Criminal Victimization and the Quality of Life

ALEX C. MICHALOS and BRUNO D. ZUMBO

CRIMINAL VICTIMIZATION AND THE QUALITY OF LIFE ?

(Accepted 19 January, 1999)

ABSTRACT. The aim of this investigation was to explain the impact of crime-related issues on satisfaction with the quality of life, satisfaction with life asa whole and happiness in the city of Prince George, British Columbia. As ex-planatory variables, we had measures of respondent fears of and actual cases ofvictimization, Indexes of Neighbourhood Problems, Police Performance, Neigh-bourhood Worries, Defensive Behaviour, beliefs about increases in local crime,satisfaction with personal and family safety, and satisfaction with a variety ofdomains of life (e.g., friendships, financial security, health). Collectively suchvariables could explain only 5% of the variation in happiness scores, 7% of thevariation in life satisfaction scores and 9% of the variation in satisfaction withthe quality of life scores. However, they could explain 38% of the variation inoverall neighbourhood satisfaction scores. When measures of satisfaction withfamily life, health, self-esteem, etc. were added, we found that crime related issueswere simply displaced by the other measures and that we could explain 31% ofthe variation in overall happiness scores, 58% of the variation in life satisfactionscores and 59% of the variation in satisfaction with the overall quality of lifescores. We conclude, therefore, that crime-related issues have relatively little im-pact on people’s satisfaction with the quality of their lives, with life satisfactionor happiness here.

INTRODUCTION

The aim of this investigation was to explain the impact of crime-related issues on satisfaction with the quality of life, satisfactionwith life as a whole and happiness in the city of Prince George,British Columbia. In city surveys conducted in June 1994 andJune 1997, the reduction of crime was among the top three thingsrespondents recommended to improve the quality of life here

? The authors would like to express their appreciation to Toni Fletcher, JoanneMatthews, Gordon Molendyck, Shelley Rennick, Wayne Roberts, Anita Hubleyand John Shultis for the help they gave us in the development, administration,analysis and reporting of the survey.

Social Indicators Research50: 245–295, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

246 ALEX C. MICHALOS AND BRUNO D. ZUMBO

(Michalos and Zumbo, 1999). Accordingly, we decided to conduct asurvey to examine these relationships in greater detail. As explana-tory variables, we had measures of respondent fears of and actualcases of victimization, indexes of neighbourhood problems, policeperformance, neighbourhood worries, defensive behaviour, beliefsabout increases in local crime, satisfaction with personal and familysafety, and satisfaction with a variety of domains of life (e.g.,friendships, financial security, health).

It is not an exaggeration to say that virtually every list of socialindicators produced in the last 30 years purporting to provide acomprehensive set capable of capturing all important aspects ofthe quality of life of a population has included some measures ofcrime or personal safety (Michalos, 1980, 1992). However, there arerelatively few studies that systematically try to connect individual-level criminal victimization measures to measures of happiness, lifesatisfaction or satisfaction with the overall quality of life. Indeed,we have searched very carefully through over 6000 abstracts andso far we have found no publications which explicitly try to makeprecisely these connections. Although we are continuing our searchfor predecessors, what we have found to date are the following.

In their classic study, Andrews and Withey (1976) measuredAmericans’ satisfaction with their safety and security from theft,and found correlations among these measures and life satisfactionrunning from 0.24 to 0.43 in four national surveys in 1972 and 1973(p. 113). However, subsequent multivariate analyses revealed thattheir safety and security measures had no explanatory power forlife satisfaction in the context of a wide variety of other variables(pp. 122–142).

Hartnagel (1979) found that Edmontonians’ feelings of safety intheir neighbourhoods were negatively associated with their satisfac-tion with their neighbourhoods.

Silverman and Kennedy (1985) combined data from the Winni-peg and Edmonton Area Studies of 1981 to explore the hypothesisthat fear of crime and some sort of satisfaction (e.g., with life as awhole, withne’s neighbourhood or friends) are reciprocally related.They had two measures of satisfaction, a single-item measure aboutlife as a whole and a six-item measure combining responses tosingle items about satisfaction with “non-work activities; family

CRIMINAL VICTIMIZATION AND THE QUALITY OF LIFE 247

life; health; amount of time to do what one wants to do; friend-ships; standard of living; and job”. Fear of crime was measuredby responses to the question “How safe do you feel walking aloneat night?” Using several path analytic models, they concluded that“The direction of the relationship between [life] satisfaction and fearis in the predicted direction but not significant. The relationship isconsistent with a hypothesis that suggests that more satisfied indi-viduals probably have more to lose in becoming a victim of crimeand, therefore, express more fear” (Silverman and Kennedy, 1985:p. 10).

Ross (1993: p. 159) claimed that

Garofalo and Laub (1978) develop, but do not test, the idea that fear of crimeaffects the subjective quality of life. Moore and Trojanowicz (1988) speculate,but present no evidence, that fear ‘produces a loss in personal well-being’ (p. 3):fear makes people feel vulnerable, isolated, and anxious; it means that people stayindoors instead of enjoying a walk to the grocery store, school or work.

Then Ross used some survey data to show that fear of victimization,distress and inactivity had a negative impact on people’s subjectivelymeasured health.

Norris and Kaniasty (1994) measured the impact of victimizationfor those suffering violent (N = 105) and property crimes (N = 227)at 3, 9 and 15 months after the incidents, and found that

Across time, crime victims, especially violent crime victims, experienced levels ofsymptoms much higher than those of nonvictims. Initially, crime victims showedclear signs of recovery, that is, they showed declines that could eventually ‘return’them to a state of well-being comparable to that possessed by nonvictims. For themost part, however, these declines leveled off between [time 2 and time 3], thusproviding little evidence that crime victims would thereafter continue to improve.

Their symptom measures included measures of depression andanxiety which are generally negatively correlated with measures oflife satisfaction and happiness (Michalos, 1991).

For a self-selected group of people who “were experiencingcrime-related problems”, Davis, Taylor and Lurigio (1996) foundthat their “perceptions of the meaningfulness of the world [was]the best predictor of adjustment” following criminal victimization.The outcome measures used in this study included the Derogatis(1975) Affect Balance Scale, which is a type of overall subjectivewell-being measure.


In summary, then, our purpose was to investigate the relativelyunexplored relation among individual-level criminal victimizationand happiness, life satisfaction, and satisfaction with the overallquality of life.

SAMPLING TECHNIQUE AND QUESTIONNAIRE

Two thousand 12-page questionnaires were mailed to a simplerandom sample of Prince George’s 34 000 households in November1997. Although some research (Fischer, 1981) has shown that fearof victimization has been “a primary cause of nonresponse in urbansurveys”, we prefer surveys over personal interviews because theformer are much less expensive and provide better protection forconfidentiality. Moreover, Warr (1987: p. 32) claimed that “18% of[his Seattle] respondents reported that they refused to answer theirdoors due to fear of victimization”.

The first nine pages of our survey contained closed-type ques-tions usually in Likert formats about some aspect of crime or crimi-nal victimization. Broadly speaking, there were questions aboutperceived increases in crime, feelings of safety in respondents’neighbourhoods, worries about becoming a victim, perceived neigh-bourhood problems, respondents’ crime-related defensive beha-viours, the possession of guns and other potential defensiveweapons, respondents being victims of crime, relatives and closefriends of respondents being victims, kinds of victimization, reasonsfor not reporting crimes to police, relative harmfulness of variouskinds of crime, and satisfaction with police performance. Thesewere followed by one page of questions about respondents’ satis-faction with particular domains of their lives (e.g., their familyrelations, jobs and health), their overall happiness and satisfactionwith the overall quality of their lives. The questionnaire ended withtwo pages of demographic questions.

SAMPLE CHARACTERISTICS

By the end of December 1997, 737 (37%) useable questionnaireswere returned, which formed the working data-set for the survey.


Of the 730 respondents who identified their gender, half (365) werefemales. Sixty-one percent (443) of those who answered the maritalstatus question were now married and living with their spouse. Theaverage age was 45, and the range ran from 18 to 88 years. Fivehundred and eighty-two (80%) owned or were in the process ofbuying their home. One hundred and sixty-seven (23%) had someuniversity education, with 116 (16%) holding a university degree.The first language of 656 (89%) respondents was English. Fourhundred and thirty (59%) were employed full-time and 80 (11%)part-time. Two hundred and sixteen (31%) had total family incomesof $40 000 to $69 999, with the median income in the $50 000 to$59 999 category.

According to the 1996 census, which is the last official full popu-lation count available from Statistics Canada, the average age ofPrince George residents over 18 years of age was 41, 49% of thecity’s adult residents were female, 50% were married and livingwith their spouse, 17% had some university education, 9% held auniversity degree, 66% were employed and 87% had English astheir first language. About 33% of our population is in the 1–19age group, 34% in the 20–39 age group and 33% in the 40 orolder group. The 1995 median family income for Prince George was$56 125. Broadly speaking, then, our working sample is a bit older,and has a few more married and university educated people than thepopulation from which it was drawn.

PERCEIVED CRIME INCREASES AND FEAR

Exhibit 1 shows the percentages of respondents affirming that, com-pared to two years ago, crime increased, remained the same ordecreased in their neighbourhood, the city, Canada and in localschools. The first thing that strikes one inspecting this exhibit is theapparent ego-centric bias (Michalos, 1995) regarding people’s judg-ments about their own neighbourhoods compared to everywhereelse. Just as people typically report that, for example, there is adeterioration in health care all over the country but the care theyget from their own physicians is fine, only 41% of our respond-ents thought that crime had increased in their own neighbourhoodsalthough almost twice as many (78%) thought it had increased in


Exhibit I

Percentages of Respondents Saying that Crime Increased, Remained the Sameor Decreased Compared to Two Years Ago

Increased Remained Decreased Don’t know

the Same

Your neighbourhood 41 45 4 9

This City 78 16 1 5

Canada 64 20 4 12

Local Schools 74 13 2 12

the whole city, 74% thought it increased in local schools and 64%thought it increased in Canada. In fact, according to the most recentreport of the Ministry of the Attorney General for the Province ofBritish Columbia (1997: p. 130), the official crime rate per 1 000residents in Prince George decreased every year from 1993 to 1996,and according to the Canadian Centre for Justice Statistics (1996:Table 3.1) the official crime rate per 100 000 inhabitants in Canadadecreased every year from 1992 to 1996. Although, official crimerates are notoriously problematic (Michalos, 1980a) and practicallyunrelated to news media reports on which people base their assess-ments (Garofalo, 1981), they cannot be dismissed as irrelevant oruseless.

While 25% of our respondents said that they never walked alonein their neighbourhoods at night and another 27% said that theyrarely did so, 34% said they occasionally did so and only 14% did soregularly. Following up this question, we asked them howsafetheyfelt or how safe they would feel walking alone in their neighbour-hoods after dark. Only 8% reported that they would feel very safe,but 46% said they would feel reasonably safe. Thirty-four percentsaid they would feel somewhat unsafe and 11% would feel veryunsafe. The correlation between responses to the behavioural ques-tion about walking alone and the attitudinal question about feelingsof safety walking alone was 0.60, which was a bit above average formost behaviour/attitude correlations.


Exhibit II

Mean Scores of Perceived Neighbourhood Problems

Misbehaving youth 4.5

Drug or alcohol abuse 3.5

People loitering on the streets 3.2

Neighbourhood drug trafficking house 2.7

Run-down buildings and houses 2.0

0 = not at all a problem 10 = a big problem

NEIGHBOURHOOD PROBLEMS AND CRIME-RELATED WORRIES

Pursuing the neighbourhood safety issue more precisely, respond-ents were asked to rate the degree to which they perceived fiveneighbourhood problems on a scale running from ‘0’ indicating ‘nota problem at all’ to ‘10’ indicating ‘a big problem’. Then anIndex ofNeighbourhood Problemswas created by calculating each respond-ent’s average score on the five items. The average score for the 734individuals who responded to the questions used in the constructionof this index was 3.2, the median was 2.6 and the range ran from0.00 to 10. The Cronbach reliability coefficient alpha for the Indexwas 0.89. Exhibit 2 lists the items and their mean scores. The biggestperceived neighbourhood problem was misbehaving youth, with amean score of 4.5, and the smallest problem was run-down buildingsand houses (2.0).

Pursuing the perceived personal impact of general feelings aboutsafety and neighbourhood problems even more deeply, respondentswere asked to rate the degree to which they worried about becominga victim of six kinds of crimes on a scale running from ‘0’ indicating‘never worry’ to ‘10’ indicating ‘worry a great deal’. Then anIndexof Crime-Related Worrieswas created by calculating each respond-ent’s average score on the six items. The average score for the 735individuals who responded to the questions used in the constructionof this index was 3.9, the median was 3.7 and the range ran from0.00 to 10. The Cronbach alpha for the Index was 0.84. Exhibit 3lists the items and their mean scores. Respondents’ biggest worrywas that a thief would break into their home, with a mean score of


Exhibit III

Mean Scores of Crime-related Worries

A thief will break into your home while you are away. 5.6

Someone will steal your coat when you have left it somewhere 4.4unattended.

Someone will assault you. 3.9

A thief will break into your home while you are home. 3.7

Someone will use a weapon to take something from you by force. 3.5

Someone will cheat or con you out of a large amount of your money. 2.0

0 = never worry 10 = worry a great deal

5.6, whereas their smallest worry was that someone would cheat orcon them out of a large amount of money. Hale, Pack and Salked(1994) remarked that this sort of a measure of fear of crime wasmore useful than those related to street crime because there are manymore household and other property crimes than street crimes.

DEFENSIVE BEHAVIOUR

Having explored respondents’ worries, we wanted to see if therewere any particular behaviours or activities that might be relatedto these worries. So, anIndex of Defensive Behaviourwas createdby adding each respondent’s scores on eight items with the dicho-tomous response categories ‘yes = 1’ and ‘no = 0’. The mean scorefor the 736 individuals who responded to the questions used in theconstruction of this index was 4.1, and the range ran from 0.0 to8.0. The Cronbach alpha for the Index was only 0.51. Exhibit 4 liststhe eight items and the percentages of respondents indicating eachresponse.

By far the most frequently used defensive behaviour was thesimple act of locking one’s doors when one leaves home. Ninety-seven percent of respondents said they did that, and 72% said theyhad special locks on their doors. Although respondents’ greatestcrime-related worry was that a thief would break into their homewhile they were away, only 24% bothered to get a burglar alarm.


Exhibit IV

Percentages of Respondents Indicating They Did or Did Not Engage in VariousDefensive Behaviours

Behaviours Yes No

Are there any special locks on your doors? 72 28

Are there locks or bars or anything else like that on your 43 57windows?

Is there a burglar alarm? 24 76

Do you stay off streets in this neighbourhood at night? 46 54

Keep the doors locked when you are at home during the day? 64 36

Avoid going to downtown because of concern about crime? 30 70

Lock the doors whenever you leave home? 97 3

Have you done anything else to protect yourself and your family 42 58against crime?

Seventy percent denied that they avoided going to the downtownarea because of a concern about crime.

RESPONDENT VICTIMIZATION

Seventy percent of respondents said that they were aware of crimesthat had been committed in their immediate neighbourhood, and38% (278 people) said they had been a victim of a crime in the lastyear. When Statistics Canada conducted its national General SocialSurvey (GSS) in 1988, it was found that 33% of the respondents hadbeen criminally victimized in the previous year, and the 1993 surveyfound that the figure dropped to 25% (Ministry of Attorney Generalof B.C., 1997: p. 31; Sacco and Johnson, 1990: p. 11). Because theGSS samples include people aged 15 years and older, results fromthose surveys are not strictly comparable to ours. However, given theinclusion of the younger aged respondents, one would have expectedto find relatively more, than less, victimization.

Exhibit 5 lists the numbers and percentages of the variouscrimes of which our respondents were victims. Since respond-ents were often victims of more than one crime, although there


Exhibit V

Numbers and Percentages of Crimes of Which Respondents Were Victims

N %

Theft of less than $5 000 163 35.3

Break and enter dwelling, house, outbuilding or business 78 16.9

Vandalism causing damage of less than $l 000 40 8.7

Break and enter vehicle 40 8.7

Theft of $5 000 or more 40 8.7

Vandalism causing damage of $1 000 or more 29 6.2

Assault 22 4.8

Fraud 13 2.8

Other 13 2.7

Spousal abuse 7 1.5

Drunken driving causing injury 6 1.3

Stalker 4 0.8

Elder abuse 4 0.8

Sexual assault 3 0.7

Total 462 100

were 278 victims, altogether they mentioned 462 criminal offences.On average, each victim suffered 1.7 offences (e.g., a break andentry of a vehicle might be combined with a theft of less than$5000). The most frequently mentioned offence was theft of lessthan $5000. It was mentioned 163 times, making it 35.3% of allmentioned offences. Comparing the figures in this exhibit with thosein Exhibit 8, mean scores for perceived harmfulness of crimes, onefinds that this offence was judged to be second-least harmful. Thesecond most frequently mentioned offence was breaking and enter-ing a dwelling, house or outbuilding, with 78 (16.9%) occurrences.The least frequently mentioned offence was sexual assault, with 3(0.7%) occurrences.

Of the 13 distinct kinds of offences listed in Exhibit 5, thereare five which might be presumed to cause some physical injury,namely, drunken driving causing injury, spousal abuse, assault,sexual assault and elder abuse. There were 42 occurrences of such


offences, which is 9% of the all the offences. So, it seems fair to saythat about 90% of the offences against our victims were non-violent,property crimes. Since it is often possible for victims of such crimesto get compensation from insurance companies for their losses and,failing that, it is still often possible to fix or replace whatever wasstolen or destroyed, such crimes might be regarded by victims moreas unpleasant nuisances than as significant harms. This possibilitywill become more plausible in the light of analyses introduced lateron.

Farrell (1992: p. 98) wrote a fine paper on multiple victimiza-tion (also called repeat victimization and recidivist victimization) inwhich he showed that

. . . a relatively small proportion of the population seems to experience a largeproportion of all the crime. There is a highly skewed distribution of crime inthe population which is not due to chance. This observation would appear tohold up to rigorous testing from a variety of different sources. In this paper,nine different research methods have generated similar patterns in the distributionof victimisation. Similar patterns of multiple victimisation have emerged from:hospital records . . . , interviews generated from recorded crime . . . , local victimsurveys . . . , national victim surveys . . . , international victim surveys . . . , a surveyof hospitalised victims of assault . . . , participant observation . . . , victim referralsto a Victim Supportscheme . . . , police recorded crimes . . . , and police incidentlogs . . . . In addition, the degree of skew in the distribution of victimisation is suchthat the two or three percent of respondents to victim surveys who are the mostvictimised report between a quarter and a third of all incidents.

Farrell (1992: p. 86) noted that “The information gathered by the1982 British Crime Survey suggests that over 70%, or over seven inten, of the offences it covered, were experienced by just 14% of thetotal population”.

Although the figures just cited are remarkable in themselves,Farrell also reminded us that they almost certainly under-estimatethe degree of concentration of victimization because victims offamily violence are notorious for under-reporting and not reportingoffences (see also, Poff and Michalos, 1991), and survey researcherstypically limit responses to those over 16 years of age. Regardingfamily violence, Farrell (1992: pp. 94–97) cited Horley’s (1988)suggestion that “a woman who calls the police has, on average, beenthe victim of 35 previous beatings by a male partner” and regardingyouth he wrote that “There would as yet appear to be little infor-


mation available about the extent of multiple victimisation of youngpeople”.

For our Prince George sample, of the 737 respondents, there were132 (18%) who were victims of more than one offence. Altogether,132 (18%) of our sampled population accounted for 319 (69%) ofthe 462 mentioned criminal offences. The 6% of our respondentswho were victimized 3 or more times accounted for 30% of allthe incidents. So, compared to the general figures provided aboveby Farrell, victimization in the Prince George sample was not quiteas concentrated as in the Great Britain sample, although there wascertainly a significant amount of concentration.

INFORMING THE POLICE

Regarding the most recent crime of which respondents were avictim, we asked them if the police were informed about the incid-ent. Of 259 people who answered this question, 180 (70%) saidthe police were informed by themselves or others, 71 (27%) saidthe police were not informed and 8 (3%) did not know. Fifty-four percent of those who reported the incidents to the police werethe victims themselves. In the previously mentioned national GSSsurveys, for 1993 there were 42% reported to the police and for1987 there were 37% reported. So, granted that the results of oursurvey are not strictly comparable to those of the GSS, it is reason-able that the Prince George respondents had both higher levels ofvictimization and higher levels of reporting to the police.

Exhibit 6 lists number and percent of times that 14 kinds ofreasons were given for not reporting the most recent crimes to thepolice. Because many respondents had more than one reason fornot reporting the various crimes, one or another of the 14 kindsof reasons given in the exhibit were cited 240 times. The mostfrequently mentioned reason for not reporting an incident was thevictim’s belief that there was a lack of proof or no way to identifythe offender. This reason was cited 46 (19%) times. Immediatelyfollowing this reason, there was the victim’s belief that the incidentwas not important enough. This reason was cited 41 (17%) times.Interestingly enough, the third most frequently mentioned reason fornot reporting a crime was the victim’s belief that the police would


Exhibit VI

Reasons Crimes Were Not Reported to the Police

N %

No Need To Call 37

Did not think it important enough 41 17

Reported to someone else 16 7

Private or personal matter 14 6

Object recovered or offender unsuccessful 11 5

It took care of itself 7 2

Police Could Not Do Anything 37

Lack of proof, no way to identify offender 46 19

Did not realize crime happened until later 22 9

Property difficult to recover due to lack of serial or I.D. number 19 8

Police Would Not Do Anything 17

Police would not think it was important enough, would not want 25 10to be bothered

Police would be inefficient, ineffective 11 5

Police would be insensitive 4 2

Some Other Reason 10

Did not want to take time, too inconvenient 14 6

Afraid of reprisal by offender, his family or friends 5 2

Concern that it would affect insurance premiums 5 2

Total 240 100

not think it was important enough or that they would not want to bebothered. This reason was cited 25 (10%) times. There were only5 (2%) times when a victim mentioned failing to report a crimebecause of her or his fear of reprisal by the offender or the offender’sfamily or friends.

When Singer (1988: p. 289) examined 20,417 incidents ofvictimization of Americans in 1974 and 1975, he found that “Mostpeople who fail to report their victimizations to the police stateeither that the incident was not serious enough to warrant officialattention or that nothing could be done”. Five percent of his respond-


ents said they did not report the incidents because they were afraidof reprisal.

VICTIMIZATION OF RESPONDENTS’ RELATIVES AND CLOSEFRIENDS

Besides asking respondents about their own victimization, we askedthem if any relatives or close friends had been victims of crimesin the last year. Of the 737 people who answered this question,355 (48%) said ‘yes’, 300 (41%) said ‘no’, and 82 (11%) said theydid not know. So, adding the 48% to the original 38%, we havethe rather remarkable statistic that 88% of our sample had eitherbeen a victim or had a relative or close friend who had been avictim of a crime in the last year. Since our ‘relative or close friend’question did not specifically say that the victim had to be living inPrince George, one should not assume that the 88% figure some-how indicates the level of crime in this city. Still, it does seem toindicate that criminal victimization is very familiar or close to mostpeople’s psychological space. Norris and Kaniasty (1994: p. 111)reported that “. . . 83% of the United States population will experi-ence a violent crime at some point in their lives and that virtually allpersons (99%) will experience a personal theft”.

Exhibit 7 lists the numbers and percentages of the various crimesof which respondents’ relatives or close friends were victims. Alto-gether the 355 respondents who knew such people mentioned 770criminal offences. The most frequently mentioned offence was againtheft of less than $5000. It was mentioned 223 times, making it 29%of all mentioned offences. The second most frequently mentionedoffence was again breaking and entering a dwelling, house oroutbuilding, with 178 (23%) occurrences. The least frequentlymentioned offence was child pornography, with 2 (0.3%) occur-rences.

PERCEIVED HARMFULNESS OF CRIMES

In order to assess respondents’ views about the relative harmfulnessof various crimes, they were asked to rate 18 kinds of crimes on


Exhibit VII

Numbers and Percentages of Crimes of Which Respondents’ Relatives orClose Friends Were Victims

N %

Theft of less than $5 000 223 29.0

Break and enter dwelling, house, outbuilding or business 178 23.0

Theft of $5 000 or more 97 12.6

Vandalism causing damage of $1 000 or more 68 8.7

Assault 40 5.2

Spousal abuse 32 4.2

Fraud 17 2.2

Selling illegal drugs 16 2.1

Sexual assault 14 1.8

Drug abuse 14 1.8

Drunken driving causing injury 12 1.6

Drunken driving causing death 9 1.2

Murder 8 1.0

Child Abuse 8 1.0

Elder abuse 8 1.0

Break and enter – vehicle 6 0.8

Abduction 5 0.7

Vandalism causing damage of less than $l 000 5 0.7

Other 5 0.7

Prostitution 3 0.4

Child pornography 2 0.3

Total 770 100

a 20 point scale in which ‘1’ indicated victims were caused theleast severe harm and ‘20’ indicated victims were caused the mostsevere harm. AnIndex of Perceived Harmfulness of Crimeswas thendefined as the average of the scores assigned by a respondent to theperceived amount of harm caused by 18 kinds of crimes. The aver-age score for the 708 individuals who responded to the questionsused in the construction of this index was 14.7, the median was 15.6


Exhibit VIII

Mean Scores for Perceived Harmfulness of Crimes

Murder 18.8

Drunken driving causing death 18.2

Child abuse 18.1

Child pornography 18.1

Sexual assault 18.0

Abduction 17.4

Elder abuse 17.3

Drunken driving causing injury 16.9

Spousal abuse 16.8

Assault 15.7

Selling illegal drugs 15.5

Break & enter 13.9

Drug abuse 13.7

Theft of $5 000 or more 12.6

Vandalism causing damage of $1 000 or more 12.3

Fraud 12.2

Theft of less than $5 000 11.0

Prostitution 10.0

1 = least harmful, 20 = most harmful

and the range ran from 0.33 to 20. The Cronbach reliability coeffi-cient alpha for the Index was 0.93. The average scores assigned toeach of the 18 kinds of crimes is given in Exhibit 8.

The rank ordering of the types of crimes in this exhibit is inter-esting. One would have expected to find murder at the top of thelist, and it is there with a mean score of 18.8. At the bottom of thelist one finds prostitution, with a mean score of 10.0. If these scoresindicated a ratio scale, one might say that since one case of prosti-tution was judged to be just a bit over half as harmful as one case ofmurder, one could infer that preventing two incidents of prostitutionwould eliminate a bit more harm than preventing one incident ofmurder. Similarly, one might say that one case of assault (15.7) wasjudged to be about 84% as harmful as one case of murder and about11

2 times as harmful as one case of prostitution. One might, then, go


Exhibit IX

Item Means and Percentages of Respondents Saying that Police Were Doing aPoor, Average or Good Job, or Indicating ‘Don’t Know’

Mean Poor Average Good Don’t

job job job know

Being approachable and easy to talk 2.6 8 26 58 9with

Supplying information to the public 2.6 5 30 60 5on ways to prevent crime

Treating minority groups fairly 2.5 6 25 36 33

Enforcing the traffic laws 2.4 12 38 46 4

Dealing with family violence 2.3 8 29 25 38

Enforcing the laws related to your 2.2 12 49 31 9personal property & safety

Making your neighbourhood safe 2.2 12 50 29 9

Promptly responding to calls 2.2 15 39 29 17

a step further and use such harmfulness scores to allocate resources;e.g., one might spend X tax dollars to try to prevent one case ofmurder or two cases of prostitution, and roughly the same amount ofharm would be prevented. Unfortunately, our mean scores cannot beregarded as measures on a ratio scale, as the ‘one case of murder =two cases of prostitution’ equation seems to demonstrate. All we caninfer from the mean scores is the rank ordering of the various crimesaccording to their perceived harmfulness; e.g., the mean scores tellus that murder is certainly perceived to be more harmful than assaultand the latter is perceived to be more harmful than prostitution, butthe scores do not tell us exactly how much more harmful any sort ofcrime is than any other sort of crime.

POLICE PERFORMANCE

Respondents were asked to rate the degree to which they thoughtthe police were performing their jobs well on a scale in which ‘1’indicated ‘a poor job’, ‘2’ indicated ‘an average job’ and ‘3’ indic-


Exhibit X

Numbers and Percentages of Gun Uses

N %

Hunting 213 44.6

Family heirloom 123 25.7

Protection against wild animals 64 13.4

Protection against criminals 38 8.0

Collecting/trading 34 7.1

Other 6 1.3

Total 478 100

ated ‘a good job’. Then anIndex of Police Performancewas createdby calculating each respondent’s average score on eight items. Themean and median scores for the 729 individuals who responded tothe questions used in the construction of this index were both 2.4,and the range ran from 1 to 3. The Cronbach alpha for the Index was0.78. Exhibit 9 lists the items, their mean scores and the percentagesof respondents indicating each response.

It is clear from the item mean scores (first column) that the policereceived highest marks (2.6) for being approachable, easy to talkwith and providing information on ways to prevent crime. Inspec-tion of the fourth column reveals that 58% to 60% of respondentsthought the police did a good job in these areas. Lowest marks (2.2)were received for enforcing the laws related to personal property andsafety, promptly responding to calls and making neighbourhoodssafe. The figures in the fourth column show that from 39% to 50%of respondents thought the police did an average job in these areas.It is worthwhile to notice that even the lowest marks received for thejob performance of the police were above average.

GUNS AND OTHER POTENTIAL WEAPONS

Since many people in Prince George opposed the federal govern-ment’s new gun registration law, we supposed that many residentsowned guns and might regard them as handy weapons against poten-


Exhibit XI

Numbers and Percentages of Potential Weapons

N %

Baseball bats 64 33.0

Knives 26 13.4

Dogs 16 8.3

Bear spray 14 7.2

Pepper spray 13 6.7

Clubs, sticks or golf clubs 12 6.2

Call 911 6 3.1

Personal alarms, motion sensor lights 5 2.6

Other 38 19.6

Total 194 100

tial criminals. So, we asked if respondents or anyone else in theirhomes owned guns. Of the 673 people who answered this question,283 (42%) said there were guns in their homes and 390 (58%)said there were not. Those who said that there were guns in theirhomes were then asked to indicate their main uses. Exhibit 10shows the number and percentages of the various uses of guns. Themost frequently mentioned use was for hunting, which was cited213 (44.6%) times. This was consistent with the responses to ourfollow-up question that asked those who indicated more than oneuse, which was the most important. Forty-four percent said that themost important use of their guns was for hunting. The second mostfrequently mentioned use was not strictly a use at all. Many peoplehad guns in their homes that were regarded as family heirlooms.Such ‘uses’ were cited 123 (25.6%) times. Protection against crim-inals was indicated as a use only 38 (8%) times, which was even lessthan protection against wild animals at 64 (13.4%) times.

Pressing our search for other potential weapons that might beused against criminals, we asked respondents if they had any otheritems that they would probably use to protect themselves or theirproperty. Exhibit 11 lists the numbers and percentages of the itemsmentioned. Forty-five respondents said they did not have such items.


The most frequently mentioned items were baseball bats, whichwere cited 64 (33%) times. Knives were cited 26 (13.4%) times,followed by dogs (8.3%). The most interesting responses to thisquestion are hidden in the ‘other’ category, which accounted for38 (19.8%) citations. Other things included swords, machetes, poolcues, pottery, fake guns, steel bars, cleaners, chains, wire cable,hammers, axes, flashlights, hockey sticks, 2× 4s, slingshots, a leadball on a shoelace, neighbours with guns, the respondent herselfor himself, fists, anything handy, cats and even a ‘killer cat’. Onewonders if potential criminals have any idea of the lethal arsenalbeyond guns that awaits them in the homes of gentle Canadians.

SATISFACTION, HAPPINESS AND THE QUALITY OF LIFE

As in previous quality-of-life surveys (e.g., Michalos and Zumbo,1999), we asked respondents to rate the levels of satisfaction theyobtained from their whole lives and particular domains of their liveson a seven-point scale running from ‘1’ indicating ‘very dissatis-fied’, through ‘4’ indicating ‘evenly balanced’, to ‘7’ indicating‘very satisfied’. We also asked them to rate their overall level ofhappiness on a seven-point scale running from ‘1’ indicating ‘veryunhappy’, through ‘4’ indicating ‘evenly balanced’, to ‘7’ indicat-ing ‘very happy’. Exhibit 12 lists the numbers of respondents andmean scores for the 19 items in this set, along with the mean scoreson most of these items from two earlier surveys of Prince Georgeresidents (i.e., Michalos and Zumbo, 1999; Michalos, 1996).

Highest mean levels of satisfaction were reported for satisfactionwith respondents’ living partners (6.3) and with their family rela-tions generally (6.0), as in the earlier surveys. Broadly speaking,for the sample sizes in this exhibit, differences between any twoscores must be 0.3 or greater in order to be statistically significantat the 0.05 level of confidence; i.e., 19 times out of 20 one wouldnot see differences so big as a result of mere chance variation. So,we can say that respondents for the current survey were statisticallysignificantly more satisfied with their living partners than with theirfamily relations generally, but across the three time periods (Novem-ber 1997, June 1997 and June 1994) there were no statisticallysignificant differences. Similarly, respondents for the current survey


Exhibit XII

Numbers of Respondents and Mean Scores for Satisfaction With:

N M M M

1997 1997 1994

Nov June June

Your house, apartment, mobile home 729 5.5 5.4 5.8

Your neighbourhood as a place to live 728 5.3 5.5 5.7

Your personal safety in your neighbourhood 710 5.0 NA NA

Your family’s safety in your neighbourhood 698 4.7 NA NA

Your family relations, generally 721 6.0 5.9 5.9

Your living partner 613 6.3 6.1 6.1

Your job 659 5.4 5.4 5.4

Your life as a whole 724 5.8 5.8 5.6

Your friendships 728 5.8 5.9 5.8

Your health 727 5.4 5.6 5.6

Your religion or spiritual fulfilment 688 5.3 5.4 5.4

Your financial security 722 4.6 4.8 4.7

Your recreation activities 724 4.9 4.9 5.0

Your self-esteem 713 5.5 5.6 5.5

Federal government officials 725 2.8 3.5 3.0

Provincial government officials 727 2.5 3.4 3.0

Local government officials 726 3.6 4.1 3.5

Your overall quality of life 726 5.6 5.6 5.7

Your overall happiness 725 5.5 5.6 5.6

Average 711 5.0 5.2 5.1

had relatively high levels of satisfaction with their lives as a wholeand with their friendships (5.8), and these levels were essentiallystable over the three time periods. Levels of overall happiness andsatisfaction with the overall quality of their lives were the same, 5.6,and were also stable over the three time periods. Inspecting all thefigures in the exhibit, one finds only three cases of statistically signi-ficant changes in satisfaction levels in different time periods. Levelsof reported satisfaction with federal, provincial and local govern-ment officials dropped significantly from the June to the November


1997 survey. There was a 20% reduction in the federal case (3.5 to2.8), a 27% reduction in the provincial case (3.4 to 2.5) and a 12%reduction in the local case (4.1 to 3.6). The average levels of satis-faction and happiness for all the items across the three time periodswas stable at 5.0 to 5.2.

MEAN DIFFERENCES AMONG VICTIMS VERSUS NON-VICTIMS,MALES VERSUS FEMALES AND YOUNG VERSUS OLD

While the previous exhibit compared mean satisfaction scores fora selection of indicators across three time periods, the next threeexhibits compare mean scores for 31 indicators in this survey. Thetotal sample of respondents was divided into two mutually exclu-sive groups in three different ways, namely, those who had beenvictimized versus those who had not been victimized, males versusfemales, and those aged 18 to 39 versus 40 to 88. T-tests wereapplied to each pair of mean scores for 38 indicators in each of thethree groups in order to identify statistically significant differences.

Exhibit 13 lists the 19 indicators that showed some significantdifferences in the means for those who had been victimized versusthose who had not been victimized, along with the sizes and meanscores of each group. Because tests of statistical significance arevery sensitive to sample sizes, the exhibit includes pairs of figureswith differences that are too big to be regarded as merely the resultof chance but also too small to be regarded as important in anysubstantive or policy-relevant sense. For example, victims’ meanscores for satisfaction with the overall quality of life and happinessare about 97% as high as non-victims’ scores. So, although thedifferences are statistically significant, one would be hard-pressedto show that these numbers designate some substantive or import-ant differences in the life experiences of victims and non-victims.Generally speaking, our crime-related results are consistent withmost other studies of these variables and, while we have very littleto compare with our quality-of-life-related variables for victimsversus non-victims, what we found is fairly consistent with whatone might have expected one would find. In particular, victimstended to be younger (Johnson and Lazarus, 1989; Collins, Coxand Langan, 1987), to be more convinced that neighbourhood crime


Exhibit XIII

Indicators with Statistically Significant Mean Score Differences forVictims Versus Non-victims

N M

Age Non-victims 441 45.6

Victims 272 43.5

Relatives or close friends victimized Non-victims 407 1.5

Victims 248 1.3

Neighbourhood crime increased Non-victims 407 1.7

Victims 252 1.5

Index of Police Performance Non-victims 453 2.4

Victims 276 2.3

Index of Defensive Behaviour Non-victims 459 4.0

Victims 277 4.3

Index of Crime-Related Worries Non-victims 458 3.5

Victims 277 4.4

Index of Neighbourhood Problems Non-victims 458 2.7

Victims 276 4.0

Satisfaction with personal safety Non-victims 444 5.2

Victims 266 4.7

Satisfaction with family safety Non-victims 433 4.9

Victims 265 4.4

Satisfaction with housing Non-victims 455 5.7

Victims 274 5.3

Satisfaction with neighbourhood Non-victims 455 5.5

Victims 273 4.8

Satisfaction with family relations Non-victims 452 6.1

Victims 269 5.8

Satisfaction with living partner Non-victims 386 6.4

Victims 227 6.1


Exhibit XIII

Continued

N M

Satisfaction with life as a whole Non-victims 453 5.9

Victims 272 5.6

Satisfaction with financial security Non-victims 451 4.7

Victims 271 4.3

Satisfaction with federal gov’t officials Non-victims 454 3.0

Victims 271 2.6

Satisfaction with local gov’t officials Non-victims 456 3.7

Victims 270 3.3

Satisfaction with overall quality of life Non-victims 454 5.7

Victims 272 5.5

Happiness Non-victims 454 5.6

Victims 271 5.4

had increased (Bennett, 1994), to be more worried about beingvictimized (Bennett, 1994; Skogan, 1987), to perceive more neigh-bourhood problems (Bennett, 1994), to engage in more defensivebehaviours, to be less satisfied with their own and their family’ssafety in their neighbourhoods and to be slightly less pleasedwith police performance (Bennett, 1994; Sprott and Doob, 1997;Homant, Kennedy and Flemming, 1984) than non-victims. Victimsalso tended to have lower levels of reported satisfaction with theirhousing, neighbourhoods, family relations, living partners, financialsecurity, federal and local government officials, life as a whole andthe overall quality of life, and happiness than non-victims. Contraryto findings of Shoemaker and Bryant (1987), we did not find anydifferences in the perceived harmfulness of crimes depending onwhether one had or had not been a victim.

Exhibit 14 lists the 17 indicators that showed some significantdifferences in the means for females and males, along with the sizesand mean scores of each group. Generally speaking, our crime-


Exhibit XIV

Indicators with Statistically Significant Mean Score Differences forFemales Versus Males

N M

Marital status Females 358 2.3

Males 367 1.6

Age Females 350 42.5

Males 362 47.0

Crime in Canada increased Females 297 1.2

Males 317 1.4

Crime in this city increased Females 323 1.1

Males 342 1.3

Crime in local schools increased Females 312 1.1

Males 304 1.2

Crime in your neighbourhood increased Females 310 1.5

Males 342 1.7

Index of Perceived Harmfulness of Crimes Females 352 15.2

Males 351 14.1

Walk alone in your neighbourhood at night Females 361 2.0

Males 368 2.8

Index of Defensive Behaviour Females 361 4.5

Males 369 3.6

Index of Crime-Related Worries Females 359 4.3

Males 369 3.4

Index of Neighbourhood Problems Females 359 3.5

Males 369 2.9

Satisfaction with personal safety Females 346 4.7

Males 359 5.3

Satisfaction with family safety Females 337 4.5

Males 356 4.9


Exhibit XIV

Continued

N M

Satisfaction with your neighbourhood Females 355 5.1

Males 369 5.4

Satisfaction with financial security Females 355 4.4

Males 364 4.7

Satisfaction with your recreation activities Females 355 4.8

Males 366 5.0

Satisfaction with local gov’t officials Females 357 3.4

Males 365 3.7

related results are again consistent with most other studies of thesevariables. Reflecting on the figures in this exhibit, readers shouldremember that in most societies men have a “greater unwillingnessto admit to or talk about their fears relating to criminal victimi-zation in general” (Walklate, 1997: p. 40; Crawford et al., 1990;Stanko and Hobdell, 1993). We found that females tended to bemore convinced that crime in Canada, this city, local schools andtheir own neighbourhoods had increased, to be more worried aboutbeing victimized (Lira and Andrade-Palos, 1993; Giles-Sims, 1984),to perceive crimes as more harmful, to perceive more neighbour-hood problems, to engage in more defensive behaviours, to be lesssatisfied with their own and their family’s safety in their neighbour-hoods, and to be less likely to walk alone in their neighbourhoods atnight than males (Sprott and Doob, 1997; Gomme, 1988). Femalesalso tended to have lower levels of reported satisfaction with theirneighbourhoods, financial security, local government officials andrecreation activities than males.

Exhibit 15 lists the 12 indicators that showed some significantdifferences in the means for those aged 18 to 39 versus those aged40 to 88, along with the sizes and mean scores of each group. Thesedivisions were used in the national analysis by Johnson and Lazarus(1989). Those in the older group tended to be home owners, less


Exhibit XV

Indicators with Statistically Significant Mean Score Differences forThose Aged 18–39 Versus 40–88

N M

Own or rent your home 18 to 39 263 1.4

40 to 88 449 1.2

Education 18 to 39 263 8.1

40 to 88 447 7.5

Crime in Canada increased 18 to 39 224 1.4

40 to 88 377 1.3

Index of Police Performance 18 to 39 260 2.3

40 to 88 446 2.4

Index of Neighbourhood Problems 18 to 39 264 3.6

40 to 88 447 2.9

Satisfaction with housing 18 to 39 264 5.3

40 to 88 445 5.7

Satisfaction with neighbourhood 18 to 39 264 5.0

40 to 88 444 5.5

Satisfaction with one’s job 18 to 39 252 5.2

40 to 88 389 5.5

Satisfaction with health 18 to 39 262 5.6

40 to 88 446 5.3

Satisfaction with financial security 18 to 39 263 4.1

40 to 88 439 4.9

Satisfaction with federal gov’t officials 18 to 39 263 2.7

40 to 88 442 2.9

Satisfaction with local gov’t officials 18 to 39 261 3.4

40 to 88 444 3.7


educated, less likely to be employed full-time, more convinced thatcrime in Canada had increased, more pleased with police perform-ance and they perceived fewer neighbourhood problems than thosein the younger group. Those in the older group also tended to havehigher levels of reported satisfaction with their neighbourhoods,their jobs, financial security, federal and local government officials,but lower levels of satisfaction with their health than those in theyounger group.

BIVARIATE RELATIONSHIPS

Having described our individual variables and indexes, our next taskwas to measure the linear relationships among all the demographicand crime-related variables on the one hand and the global indica-tors of happiness, life satisfaction and satisfaction with the overallquality of life on the other hand. Exhibit 16 lists the first resultsof the exploration, using only bivariate analysis. Although bivariateanalysis can be misleading because measured relationships betweenany two variables may mask more complicated relationships amongone or more variables not included in the analysis, the simplicity ofbivariate analysis makes it a good place to begin.

Because beliefs about increased crime in the city and in Canada,and the presence of a neighbourhood block watch program inrespondents’ neighbourhoods had no statistically significant rela-tionships to any of our dependent variables, these variables wereomitted from the exhibit. Variables were retained provided that theyhad a statistically significant relationship with at least one of thedependent variables.

Because we believed that the kind of crime of which one wasa victim would make a difference to its overall impact on his orher life, we constructed a more sophisticated measure of victimi-zation by weighting each offence against an individual by its meanharmfulness score from Exhibit 8. So, two measures of victimizationoccur in Exhibit 16. One is called ‘being a victim’, which is simplya sum of the number of times a respondent was a victim of somecrime. The other is called ‘self-harm-weighted victimization’, andit was calculated by multiplying the mean harmfulness score for


Exhibit XVI

Correlations among Crime-related and Demographic Variables, and Happiness,Life Satisfaction and Satisfaction with the Overall Quality of Life

Quality Happi- Life Neighbour-

of life ness Satisfac- hood

tion Satisfaction

Age 0.10 ns 0.12 0.17

Sex ns ns ns 0.08

Marital status –0.12 –0.14 –0.14 –0.15

Own or rent home –0.12 –0.13 –0.10 –0.21

Education ns 0.12 ns ns

Household income 0.13 0.09 0.13 0.20

Self-harm-weighted victimization ns ns ns –0.19

Being a victim –0.10 –0.09 –0.09 –0.21

Relative/close friend victimization ns ns ns 0.18

Neighbourhood crime increased ns ns 0.10 0.22

Local school crime increased ns ns ns 0.08

Walk alone in neighbd. at night ns ns ns 0.13

Index of Police Performance 0.18 0.14 0.17 0.26

Index of Defensive Behaviour ns ns ns –0.17

Index of Crime-Related Worries –0.14 ns –0.12 –0.26

Index of Neighbourhood Problems –0.23 –0.11 –0.16 –0.54

Satisfaction with personal safety 0.24 0.22 0.20 0.45in one’s neighbourhood

Satisfaction with family’s safety 0.27 0.22 0.22 0.45in one’s neighbourhood

All entries significant at 0.05or better. ns = not significant

each kind of offence times each occurrence of the offence for everyrespondent, and adding up all these products for each respondent.

Inspection of the cells in the exhibit reveals that while there were17 significant relationships between the independent variables andneighbourhood satisfaction, there were only 11 such relationshipsbetween the independent variables and satisfaction with life as awhole, 10 between the independent variables and satisfaction with


the overall quality of life and 9 between the independent variablesand happiness. While every one of the 12 crime-related independentvariables had a significant relationship to neighbourhood satisfac-tion, 7 were related that way to life satisfaction, 6 to satisfactionwith the overall quality of life and 5 to happiness.

The strongest relationship in the exhibit is between respond-ents’ satisfaction with their neighbourhoods and the Index ofNeighbourhood Problems. The Pearson Product-Moment Correla-tion Coefficient is negative, –0.54, meaning that the more problemsa respondent perceived in her or his neighbourhood, the lowerthe respondent’s satisfaction with that neighbourhood. The moresatisfied respondents were with their own safety and the safetyof their families in their own neighbourhoods, the more satisfiedthey were in general with their neighbourhoods. The correlationsbetween respondents’ satisfaction with their personal safety andwith their neighbourhoods, and with respondents’ satisfaction withtheir families’ safety and with their neighbourhoods were both posi-tive and the same, 0.45. The similarity was not surprising becausethe correlation between the personal and family safety items was awhopping 0.87.

After these things, the most robust relationships were betweenrespondents’ Indexes of Police Performance and their neighbour-hood satisfaction, and between respondents’ Indexes of Crime-Related Worries and neighbourhood satisfaction. The correlationcoefficients had the same absolute value, 0.25, but the associationwas positive in the case of Indexes of Police Performance andnegative in the case of Indexes of Crime-Related Worries. Thus,the more highly respondents’ rated police performance, the moresatisfied they were with their neighbourhoods, and the more crime-related worries respondents had, the less satisfied they were withtheir neighbourhoods. Our more sophisticated measure of harm-weighted victimization appears to have had a weaker association(–0.19) than the unweighted measure of victimization (–0.21) toneighbourhood satisfaction, but the small difference may be onlya result of measurement error. Regarding demographics, age, sex,education and total household income were all positively and beingunmarried and/or renting one’s place of residence were negativelyrelated to neighbourhood satisfaction.


Compared to the neighbourhood satisfaction case, the other threecases summarized in Exhibit 16 are not as impressive. The mostrobust relationships in the latter three cases occur for the correla-tions between respondents’ satisfaction with their personal safetyand the overall quality of their lives (0.24), and with respondents’satisfaction with their families’ safety and the overall quality of theirlives (0.27). Their Indexes of Neighbourhood Problems had nearlyas strong a negative association with their satisfaction with the over-all quality of their lives, –0.23, and this was followed by the positivecorrelation of 0.18 between their Indexes of Police Performance andtheir satisfaction with the overall quality of their lives. Although thenumbers are a bit smaller for the relationships between these inde-pendent variables, happiness and life satisfaction, the relationshipshave the same rank orderings and signs as in the quality of life case.Similarly, in all three cases there was a modest negative association(–0.09 to –0.10) between being a victim and happiness, life satisfac-tion and satisfaction with the overall quality of life. These particularfigures tell us that, at best, one percent of the variation in respond-ents’ reported happiness, life satisfaction and satisfaction with theoverall quality of their lives may be explained by their experience ofvictimization. In other words, 99 percent of the variation in respond-ents’ reported happiness, life satisfaction and satisfaction with theoverall quality of their lives must be explained by something otherthan their victimization experiences. Again, when one reflects onthe nature of most of the crimes against our respondents, the verymodest affects that they had on their global assessments of theirlives is perhaps not surprising. (Harm-weighted victimization hadno statistically significant relationships to the global indicators.) It isalso not surprising that total household incomes are positively whilerenting rather than owning one’s residence and being without asignificant other are negatively related to the three global indicators.These are typical findings around the world (Michalos, 1991).

SATISFACTION WITH GOVERNMENT OFFICIALS

Because the City of Prince George and other public agencies are thesponsors of this research, among other things, we asked respondentsto indicate their levels of satisfaction with federal, provincial and


local government officials. When one reviews all the statisticallysignificant relationships among these three items and the variety ofother questions in our survey, one is struck first by the large numberof such relationships and second by the recurring pattern in them.For illustrative purposes, we included an Appendix indicating mostof the important relationships. There are 37 rows in the table and for20 (54%) of them, the strongest associations are with satisfactionwith local government officials, then with federal government offi-cials and finally with provincial government officials. For example,the top row shows that there is a positive association betweenrespondents’ satisfaction with officials in all three levels of govern-ment on the one hand and their belief that neighbourhood crime haddecreased over the past two years on the other. The relationshipis strongest for local government officials (0.17), then for federalgovernment officials (0.10) and finally for provincial governmentofficials (0.08). We have no grand hypothesis or theory to accountfor these and all the other similar figures in the table, but we thinkthey are interesting enough to be included for others to think about.

FEAR AND OTHER CRIME-RELATED ISSUES

Researchers have operationalized fear of criminal victimization inseveral different ways and explored their relationships with a varietyof other crime-related issues. The question regarding people’s feel-ings about walking alone in their neighbourhoods after dark isprobably the most frequently used measure of fear in this area ofresearch. Bennett (1994) found that such feelings could be explainedpartly by people’s perceptions of increases in crime, crime-relatedworries and perceived neighbourhood problems, as well as genderand age. Box, Hale and Andrews (1988) and Baumer (1985) alsoreported positive associations between low levels of fear and highlevels of satisfaction with police performance.

Jones (1987) suggested, but did not test, the hypothesis that fearmight be related to “negative self image and low morale”. Sincethere are fairly strong correlations between our global measuresof life satisfaction, happiness and satisfaction with overall qual-ity of life on the one hand and with measures of morale on theother (Michalos, 1991), we could indirectly test Jones’s hypothesis.


Exhibit 16 shows that there were no significant associations betweenour walking alone variable and our global measures. However, ourIndex of Crime-Related Worries was negatively associated with lifesatisfaction and satisfaction with overall quality of life scores, whichdoes support her hypothesis. Although we did not have a measureof self-esteem, our measure of satisfaction with one’s own self-esteem is correlated to measures of actual self-esteem at about 0.35(Michalos, 1991). So, the former is not a bad substitute for thelatter. Scores for feelings of fear about walking alone were verymodestly correlated (0.09) with self-esteem satisfaction. Still, self-esteem satisfaction scores were a bit more highly correlated withscores for satisfaction with respondents’ own and their families’neighbourhood safety (0.18 for both), and the latter two satis-faction items were strongly positively correlated (0.51 and 0.43,respectively) with the walking alone item. All things considered,Jones’s hypothesis has some support and would be worth pursu-ing further. Janoff-Bulman (1989) also found support for thehypothesis.

Costa (1984) reported that elderly Philadelphians limited theiractivities as a result of their fear of crime. For our respondents therewas a good correlation of –0.43 between scores for feelings of fearabout walking alone and our Index of Defensive Behaviour, mean-ing that the more people tended to engage in defensive activitieslike staying off the streets at night, the more likely they were tobe afraid of being on the streets. There was also a good positiverelationship (0.38) between respondents’ Index of Crime-RelatedWorries and their Index of Defensive Behaviour, meaning that themore they worried, the more their worries were translated into actualbehavioural changes (Norris and Kaniasty, 1994).

Norris and Kaniasty (1994: p. 120) did some excellent analysesof relationships between defensive behaviour and victimization, andconcluded that

It is important to note . . . that although we measured four different aspects ofprecautionary behavior, none had any predictive value. Victims of crime wereneither more nor less cautious than others. Thus victims’ behavior was not toblame, and we should be careful not to give that impression to either the victimor the public at large. These findings imply that ‘victim control’ (i.e., blaming)models are inappropriate as a basis for either public policy . . . or psychologicaltreatment . . . .


In an earlier paper, Norris and Kaniasty (1992: pp. 644–645)challenged those who spent all their time reflecting on defensivebehaviour. In their view,

Theoretically, the focus on precautionary behavior is an outgrowth of theoppor-tunity reduction modelof crime prevention . . . . While well-intentioned, advocatesof this approach have implied that the goal is the prevention of victims. Impli-citly, it blames victims by suggesting that they were somehow responsible fortheir misfortune. If they had just been more careful, it would not have happened.. . . our results challenge the opportunity reduction model as a framework for crimeprevention policy. A legitimate substitute is thesocial problem modelwhereincrime is reduced via the amelioration of broad social conditions that breed crimi-nal activity . . . . Effective law enforcement and adjudication systems are alsoessential.

Earlier researchers (Gottfredson and Hindelang, 1979; Skogan,1976) reported that the probability of reporting incidents to offi-cials increased with the seriousness of the offence. Although therewere no significant relationships between our respondents’ harm-weighted victimization scores and the victims’ reporting behaviouror walking alone at night behaviour, the former scores were posi-tively associated with our Index of Crime-Related Worries (0.19)and our Index of Neighbourhood Problems (0.21), and negativelyassociated with our Index of Police Performance (–0.13) andwith respondents’ feelings of safety about walking alone at night(–0.12). The negative correlation between harm-weighted victimi-zation scores and feelings of safety (i.e., fear) is consistent withfindings by Warr and Stafford (1983). The negative correlationregarding the police just means that the probability of a relativelynegative assessment of police performance increased with increasesin the amount of harm that victims thought had been inflictedupon them by offenders. As one might have expected, the probab-ility of a negative assessment of police performance also increasedwith increases in respondents’ Index of Neighbourhood Problems(–0.19).

Maxfield (1984) found positive associations between sampledSan Franciscans’ fears about being ‘out alone after dark in yourneighborhood’ and a set of items similar to our Index of Neighbour-hood Problems.


EXPLAINING HAPPINESS, LIFE SATISFACTION ANDSATISFACTION WITH THE OVERALL QUALITY OF LIFE WITH

MULTIVARIATE ANALYSIS

In order to sharpen our investigation of the relationships between allof our crime-related variables and our global indicators (happiness,life satisfaction and satisfaction with the overall quality of life), weundertook a series of step-wise multivariate regression analyses.Such analyses allow one to measure the percent of variation independent variables that might be explained by a set of one or moreexplanatory variables both collectively (measuring the impact of allthe explanatory variables together) and individually (measuring theimpact of each variable one at a time with the influence of all othervariables in the set held constant). They are good techniques forrevealing which of the previously mentioned bivariate relationshipsare spurious and misleading.

The next three exhibits (17, 18, 19) summarize fifteen separateregressions, consisting of the same kinds of analyses for each of thethree global indicators. First, each global indicator was regressed onthe set of 6 demographic and 12 crime-related variables listed in theprevious Exhibit 16. Second, each global indicator was regressed onthe same set of demographic variablesplusa set of 14 domain satis-faction variables listed in Exhibit 12 (i.e., satisfaction with housing,neighbourhoods, families, living partners, jobs, friendships, health,religion, financial security, recreation activity, self-esteem, federal,provincial and local government officials) butminus any crime-related variables. Third, each global indicator was regressed on thesame set of demographic variablesplusthe domain satisfaction vari-ablesand the crime-related variables. Finally, the whole group ofrespondents was divided into those who had been and those whohad not been victimized, and the third sort of regression analysis wasrun on each group (without the variables being a victim and harm-weighted victimization). Following the steps of the analyses for thefirst three columns, in the context of a common set of demographicvariables, one is able to measure the marginal benefit or additionalexplanatory power that crime-related variables bring to our capacityto explain respondents’ happiness, life satisfaction and satisfactionwith the overall quality of their lives. Applying the third sort ofanalysis to victims versus non-victims, one is able to measure the

28

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Exhibit XVII

Regression of Happiness Scores on 6 Demographic, 12 Crime-related and 14 Domain Satisfaction Variables, All Respondents,Victims, Non-victims

6+12 6+14 6+12+14 6+10+14 6+10+14variables variables variables victim non-victim

Explanatory variables in equation Beta Beta Beta Beta BetaOwn or rent home –0.09 a a a aSatisfaction with personal safety in one’s neighbourhood 0.22 a 0.10 a 0.19Walk alone in neighborhood at night a a a 16 aAge a a a –0.26 aIndex of Crime-Related Worries a a a a 0.15Satisfaction with one’s housing a a a 0.21 aSatisfaction with one’s job a a a 0.19 aSatisfaction with financial security a a a a 0.20Education a 0.09 a 0.24 aSatisfaction with recreation activities a 0.11 0.12 a aSatisfaction with one’s religion a 0.14 0.15 0.32 aSatisfaction with one’s health a 0.16 0.16 a aSatisfaction with one’s self-esteem a 0.19 0.19 0.33 0.35Satisfaction with one’s living partner a 0.20 0.16 a aTotal percent of variance explained 6 35 34 57 27

a =variable not in equation, total N = 633, victim N = 132, non-victim N = 188


difference, if any, that being a victim makes to explanatory modelsfor the three global indicators. As usual, the only variables listed inthe exhibit are those with some statistically significant impact on theglobal indicators.

The first column of figures in Exhibit 17 gives the resultsof regressing happiness scores on 6 demographic and 12 crime-related variables. The most striking feature of the column is thefact that only two of the 18 potentially explanatory variables hadany explanatory power. Only 6% of the total variance in happi-ness scores was explained by respondents’ owning or renting theirresidence and their satisfaction with personal safety in their neigh-bourhoods. The standardized regression coefficients (Betas) indicatethat satisfaction with personal safety (B = 0.22) was a bit over twiceas influential as owning or renting one’s residence (B = –0.09).Figuratively speaking, one might say, for example, that with all vari-ables standardized to have means of zero and standard deviations ofone, for every full unit of increase in one’s satisfaction with personalsafety, one would have 22% of a unit of increase in one’s happiness.As indicated earlier in our bivariate analyses, renting rather thanowning one’s residence was usually negatively related to all ourglobal indicators. Our multivariate analysis confirmed the directionof the relationship and washed out some of its absolute value.

The second column of figures in Exhibit 17 gives the results ofregressing happiness scores on the 6 demographic and 14 domainsatisfaction scores. Seven of the 20 predictors had statisticallysignificant impacts on the dependent variable, with none of thedemographic variables remaining in the equation. Altogether, the 7variables explained 35% of the variance in happiness scores. Satis-faction with respondents’ living partners (B = 0.20) and their ownself-esteem (B = 0.19) were the most powerful explanatory variablesin the set, with satisfaction with their own health (B = 0.16) and theirreligion or spiritual fulfilment (B = 0.14) coming closely behindthem.

The third column of figures in Exhibit 17 gives the results ofregressing happiness scores on the 6 demographic, 12 crime-relatedand 14 domain satisfaction scores. Only 7 of the 32 potentiallyexplanatory variables had statistically significant impacts on thedependent variable, with none of the demographic and only one of


the crime-related variables remaining in the equation. Altogether,the 7 variables explained 34% of the variance in happiness scores.Satisfaction with respondents’ self-esteem (B = 0.19) was the mostinfluential variable in the set, with satisfaction with living partners(B = 0.16), health (B = 0.16) and religion (B = 0.15) coming closelybehind it.

The fourth column of figures in Exhibit 17 gives the resultsof regressing happiness scores on the 6 demographic, 10 crime-related and 14 domain satisfaction scores only for those who hadbeen victims of some crime. Again, only 7 of the 30 potentiallyexplanatory variables had statistically significant impacts on thedependent variable, with two of the demographic and only one ofthe crime-related variables remaining in the equation. Altogether,the 7 variables explained 57% of the variance in happiness scores,which was a relatively high percentage for this global indicator. Itwas so extra-ordinary that we suspected it was some sort of statist-ical artifact, but we were unable to find any anomaly in the analysis.The most powerful explanatory variable is satisfaction with one’sown self-esteem (B = 0.33), followed immediately by satisfactionwith one’s religion (B = 0.32). Age is then negatively related tohappiness scores (B = –0.26), meaning that as the age of victimsincreases, their happiness scores decrease; e.g., if a 30 year old anda 60 year old man were burglarized, it would depress the older man’shappiness score more than it would depress the younger man’sscore.

The fifth column of figures in Exhibit 17 gives the results ofregressing happiness scores on the 6 demographic, 10 crime-relatedand 14 domain satisfaction scores only for those who had not beenvictims of some crime. Only 4 of the 30 potentially explanatory vari-ables had statistically significant impacts on the dependent variable,with one of the crime-related variables remaining in the equation.Altogether, the 4 variables explained 27% of the variance in happi-ness scores. Satisfaction with one’s own self-esteem (B = 0.35)pretty well dominates the set of predictors, trailed at some distanceby satisfaction with one’s financial security (B = 0.20).

The first column of figures in Exhibit 18 gives the results ofregressing satisfaction with life as a whole scores on 6 demographicand 12 crime-related variables. Just 4 of the 18 potentially explana-

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Exhibit XVIII

Regression of Satisfaction with Life as a Whole Scores on 6 Demographic, 12 Crime-related and 14 Domain SatisfactionVariables, All Respondents, Victims, Non-victims

6+12 6+14 6+12+14 6+10+14 6+10+14

variables variables variables victim non-victim

Explanatory variables in equation Beta Beta Beta Beta Beta

Age 0.10 a a a a

Marital status a –0.11 –0.10 –0.15 a

Total household income 0.12 a a a a

Index of Police Performance 0.10 a a a a

Satisfaction with personal safety in one’s neighbourhood 0.16 a a a a

Own or rent housing a a a 0.13 a

Satisfaction with one’s religion a a a 0.16 a

Satisfaction with family relations a a a a 0.13

Satisfaction with one’s job a 0.23 0.24 0.24 0.31

Satisfaction with one’s friendships a 0.25 0.21 0.23 0.28

Satisfaction with one’s health a 0.10 0.13 a 0.15

Satisfaction with one’s self-esteem a 0.29 0.30 0.31 0.20

Satisfaction with one’s living partner a 0.11 0.11 a a

Total percent of variance explained 7 60 59 59 66



tory variables had any explanatory power. Seven percent of the totalvariance in satisfaction with life as a whole scores was explained byrespondents’ age (B = 0.10), household income (B = 0.12), Index ofPolice Performance (B = 0.10) and their satisfaction with personalsafety in their neighbourhoods (B = 0.16).

The second column of figures in Exhibit 18 gives the resultsof regressing life satisfaction scores on the 6 demographic and14 domain satisfaction scores. Six of the 20 predictors had statis-tically significant impacts on the dependent variable, with one of thedemographic variables remaining in the equation. Altogether, the 6variables explained 60% of the variance in life satisfaction scores.Satisfaction with respondents’ own self-esteem (B = 0.29) was themost powerful explanatory variable in the set, with satisfaction withtheir friendships (B = 0.25) and their jobs (B = 0.23) coming closelybehind them. As indicated earlier in our bivariate analyses, beingunmarried was usually negatively related to all our global indicators.Our multivariate analysis confirmed the direction of the relationshipand washed out some of its absolute value.

The third column of figures in Exhibit 18 gives the results ofregressing life satisfaction scores on the 6 demographic, 12 crime-related and 14 domain satisfaction scores. Again, only 6 of the 32potentially explanatory variables had statistically significant impactson the dependent variable, with one of the demographic and none ofthe crime-related variables remaining in the equation. Altogether,the 6 variables explained 59% of the variance in life satisfactionscores. Satisfaction with respondents’ self-esteem (B = 0.30) wasthe most influential variable in the set, with satisfaction with jobs(B = 0.24) and friendships (B = 0.21) coming behind it.

The fourth column of figures in Exhibit 18 gives the results ofregressing life satisfaction scores on the 6 demographic, 10 crime-related and 14 domain satisfaction scores only for those who hadbeen victims of some crime. Only 6 of the 30 potentially explana-tory variables had statistically significant impacts on the dependentvariable, with two of the demographic and none of the crime-relatedvariables remaining in the equation. Altogether, the 6 variablesexplained 59% of the variance in life satisfaction scores, whichwas the same percentage for the total set of respondents. The mostpowerful explanatory variable was again satisfaction with one’s own


self-esteem (B = 0.31), followed by satisfaction with one’s job (B =0.24) and friendships (B = 0.23). Being unmarried was negativelyrelated to life satisfaction scores (B = –0.15).

The fifth column of figures in Exhibit 18 gives the results ofregressing life satisfaction scores on the 6 demographic, 10 crime-related and 14 domain satisfaction scores only for those who had notbeen victims of some crime. Only 5 of the 30 potentially explana-tory variables had statistically significant impacts on the dependentvariable, with none of the demographic or crime-related variablesremaining in the equation. Altogether, the 5 variables explained 66%of the variance in life satisfaction scores. Satisfaction with one’s job(B = 0.31) and friendships (B = 0.28) dominate the set of predictors,followed by satisfaction with one’s self-esteem (B = 0.20).

The first column of figures in Exhibit 19 gives the results ofregressing satisfaction with respondents’ overall quality of lifescores on 6 demographic and 12 crime-related variables. Just 4 ofthe 18 potentially explanatory variables had any explanatory power.Nine percent of the total variance in satisfaction with overall qual-ity of life scores was explained by respondents’ owning or renting(B = –0.08), household income (B = 0.09), Index of Police Perform-ance (B = 0.12) and their satisfaction with personal safety in theirneighbourhoods (B = 0.21).

The second column of figures in Exhibit 19 gives the resultsof regressing satisfaction with overall quality of life scores on the6 demographic and 14 domain satisfaction scores. Eight of the20 predictors had statistically significant impacts on the dependentvariable, with none of the demographic variables remaining in theequation. Altogether, the 8 variables explained 58% of the variancein satisfaction with overall quality of life scores. Satisfaction withrespondents’ own self-esteem (B = 0.29) was the most powerfulexplanatory variable in the set, with satisfaction with their jobs (B =0.17), their health (B = 0.18) and living partners (B = 0.15) comingbehind it.

The third column of figures in Exhibit 19 gives the results ofregressing satisfaction with overall quality of life scores on the 6demographic, 12 crime-related and 14 domain satisfaction scores.Again, only 8 of the 32 potentially explanatory variables had statis-tically significant impacts on the dependent variable, with none of

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Exhibit XIX

Regression of Satisfaction with the Overall Quality of Life on 6 Demographic, 12 Crime-related and 14 Domain SatisfactionVariables, All Respondents, Victims, Non-victims

6+12 6+14 6+12+14 6+10+14 6+10+14variables variables variables victim non-victim

Explanatory variables in equation Beta Beta Beta Beta BetaOwn or rent home –0.08 a a a aTotal household income 0.09 a a a aIndex of Police Performance 0.12 a a a aSex a a a –0.17 aIndex of Neighbourhood Problems a a a a –0.12Satisfaction with financial security a a a a 0.20Satisfaction with personal safety in one’s neighbourhood 0.21 a 0.08 0.18 aSatisfaction with local gov’t officials a 0.09 0.08 a 0.10Satisfaction with one’s religion a 0.09 0.10 0.19 aSatisfaction with one’s neighbourhood a 0.08 a a aSatisfaction with one’s job a 0.17 0.18 0.28 0.22Satisfaction with one’s housing a 0.13 0.15 0.14 aSatisfaction with one’s health a 0.18 0.20 0.25 0.12Satisfaction with one’s self-esteem a 0.29 0.27 0.24 0.19Satisfaction with one’s living partner a 0.15 0.15 a 0.29Total percent of variance explained 9 58 60 61 66



the demographic and one of the crime-related variables remaining inthe equation. Altogether, the 8 variables explained 60% of the vari-ance in satisfaction with overall quality of life scores. Satisfactionwith respondents’ self-esteem (B = 0.27) was the most influen-tial variable in the set, with satisfaction with health (B = 0.20),jobs (B = 0.18), housing (B = 0.15) and living partners (B = 0.15)coming behind it. Satisfaction with personal safety in one’s neigh-bourhood was considerably less influential (B = 0.08) than theseother variables.

The fourth column of figures in Exhibit 19 gives the results ofregressing satisfaction with the overall quality of life scores on the6 demographic, 10 crime-related and 14 domain satisfaction scoresonly for those who had been victims of some crime. Seven of the 30potentially explanatory variables had statistically significant impactson the dependent variable, with one of the demographic and one ofthe crime-related variables remaining in the equation. Altogether,the 7 variables explained 61% of the variance in satisfaction withthe overall quality of life scores, which was one percentage pointbetter than that achieved for the total set of respondents. The mostpowerful explanatory variable this time was satisfaction with one’sfinancial security (B = 0.28), followed by satisfaction with one’shealth (B = 0.25) and self-esteem (B = 0.24). Being male was nega-tively related to satisfaction with the overall quality of life scores(B = –0.17).

The fifth column of figures in Exhibit 19 gives the results ofregressing satisfaction with the overall quality of life scores on the6 demographic, 10 crime-related and 14 domain satisfaction scoresonly for those who had not been victims of some crime. Seven ofthe 30 potentially explanatory variables had statistically significantimpacts on the dependent variable, with none of the demographicand only one of the crime-related variables remaining in the equa-tion. Altogether, the 7 variables explained 66% of the variance insatisfaction with the overall quality of life scores. Satisfaction withone’s living partner (B = 0.29) led the field, with satisfaction withone’s job (B = 0.22) and financial security (B = 0.20) behind it.The Index of Neighbourhood Problems was negatively related (B =–0.12) to the dependent variable.


Exhibit XX

Regression of Satisfaction with One’s Neighbourhood on 6 Demographic and12 Crime-related Variables

6 + 12

variables

Explanatory variables in equation Beta

Age 0.11

Total household income 0.09

Being a victim –0.10

Neighbourhood crime increased –0.09

Index of Police Performance 0.11

Satisfaction with personal safety in one’s neighbourhood 0.23

Index of Neighbourhood Problems –0.42

Total percent of variance explained 38

EXPLAINING NEIGHBOURHOOD SATISFACTION

Because our bivariate analyses revealed that crime-related variableshad more robust relationships to satisfaction with neighbourhoodsthan with any of the global indicators, we thought it would be worth-while to regress neighbourhood satisfaction scores on our demo-graphic and crime-related variables. Exhibit 20 gives the resultsof regressing satisfaction with respondents’ neighbourhoods scoreson 6 demographic and 12 crime-related variables. Seven of the 18potentially explanatory variables had any explanatory power. Alto-gether, 38% of the total variance in satisfaction with neighbourhoodscores was explained. Respondents’ ages (B = 0.11) and householdincomes (B = 0.09) were the only 2 demographic variables remain-ing in the equation. The most powerful explanatory variable in theset was the Index of Neighbourhood Problems (B = –0.42), followedat some distance by respondents’ satisfaction with personal safety intheir neighbourhoods (B = 0.23). Following again at some distancewere respondents’ Indexes of Police Performance (B = 0.11), beinga victim (B = –0.10) and perceptions that neighbourhood crime hadincreased over the past two years (B = –0.09).


EXPLAINING RESPONDENTS’ EVALUATION OF POLICEPERFORMANCE

Since police services are the public services most directly relatedto criminal victimization and our Index of Police Performanceprovides a pretty good overall assessment of police services, it wasworthwhile to undertake a multivariate analysis to try to explainrespondents’ Index scores. Working from our correlation matrix, weused 27 potentially explanatory variables, each of which had a statis-tically significant correlation with the Index of Police Performance.They included perceived increases in neighbourhood crime, feelingsafe walking alone in one’s neighbourhood, Index of Crime-RelatedWorries, Index of Neighbourhood Problems, being a victim, harm-weighted victimization, satisfaction with personal safety and familysafety in one’s neighbourhood, satisfaction with one’s housing,neighbourhood, family relations, living partner, job, life as a whole,friendships, health, religion, financial security, recreation activities,self-esteem, federal government officials, provincial governmentofficials, local government officials, satisfaction with one’s over-all quality of life, happiness, and age. The result of the regressionanalysis was unimpressive. We could only explain 14% of the vari-ation in Index of Police Performance scores, and only four variablesfound their way into the regression equation. Satisfaction with localgovernment officials had the greatest influence, with a Beta value of0.22. Following that there was satisfaction with personal safety inone’s neighbourhood (B = 0.15), the experience of being a victim(B = –0.14) and age (B = 0.12). Hopefully, in a later study wewill be able to identify at least some of the variables that accountfor the other 86% of the variation in peoples’ evaluation of policeservices.

CONCLUDING REMARKS

The aim of this investigation was to explain the impact of crime-related issues on satisfaction with the quality of life in PrinceGeorge, overall happiness, and satisfaction with life as a whole.As explanatory variables we had an Index of Neighbourhood Prob-lems (e.g., drug or alcohol abuse in the neighbourhood), Index of


Police Performance (e.g., police promptly respond to calls), Indexof Neighbourhood Worries (e.g., thief will break in while you areaway), Index of Defensive Behaviour (e.g., special locks on yourdoors), beliefs that crime increased in respondents’ neighbourhoods,the city, Canada and local schools compared to 2 years ago, respond-ents’ frequency of walking alone in their neighbourhoods at nightand their feelings of safety about such walks, satisfaction withpersonal safety in neighbourhoods, satisfaction with family safetyin neighbourhoods, being a victim of some crime in the past year(e.g., one case of theft under $5000), being a victim with eachtype of crime weighted by the average amount of harm done bysuch crimes (e.g., 1 theft under $5000 times its average measureof harm). Collectively such variables could explain only 5% of thevariation in happiness scores, 7% of the variation in life satisfactionscores and 9% of the variation in satisfaction with the quality oflife scores. However, they could explain 38% of the variation inoverall neighbourhood satisfaction. When we added in measures ofsatisfaction with family life, health, self-esteem, etc., we found thatcrime related issues were simply displaced by the other measuresand that we could explain 31% of the variation in overall happi-ness scores, 58% of the variation in life satisfaction scores and59% of the variation in satisfaction with the overall quality of lifescores.

We conclude, therefore, that all things considered, while previoussurveys in Prince George have shown that crime reduction is alwaysmentioned as important to improving the quality of life here, crimi-nal victimization, beliefs, feelings and worries about safety, andspecial defensive behaviour related to personal safety have relativelylittle impact on people’s satisfaction with the quality of their lives,with life satisfaction or happiness here.


APPENDIX

Correlations among scores for satisfaction with government officials and selecteditems

Sat. Sat. Sat.

with with with

federal prov. local

gov’t gov’t gov’t

Neighbourhood crime decreased 0.10 0.08 0.17

City crime decreased 0.13 0.08 0.16

Crime in Canada decreased 0.17 0.13 0.15

Local school crime decreased 0.17 0.15 0.14

Walk alone in neighbd. at night 0.08 0.08 0.11

Feel safe walking alone at night 0.11 0.07 0.21

Index of Crime-Related Worries –0.12 –0.09 –0.18

Index of Neighbourhood Problems –0.13 –0.06 –0.21

Being a victim –0.12 –0.07 –0.10

Relative/close friend victimization –0.13 –0.11 ns

Harm-weighted victimization –0.12 –0.11 –0.10

Index of Police Performance 0.26 0.17 0.29

Index of Defensive Behaviour –0.10 –0.09 –0.10

Satisfaction with personal safety in one’s neighbourhood 0.19 0.11 0.31

Satisfaction with family safety in one’s neighbourhood 0.24 0.16 0.32

Satisfaction with housing 0.12 ns 0.19

Satisfaction with one’s neighbourhood 0.20 0.13 0.30

Satisfaction with one’s family relations ns ns 0.14

Satisfaction with one’s living partner ns ns 0.10

Satisfaction with one’s job 0.08 ns 0.15

Satisfaction with life as a whole 0.17 0.10 0.26

Satisfaction with one’s friendships ns ns 0.15

Satisfaction with one’s health 0.15 0.09 0.22

Satisfaction with one’s religion 0.16 0.08 0.14

Satisfaction with one’s financial security 0.19 0.09 0.30


APPENDIX

Continued

Sat. Sat. Sat.

with with with

federal prov. local

gov’t gov’t gov’t

Satisfaction with recreation activities 0.13 0.08 0.21

Satisfaction with one’s self-esteem 0.11 ns 0.21

Satisfaction with federal gov’t — 0.75 0.59

Satisfaction with provincial gov’t 0.75 — 0.49

Satisfaction with local gov’t 0.59 0.49 —

Satisfaction with one’s overall quality of life 0.21 0.13 0.30

Happiness 0.16 0.09 0.19

Age 0.11 ns 0.16

Household income –0.05 –0.10 ns

Sex ns ns 0.12

Own or rent housing ns ns –0.08

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College of Arts, Social and Health SciencesUniversity of Northern British ColumbiaPrince George, B.C.e-mail [email protected]

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Criminal Victimization and the Quality of Life

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