This study examines factors relating to burglary incidence in England and Wales, the United. States, and the Netherlands. Negative binomial regression models ...
BRIT. J. CRIMINOL.
(2004) 44, 66–91
BURGLARY VICTIMIZATION IN ENGLAND AND WALES, THE UNITED STATES AND THE NETHERLANDS A Cross-National Comparative Test of Routine Activities and Lifestyle Theories A N DR OM A C H I T S E L O N I , K A R I N W I T T E B R O O D , G R A HA M F A R R E L L
AND
K E N P E A SE *
This study examines factors relating to burglary incidence in England and Wales, the United States, and the Netherlands. Negative binomial regression models are developed based on routine activities theory. Data are drawn from national victimization surveys of about the same time: the 1994 British Crime Survey, the 1994 National Crime Victimisation Survey, and the 1993 Police Monitor, respectively. Relative to the two European countries, US households have more idiosyncratic patterns of burglary victimization. Despite differences across the three data sets, several similar effects emerge of variables tapping lifestyle characteristics on burglary victimization. Four variables had significant effects in the same direction in two or more countries where the third country showed a non-significant effect in the same direction. These were age, lone parent household status, urbanization, and the presence of security measures in the home. Some variables had significant effects in opposite directions according to country: rented accommodation was associated with higher burglary rates in the UK but lower rates in the Netherlands; household affluence was linked with higher rates of burglary in the UK and lower rates in the United States. The present study seeks to contribute to crime research in two main areas. The first contribution is to substantive knowledge about crime. The contribution derives from an examination of three main research questions:
• Do indicators derived from routine activity and lifestyle theory affect household burglary incidence similarly across three countries? • If they do, which are the most consistent? • What are the potential implications of the findings? The second hoped-for contribution is to methodology, though this is necessarily also linked to the area of substantive knowledge. There are, to the writers’ knowledge, no previous cross-national studies which use comparative negative binomial models to examine routine activity and lifestyle theories. The negative binomial model has two main advantages for present purposes. First, it accounts for the role of repeat victimization in the composition of crime. Second, it allows an examination of the extent of unexplained heterogeneity between households in different countries (this is explained more fully below). Further, instead of concentrating on relatively few variables as in some previous studies, all available indicators of routine activities and lifestyle are * Andromachi Tseloni, International & European Economic and Political Studies, University of Macedonia, Thessaloniki, Greece; Karin Wittebrood, Social and Cultural Planning Office, The Hague, The Netherlands; Graham Farrell, Department of Social Sciences, Loughborough University, UK, Ohio and Jill Dando Institute of Crime Science, University College, London; Ken Pease, Jill Dando Institute of Crime Science, University College, London. Please address correspondence to Professor Ken Pease, 19 Withypool Drive, Stockport SK2 6DT, UK. Many thanks to Professors Gary LaFree and John Laub for their insightful comments. We are responsible for remaining errors.
66 British Journal of Criminology 44(1) © the Centre for Crime and Justice Studies (ISTD) 2004; all rights reserved
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included, and statistical controls for the effects of demographic characteristics are incorporated. In short, the study method allows an examination of the explanatory power of routine activities and lifestyle theories. If, controlling for method, the theory does not explain burglary equally well across the three countries, then there may well be implications for theory and policy. Previous Cross-National Comparative Models of Routine Activities and Lifestyle Theories The influential routine activities and lifestyle theories of victimization risk were proposed two-and-a-half decades ago (Hindelang et al. 1978; Cohen and Felson 1979). Most testing of the theories cross-nationally have relied primarily on bivariate analyses. Such analyses have compared victimization risk to characteristics of individuals (Block and Block 1984; Block 1986 and 1989; Van Dijk and Mayhew 1992; Gottfredson 1986; Mayhew 1987; Mayhew and Van Dijk 1997). These studies have thereby largely ignored the magnitude and simultaneity of effects, risking false inferences (Johnston 1984). Tests of routine activity and lifestyle theories which draw upon cross-national empirical models of victimization risks are limited in number and scope (Van Dijk and Steinmetz 1983; Maxfield 1987).1 Both the studies cited here examined the victim/non-victim dichotomy in two countries and focused on few indicators of routine activities. Van Dijk and Steinmetz (1983) examined the effects of age, going out at night, living in a big city, social class and prior victimization on the risk of burglary, theft, motor vehicle theft, vandalism, assault and sexual attacks, as well as their aggregate in the Netherlands and Vancouver (Canada). They used data from the 1980 Dutch Victim Survey and the 1978 Greater Vancouver Area Victim Survey, and found that, in both countries, those under 25 years and people who go out frequently at night are more likely to be victimized. Maxfield (1987) tested the effects of household composition and routine activities, both vocational and leisure, on personal and household victimization risks in the US and England and Wales. He used data from the 1983 National Crime Survey and the 1982 British Crime Survey. In both countries household composition was strongly related to property crime while night-time activity was associated with personal victimization. The Importance of Incorporating Repeat Victimization into Statistical Modelling of Crime Although the concentration of crime incidents on repeat victims was recognized in early victimization studies (e.g. Hindelang et al. 1978; Reiss 1980), it has attracted policy-related attention during the last decade (see e.g. Farrell 1992; Pease 1998). Work on repeat victimization highlights the need to model the distribution of crime events rather than the victim-non victim dichotomy, since the factors which influence 1 Sousa (1997) and Van Dijk (1997), who have modelled international victimization risks using the International Crime Victimization Survey (ICVS, see Van Dijk and Mayhew 1992; Mayhew and Van Dijk 1997) are in the process of publishing their results at the time of writing. Since the present study utilizes large-scale surveys from three countries, the principal benefit it has over ICVSrelated studies, apart from methodological, is the wider range of variables that are available and the significantly larger sample sizes. Bennett (1991) tested the routine activities theory at the macro level, namely ‘investigat[ing] the empirical relationship between social structure and risk [without] measuring or testing [the routine activities] within the model’ (Bennett 1991: 147–8). Thus his study is not directly comparable to the present study. This is also true for the work by LaFree and Birkbeck (1991) who examined the situational characteristics of criminal incidents. They estimated empirical models of personal contact crimes using victimization survey data from the US and Venezuela.
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repeat victimization may differ from those that explain an isolated crime experience. Osborn and Tseloni (1998) showed that modelling crime incidence (crimes per capita or household at risk) rather than prevalence or risk (targets per capita or household at risk) predicts the entire distribution of victimization identified by previous descriptive studies (e.g. Ellingworth et al. 1995; Chenery et al. 1996). Osborn and Tseloni used the negative binomial model (see Cameron and Trivedi 1986) of crime counts. In the context of cross-sectional data, the negative binomial model assumes that households or individuals face differing crime risks which remain constant over time (Osborn and Tseloni 1998: 308). The model is therefore limited in that it only explains repeat victimization in terms of risk heterogeneity. ‘Risk heterogeneity’ refers to the fact that some targets are inherently more attractive and remain so over time, thereby inducing repeated victimization by either the same or different offenders.2 It is unfeasible to measure everything in a single study. Knowledge is accrued via an iterative process, often requiring triangulation from multiple studies. In the context of cross-national comparative theory testing, the negative binomial regression model is an incremental step forward. It allows for the estimation of any unexplained3 heterogeneity between the units of analysis, whether households or individuals, and does so in a manner which distinguishes the estimates from the effects of crime covariates (Tseloni 1995; Osborn and Tseloni 1998). In the present context, the term ‘unexplained heterogeneity’ refers to the fact that ‘two households can face risks which are systematically different from each other even when these households have identical measured characteristics’ (Osborn and Tseloni 1998: 308). The greater the level of unexplained heterogeneity, the less predictable are the crime risks of households or individuals (Tseloni 2000). Unexplained heterogeneity exists for two reasons: either the theory is imperfect, or the empirical measures are imperfect. For example, consider the case in which the model predicts the burglary rate of one nation better than that of another. In this case, either the lifestyle variables or the manner in which they are operationalized are less perfect in relation to the country that is more poorly predicted. Two Methods of Cross-National Comparative Modelling There are two principal methods for cross-national empirical modelling (Lynch 1995). In the first, data from different countries are pooled in a single model. This method allows for the identification of national differences in the intercept (via country dummy variables or country-level variance) and allows the effects (via country interactions or random effects) of the explanatory variables or country-specific models to be estimated. In the second principal method, each country’s data is modelled separately. While the two methods are equivalent, the second accommodates cross-national differences more readily. If different countries are similar, elegant and concise results can be derived utilising the first method. Tseloni and Farrell (2002) modelled burglary incidence across eight European countries using the 2000 International Crime Victims Survey (see Van Kesteren et al. 2000) via a single multilevel negative binomial regression model 2 The other explanation is event dependence (Lauritsen and Davis-Quinet 1995; Wittebrood and Nieuwbeerta 2000) whereby one victimization increases the risk of subsequent victimization. However this explanation cannot be tested without panel data or information on prior victimization by same-crime type (Osborn and Tseloni 1998). 3 As used here, this term is equivalent to ‘unobserved heterogeneity’. However we prefer ‘unexplained’ since, while the negative binomial model cannot assign it to any particular factor(s), it does capture it.
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(Tseloni 2000). The model showed similar burglary victimization patterns across Europe despite the relatively few household-level independent variables yielded by the limited number of measures available in the ICVS. The present study tests routine activity and lifestyle theories, using models of burglary incidence drawn from comparable national data sets in three countries. The countries are England and Wales (for brevity, hereafter referred to as the UK, with apologies to Scots and Ulster people, and island-dwellers within the UK), the US and the Netherlands. Each has a long history of self-report victimization surveys, and is among the Western industrialized countries with the highest burglary rates (Mayhew and Van Dijk 1997: 21). It has been suggested that they best fit macro-level routine activity approaches (Bennett 1991: 159). Estimating three comparable yet country-specific models is necessitated by differences between the three data sources. This paper’s next section describes the data sets. That is followed by a comparison of the distribution of burglary in the three countries, an overview of the routine activity and lifestyle victimization theories and the development of measurable theoretically informed variables. Following a presentation of the results of the analysis, the final section presents a discussion and conclusions together with suggestions for future research. Data This study employs data from the national victimization surveys of three countries, each covering roughly the same period. The surveys are the 1994 British Crime Survey (BCS) for England and Wales, the 1993 Police Monitor (PM) for the Netherlands and the 1994 National Crime Victimization Survey (NCVS) for the US. The British Crime Survey (BCS) The 1994 BCS was conducted by the Social Survey division of the (then) Office of Population Censuses and Surveys, on behalf of the Home Office. The BCS, whose first sweep was in 1982, has been administered biannually since 1988, and since 2001 on a rotating annual basis. It employs a multistage stratified sample, in principle representative of the adult (16 years or older) population of England and Wales living in private accommodation. The sampling frame is the Postcode Address File. One adult (aged 16 or over) from each selected household is randomly chosen by the interviewer, using selection tables (White and Malbon 1995: 8). In the 1994 BCS, respondents were invited to report any victimization experienced since January 1994. Most interviews took place between February and April 1995 and some (7 per cent) were as late as June 1995 (White and Malbon 1995: 12). The survey also gathers information on attitudes towards crime, policing, fear of crime etc. The core sample of the 1994 BCS includes 14,520 respondents with a response rate of 77 per cent (White and Malbon 1995: 16). Computer Assisted Personal Interviewing (CAPI) was used for the full sample (White and Malbon 1995: 12). The Police Monitor (PM) Victim Survey in the Netherlands The PM, initiated and funded by the Dutch Ministry of Internal Affairs and the Ministry of Justice, has been conducted biannually since 1993. This national crime survey is 69
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primarily meant to serve the police and public prosecutors by informing them about victimization rates within the Dutch population, its fear of crime and perception of the police. The PM is designed as a representative survey of households in the Netherlands. In 1993 data were collected on 50,704 individuals aged 15 years or older. The response rate was 52 per cent. Individuals were selected by using the national telephone number as a sampling frame. Within each household contacted by telephone the person whose birthday was closest in the future was selected. The questionnaire was taken by Computer Assisted Telephone Interviewing (CATI) in early 1993. In principle the reference period in the PM is 12 months. The US National Crime Victimization Survey (NCVS) The NCVS has been conducted by the Census Bureau of the US on behalf of the Bureau of Justice Statistics since the mid-seventies and since 1991 in its current format. It includes non-institutionalized US permanent residents aged 12 and over (though those aged 16 and over were included in the present study). The Address List of the Decennial Census provides the sampling frame for a rotating panel of housing units. Every six months over a three-and-a-half year period, detailed information about the victimization experiences of all members of the households in the selected housing units is collected using, alternately, face-to-face interviews and CATI. The first contact with the household at the selected address is called a bounding interview. This is a pre-survey interview, which ensures the absence of telescoping memories. Household crimes, together with other household information, are detailed by a designated household respondent. This individual, chosen by the interviewer, is generally at least 18 years old and appears to be the most knowledgeable about household composition. The NCVS response rate is about 95 per cent. As in most panel designs there is considerable attrition since individuals and/or households move in and out of the selected housing units during the survey. The 1994 NCVS sample analysed in this study comprises all households occupying selected residential units during January to June 1994, for as long as they remained in the survey. Thus, the reference period of the sample varies. Indeed, 2.5 per cent of households remained in the survey for one and a half years, 34 per cent for one year and 63.5 per cent for six months. The dummy variable ‘Moved out during reference period’ in the NCVS model (detailed below) captures the lower burglary incidence due to the shorter reference period. Information on the set of explanatory variables in the NCVS model is drawn from answers given by the household respondent at the time of her or his last interview, as in the cross-sectional designs of the BCS and the PM. Burglary: Definition and Distribution Burglary is a crime type most suitable for international comparison since it is relatively well-defined across national borders (Mayhew 1987). Indeed the identification of burglary victims by crime surveys has been claimed to be more accurate than for any other crime type (Schneider 1981: 832, 837). The data on burglary victimization for this study are taken from answers to the screening questions of the three surveys about any number of completed or attempted burglaries, which may have occurred during 70
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the reference period.4 Table 1 shows the observed frequency distributions of burglaries experienced in the UK,5 the US and the Netherlands. Three burglary rate measures are used herein and are defined as follows:
• Burglary prevalence or risk is the proportion of burgled households to the total number of households in the population. • Burglary incidence or rate is the ratio of burglaries to the total number of households in the population. • Burglary concentration is the number of burglary incidents per burgled household, that is, the mean repeat burglary rate. In all three countries the majority of the respondents reported zero incidents. The distributions are remarkably similar for the UK and the Netherlands. Burglary prevalence in the two European countries is 8.1 per cent, while the prevalence of repeat burglary in the total population is 1.6 per cent. However, the central role of repeat burglary is evidenced by the fact that these 1.6 per cent of households account for 41 per cent of burglaries in England and Wales, and 40 per cent of Dutch burglaries (Table 1). By contrast, US burglary and repeat burglary prevalence are over four times less, namely 1.7 per cent and 0.3 per cent, respectively. The 0.3 per cent of US households that are repeatedly burgled account for 30 per cent of total burglaries.6 In the US the estimated burglary incidence is roughly five times less than that of the two European countries. Although among the three countries the US showed the lowest (4.9 per cent) burglary prevalence in the 1996 ICVS (Mayhew and Van Dijk 1997: 21), each of prevalence, incidence and repeat burglaries in the US are underestimated in the present study. Research based on the NCVS showed that changing residence and thereby moving out of the survey before the three-and-a-half years of interviews have elapsed is related to property crime (Dugan 1999). The annual rate of repeat burglary is underestimated in the NCVS due, in part, to missing data (as demonstrated by the highly negative effect of moving out during reference period on US burglary incidence in Table 3) since victims who left the survey prematurely are likely to have been revictimized during the missing period (Ybarra and Lohr 2002). Similarly, the six-month reference period of the NCVS may understate the repeat burglary rate by over 40 per cent relative to the one-year reference period of the BCS and the PM (Farrell et al. 2002 estimated it at 42 per cent). Indeed, if this figure were used to produce a grossed-up estimate of the extent of repeat burglaries, it would suggest that repeatedly burgled US households accounted for 42.6 per cent of all burglaries (since 30 per cent × 1.42 = 42.6 per cent). Hence when the short NCVS reference period is accounted for, the extent of repeat burglaries in the US is almost identical to that in the UK and the Netherlands. 4 The reason for not working with the victim forms or incident level data was to avoid the differential censoring of the answers of the respondents across the three surveys. For instance, the BCS collects information for up to 5 crimes per person whereas the NCVS up to 11 incidents per person. Series are also differentially defined (see also footnote 15). The problems with working with screener questions are acknowledged. 5 The BCS includes incidents at the previous address if respondents had moved within the reference period of the survey. Since such information is unrelated to the set of explanatory variables, which are drawn from information on the characteristics of the dwelling at the time of the interview, burglaries at the previous address are not considered in this study. 6 Although elsewhere we have argued against such practices, in order to produce an extremely conservative estimate of the extent of repeat burglaries, we artificially capped the number of burglaries that a person can report in a series at an upper limit of 11.
71
0 1 2 3 4 5 6 7 8 9 10 11 or more Total cases Incidence (Mean) Variance Concentration % burglaries at repeatedly burgled households
Number of burglaries
11,806 833 139 34 15 4 5 1 1 0 3 4 12,845 0.114 0.162 1.407
N 91.9 6.5 1.1 0.3 0.1 0 0 0 0 0 0 0 100
%
Households
0 833 278 102 60 20 30 7 8 0 30 44 1412
N
72 41.0%
0.0 59.0 19.7 7.2 4.2 1.4 2.1 0.5 0.6 0.0 2.1 3.1 100.0
%
Burglaries
England and Wales
71,208 1,048 107 29 6 1 5 0 0 1 1 6 72,412 0.022 0.091 1.294
N 98.3 1.4 0.1 0 0 0 0 0 0 0 0 0 100
%
Households
0 1048 214 87 24 5 30 0 0 9 10 66 1493
N
29.1%
0 70.9 14.3 5.8 1.6 0.3 2.0 0 0 0.6 0.7 4.4 100
%
Burglaries
United States
TABLE 1 Observed frequency distribution for burglary victimization
36,632 2,548 448 134 51 19 8 2 1 2 2 2 39,849 0.11 0.19 1.358
N
91.9 6.4 1.1 0.3 0.1 0 0 0 0 0 0 0 100
%
Households
0 2,548 896 402 204 95 48 14 8 18 20 22 4,275
N
40.4%
0 59.6 21.0 9.4 4.8 2.2 1.1 0.3 0.2 0.4 0.5 0.5 100
%
Burglaries
The Netherlands
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The distributions of burglary given in Table 1 are overdispersed. ‘Overdispersion’ means that, due to the long tail of repeat burglaries, the sample variance is greater than the sample mean number of burglaries. The observed overdispersion is 4.1, 1.4 and 1.7 in the US, the UK and the Netherlands, respectively. Due largely to the relatively low burglary prevalence rate, burglary distribution in the US is over three times more dispersed relative to that of the UK and the Netherlands (with respective coefficients of variation of 13.7, 3.5 and 4). This issue is revisited in the results section. Identifying and Operationalizing the Explanatory Variables The routine activity or lifestyle theory is based on two central assumptions (Miethe and Meier 1990: 245). First, patterns of routine activities and lifestyles are assumed to create a criminal opportunity structure by enhancing the contact between potential offenders and victims. Second, the subjective value of a target and its level of guardianship are assumed to determine the offender’s choice of victim. Four risk factors are generally detailed: target exposure, absence of capable guardianship, attractiveness, and proximity to potential offenders. The nature of the four risk factors and the manner in which they were operationalized for the study are described in this section. The variables that were used as statistical controls are also described. For each explanatory variable, one value was nominated as the base category, and burglary rates of the other values were expressed as a ratio to the base category. For example, in the variable dwelling type, detached/semi-detached homes form the base category. The risks of other dwelling types are expressed in relation to the burglary rate of a detached or semi-detached household. Target exposure Target exposure refers to one’s availability to be a victim (see Meier and Miethe 1993). In the context of burglary, the physical visibility and accessibility of the residence are the relevant concepts (Bennett and Wright 1984: 93–4). Dwellings obscured from public view by trees, shrubs, or fences have a relatively high risk of burglary victimization (Hope 1984). The design and type of an accommodation can indicate the degree of accessibility. Terraced houses and flats, especially those with entry points on the second floor and above, are less likely to be burgled than detached or semi-detached houses in the UK (Ellingworth et al. 1997; Osborn and Tseloni 1998). In the US a dwelling was found to have a high exposure to burglary if detached from other units (Miethe and Meier 1990). Van Burik et al. (1991) found that Dutch burglars prefer houses to apartments, and that houses at the end of terraced row or detached houses are especially preferred. Target exposure is reduced here to residence type of which four types were distinguished: Semi-detached or detached houses were the base category; Rows of terraced houses; Flats (apartments); and Other residences. ‘Other’ residence types could include a non-transient room in a hotel or motel, or a caravan (trailer). However, since the NCVS groups houses and flats as a single category it was necessary to use this as the base category for the NCVS model. 73
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Guardianship Guardianship refers to the capability of persons and objects to prevent crime from occurring. Two types of guardianship can be distinguished: social (interpersonal) and physical (Meier and Miethe 1993; Garofalo and Clark 1992). Social guardianship: This includes household composition, house occupancy and having neighbours who watch a dwelling when it is unoccupied. Most burglars avoid dwellings which are occupied or show signs of occupancy (Bennett and Wright 1984: 94; Maguire and Bennett 1982). Many of those at work or in education leave and return home at approximately the same time every weekday, making these activities quite predictable (Miethe et al. 1987). Furthermore, the type of daily activity indicates the amount of time spent outside the home. Homes which are left empty for several hours a day are more vulnerable than those which are not (Hough 1984: 21). Ellingworth et al. (1997) found a strong positive effect of lone parenthood on burglary risk. Similarly, Osborn and Tseloni (1998) found a strong negative effect of having two or more adults in the household on burglary incidence in the UK. In the present study, social guardianship was measured by household composition, marital and employment status, lifestyle indicators, and (only in the PM) whether or not occupants had an informal arrangement with neighbours to watch each others’ homes. Household composition was operationalized as the number of adults in the household, whether there were children under 16 (12 for the US), and whether the residence was a lone parent household.7 Employment status was operationalized as whether or not residents worked part time, unemployed, homemaker, retired or sick/ disabled and student against working full time.8 The variable for ‘Marital status’ distinguishes married (the base category) from two other categories: divorced or separated; widowed or single. Together, these variables serve as proxy lifestyle measures of the extent to which the residence is left unoccupied, that is, as partial indicators of social guardianship. Other measures of social guardianship were derived from more direct lifestyle indicators: the number of hours the house is empty; number of evenings out per week; and amount of time spent shopping. The last of these lifestyle measures was only available for the NCVS. In addition, the number of hours a residence is empty was not directly comparable between the BCS and the PM since the BCS asked about the activities of the respondent whereas the PM asked how often there was no one home in a typical week. Physical guardianship: This involves the use of self-protection measures and participation in collective crime prevention enterprises (Meier and Miethe 1993). Physical guardianship in principle decreases the risk of burglary (see for instance, Miethe and Meier 1990; Miethe and McDowall 1993; Budd 1999). However, the opposite effect has also been found (Tseloni and Farrell 2002), an issue that is returned to later. In the present study physical guardianship was measured by whether the household participated in a neighbourhood watch programme as well as by the presence of security measures. Security measures included additional locks, leaving lights on when house is empty, having a light timer or special outside lighting, a burglar alarm, or a dog. 7
A lone parent household consists of one adult living with one or more children. Being unemployed, homemaker and retired/sick or disabled are conpounded in the US models under the general label not in paid work. 8
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Target attractiveness Target attractiveness or desirability refers to the value of goods available in the residence (Meier and Miethe 1993). The higher the apparent economic or symbolic value of these properties, the more attractive they tend to appear and, thus, the higher the risk of being victimized (Miethe and Meier 1990). Therefore, households with higher income, status and/or education, would probably own more valuable property and, therefore, be more attractive to burglars (Cohen et al. 1981; Miethe and Meier 1994). Potential offenders may be similarly attracted by a property as a consequence of its geographical location or its exterior condition (Meier and Miethe 1993; Cromwell et al. 1991: 33–4). Owning two or three cars increases burglary risk but having four or more decreases burglary risk in the UK (Ellingworth et al. 1997; Osborn and Tseloni 1998), suggesting that, within limits, affluence revealed by cars may attract burglars. The proxy measures of target attractiveness utilized in the present study were householder income and educational level, household tenure type and the number of cars owned by household members. The variable ‘Income’ contrasts poor and affluent households to the ones with average annual income (the base category).9 The variable ‘Educational level’ was indexed according to the most recent educational level attained: primary education, middle and high school (combined in the US model) and university degree. The variable for household tenure type contrasts private rented and owner occupied accommodation.10 Proximity to potential offenders Proximity to potential offenders refers to the physical distance between potential targets and populations of potential offenders (Meier and Miethe 1993). Living in a high-crime area increases one’s risks of victimization since offenders tend to target victims in close proximity to their residences (Cornish and Clarke 1986). Miethe and Meier (1990) found that those who lived in areas with higher levels of offenders had higher risks of burglary. Giving indirect support to the link, Cohen et al. (1981) found that dwellers in central cities and low-income areas suffered higher risks of burglary than people who lived in other types of area. Similarly, inner city residents or council housing occupiers face higher burglary risks and rates in the UK (Ellingworth et al. 1997; Osborn and Tseloni 1998). Proximity to potential offenders was measured by various proxy indicators.11 From the NCVS a dummy variable was used that indicates whether the respondent lived in an urban area. A categorical variable was also used to indicate the population size of the area of residence. The inference is that offenders congregate in big cities. An inner city dummy (for definition see White and Malbon 1995) and region within the UK were the area variables taken from the BCS. The implication is that English regions and Wales 9 Due to the high non-response for the household’s income in the NCVS we include non-respondents as another income category in the US model. Income information is not available in the Dutch data. 10 Public housing (equivalent to council housing in the UK) is not identified in the 1994 NCVS. In the Netherlands a type of tenure equivalent to public/council housing does not exist. Thus, to ensure comparability council housing and rented are collapsed into the same category for the BCS models. 11 Area-level information was unavailable in each of the three surveys due to statistical confidentiality.
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have varying degrees of urbanization. For the Netherlands, the degree of urbanization of respondents’ areas of residence was utilized.12 Control variables The demographic characteristics of the respondent, such as her or his age, sex and race, were included in the regression models as control variables. Age was a count variable in all three data sets. The minimum age of respondents was 15 years in the PM and 16 years for the BCS and the NCVS.13 With regard to race, in the PM the variable takes the value 1 if the respondent is not Dutch by origin, whereas the BCS and NCVS models utilized a non-white dummy variable.14 Length of residence at the same address for the UK and the US was used as a variable to indicate familiarity with the area of residence. The variables ‘living for less than a year at the same address’ in the BCS and ‘moved out during the reference period’ in the NCVS were used to control for the risk reference period and thus the duration of exposure to crime. Table 2 sets out the proportions of homes falling under each value of the relevant explanatory variable. Thus, for example, in England and Wales 54.2 per cent of surveyed homes were detached or semi-detached, 29.7 per cent terraced, 15.8 per cent flats, and 0.4 per cent mobile. Missing values were eliminated from each data set. The abbreviation ‘n.a.’ in cells of Table 2 indicates that a variable did not exist in the relevant data set. The variable descriptions demonstrate some cross-national differences in the resultant variables. In some instances there are partially omitted characteristics or incongruous classifications of the same variables in the three data sets. However, such differences were eliminated where possible, so that the bulk of information incorporated in the regression was comparable across the three countries. Further, where some variables were not directly comparable, it is arguably possible to account for this in the interpretation of findings. Analysis To model the distribution of burglary in each country, the negative binomial regression, a compound Poisson model, was employed. The negative binomial model accounts for the overdispersion of the empirical burglary distributions, that is, the long ‘tail’ of repeat burglary that existed in the US, the UK and the Netherlands (Table 1). Osborn and Tseloni (1998) provided a more comprehensive description of the negative binomial model and Tseloni et al. (2002) discussed its applicability and implications for victimization research. For modelling purposes the number of burglary counts was truncated at four events. This was because the measure of interest for present purposes was the proportion of households rather than the proportion of burglaries. Typically 0.1 per cent or less of households reported five or more burglaries even though they accounted for a greater proportion of burglaries (Table 1). An advantage to truncating the observed burglary 12 This is based on the population density of the place of residence. Municipalities with the highest level of urbanization were coded 1. 13 The very few cases with household respondent younger than 16 were dropped from the employed NCVS file. 14 Although the original race variables offers a detailed list of categories in the BCS and the NCVS they were collapsed into nonwhite categories to improve comparability with the PM and to increase category sample sizes.
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TABLE 2
TARGET EXPOSURE Type of accommodation detached/semi-detached house row/terraced house apartment/flat mobile SOCIAL GUARDIANSHIP Number of adults in household 1 2 3 or more Any children in household Lone parent Marital status single married divorced/separated widowed Employment status paid job (>15 hours) paid job (15 hours) paid job (
