this required the identification of targets in

2 This being reported in full reports of SDPs that were prepared for the Home Office.

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advance of action and an assessment of how closely those targets had been met. Yet a number of very significant practical and conceptual difficulties meant that measuring outputs in this way was at best highly problematic and, at worst, misleading (see Crawley and Hope, 2003).

In sum, the pilot work suggested that the forms, as originally designed, seemed ill-suited to capture the full complexity of project work within SDPs. In preference, a new research instrument was developed – the Calendar of Action (CoA) – to capture both change in the nature or focus of the intervention, and the ‘non-sequential’ nature of implementation. The CoA forms a critical link between the various strands of evaluation - the measurement of process, the measurement of resources used, and the estimation of project impact. The three principal aims of the Calendar of Action were to: aid the modelling process; assist the process evaluation of each intervention; and facilitate the measurement of the intensity of output. The CoA allows for costs incurred in each quarter to be checked against the process evaluation and against the progress of interventions.

Intensity of project output

In order to establish the overall effect a project has had on the level of burglary it is necessary to quantify project outputs systematically. In most instances, however, projects combined various interventions, each attempting to reduce burglary by employing differing theories and techniques. Nevertheless, the outputs from individual interventions had to be amalgamated into a standardised variable – the Intensity of Output indicator – in order to allow the project outcome, as a whole, to be modelled.

An examination of the information obtained within the CoAs for all the SDPs in the study led to three underlying principles for the measurement of the Intensity of Output.

• Different types of intervention require differing measurement procedures – given the differing nature of interventions it is appropriate to classify and quantify according to the attributes exhibited by each type. The principal criteria for classification were the duration and investment required to bring the intervention to a state of full implementation. Three generic categories of intervention were deemed both necessary and sufficient to capture the numerous options used: hardware – measures that are primarily dependent upon the installation of equipment, e.g. target hardening individual households, erection of alley-gates or fences etc. Additionally, there is normally a degree of permanency related to these interventions such that once installed they should offer protection against burglary beyond the project finish date without further involvement from project personnel; software interventions (such as additional policing) that are more personnel-based and may or may not continue beyond the life of the project, though they will require a continuing use of resources (e.g. wages and rent) from some source otherwise they will cease to operate; and ad hoc interventions such as the distribution of crime prevention leaflets, visits to schools, talks with residents, awareness days etc. that occur only once or on an irregular basis. Intensity measures should relate to what has actually happened rather than to what could have taken place- problems of differential take-up, changes of plan, unexpected problems etc. meant that the estimation of intensity should be based on what actually occurred, not on what might have taken place. Consequently, full implementation of individual interventions is achieved at the point when work on the intervention ceased.

• The intensity of each intervention should reflect the quantity of inputs utilised by the intervention – this is required in order to standardise each intervention’s input into the overall project Intensity of Output measure. For individual interventions, the proportion of project resources used within its implementation is multiplied by the percentage of action taken each month. The overall project level variable, which is the summation of the separate intervention measures, could range from zero to one, where one will only be achieved if all interventions are at full implementation during the same month. Information

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on the cost of resources used by each SDP was collected according to Home Office guidelines. The underlying principle was to establish the level of resource commitment that would be required if the project were to be replicated in another area. Information was collected under the following headings: personnel; training; equipment; premises; transport; research, advertising and publicity. For each project, data was collected on all resources used by public agencies in support of project activities.

The estimation of project outcome

It is not enough just to collect information on project outputs and outcomes. Data have to be placed within a framework of understanding – often known as a research design – so that when a result is found, researchers know how to make sense of it in terms of the purposes of the enquiry. In evaluation research, the simplest, most commonsensical, research design is a comparison of ‘before’ with ‘after’. Both periods sit either side of the point of intervention of the project (call this X) and allow researchers to make a comparison of what had transpired after they intervened with what had happened beforehand. The purpose of doing this, of course, is to find out whether there was a change – perhaps a reduction in burglary – from which the effect of the intervention might be inferred. In doing this, the researchers are relying implicitly upon the temporal sequence of events to sort out our interpretation of cause and effect. For example, if X intervenes between observations taken ‘before’ (call them O1) and observations taken ‘after’ (call them O2) then if O1 > O2 (i.e. if there were more burglaries ‘before’ than ‘after’), then ceteris paribus it could be said that the intervention X reduced the number of burglaries. Unfortunately, in the real (or observed) world ‘other things’ are rarely ‘equal’. This is where research design comes in – to help ‘control’ for, or discount, the possible influences of ‘other things’ so that the intervening effect of X can be estimated more accurately, presupposed in themost simple research design: O1 X O2.

An important principle of evaluation research design is to establish an hypothetical ‘counter-factual’ condition; meaning that it is necessary, in order to establish cause and effect between project outputs and outcomes, to set up a comparison between what the researchers observe in their target or ‘experimental’ situation (i.e. that which they are intervening upon) and a situation which represents what would have happened had the intervention not taken place (i.e. the counter-factual condition)3. Clearly, since the counter-factual condition is hypothetical, the research design has to select a way in which to assess the counter-factual hypothesis – that is, to estimate whether the observed changes are due to the intervention X and not to anything else which would have occurred had X not been present. A basic approach is to ‘control’ for other influences affecting the criterion of effect (in this case, change in monthly burglaries) so that the specific effect of the intervention might be detected; thus, the counter-factual situation represents all these relevant influences which are also to be found in the intervention situation, except for the presence of the intervention itself.

There are a range of research designs and techniques for establishing hypothetical counter-factual conditions, all of which have their advantages and disadvantages, and which vary in applicability to different situations. One popular approach, for example, would be to allocate cases to ‘experimental’ (i.e. receiving the intervention) and ‘control’ (i.e. not receiving) groups – either by random allocation, matching or some combination of the two. However, as is widely recognised, true or effective randomisation and matching are seldom feasible where communities or areas comprise the cases that are subject to the intervention4. Certainly, in this case, the ability to manipulate the circumstances of the RBI, including the selection of areas, so as to create counter-factual conditions in this way was not within the researchers control – their brief was to assess the effects of interventions after decisions about the selection of projects and areas for intervention had been taken. Nor was it ever intended, apparently, that the RBI should be organised as this kind of ‘experiment’. Yet even where ex post facto matching of communities is attempted following intervention decisions (in order to reconstruct quasi-experimental conditions), 3 Hollister and Hill, 1999. 4 See Sherman et al., 1998.

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it is rarely successful either in capturing all the relevant differences or in accounting for differing patterns and experiences between community contexts.5 In contrast, then, an alternative approach was chosen – more common in the econometric evaluation literature – which took advantage of established analytic methods for studying time-series data in order to establish an appropriate and feasible counter-factual condition.

A distinguishing feature of the approach is that the analysis of time-series is conducted as a single-case study – in this case, one for each SDP. For the purposes of analysis, each project area’s time-series of burglary acts as its own ‘control’ of itself. In this research design the ‘control’ over extraneous influences is attained through the statistical estimation of what the trend in the time-series (predicted from previous periods in the time-series) would have been without the project (as measured by the Intensity of Outcome variable). Here, the unit of analysis is neither people nor groups nor even areas but rather the series of data itself – that is, the monthly number of burglaries occurring in each case-study area.

The time-series analytic research design

Crime data

In this report the data on burglary comprise the monthly number of notifiable burglary offences recorded and supplied by the respective local police services. The time-series analysed comprised a period of 24 months prior to the inception of the RBI (in April 1999) and a subsequent period ending sometime during or after February 2002, depending on the availability of data. Data was also collected covering the same period on Other Acquisitive Crimes (OAC), including burglary from a property other than a dwelling, theft from a dwelling, theft from a shop and theft from a vehicle. The OAC variable was included in the models of burglary as an ‘explanatory’ variable (alongside the Intensity of Output variable) to take into account general trends in property crime and offending occurring simultaneously with project implementation. Separate models are also estimated using the trend in OAC as the dependent variable (with the Intensity of Output variable as an explanatory variable) to test for cross-crime displacement from burglary to other forms of property crime as a consequence of projects’ activities.

Comparison of trends

All data used in the analysis are measured on a monthly basis and relate to three distinct geographical areas: namely, the target area, the buffer zone and the rest of the police Basic Command Unit (RoBCU) area in which the SDP is located. The target area is the area covered by the burglary reduction interventions under evaluation and for the majority of projects is equivalent to either one or two adjacent police beat areas. The police beats immediately surrounding the designated SDP are referred to as the buffer zone. This area is believed to be the most susceptible to geographic displacement effects as a result of the project. The considerable research literature available on the subject suggests that such displacement may be either negative crime displacement – producing an increase in burglary in the buffer zone – or positive diffusion of preventive benefit – producing a simultaneous reduction in the buffer zone. All remaining beats make up the rest of the BCU (RoBCU) area – which serves the purpose of standing for the trend in burglary occurring generally. For each of the SDPs studied, appropriate boundaries (comprising relevant groupings of police beat areas) were agreed between research field staff and local project personnel and took into account natural boundaries and other physical features (e.g. open space and motorways). Figure 3.1 gives a pictorial representation of these three types of area.

5 See Pawson and Tilley, 1997.

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Figure 3.1: Areas of investigation

Within each area, two categories of crime type are included in the analysis: burglary of a domestic property and Other Acquisitive Crimes. The OAC category encompasses burglary from a property other than a dwelling, theft from a dwelling, theft from a shop and theft from a vehicle. These crimes are considered to be those most likely to be affected by any cross-crime displacement following project implementation – within both the target and buffer areas.

Analytic model

The general model that was applied to each individual time-series consisted of:

Dependent variable:

Y = number of burglaries in target area per month

Explanatory variable:

X1 = the intensity of project output variable (ranging from 0 to ≈ 1)

Control variables:

X2 = number of other acquisitive crimes (OAC) in target area per month

X3 = number of burglaries in buffer zone area per month

X4 = number of OAC in buffer zone area per month

X5 = number of burglaries in the rest of the BCU (RoBCU) area per month

X6 = number of OAC in the rest of the BCU (RoBCU) area per month.

The control variables served two purposes: controlling for the effect of crime displacement from the other areas/crimes into the target area; and controlling for common trends and patterns in crime affecting all or most of the areas (a set of effects known in the literature as ‘history’). They were only retained in the final model estimating project effect if they proved significant.

SDP

Buffer Zone

Rest of BCU

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Serial dependency

In seeking to estimate the independent effect of the projects’ Intensity of Output, net of other things (whether measured or not) that might influence the trend in burglary, the researchers have to control for the various temporal properties, which bear equally on the project as upon all the other things that might be shaping the local burglary trend. If not, they will bias the statistical estimate of the independent effect of the intensity variable, making it unreliable. Aside from these other influences it is reasonable to assume that the trend in burglary, as in most things, is serially dependent – that is, observations later in time (e.g. O2) are more likely than not to be influenced by prior observations (e.g. O1), irrespective of any other extraneous influence, including that of the Intensity of Output (X). Without taking account of serial dependency in the observations (literally controlling for the effect of time itself) it will not be possible to assess the effect of any extraneous influence since the statistical tests will be biased by the uncontrolled presence of serial dependency 6. A common statistical ‘operation’ on time series data is to include necessary corrections for diagnosed serial-dependency7. Not only do such corrections remove serial dependency but they also provide additional ‘diagnostic’ information on the nature of the general trend in the area, for instance, whether there are cycles or long-term trends, and whether the trend itself might be auto-regressive – i.e. that what has happened previously has indeed affected what is happening currently.

Model specification

Problems of serial dependency are often encountered in time-series analysis and usually can be overcome by established time-series statistical modelling techniques8. However, examination of local crime trends in the project areas through descriptive statistics, histograms and line graphs raised questions regarding the suitability of traditional econometric time-series models for estimating the effect of the reducing burglary initiatives. In the early stages of data analysis non-parametric statistical tests were used9 (which make few restrictive assumptions about the distribution) in order to look at burglary rate changes ‘before’ and ‘after’ the start of the projects. These tests were also helpful in indicating whether shifts in the distribution of monthly burglary rates, before versus after, were due to other kinds of shift in the monthly distribution of burglary as well as to shifts in the mean monthly rate. Yet these simple, descriptive analyses ignored the most obvious general characteristic of the various area crime trends – that is, their considerable volatility in the number of burglaries per month. Were conventional time-series models to be used to estimate the net impact of projects, they would increase the risk of bias in the estimates and of error in the conclusions.

Conventional time-series models – such as the ARIMA technique – are predicated on the assumption that the error process associated with the model has a zero expected value and constant variance for all time-period observations. Amongst the things such models assume is that month-on-month trends will be relatively stable and regular over the entire period so that changes in the level of burglary can be clearly identified. While not all of the local crime trends looked at appeared to violate such assumptions, there was enough evidence that most showed considerable volatility and unpredictability over the time-periods in question sufficient for the researchers to take some necessary precautions in their statistical modelling.

In order to measure the trend accurately, and allow the researchers to relate outputs to outcomes, it was felt that an alternative modelling approach was required which would provide some assurance that they were not making errors of inference because they were using

6 Judd and Kenny (1981, p137) liken serial dependency to a ‘disease’ that infects time-series data, which has first to be diagnosed and then removed by a statistical ‘operation’. 7 A widely used set of statistical modelling techniques include Auto-Regressive (AR) and Moving Average (MA) corrections. 8 Such as the Box-Jenkins (or ARIMA) approach. 9 Including the Mann-Whitney and Kolmogorov-Smirnoff tests.

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inappropriate modelling techniques. The statistical consultants10 agreed that the ARCH (Autoregressive Conditional Heteroscedasticity) family of statistical models could prove very useful where local crime data were exhibiting volatility over time11. The use of ARCH models involves the specification of two equations rather than one. The mean equation is equivalent to the conventional regression equation utilised in time-series models. Often it is assumed that the mean of the dependent variable is a linear combination of lagged endogenous and exogenous variables. For the purposes of this analysis, the level of monthly crime (either burglary or OAC) is assumed to be a function of a lagged Intensity of Outcome variable (because it is reasonable to assume that outputs produced in one period will have their effect in the next), a lagged dependent variable (because it is assumed that crime levels in the last period will affect burglary or OAC levels in the present period) and a lagged OAC level variable (standing as an albeit crude proxy for general criminal activity). In practice, the latter two variables were only included in final models if they were estimated to have a statistically significant effect on burglary (the dependent variable).

In addition to the conventional mean equation common to most regression-based time-series techniques, the ARCH regression model also includes a specification for the ‘disturbance term’, which is known as the variance equation, where the conditional (present) variance is defined as a linear function of past variances. The inclusion of this equation is to take account of changes occurring in the temporal distribution (or volatility) of crime occurring simultaneously which contributes to changes in the level (mean) of crime. Numerous extensions and adaptations to the ARCH model have been introduced since 1982, the most frequently utilised is the Generalised ARCH (GARCH) model12. In practice, this allows selection of the most appropriate model specification for the particular local series being examined. Of course, different local trends, with different time-series characteristics, will require different specifications. Explanatory variables (such as the Intensity of Output variable) may be introduced in either the Mean or Variance equation or indeed both. In theory, therefore, it is possible to estimate the effect that the Intensity of Output variable has on both the mean level of crime and also the variance – or monthly volatility - around this level.

It should be noted that this is an empirically-based approach resting solely on the practical requirement to incorporate immediate past information on burglary into the model. In practice, the process of model fitting is an iterative one somewhat akin to trial and error – a process of varying the technical specifications for each equation until the best-fitting and/or most parsimonious model can be found for each time-series case. It follows that the selected models for each project’s time-series may have different specifications. Finally, since the ARCH/GARCH model incorporates the assumptions of conventional time-series approaches (e.g. the ARIMA method) as well as its additional assumptions, it cannot make ‘worse’ predictions than conventional time-series models. At worst, it will reproduce the same results as conventional models, if the latter’s assumptions are not violated; but if they are, it promises to provide more accurate and unbiased results than could be obtained from conventional time-series techniques. In the light of this general methodological approach, it was decided not to take the risk of error by using conventional methods and thus a less well-known but arguably more appropriate and safer approach to modelling local burglary time-series was chosen.

10 Professor Chris Hale (University of Kent) and Len Gill (University of Manchester). 11 The major difference between traditional econometric time-series and ARCH-type models is with regard to the assumptions about the one-period forecast variance. For instance, many empirical analysts working with financial market data using traditional econometric forecasting techniques discovered that their ability to predict future values varied considerably from one time-period to another. Engle (1982) introduced a new class of stochastic processes, namely the ARCH process, to deal with such instances. These processes, that define the variance, are still assumed to be serially uncorrelated with zero mean (as are more conventional time-series methods), but also exhibit non-constant variances conditional upon past information. 12 As with the other adaptations, the difference arises in the specification of the variance equation - in particular, the GARCH model allows for a much more flexible lag structure than the ARCH process.

13

Estimating net effects

As noted, separate models of the time-series for burglary and Other Acquisitive Crime were estimated for both target and buffer zone areas. This afforded the possibility of estimating the net effect of projects in the combined target and buffer zone area, taking into account the various positive and negative geographical and cross-crime displacement/diffusion of benefit effects. To do this, it was first necessary to convert estimates of project effect on OAC into a standard measure of burglary-equivalents – the method is set out in Appendix 1. Having done this, it was then possible to calculate three measures of net effect:

• gross impact – that is, the sum of all estimated crime reductions (in burglary equivalents) due to the project in both target and buffer zones;

•

displacement – i.e. the sum of all estimated crime increases;

• net impact - i.e. the sum of all effects, both reductions and increases.

Measuring repeat burglaries

Perhaps not surprisingly, given the importance attached to it in recent years, many of the Strategic Development Projects aspired to reduce repeat burglary victimisation. In recognition of this the researchers were contracted by the Home Office to measure the effect of projects on repeat burglary victimisation. Measuring repeat victimisation is not without difficulty. There are two principal issues to be addressed: collecting data that would uniquely identify repeat victimisation – e.g. by the address of the dwelling; and standardising the period of observation over which to count repeat victims – since the length of the period affects the probability of observing or counting a repeat victimisation. Unlike the analysis of area burglary trends, the analysis of changes in repeat victimisation requires disaggregated crime data – that is, individual records of burglaries. The research design employed was relatively simple and straightforward and was intended to answer one particular question: was there a reduction in the rate of repeat victimisation following the introduction of project prevention measures? Our method for measuring the impact of projects on repeat burglary is described in Appendix 2.

There was considerable variation amongst the ten separate police forces dealt with in the regions, both in their capacity to supply data in the appropriate form, and in the conditions under which the researchers were required to store and use this data. Difficulties and uncertainties regarding the relevant data protection legislation were frequently cited. In the event, although the majority of police forces were helpful in addressing the requirements, it was not possible to collect sufficient data for analysis in respect of eight out of the 21 SDPs.

14

4. The impact of the projects on local burglary

Table 4.1 lists the percentage changes in burglary occurring before and following the actual start of implementation of each project (i.e. when the project intensity variable took on a non-zero value), estimated by the statistical methods outlined above.

Table 4.1: Impact of projects on local burglary

Project Col 1 Change in target area

Col 2 Change in target area due to project (modelled)

Col 3 Other change in target area (Col 1 – Col 2)

Group A A1 -36 -49* 13 A2 -40 -43* 3 A3 -47 -37* -10 A4 -3 -35* 32 A5 -29 -27* -2 A6 -20 -4* -16 Group B B1 -40 -27 -13 B2 -42 -21 -21 B3 -24 -20 -4 B4 -2 -15 13 B5 13 -10 23 B6 -32 -7 -25 B7 -37 -6 -31 Group C C1 -47 4 -43 C2 -16 6 -10 C3 -36 6 -42 C4 29 11 18 C5 -14 12 -26 C6 13 34 -22 C7 14 39* -25 * = significant at p. <.05. There were three groups of projects:

• group A (n = 6) - projects that brought about a significant reduction in burglary in the target area over and above the local burglary trend (column B);

• group B (n = 7) - projects that brought about a reduction in burglary in their target area, though this was not significantly different from the local burglary trend;

• group C (n = 7) - projects that seemed to bring about an increase in burglary in the target area, though only one of these (project C7) was significantly different from the local trend.

The existence of group C projects may seem counter-intuitive, given that all projects shared the same purpose of reducing crime. However, as described below, projects may have unanticipated

15

and unintended effects which may well intervene in the local ‘chemistry of burglary’ in such a way as to make things worse than they might have been. This point can be illustrated with reference to Table 4.1, column 3, which lists the percentage change in burglary in the target area, net of the changes due to the project. Here, it can be inferred that some projects achieved a significant reduction despite other possible factors in the target area leading to an increase (e.g. projects A1, A2 and A4), while others were responsible for most of the reduction in their area (A3, A5). Although project A6 achieved a significant reduction, much of the overall reduction in its area was the result of other factors. Much the same mixture of results were obtained by projects in group B, though none had a significant impact in its own right. Finally, with the exception of only one project (C4), all the projects in group C appeared to be responsible for activities that might have brought about increases in the local burglary rate which might otherwise not have occurred; one project (C7), in particular, appearing to bring about a 39 per cent increase, as against a possible 25 per cent reduction that might have been due to other factors operating in the local area.

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5. Net effect – crime displacement or diffusion of benefit?

As noted above, the research sought to examine whether negative crime displacement and positive diffusion of benefit displacement may have occurred as a result of the projects for both burglary and ‘other acquisitive crime’ both in the target area and the buffer area. Table 5.1 sets out the results, in burglary-equivalents (Appendix 1), for group A and group B projects. Generally, there would appear to have been relatively few adverse effects for projects that achieved some kind of burglary reduction in their target areas13. Indeed, only three projects out of the 13 in either group, appeared to have produced an overall net increase in burglary-equivalents14; while eight of these projects had a positive overall net impact at least twice the size of their impact in the target area. In general, then, more projects achieved an overall diffusion of benefit in their wider area, taken as a whole, than in their specific target area.

13 The only ones appear to be for projects: A2 (increase in OAC in buffer area); A4 (increase in OAC in both target and buffer areas); B4 and B5 (increases in OAC in buffer areas). 14 Though even here the increases were due largely to increases in OAC in the buffer area.

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Table 5.1: Impact of the projects on burglary in the project area, displacement and diffusion of benefit (measured in burglary-equivalent terms) – burglaries per month

Impact in the target area Impact in the buffer areaa

Burglary Other acquisitive

crimesc

Burglary Other acquisitive

crimes

Gross impact, displacement, and net

impactb

Project Area

Effect p-value

Effect p-value

Effect p-value

Effect p-value

Gross impact

Displace-ment

Net impact

Group A: Projects associated with a significant reduction in burglary in the target area A3

-13.47 0.02 -1.45 0.79 -18.16 0.01 -8.62 0.35 -41.7 0.0 -41.7

A1

-2.44 0.00 0.74 0.28 -20.02 0.06 -1.93 0.65 -24.39 0.74 -23.65

A5

-5.95 0.00 -6.75 0.01 -5.72 0.11 4.76 0.31 -18.42 4.76 -13.66

A6

-4.31 0.03 -1.37 0.02 1.39 0.67 -2.97 0.43 -8.65 1.39 -7.26

A2

-9.30 0.00 -4.51 0.14 -12.45 0.04 23.78 0.01 -26.26 23.78

-2.48

A4

-12.77 0.00 6.36 0.07 -9.19 0.57 26.34 0.04 -21.96 33.0 10.74

Group B: Projects associated with a non-significant reduction of burglary in the target area B7

-0.29 0.55 0.10 0.87 -28.01 0.01 -18.36 0.04 -46.66 0.10 -46.56

B2 -14.41 0.14 -4.21 0.01 -33.30 0.00 5.87 0.17 -51.92 5.87 -46.05 B6

-0.90 0.58 -3.26 0.14 -13.37 0.14 -14.92 0.01 -32.45 0.0 -32.45

B1

-7.39 0.31 -1.61 0.32 -2.41 0.20 -2.44 0.19 -13.85 0.0 -13.85

B3

-4.10 0.31 -1.15 0.60 4.84 0.48 0.33 0.95 -5.25 5.17 -0.08

B4

-2.18 0.49 -1.12 0.50 -5.75 0.31 19.23 0.01 -9.05 19.23

10.18

B5

-2.06 0.52 -5.53 0.25 -2.97 0.46 22.81 0.01 -10.56 22.81

12.25

Notes ‘Other Acquistive Crimes’ were converted into ‘burglary-equivalents’ primarily on the basis of estimates of the relative costs for offences provided by the Home Office. Details of the conversion process are contained in Appendix 1. a The buffer area consists of the police beats immediately adjacent to the project area. b Gross Impact is equivalent to the summation of all negative values’, displacement is the summation of positive values while the Net Impact sums all effects regardless of sign. c Other Acquisitive Crimes include burglary not in a dwelling, shoplifting, theft from a vehicle, and theft in a dwelling.

This suggests two, related, possibilities: 1. Projects were part of, and embedded within, crime reduction initiatives covering wider areas

beyond the target area.

In hindsight, a weakness of the approach to evaluation and also of the way in which projects were perceived centrally may have been to focus too specifically on tracking and monitoring the specific impact of the RBI investment on its designated targets at the expense of taking into

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account sufficiently its place in the wider fabric of local crime reduction and community safety activity. It is evident that the SDPs and their target areas were not ‘islands’; indeed, both their organisational and territorial boundaries would be highly porous, not only to ‘burglars’ but also to ‘preventers’ or ‘regenerators’! In practice, the SDPs often had an influence beyond their target areas and were, in turn, influenced by programmes and activities occurring beyond their specific borders. Thus, a comprehensive analysis of project impact would need also to take into account these various externalities and interdependencies.

2. Projects affected the perceptions, attitudes and behaviour of a wider population of offenders, victims and members of the community, beyond the specific target area.

It also seems plausible that these mutual influences would not be contained easily within the particular residential population of the target areas which were, after all, relatively small. Not only were the target areas not islands, they were also not ‘villages’. Again, burglars are no respecters of administrative boundaries and local residents have connections to wider social networks than the target areas. Neither the SDPs themselves, nor the evaluation team, had the resources to map the wider social networks in which offenders, victims and the communities of the target areas were embedded (this by itself, would have been a substantial task). Thus, for example, it is not easy to say whether local burglars were persuaded to go elsewhere or whether less-local burglars were dissuaded from coming into the target area. Nor can the net trade-offs between target and buffer areas of simultaneous, though separately funded, prevention activities be easily estimated. However, as with the findings of the Home Office Safer Cities Programme (Ekblom et al., 1996), it seems likely that the projects had area-wide effects going beyond the targeted area, and were also so affected. Though this might have made the administration and evaluation of the RBI harder, as far as can be judged, these wider effects would seem, on balance, to have been beneficial to the specific communities involved.

Generally, these findings suggest that the scale and impact of the SDPs was, in practice, greater than that funded specifically by the RBI. The overall costs and proportion of resources levered in from other local agencies suggests that support from the RBI, although enabling, comprised only a part of the local crime and reducing burglary initiatives covering a wider area than the target area specified for the RBI intervention. While, for accounting purposes, local partnerships kept RBI funding and management distinct, in a number of cases in practice, the SDPs were embedded – often in complex ways – within a network of wider crime reduction and regeneration initiatives funded from a variety of sources (including the Single Regeneration Budget and Neighbourhood Renewal) as well as the other work of the local Crime and Disorder Reduction Partnerships.

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6. Anticipatory benefit Recently, Smith et al. (2002) have introduced the concept of the anticipatory benefit that might accrue to crime prevention projects, by which they mean a reduction in crime following the public announcement or launch of a project but in advance of the implementation of specific, tangible prevention measures or activities. Various explanations are offered for this effect, apparently observable, though hitherto largely unremarked, in the findings of a number of crime prevention evaluations. The term ‘anticipatory benefit’ refers to one such possibility: the deterrent effect on offenders’ readiness to commit crimes against targets which seem to them likely to be subject to prevention activity. Bowers and Johnson (2003) re-analysed burglary data collected by the Consortium, along with that collected by the Northern Consortium, purporting to demonstrate the existence of such an effect, and claiming that observed reductions could have been due to advanced and/or informal publicity about the projects, which it is presumed may have been communicated to offenders in a way that altered their perception of risk in the target area.

The researchers do not wish to comment upon the validity of this explanation, nor upon its theoretical underpinning. Nevertheless, since data from the research study has been used by Bowers and Johnson (2003), it is appropriate to comment as to why the Midlands Consortia have not drawn a similar inference in our their analysis15.

Measurement of intensity of output

One reason why the researchers have not been drawn to identify anticipatory benefit effects is to do with the precision with which they have measured the respective intensities of project outputs. Their measure only takes on a non-zero value when actual resources, however slight, were expended by projects, including those spent on publicity. For most projects, very little, if any, tangible resources were used in the first three or so months following the common launch date of the RBI – Phase 1 Strategic Development Projects (April 1999) and, of course, none were spent specifically by the SDPs prior to the launch. Thus, by the Midlands Consortia definition, there was negligible measured activity attributable to the SDPs for the six month period either side of the common launch date – though this is the period where Bowers and Johnson detect the maximum anticipatory benefit (2003: Figure 4.1). Since the researchers objective was to estimate the effect of actual project expenditure and effort, they did not measure other publicity or other activity, or its effects upon its recipients, whatsoever that might have been, and regardless of who might have broadcast or received it. By the same token, nor did Bowers and Johnson (2003) have access to any information about these activities in the target areas either. But because the Midlands Consortia did measure actual effort and activity they can be reasonably sure that any apparent anticipatory benefit effect was not due to specific project activity, since the projects as specific organisations did not formally exist prior to April 1999, nor did most of them expend resources for the first three months or so.

Smoothing and aggregating data

As noted above, the method was also concerned to estimate the effect of projects precisely. This was done both by analysing the data for each project separately but also by using a disaggregated time-series composed of monthly periods. Although such data provided more of a statistical challenge, they also gave the researchers more ‘degrees of freedom’ to work with, thereby increasing the accuracy and precision of the estimates of effect and supporting the validity of the inferences to be drawn about them. In contrast, Bowers and Johnson’s (2003) analysis: combines all projects together, effectively reducing 42 separate observations per time period to one; and aggregates monthly data to quarters, again reducing 54 time-periods to 18. Not only is much statistical precision in estimation lost by using a cruder time-space framework

15 It should be noted that the researchers were not consulted about any aspect of Bowers and Johnson’s (2003) analysis.

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but also the possibility of aggregation bias in estimation is increased, especially if the contribution of each time-space observation, in its most disaggregated form, is unweighted in the analysis, which is the case.

Estimates of project effect

Again, in contrast to the Midlands Consortia method, Bowers and Johnson’s (2003) analysis fails to separate the independent effect of the projects from that which might be due to all other factors and events occurring in the target area. What might then appear to be an anticipatory benefit associated indirectly with the project need not actually have any relationship to the project at all, given that in many cases the researchers have estimated that much of the trend in many project’s target areas could have been due to other things, occurring simultaneously but probably (according to the Midlands Consortia method) independently of the project’s existence (see Table 4.1). Further, Bowers and Johnson (2003) use the ratio of burglary in the target area (the numerator) to burglary in a ‘comparison area’ (the denominator, which in the case of these projects is actually the RoBCU) as their dependent variable; as is well known, and demonstrable in this case (Hope, 2004), change in ratios can be affected by change in the denominator, quite independently of change in the numerator.

Temporal issues of causal attribution

Finally, unlike Bowers and Johnson (2003), the Midlands Consortia method sought to discount various sources of spurious causal attribution, deriving from the particular nature of trend data – namely, selection/regression artefacts, which for many years have been regarded as common ‘threats’ to the validity of causal inference in evaluation research. Their time-series diagnostics detected significant auto-regressive properties in ten of the target areas.

Taking these into account, the researchers analyses suggested that the RBI projects themselves managed to achieve additional reductions over and above the general trend (which tended to be downwards) in only two of these areas; the remaining successful projects (4) achieved reductions without benefiting from, or contributing to, these ostensibly favourable conditions, while five group B and three group C projects did not achieve any significant effect on burglary over and above the general trend.

One possible explanation of this disparity is that where a possible self-sustaining burglary trend had not become established in an area, the projects themselves had a better chance of intervening to change the situation, or halt an otherwise adverse trend; paradoxically, where burglary was already declining in an apparently self-sustaining way – possibly as a result of previous crime reduction successes by partnerships in these areas (which also may have led to their selection for further prevention investment in the RBI) – the projects themselves may well have had their work cut out to add anything more appreciably to the ‘virtuous’ spiral of burglary reduction already occurring. For instance, nine out of the ten areas with a self-sustaining downward trend were also estimated to have had burglary reductions resulting from other influences occurring in the target area coincidental with their projects’ activities. The implication for Bowers and Johnson’s (2003) analysis is that what is thought to be an anticipatory benefit effect may be merely the continuation of crime reductions already occurring, by whatever agency, before the projects started. And this error of causal attribution is compounded in Bowers and Johnson’s (2003) case (though guarded against in the Midlands Consortia statistical model) by the likelihood of regression to the mean – the general statistical probability that rates of burglary in high crime areas are more likely to decrease rather than increase over time (see Bowers and Johnson, 2003, Figure 4.1, p30).

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7. Did targeted (repeat victimisation) prevention work? Although projects were encouraged to devise packages of prevention measures appropriate for the specific local chemistry of burglary, perhaps not surprisingly, given its heavy promotion over the past decade as a performance indicator for police, seventeen out of the twenty SDPs in the regions proposed some form of repeat victimisation prevention strategy as part of their agreed plans. Eight projects proposed solely to target new burglary victims shortly after they had been victimised, while nine projects added further targeting components such as targeting already repeat victims, ‘cocooning’ nearby neighbours of recent victims, and targeting other vulnerable groups, for example elderly households.

Two major flaws with this strategy emerged across the projects. First, Table 7.1 shows that, where changes in RV could be measured16, there were no projects where the rate of repeat victimisation reduced – and some where it actually increased, even in situations where projects achieved a significant reduction in burglary. Consistent with some other community-based research (Hope, 2001), the researchers could find no consistent evidence across the projects in support of the major RV prevention hypothesis – that reductions (or increases) in a community’s burglary rate are correlated with reductions (or increases) in its rate of repeat victimisation.

Table 7.1: M easured change in rates of repeat victimisation by estimated effect of project on burglary Significant change in repeat victimisation

Group A Significant reductions

Group B Non-significant

reductions

Group C Apparent increases

Total

Increase

3

2

2

7

No change 1

4

0

5

Reduction 0

0

0

0

Data unavailable 2 1 5 8

Total

6 7 7 20

Second, it emerged that part of the reason for this was that the plans for RV prevention proposed by the projects – which were consistent with Home Office advice and had been approved by the expert project ‘developers’ – faced considerable practical difficulties when implementation was attempted, to the extent that several, though not all, abandoned or considerably modified their targeting strategy. The following are the main reasons why this occurred:

• Too few victims/repeat victims – although it has been assumed, on the basis of British Crime Survey data, that repeat victimisation is to be found in high crime areas, and although the project areas were selected ostensibly because they had high crime rates, in practice some areas simply failed to generate enough victims or repeat victims commensurate with the numbers anticipated in the projects’ planning, a fact corroborated

16 A range of difficulties in collecting data to measure changes in repeat victimisation were encountered including difficulties in gaining access to data, differing interpretations of the data protection legislation, incomplete data and infrequent numbers of repeat victims.

22

by the researchers independent analysis of local burglary records 17.

• Referral/delivery problems – aside from low numbers, some projects had persistent problems associated with the system of referrals of eligible cases – from the crime-reporting and investigation process – and/or delivery of prevention (usually some kind of target hardening) to the identified target dwelling.

•

Low take-up and non-compliance – even when a project was capable of delivering prevention to identified targets, several projects faced low take-up, and high rates of refusals and non-compliance with the prevention offered them, often much to the surprise of the projects themselves. There were a variety of reasons for this, including: an evident preference on the part of victims for self-help, linked to suspicion of the project’s motives or personnel; victims’ inability or refusal to contribute a share to the costs of the installation; and particularly in the case of households in multiple occupation, an inability to locate or ensure the compliance of the property owner).

Perhaps the most significant aspect of the problems encountered with the RV strategy was what projects did when they encountered implementation difficulties. Table 7.2 shows clearly that the more successful projects – that is, those that achieved some level of burglary reduction – were more likely to have abandoned or seriously modified their RV plans – usually to widen coverage or eligibility to other vulnerable groups or to residents generally. In contrast, the least successful projects – that is, those in group C – were much more likely to have persisted in seeking to implement their RV plans despite evidence that they were encountering difficulties. Since four out of the six projects that persisted with their RV strategies ended up in group C – making-up the majority of the projects that seem to have brought about an increase in burglary by their activities – it is hard, on this evidence, to avoid the conclusion not only that their RV prevention tactics were ineffectual but that persisting with them was also misguided.

Table 7.2: Project delivery of target-hardening to burglary victims (RV)

Group A

Significant reductions Group B

Non-significant reductions

Group C Apparent increases

Projects with RV in original bid

6

5

6

Projects that abandoned/ modified RV plans

6

3

2

Projects that persisted with RV plans

0

2

4

There are two lessons emerging from this experience. First, while there has been persuasive evidence of the benefits to be had from targeting repeat victims, especially in the promise of bringing down the crime rate cost-effectively (Laycock, 2001; Pease, 1998), evidently, there has been little warning about the possible risks attaching to such a strategy, especially if it fails to

17 In a few cases, on independent inspection of the data, it turned out that the project areas did not have nearly as high a rate of burglary – and consequently as much repeat burglary - as had been claimed in the project’s bids. Nevertheless, the reasons why seemingly high-crime, local areas may not generate the numbers of repeats implied by reference to national-level data are likely to be complicated and can only be addressed by engaging with the central propositions and assumptions of the RV prevention ‘theory’ itself (see, for example, Hope and Trickett, forthcoming; Hope et al., 2001; Morgan, 2001). The findings from these albeit limited number of cases suggest that there may be some urgency in doing this, if the reduction of RV is to remain a central Performance Indicators for police services.

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reach or cannot alter its intended targets in the way envisaged. Unfortunately, it would seem that the consequence of such targeting failure is not merely neutral but rather may set in train adverse, unintended consequences. Working out what happened to targets (e.g. potential RVs) in the target areas when they were ‘missed’ would be a complicated process – possibly there were various kinds of target displacement, including:

• failure to target – and to target harden – the most vulnerable properties;

• target-hardening the less vulnerable properties;

• displacing burglary from the less vulnerable to the more vulnerable;

• or, where the vulnerable were protected, displacing burglary to the marginally less vulnerable;

• failing to protect the most vulnerable from the displacement caused by area residents’ own ‘private’ security activities.

At any rate, the local ‘chemistries of burglary’ appeared to be much harder to understand than the projects had been led to believe (e.g. Tilley et al., 1999).

Another reason for targeting failure may be the failure to anticipate that the most vulnerable dwellings may also have been the most intractable from a prevention point of view. This appeared especially the case with rental houses in multiple occupation (HMOs), in many cases occupied by university students or low-income households. Where these arose, projects experienced difficulties in contacting and persuading the usually absentee property owners to invest in, or comply with, the desired dwelling security measures. Finally, even where protection may have been extended to the less vulnerable or the generality of victims – even resulting in reduced burglary rates – this may actually have concentrated risk even more on the most vulnerable who had been missed.

A second lesson is that persistence with plans, however compelling, is unwise in the face of day-to-day practical evidence that implementation and delivery is not going according to plan. As Table 7.2 suggests, some projects appeared wiser than others, abandoning or modifying their plans, and mostly were rewarded with greater success. The reasons why some projects were more flexible while others stuck rigidly to their plans are discussed below.

What works The majority of the resources used by all the projects were spent on situational crime prevention measures, as defined in the agreed classification. In terms of the percentage of expenditure on different kinds of prevention measures by each burglary outcome group, Table 7.3 shows that within the category of situational prevention measures, the most successful group of projects (group A) spent the greatest amount on SCP measures - about 70 per cent of its expenditure – while the least successful group (group C), spent the least, about 62 per cent. Although a greater proportion of group A’s expenditure went on area-wide SCP, this was largely accounted for by two ‘alley-gating’ projects. In terms of expenditure, at least, certain types of interventions also seemed more likely to be associated with unsuccessful projects. A greater proportion of group C’s (and group B’s) expenditure went on police ‘enforcement’ activities – including ‘gathering, analysing and using intelligence’, ‘disrupting offending behaviour’ and ‘high visibility policing’ – apparently to little avail. Similarly, a higher proportion of expenditure in group C was devoted to measures intended to tackle offending behaviour. Finally group C spent the least resources on ‘stakeholding’ projects to engage the local community, including education, publicity and awareness schemes.

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Table 7.3: Average total and percentage expenditure on burglary reduction measures by project outcome group

Group A

Significant reductions

Group B Non-significant

reductions

Group C Apparent increases

Per cent Total (£000s)

Per cent Total (£000s)

Per cent Total (£000s)

Area-wide SCP 18 109 3 21 1 6 Enforcement 9

55 12 77 13 91

Location-specific SCP

52 311 63 400 61 435

Tackling offender behaviour

6 34 8 49 9 67

Property 5 32 0 1 10 74 Stakeholding 10 62 14 85 5 39 TOTAL 100 603 100 633 100 712 One interpretation that might be placed upon these results is that the least successful projects relied most (though not exclusively) upon interventions intended to influence directly the behaviour and activities of the local offender population, through services delivered by public agencies. Yet, in parallel with the difficulties associated with targeting target-hardening (RV), such targeted ‘intelligence-led policing’ activities were likewise not associated with success in burglary reduction. Equally, though, schemes aimed directly at tackling offender behaviour were also unsuccesful, particularly those projects that supported youth diversion and drug abuse prevention schemes. Of course, the RBI was not intended to evaluate the effectiveness of either of these approaches to crime reduction per se – in this regard much may depend both upon the way that programmes are delivered to their intended recipients and the duration over which they are run. Nevertheless, it would seem that in the context of short- to medium-term burglary reduction, those interventions most likely to be associated with successful projects were those that sought to influence offenders indirectly, largely by increasing the level of ‘private’ security and security-consciousness amongst local residents. Here, eighty per cent of the expenditure of group A was directed towards situational crime prevention delivered to the community, reinforced with ‘stakeholding’ publicity and community-involvement schemes.

Implementation, delivery and problem-solving The experience of targeting suggests that successful burglary reduction is not just a matter of having the resources available for delivering prevention to the community but also about how that delivery is organised and managed. Table 7.4 sets out some aspects of project delivery. First of all, the outcome groups of projects, as a whole, differed little in their overall amount of expenditure or in the proportion of external funds levered in. Indeed, examples of both high- and low-expenditure projects were found in all three groups. Generally, though, many planned interventions were never fully implemented (in the sense of using more than 5 per cent of a project’s total resources) – only about 58 per cent of the total number of planned interventions (116) achieved this level of implementation. Nevertheless, Table 7.4 shows that although the more successful projects (group A) planned fewer interventions, they implemented proportionately more of them than the least successful projects (group C).

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Table 7.4: Number of interventions planned and implemented, leverage of funds and total project expenditure (averages)

Project Number of

interventions planned

Number implemented a

Proportion implemented of planned

Leverage (per cent funding from other sources)

Total expenditure (adjusted)b

(£000s) Group A 5.2 3.3 .65 54 100 Group B 6.0 4.0 .66 56 90 Group C 6.1 2.7 .44 50 105 Total 5.8 3.4 .58 53 98 a Number of interventions attracting at least five per cent of total funding b After deflation and discounting adjustments Though partly with the benefit of hindsight, this suggests that advanced action-planning may have a pay-off – the more successful projects may have been those that put forward proposals that they knew would have a good chance of being implemented, even if this meant limiting the number and range of innovative ideas; in contrast, the less successful projects may have been those that were tempted to suggest a ‘scatter gun’ approach which may have been more innovative but also more speculative or novel to the project. It is likely also that the more successful projects operated more integrated packages of measures with less falling-off of redundant or impractical schemes. As noted above, the more successful projects were more likely to focus most of their effort on disseminating target-hardening within the community, and to reinforce residents’ take-up with stakeholder engagement activities. In contrast, although the target hardening of individual dwellings was a major activity of all projects, the less successful projects were more likely also to have invested in human resource-intensive, service-led activities – especially policing and other operations targeted at potential offenders – which may have borne little relationship to their other activities which were targeted on residents or victims.

But the experience of implementing RV strategies also shows that advanced planning is insufficient by itself to guarantee implementation and outcome success. The experience of some of the successful projects – and some of the least – suggests that the capacity to solve problems and change plans during implementation, when difficulties are encountered, is an equally vital component of success. For example, Figures 7.1 and 7.2 contrast the implementation patterns of two different projects – A3 and C7 – which achieved very different results, the former a significant reduction in burglary, the latter apparently permitting a significant increase (see Table 4.1). Both spent roughly the same amount of resources (£62,000 and £65,000 respectively) and both planned the same number of interventions (4), though A3 implemented one fewer intervention (2) than C7 (3). Both also planned, initially, targeted target-hardening (RV) prevention strategies and both encountered difficulties early on. These were not of the same kind – project A3’s problems were more about encouraging take-up by private residents while project C7’s problems were more about the referral of victimised dwellings and securing the compliance of property owners18. However, prior to the project both areas had experienced a favourable long-term decline in burglary rates; one that A3 seems to have capitalised upon.

18 Project A3 operated in an area of mainly privately-owned properties adjoining a large area of social housing; project C7 was in an area with a high concentration of rental property, including HMOs, student and benefit dependent households. A3 had a largely stable, ageing population, while C7 was becoming ‘studentised’ due to the increasing numbers of students from the large universities within their respective cities moving into the rental accommodation in these areas.

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Figure 7.1: Project A3 - monthly burglaries in SDP area and project progression

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Figure 7.2: Project C7 - monthly burglaries in SDP area and project progression

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A crucial difference between the projects, though (illustrated by the different trajectories of implementation in Figures 7.1 and 7.2), was that, having encountered difficulties, project A3 effectively ceased implementation for a period while it came to grips with new ways and strategies for disseminating a greater level of security amongst the households of its target area. It then initiated a project ‘relaunch’, with different delivery approaches, that the project intended to pursue beyond the duration of the RBI. In contrast, project C7 continued doggedly to implement

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its original targeting plans – though continuing, evidently, to ‘miss’ the target – until its resources had been exhausted, whereupon the project itself ceased to operate.

Project management Project delivery requires three levels of management: these are, from the bottom-up: a practical implementation level; an executive management level; and a strategic, partnership-level. There were examples, in a number of projects, of local, community-based, ‘not-for-profit’ organisations that specialised in installing security and target-hardening measures on an individual dwelling or area-wide basis. Such organisations had usually arisen to provide services for local regeneration programmes and had developed specialist security installation services. There was little difference in the prevalence of these agencies across the three outcome groups of projects; their usual role was as a ‘contractor’ for the provision of services as specified by the project’s executive management. Nevertheless, it was noticeable that three out of the six successful group A projects had particularly active implementing agencies that were not only competent in the services provided but also took a more active role in operational decision-making. For example, both projects A3 and C7 had such agencies but whereas the relationship in C7 was strictly that of ‘customer-contractor’, the operational manager of the implementing agency in project A3 took a much more directive and hands-on approach in deciding where and how implementation should take place – with evident success.

For the other levels of project management, Table 7.5 shows a very clear and marked tendency for the more successful projects (group A) to have:

• a greater number of active partnership agencies involved at the strategic, steering level;

• executive management under the auspices of the local authority.

In contrast, it seems, the more that a project was predominantly police-led the more likely it was not to succeed.

Table 7.5: Project management by outcome group Group A

Significant reductions

Group B Non-significant reductions

Group C Apparent increases

Number of active partner agencies

Three or more 4 2 1 Two 2 4 3 One 0 1 3 Key managing agency

Local authority 5 3 1 Police 1 4 6 a Includes one separately constituted partnership agency. Of course, there were many varieties of project arrangement apparent across the projects and space precludes a detailed organisational analysis that would distil the optimum form of project management. However two main issues deserve some discussion:

• how relationships between organisational levels may be related to project outcomes; and,

• what was problematic about police-led projects.

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Relationships between levels

Albeit with some simplification, it is possible that the most successful project organisations were those where the work carried out at the different organisational levels meshed together in ways that were ‘fit-for-purpose’. Thus, at the strategic partnership level, the involvement of a wider range of partners may have ensured a community-wide agency consensus and mutual ‘ownership’ of the project. In turn, this may have created an informal network of agencies around the project that not only marshalled resources that could be brought to bear but also provided greater support and assistance, especially when projects needed to shift focus. It is possible that the more successful projects were given a greater organisational ‘drive’ and momentum by the underpinning network of support and ‘social capital’ that could be called upon if needed. As such, broader strategic partnerships may be more effective because they are more inclusive, creating a greater range of active stakeholders, jointly committed to a project’s outcomes.

However, it is also likely that too many cooks spoil the broth; at the executive level a single, competent management agency may be needed to turn aspirations into practical tasks. When it comes to actual, executive level project management – especially in the development of effective relationships with contractors and implementing agencies – it is probably still the case that local authority managers and procedures are more effective in commissioning and running projects than are those arising from police organisations. Notwithstanding the fact that the projects described here were carried out under the auspices of local Crime and Disorder Reduction Partnerships – which gives parity between police and local authorities in the task of reducing crime and disorder – in practice, the kinds of intervention attempted during these projects more often resembled the work carried out in urban regeneration and neighbourhood renewal programmes than the ‘crime fighting’ and prevention activities more familiar to the police. As noted, the most successful schemes were those that (a) implemented SCP and environmental improvements in the community, usually requiring the delivery and installation of security measures to a substantial number of households; and/or (b) were embedded in wider regeneration initiatives going beyond the confines of the project and its target area. And while the police expertise may lie in their knowledge about crime, the experience of many of these projects suggests they may be still too little prepared for effective crime reduction project management.

Problems with police-led projects

It is not within the scope of this particular evaluation to ascertain why the projects took the organisational form that they did, especially why one or another agency took on the executive role. The answers to these questions lie in the background to the initial project bids and, in turn, the character of local agency relations in the communities served by the respective CDRPs. Certainly, though, police-led projects (which predominated in the least successful group C projects) were also caught up in a particular double bind. On the one hand, as noted, they were (naturally) more likely to favour enforcement type measures, often human resource-intensive (and therefore costly) police operations, which proved relatively ineffectual in their contribution to burglary reduction; on the other hand, they did not appear to have the capacity or readiness to change or modify plans if they were proving ineffective.

Perhaps not surprisingly, given that the reduction of repeat victimisation has been set as a performance indicator for the police service (Farrell et al., 2000) – or that ‘intelligence-led policing’ has been much encouraged – there may have been some unwillingness amongst police personnel running the projects to question or abandon these pursuits in the face of difficulty or inefficacy. Rather than being able to acknowledge that it may have been the performance indicators themselves that were proving problematic in their practical application, police-led projects may have struggled on regardless, trying to demonstrate their performance against these service-based criteria. Since these particular performance criteria do not bear on local authorities, the latter personnel may have been under less constraint to demonstrate their performance in these particular ways and thus freer to pursue approaches that proved, in the end, to be more effective in reducing local burglary.

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Finally, as many commentators have noted, there are several characteristics of police organisation that act to inhibit practical, operational problem-solving on the ground (Goldstein, 1990). Indeed, the move towards ‘problem-oriented policing’ is recognition that police organisation can be reoriented towards addressing practical crime reduction issues in the community (Read and Tilley, 2000). However, as with the experience of these projects, it would seem that the key to successful problem-oriented policing lies as much in the organisation and management of implementation as it does in focusing police attention on specific crime problems (Irving and Dixon, 2003).

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Summary and conclusions By way of conclusion, the features of projects anticipated at the outset are revisited:

Strategic planning

The chances of success seem to be enhanced if targets and objectives are set in the light of local burglary patterns and trends – what was referred to as the ‘local chemistry of burglary’ (Tilley et al., 1999). However, such ‘chemistries’ would appear to be much more complex – and their dynamics perhaps more difficult to understand – than had been anticipated. In some circumstances, there may already have been a favourable ‘virtuous’ spiral of burglary reduction occurring in the community; here, there would seem to be risks in inadvertently upsetting such trends and making matters worse. The problem for crime reduction projects is therefore knowing when, or even if, to intervene and when to support what might be called the ‘private’ crime prevention activities of the community. Closer attention to pre-existing trends and patterns would seem to be needed, along with greater sensitivity to what might be going on in the community already.

Projects as packages of measures

More successful projects comprised integrated packages of measures in two senses.

• Projects were not isolated either from crime trends occurring in the wider area or from other crime reduction and area-regeneration programmes. Although a number of projects appeared to have a greater impact on their surrounding areas than in their own target areas, this can be seen as a consequence of a wider set of activities, of which the projects were part, than merely their specific burglary-reduction efforts.

• Interventions that mutually supported and reinforced themselves seemed more successful. This was especially noticeable for projects that reinforced their delivery of security and target-hardening with publicity and other ‘stakeholder-engagement’ programmes.

Crime prevention techniques and measures

A significant investment in Situational Crime Prevention measures delivered to the community was more likely to be part of successful projects than either ‘enforcement’ or measures to tackle offender behaviour. This suggest that, at least in the short run, the activities of local offenders are most likely to be addressed indirectly via an increase in the level of private security in the community than through direct intervention by public agencies.

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Crime prevention tactics

A targeted approach – represented here by the strategy of reducing repeat victimisation – would not seem to have been successful, largely because of difficulties in identifying target dwellings, encouraging take-up of preventive measures by victims, and ensuring compliance from those responsible for targeted dwellings. Moreover, risks of inadvertently increasing or re-distributing a community’s burglary may be attached to these targeting failures. The more successful projects were those that abandoned targeting in favour of widening coverage to other actual and potential targets in the community.

Action planning

Because effectiveness depends, first and foremost, on successful implementation, approaches with a lower or known risk of failure during implementation may end up as more effective than more ‘radical’ approaches that contain a greater or unanticipated risk of failure. It may be that the wiser projects chose approaches with which they were familiar rather than the more innovative approaches; though, of course, these need also to have some promise of success (see ‘Crime prevention techniques and measures’ above) rather than merely being consistent with general agency practice and orientation.

Problem-solving

While there has been much attention to ‘problem-solving’ as a method of working out what to do about specific, local crime problems, there seems to have been much less attention paid to taking a problem-solving approach to implementation.19 The more successful projects were also those that were able to take remedial action swiftly and soon when proposed interventions began to experience difficulties in practice. As the examples of RV show, the smarter and more successful projects were those that had the wisdom and/or resolve to look for alternative solutions to emergent implementation problems rather than to persist with their original plans.

Managing delivery

Although all projects were genuine partnerships, and all exemplified the new relationships embodied in local Crime and Disorder Reduction Partnerships, the most effective organisational structure would appear to be one that deploys partner agencies judiciously in ways that are fit-for-purpose. While maximising partner involvement may be best at the strategic level – to maximise local support and commitment – clear lines of responsibility and direction would seem to be needed at executive and implementation levels, which may best be obtained through the established operational procedures of specialised agencies. Here again, the placing of responsibility in the hands of a primary agency is also one that needs to be fit-for-purpose. Since most of these projects in practice more often resembled community regeneration and environmental improvement programmes, it is perhaps not surprising that the most successful projects were co-ordinated and implemented by partnerships between local authorities and regeneration contractors – agencies with some expertise in this work – rather than those that directly involved the police in day-to-day management, albeit that they were focusing directly on the reduction of a specific form of crime.

19 Despite warnings about implementation difficulties in local crime prevention projects made twenty years ago (Hope and Murphy, 1983).

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Appendix 1 Calculating burglary equivalents for other acquisitive crimes

The outcome analysis provided information on the estimated effect of project activities’ on the level of monthly burglary in the target area and surrounding buffer zone as well as on the level of other acquisitive crimes in these two areas. Depending upon the data available, the OAC category included two or more of the following: burglary not in a dwelling, theft from a dwelling, theft from a motor vehicle and theft from a shop or stall. These were considered to be those crimes most likely to be affected by any cross-crime displacement following project implementation.

In order to conduct the cost effectiveness analysis it was necessary to compare and collate the effect of the project on both categories of crime, which required a standard equivalent measure, converting the estimated impact on the OACs category into burglary equivalents. The following sets out the steps undertaken to conduct this conversion.

Using Home Office estimates of the average cost of different crimes, a burglary equivalent factor was calculated for the categories of crime included in the OACs variable, these are shown in table A.1 below. It should be noted that the average cost per crime figures do not match precisely with the categories included in the outcome analysis. In particular, within the Home Office average cost figures there are no separate entries for theft from a vehicle and theft from a dwelling, rather these are included in the overall theft category.

Table A.1: Average cost per crime and burglary equivalents

Crime category Average cost (£) Burglary equivalent Burglary dwelling 2,300 1 Burglary not in a dwelling 2,700 1.174 Shoplifting 100 0.043 Theft (all) 600 0.261 Source: Home Office In order to apply the calculated burglary equivalent factors from table A.1 it was necessary to estimate the proportion of each crime type included in the OAC category. Rather than estimate these proportions for each project individually, the national averages for England and Wales were utilised. As the exact formulation of the OAC category varied for each project, depending upon the data supplied, table A.2 indicates the relative proportions of each crime type dependent upon the number of categories included in the outcome analysis. Where both theft from a dwelling and theft from a vehicle were included in the outcome analysis, the proportions for these two categories are amalgamated into one figure in the final step of the conversion process as only one figure for the average cost of theft is available.

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Table A.2: Proportion of crimes Number of crimes Crime category 1998/99 1999/00 Average proportions Three OAC categories

Burglary not in a dwelling

479,835 463,866 0.328436

Shoplifting 281,972 292,494 0.199931 Theft from a vehicle 685,919 669,232 0.471633 Two OAC categories Burglary not in a dwelling

479,835 463,866 0.41051

Theft from a vehicle 685,919 669,232 0.58949 Four OAC categories

Burglary not in a dwelling

479,835 463,866 0.318553

Shoplifting 281,972 292,494 0.193915 Theft from a vehicle 685,919 669,232 0.457442 Theft in a dwelling 44,75 44,764 0.03009 Source: Home Office Statistical Bulletin 12/00 For each project two OAC equations were estimated; one for the target area and one for the buffer zone. Each of these impacts have been converted into burglary equivalent effects. Table D.3 highlights the calculation that was conducted for an SDP, where the OAC category included only two crime types, namely burglary not in a dwelling and theft from a vehicle.

Table A.3: Example of calculation for project C3 Estimated coefficient from GARCH model

6.939078

Conversion to burglary equivalents 6.939078 (.41051) (1.174) + 6.939078 (.58949) (.261)

4.41

Multiplied by final intensity of project value 0.965 Effect of project on OACs in burglary equivalents

4.3

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Appendix 2 Measuring repeat burglaries Unlike the analysis of burglary trends, the analysis of changes in repeat victimisation requires disaggregated crime data – that is, individual records of burglaries – since it is necessary to collate individual crime records by victim and address in order to count numbers and calculate rates of repeat victimisation occurring over time. Where these data were supplied, the individual burglary records were collated by residential address according to the months during which they were recorded by the police. Since previous experience has shown that the periods during which residents thought that the burglaries could have occurred could be anything up to two weeks long, so a monthly date of recording has been used. For each month, then, there was a count of the number of burglaries occurring at unique, individual addresses during that month. Counting the repeat rate on a monthly basis also achieves consistency with the unit of time used in the analysis of burglary trends.

Repeatedly burgled addresses were identified by hand, to ensure a more thorough check, and to allow for multiple victims at the same address to be identified and removed from the analyses. For example, if a household at which four students lived has been burgled, it would have been counted four times as each individual counted as one ‘household’ (which was one reason why it was necessary to know the names as well as the addresses of victim complainants); however for the present analyses it was counted just once.

The number of repeats thus identified was divided by the total number of burglaries in order to produce a variable which measured the extent to which burglaries were to previous burgled houses. The obvious shortfall with this methodology is that the chances of uncovering repeat burglaries increase with time. This was an especially important problem for the early months of the series as there were either no months or very few months from which to identify repeatedly burgled houses. This problem is particularly acute when seeking to identify changes occurring as a consequence of an intervention because, if the same time-period before and after the intervention is not being compared, the comparison maybe biased, one way or the other.

The solution, therefore, was to divide the data into three periods:

• the period after the intervention had started, referred to as ‘after’;

• the period immediately before the intervention started (consisting of the same number of months as the ‘after’ period), referred to as ‘immediately-before’; and,

• a period before this, referred to as ‘before-before’.

The ‘before-before’ period was the time period during which repeats would have been hardest to identify, and this portion of the data is used only to identify repeats for the ‘immediately-before’ period. The ‘immediately-before’ period (which has a better chance of detecting repeats) was therefore compared with the ‘after’ period in the assessments of the impact of the interventions on the rate of repeat burglaries. Independent samples T-tests form the basis of all data analyses. The burglaries which occurred during the ‘before-before’ period were used as the basis from which to decide which burglaries in the ‘immediately-before’ period were repeat burglaries. The burglaries in the ‘immediately-before’ period were used to identify repeats in the ‘immediately-before’ period and the ‘after’ period.

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This research design is relatively simple and straightforward and is intended to answer one particular question:

• was there a reduction in the rate of repeat victimisation following the introduction of project prevention measures?

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Hope, T., Bryan, J., Osborn, D. and Trickett, A. (2001). The phenomena of multiple victimisation the relationship between personal and property crime risk. British Journal of Criminology, 41, 595-617. Hope, T. and Sparks R. (2000). For a sociological theory of situations (or how useful is pragmatic criminology?). In Hirsch, A.V., Garland, D. and Wakefield, A. (Ed.) Situational Crime Prevention: ethics and social context. Oxford: Hart Publishing. Hope, T. and Murphy, D.J.I. (1983) Problems of implementing crime prevention: the experience of a demonstration project. The Howard Journal, XXII, 38-50. Laycock, G. (2001). Hypothesis-based research: the repeat victimisation story. Criminal Justice, 1, 59-82. Mawby, R.I. (2001). Burglary. Cullompton, Devon: Willan Publishing. Morgan, F. (2001). Repeat burglary in a Perth suburb: indicator of short-term or long-term risk?. In Farrell, G. and Pease, K. (Eds.) Repeat Victimization. Crime Prevention Studies, vol. 12. Monsey, NY: Criminal Justice Press. Pawson, R. and Tilley, N. (1997). Realistic Evaluation. London: Sage. Pease, K. (1998). Repeat Victimisation: Taking Stock. Police Research Group Paper 90. London: Home Office. Read, T. and Tilley, N. (2000). Not Rocket Science?: problem-solving and crime reduction. Crime Reduction Research Series Paper 6. London: Home Office. Russell, N., Bryan, J., Trickett, A., Hope, T., Crawley, E., Crawley, P. and Gill, L. (2002). Cost-Effectiveness Analysis of Strategic Development Projects in the Burglary Reduction Initiative (Phase I) in the Yorkshire and the Humber, East Midlands and East Regions. Report to the Home Office. Sherman, L., Farrington, D.P., Welsh, B.C. and MacKenzie, D. (Eds.) (2002). Evidence-Based Crime Prevention. New York and London: Routledge. Smith, M.J., Clarke, R.V., and Pease, K. (2002). Anticipatory benefits in crime prevention. In Tilley, N. (Ed.) Analysis for Crime Prevention. Crime Prevention Studies, Volume 13. Monsey, NY: Criminal Justice Press. Tilley, N., Pease, K., Hough, M. and Brown, R. (1999). Burglary Prevention: Early lessons from the Crime Reduction Programme. Policing and Reducing Crime Unit, Crime Reduction Research Series Paper 1. London: Home Office.

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