NEW AND OLD MEASURES OF THE FEAR OF CRIME A MULTILEVEL ASSESSMENT OF MEASURES OF INTENSITY AND FREQUENCY Ian Brunton-Smith: University of Surrey

Outline Introduction –Measures of fear –Neighbourhood influence –Multilevel modelling Area structure –Local focus within a national framework –Initial results- area differences? Expanded models –Incorporating neighbourhood data –Preliminary findings Summary

Current research The influence of local area differences on fear of crime –British Crime Survey individual level data Inclusion of area based measures to characterise neighbourhood difference –Geodemographic data from administrative sources –Local neighbourhood focus within a national survey context Examine the frequency, as well as intensity of personal fear –New questions- alternative look at fear of crime (Farrall, Jackson and Gray)

INTENSITY “How worried are you about being ‘mugged or robbed’?” Very worried, fairly worried, not very worried, or not at all worried? Use of ‘how’ is leading- suggestive that all people experience worry No specific timeframe Difficulty of summarising emotions Fail to distinguish specific events from generalised anxieties Measuring fear of crime: Introducing the frequency of fear FREQUENCY Q.1 “During the last 12 months, have you ever felt worried about being ‘mugged or robbed’?” Yes, no Q.2 “If yes, how many times have you felt like this in the past 12 months?” Relate to actual experiences (not opinions/attitudes) Refer to a specific time frame Use of a filter question reduces overestimation Distinguish between low level emotional states and intense but transitory reactions to specific events (Farrall et al, 2006)

Differences in the prevalence of fear of crime Clear differences in overall estimates of fear by crime type Reduced estimates of fearful population with frequency measures Intensity (Very worried)Frequency (Once a month or more) Mugging 34.0% (10.5%)13.5% (3.3%) Burglary 45.3% (12.2%)30.0% (7.5%) BCS 2003/04

Local area focus? “Increasing awareness of the importance that local area characteristics can have on individual outcomes” (Ellen and Turner, 1997) –Policy interventions –Understanding social processes Growing focus on the impact of local area influence on fear of crime –Recorded crime rates... –Incivilities Recent availability of Geodemographic identifiers on British Crime Survey Increasing use of multilevel analysis techniques –Incorporate context in individual level analysis

Multilevel modelling Individuals often subject to the influences of groupings (pupils within schools, individuals within neighbourhoods) –Individuals within an area will be more alike, on average, than they will be alike those of another area (dependency) Estimate neighbourhood based models within individual level analysis –Relative contribution of individual and area to unexplained variation in dependent variable Area variables included at correct level of influence –Avoiding problems of aggregation or disaggregation

Data BCS 2003/04: Sub-sample of respondents (n=4,000) asked both question types 2 crime types used, matching ‘worry’ questions Truncated to more closely resemble intensity measures INTENSITYFREQUENCY How worried are you about...During the last 12 months, have you ever felt worried about... If yes, how many times have you felt like this in the past 12 months?...being mugged or robbed?...having your home broken into and something stolen?...having you’re your home broken into and something stolen? No worry (not at all/not very) No, never worried Fairly worried Yes, up to once a month (1-12 reported episodes) Very worried Yes, more than once a month (13+ episodes)

Ordered multilevel regression modelling Proportional odds Underlying Linear threshold model- –Conservative estimate of area variance Reduced model assumptions –Improved estimates and standard errors –Particularly important for area component (typically underestimated when normality assumption violated) Logit link function –Odds of moving into a higher fear category, controlling for the influence of the other covariates

Local area structure MSOA –Clusters of census output areas –Account for population size, area proximity and social homogeneity –Average population 2,500 households –Spatially and statistically stable –6,781 in England Sample nesting –2,046 MSOA –Max 8 respondents per MSOA –Limits model complexity Individual MSOA

Results I: Existence of area differences Individual level controls –Gender; Age, Education; SEC; Ethnicity; Length of time in area... –Direct victimisation experience Significant impact of local area differences on fear of mugging –Both intensity and frequency No significant area component to burglary –No further examination required Intensity of fear Frequency fearful events Variance (S.E)Percentage of total variance Variance (S.E)Percentage of total variance Mugging.244(.078)6.9%.240(.130)6.8% Burglary.048(.059)1.4%.050(.070)1.5% BCS 2003/04

Method Area level measures –Neighbourhood data from Census and National Statistics –Range of neighbourhood characteristics identified Disadvantage; household type; housing details; land usage; population structure –Multicollinearity problem Factorial ecology “Components (factors) represent concise descriptions of patterns of associations of attributes across observations” (Rees, 1971: 221) –Identify key contextual attributes –PCA at neighbourhood level, based on data for all MSOA –4 factors extracted- 80% of total variance

Socio-Economic Disadvantage – % income support; % unemployed; % lone parents; % managerial; % car owners; % terraced housing; % local authority owned Urbanisation and overcrowding – Pop density; % flats; % people per room; % agriculture; % green-space; % domestic property Transition Communities – % in-migration; % out-migration; % vacant properties; % single-person non-pensioners Age Structure – % younger residents (<16); % older residents (65+); % owner occupied Method II: PCA Extracted Factors

Method III: Additional Area Characteristics Ethnic Diversity (Taylor and Hudson, 1972) –Herfindahl concentration formula = –Si is the share of group i out of a total of n groups –Probability that two randomly selected individuals from the same area will be of different ethnic origin Interviewer rating of area –Measure of low level area disorder –Housing quality; extent of litter; vandalism and graffiti Crime Index (from IMD) –Incidence of recorded crime for 4 major crime types –Burglary (4 offence types) –Theft (5 offence types) –Criminal Damage (10 offence types) –Violence (14 offence types)

Results II: Mugging and robbery Neighbourhood level of socio-economic disadvantage, and low level disorder associated with intensity of fear, but not frequency Recorded crime associated with frequency of fearful episodes, but not intensity of fear Ethnic fractionalisation, urbanisation and extent of transition associated with both intensity and frequency Intensity measureFrequency measure Odds RatioPrecision Odds RatioPrecision Socio-Economic Disadvantage1.11* Urbanisation and overcrowding1.23* * 2.79 Transition Community.90* * 2.29 Age Structure Ethnic Diversity2.09* * 3.38 Low-level disorder1.14* Recorded crime * 2.61 Remaining Area Variance4.7%*3.5% BCS 2003/04 * Sig p(<.05)

Summary Incorporating local neighbourhood information can provide new clues about what does and doesn’t influence fear of crimes Neighbourhood people live in makes an important contribution to fear of mugging, but has no significant influence on levels fear burglary –Extent of area variation consistent when considering frequency and intensity of fear Clear differences in the neighbourhood influences on mugging when looking at the frequency of fear compared to intensity –People’s intensity of fear more influenced by external factors Low level neighbourhood disorder, and increasing socio-economic disadvantage No significant association with recorded levels of crime –The frequency of recalled fearful incidents more directly affected by crime Positive relationship with recorded crime figures