1. Violence, Weapons and Fear in Crime Surveys Dr Simon Moore & Dr Iain Brennan Violence & Society Research Group Cardiff University

2. 2 Overview What are the long-term effects of injury on well-being? –Adaptation to change (lottery wins, crime states) Measuring & Predicting Fear of Crime –How safe do you feel walking in your neighbourhood at night? The Cost of Fear –Cost effectiveness of interventions Fear and Weapon Carrying –Does fear motivate harm avoidance through self-defence

3. 3 Well-being British Cohort Study Began in 1970 –data were collected about the births and families of babies born in the UK in one particular week (n = 17,415). All but one National Health Service hospital in the UK took part –age 34 years data contains seriousness of injury (hospitalisation) and time since injury H: What are the long-term effects of injury on well-being?

4. 4 Results Ordered logistic regression (SEs adjusted for 11 clusters in government office region) –N = 1,512 More life satisfaction: –Income (log income, β = 0.14, z = 2.03) Less life satisfaction –Victim of vandalism (β = -0.33, z = -2.20) –Poor health (β = -0.38, z = -3.30) Second order polynomial of time since accident –Satisfaction = -0.34(time) + 0.013(time²)

5. 5 British Cohort

6. 6 Implications The long-term negative effects of serious injury on well-being reduce as time passes Is the same true for the effects of crime on well- being? Essential to know in the case of understanding the intangible costs of crime British Crime Survey includes good data on the nature of victimisation but, critically, lacks temporal element Paul Dolan, Joanna Shapland, Aki Tsuchiya, Chris Cox –Bespoke survey to consider long-term intangible costs of crime –Access existing BCS respondents

7. 7 Fear Fear is a powerful motivator of behaviour (discussed more later) Are women more fearful of crime than men? Vulnerability hypothesis – that women are more vulnerable (through less physical strength compared to men) But not all crimes involve physical strength –H1: the dimensions of fear –H2: gender and fear –H3: explore other predictors

8. 8 2001/2 and 2002/3 British Crime Survey crime-specific worry about questions and one global how safe do you feel walking in your neighbourhood at night? question. For both sets of questions respondents choose one of four options for each question ranging from very worried to not very worried at all

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11. 11 Being female predicts Fear of Personal Harm, no gender differences on Fear of Loss Graffiti predicts both fear types

12. 12 The ubiquitous FOC question how safe do you feel walking in your neighbourhood at night? most associated with fear of physical harm –Women more fearful of personal harm but no gender difference for fear of personal loss –Vulnerability only half the story Gender difference in FOC research due to inappropriate FOC instrument Rubbish, graffiti & litter all increase FOC –Cost effective intervention to reduce FOC? Past victimisation increases FOC –But do people adapt?

13. 13 Cost of Fear Cost-effectiveness of interventions to reduce FOC require knowledge on the value of FOC Attempt to shadow price FOC European Social Survey: –Happiness –FOC –Income –Control variables

14. 14 Shadow pricing: –Income Happiness –FOC Happiness –What is the compensating differential in income for the transition from no FOC to FOC? u is a measure of happiness, A is a constant, Y is a measure of income, S i are dummy variables for factors associated with happiness (i.e., FOC) and the X vector includes other variables that are known to influence happiness (e.g., age), ε is an error term that subsumes individual variation

15. 15 Happiness 0.587(log income) - 0.223(FOC*) –*binary variable denoting no fear and some or more fear Shadow price of FOC = 13,538 Since replicated by Mark Cohen (The Effect of Crime on Life Satisfaction) who found the value of FOC to be $34,322

16. Predictors of weapon carrying & weapon victimisation Violent offenders report self-defence as primary motivation for weapon carrying (Brennan, 2007) BCS: How often do you carry a weapon for protection? –Reduced to binary variable –Small change in wording over years 2002/3-2007/8 BCS data sets combined –Waves combined to overcome rarity of weapon data –n=17,959 subset asked about weapons

17. Predictors of weapon carrying & weapon victimisation H 1 : Weapon carrying influenced factors related to self defence –previous victimisation –fear of harm –lack of confidence in CJS (state protection)

18. Weapon Carrying BetaS.E. Worried about Mugged0.13*0.05 Attacked0.19***0.05 Recent violent victimisation (12 mth)0.56**0.17 Recent threats (12 mth)0.54***0.12 Male-0.54***0.08 Age-0.03*0.01 Age squared0.0003*0.0001 Lack of confidence in CJS0.24***0.04 Year (Sweep)-0.07**0.03 Observations17,756 ROC0.65 Clustered by police force area

19. Findings support fear hypothesis. –This may be linked to criminal lifestyle (Du Rant, 2004), but BCS lacks offending-related items –Possible to combine BCS with offence data? Weapon carrying influenced as much by fear of, and previous, victimisation as demographics Women more fearful of personal harm, more likely to carry a weapon? Despite recent suggestions of rises in weapon carrying, likelihood decreased in recent years Panel survey to determine causality more effectively? Offending & victimisation overlap –What predicts being a victim of weapon crime…

20. Predictors of weapon victimisation For all forms of contact victimisation (burglary, violence, robbery, etc.) - Did the person have a weapon? – related to any type of crime 2002/3-2007/8 data sets (any victims: n=27,681) Hospital admissions increasing for weapon violence (Maxwell et al., 2007) H 1 : Weapon victims deprived, young males

21. Weapon Victim BetaS.E. Male0.39***0.05 Age-0.01**0.001 Age squared-0.004***0.0001 Recent violent victimisation0.77***0.05 Recent threat of violence0.37***0.04 Own area-0.18**0.07 Sweep-0.020.02 Qualificationns Household incomens Observations27,655 ROC0.65 Clustered by police force area

22. As with most violence, males more likely Repeat victimisations, threats and victimisation away from neighbourhood increase risk –Territory/gangs? Surprisingly little effect of age –Different crimes over the lifespan? Young deprived men: reactive violence Older: instrumental/coercive violence Knife crime is not helpful as a catchall term –Should focus on the crime not the weapon, e.g. Predictors of burglary more coherent than predictors of knife crime

23. 23 Summary Fear is an important facet of life –substantial deleterious effects on well-being –Motivates avoidance/self-preservation behaviour Estimated cost of fear, in terms of annual household income are high suggesting interventions (removing graffiti) could be cost- effective FOC might be adaptive (get used to fear or return to baseline following victimisation) suggesting an important temporal element in understanding the intangible costs of crime

24. 24 The relationship between fear and serious violence is frequently overlooked. The BCS, in combination with more experimental work with weapon carriers offers the opportunity to monitor and better understand this relationship What drives fear may also drive weapon carrying –Media? –Subcultures of violence (if everyone thinks everyone else is carrying a weapon then better off carrying a weapon) –If clearing up graffiti reduces FoC would it also reduce weapon carrying?

25. Thanks for listening! Simon Moore Iain Brennan Violence Research Group, Cardiff University mooresc2@cardiff.ac.uk www.vrg.cf.ac.uk/scm.html