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Plenary I Policing and crime prevention: exploring the relationships between deterrence, fairness and effectiveness Susan McVie (Edinburgh) Falling crime:

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Presentation on theme: "Plenary I Policing and crime prevention: exploring the relationships between deterrence, fairness and effectiveness Susan McVie (Edinburgh) Falling crime:"— Presentation transcript:

1 Plenary I Policing and crime prevention: exploring the relationships between deterrence, fairness and effectiveness Susan McVie (Edinburgh) Falling crime: Fact or Fallacy?

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3 Police recorded crimes and offences in Scotland since 1930 Source: Scottish Government (2009) Police Recorded Crime in Scotland 2008/09

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5 Police recorded crimes and offences in Scotland since 1930 Source: Scottish Government (2009) Police Recorded Crime in Scotland 2008/09

6 Crimes recorded by the police in Europe Source: Eurostat Crime and Criminal Justice Database (Accessed March 2014 at eurostat.ec.europa.eu)

7 The great crime drop of the 1990s The US witnessed an ‘unprecedented fall’ in violence, which saw levels of crime parallel with the 1960s (Blumstein & Wallman, 2005) ‘For a decade from the early to mid-1990s many countries witnessed the most major and widespread drops in crime they had ever experienced’ (Tseloni et al, 2010: 375) A variety of hypotheses have been put forward for falling crime, but few have been tested empirically or internationally. No consensus on the main drivers for the decline or the effect these have had on crime.

8 Problems of researching the crime drop Most of the analysis has focused on the period of decline The effect of certain factors on aggregate crime measures may change over time Little simultaneous modelling of multiple effects Analysis is hindered by a lack of data and too short timescales Underlying problem of change in crime versus change in police activity

9 AQMeN programme of research Aims to 1.Explore factors associated with the crime drop at the national level 2.Examine trends and patterns in crime at different levels of geography (police force area, local authority, datazone, local communities). 3.Identify changes in the pattern of victimisation over time 4.Identify changes in the pattern of offending over time

10 Introduction of SCRS 1. Exploring the crime drop at the national level (Humphreys, Francis and McVie, forthcoming)

11 Four groups account for 80-85% of crime

12 Factors associated with change from NS Crimes of violence Crimes of dishonesty Motor vehicle offences Miscellaneous offences Average daily prison population 5% ↓ Certainty of sanction* 3% ↓8% ↓4% ↓ Number of offenders in custody* 8% ↑1% ↑ Average sentence length* 6% ↓3% ↓ Police headcount 2% ↓ Change in GDP 4% ↓ Number of sequestrations 0.1% ↓ Average alcohol consumption 12% ↑4% ↓11% ↑14% ↑ Voter turnout 5% ↑ Note: Only significant variables shown; * refers to within crime/offence type.

13 Two time dependencies found NS Crimes of violence Pre 1994Post 1994 Police headcount 2% ↓1% ↓ Number of offenders in custody* 12% ↑5% ↑ Crimes of dishonesty Pre 1999Post 1999 Number of sequestrations 0.05% ↓0.04% ↑ Voter turnout 11% ↑12% ↓ Average alcohol consumption 11% ↓4% ↑

14 2. Examining crime at different levels of geography (Bates, Bannister and Kearns, forthcoming) Analysis was undertaken to examine crime rates across a large urban area within Scotland (882 datazones) Lowest level of publically available crime data from Scottish Neighbourhood Statistics includes only five crime types contained in the SIMD and only for 3 time periods. Group Trajectory Analysis (a type of latent class analysis) was used to identify distinct groups of datazones following different ‘crime trajectories’

15 Group Trajectory Model - 8 class model Trajectories of Mean levels of Crime in each Group

16 Average Crime drop per group (where crime fell)

17 Boundary Data: Contains Ordnance Survey data © Crown copyright and database right An Ordnance Survey EDINA supplied service. Crime Data – Police recorded crime aggregated to datazones - SIMD Crime s only © Crown copyright 2014 – source

18 Contribution to the crime drop from High, Medium and Low Level Areas

19 Proportion of datazones where crime actually fell

20 3. Changes in the pattern of victimisation over time (Pillinger, Norris and McVie, forthcoming) Victim survey data provides an alternative to police recorded crime statistics. Scottish crime surveys, since the 1990s, provide long term trends in victimisation Does it show a similar crime drop to the police recorded crime statistics? If so, does the crime drop reflect a reduction in victimisation across the board?

21 How much crime in Scotland? The Scottish Crime Surveys Source: Scottish Government (2014) Scottish Crime and Justice Survey 2012/13: Main Findings, p15

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24 Latent class analysis of victim groups Aim is to explore change in prevalence, incidence and crime mix over time Uses combined survey data from nine sweeps Crime mix analysis is restricted to four crime groups: – Vehicle crimes – Property crimes – Assaults & threats – Personal thefts No explanatory variables Prevalence over time calculated from fitted class membership probabilities for each individual Incidence over time calculated from fitted class membership probabilities and actual incidences (capped at 4) for each individual

25 Four distinct classes of victim

26 Changing prevalence of victimisation - 1

27 Changing prevalence of victimisation - 2

28 Changing incidence of victimisation One-off victim classRepeat household victim class Chronic personal crime victim class Incidence of motor vehicle theft ↓↓ - Incidence of household crime ↓ ↓ - Incidence of personal theft/robbery -- ↑ Incidence of assault and threats ↑↑↑↑

29 Does a change in victimisation explain the crime drop? The crime drop is partly explained by change in prevalence – The probability of being a non-victim has increased – The probability of being a one-off or repeat household victim has decreased But… – No significant reduction in probability of being a chronic victim of assault, threats and personal crime The crime drop is partly explained by change in incidence – Total number of crimes amongst one-off and repeat household victims has decreased – Average number of incidents of most crime types amongst one-off or repeat household victims has decreased But – There is no significant reduction in incidence of crime amongst chronic victims of assault, threats and personal thefts

30 4. Changes in the pattern of offending over time (Ben Matthews, Susan McVie)

31 Changing incidence of conviction by gender

32 Changing incidence of conviction by age

33 Far fewer young people are being convicted Rate per 1000 people proceeded against in Scottish courts, by age

34 And far fewer young people are being reconvicted Reconviction frequency rate by age

35 There’s virtually no change in the proportion of first time offenders or persistent offenders

36 The younger you are at first conviction, the more likely you are to be reconvicted

37 Change over time in the age of conviction (Ben Matthews, PhD student) Age Rate per 1000,000 population

38 A dramatic fall in youth crime Source: SCRA Online Statistics Dashboard, Number of people aged 8-15 referred to the Reporter on offence grounds

39 Number of 16 and 17 year olds with charges proved in court Average daily prison population of prisoners in Scotland Sources: Criminal Proceedings in Scottish Courts Statistical Bulletins & Prison Statistics Bulletins

40 Falling crime: Fact or Fallacy? Strong evidence to suggest that there is a real crime drop in Scotland, but it is not evenly spread across crime types, geographical locations or population groups, and there is no global explanation For some people and places – especially those at most risk - crime has remained persistently stubborn and inequality has increased The real good news story is falling youth crime – behavioural change is impossible to measure, but the crime drop appears to be mainly the result of reducing prevalence of offending The evidence suggests that keeping individuals out of the ‘system’ is more beneficial than what you do with them within it


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