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The Global Crime Drop and Changes in the Distribution of Victimisation Ken Pease1* and Dainis Ignatans2

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The Global Crime Drop and Changes in the Distribution of Victimisation Ken Pease1* and Dainis Ignatans2 Pease and Ignatans Crime Sci (2016) 5:11 DOI 10.1186/s40163-016-0059-4 RESEARCH Open Access The global crime drop and changes in the distribution of victimisation Ken Pease1* and Dainis Ignatans2 Abstract Over three decades crime counts in England and Wales, as throughout the Western world, have fallen. Less attention has been paid to the distribution of crime across households, though this is crucial in determining optimal distribu- tion of limited policing resources in pursuing the aim of distributive justice. The writers have previously demonstrated that in England and Wales the distribution of crime victimisation has remained pretty much unchanged over the period of the crime drop. The present paper seeks to extend the study of changes in the distribution of victimisa- tion over time using data from 25 countries contributing data to the International Crime Victimisation Survey (ICVS) sweeps (1989–2000). While fragmentary, the data mirror the trends discerned in England and Wales. The trends are not an artefact of the inclusion of particular countries in particular sweeps. The demographic, economical, geographi- cal and social household characteristics associated with victimisation are consistent across time. The suggested policy implication is the need for greater emphasis on preventing multiple victimisation. Keywords: Victimisation, Crime drop, Crime concentration, Distributive justice, Quantitative criminology Background proportion of the total burden than before. Disaggrega- Distributions are as important as measures of central tion by offence type (Ignatans and Pease 2015b) showed tendency for applicable research. In the burgeoning liter- that the trends were for all practical purposes uniform ature on the near ubiquitous crime drop of recent years, across crime types. an emphasis on distributions has arguably been lacking. The crime drop is common across nations. Are the Yet if (for example) the crime drop has been most marked distributional trends also similar cross-nationally? Early for those who already suffered relatively little crime (as analyses of the crime drop were flawed in their exclu- with regressive taxation) one would be concerned about sive concentration on trends in the USA. This led to the disproportionate burden which the most victimised the choice as explanatory variables which were specific continue to suffer. At the operational policing level, it is to that country, such as prison use, police strength and crucial to know how the diminished crime burden is dis- abortion legislation. These variables trended differently tributed, so as to inform resourcing and deployment deci- in other countries with similar crime drops (Tonry 2014). sions. A recent paper (Ignatans and Pease 2015a) showed The intention here, insofar as the data permit, is to that the crime drop in England and Wales in recent years examine whether the trends identified in England and was greatest in absolute terms for the most victimised Wales extend beyond its borders. Is the slight increase in households, but not so great relative to the decline of inequality of distribution of crime evident in England and crime generally as to yield a more even distribution of Wales also evident elsewhere? The implications for crime victimisation. The most victimised came to suffer fewer control are substantial and will be touched upon in the crimes, but these crimes represented a somewhat higher “Conclusions”. Main text *Correspondence: [email protected] For present purposes, four sweeps of the International 1 Jill Dando Institute, University College London, 35 Tavistock Square, Crime Victims Survey (ICVS) with a total representative London WC1H 9EZ, UK Full list of author information is available at the end of the article adult sample of over 100,000 respondents over a decade © 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Pease and Ignatans Crime Sci (2016) 5:11 Page 2 of 6 were utilised. ICVS features victimisation reports over a 25 recall period of 1 year that were gathered from 25 coun- 1989 tries contributing data in at least one sweep. Thirteen of 20 these countries featured in at least two sweeps. The data 15 used in the analyses comes from countries that were sur- 1992 veyed nationally. Roughly 1000–2000 households were 10 Proportion interviewed from each country over the phone (Nether- 1996 lands Institute for Scientific Information Services 1999). 5 Self-evidently trends require a minimum of two data 0 2000 points to discern so data from twelve countries had to 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 be discarded. The fragmentary nature of the other data, Population Percentiles and the fact that the most recent ICVS sweep was in 2000 Year 1989 1992 1996 2000 when the crime drop had been under way for less than a Total Sample 31017 20217 19892 33072 decade, represented challenges. Given the unique nature Victimised Sample 4013 3521 3033 4165 of the ICVS data and the importance of the topic in the (Vehicle) writers’ view, it was a challenge worth accepting. Total Crimes (Vehicle) 6224 5619 4596 6464 By taking into account the four-year time elapsing Fig. 2 Proportion of total vehicle victimisations by year and percen- between sweeps, national data present in two or more tile, ICVS Sweeps 1989–2000 datasets was deemed sufficient for substantive compari- sons of trends in victimisation concentration over time. Offences were coded identically in all the sweeps, per- mitting consistent comparison across time. All thirteen 6 1989 countries are used in the global analyses (Figs. 1, 2, 3, 4, 5 5, 6). 4 Multiple events against the same victim in ICVS are 1992 capped to a maximum of five for each offence type. If, for 3 Mean example, someone reports having been assaulted twenty 2 times, the number of assaults is recorded as five. This 1996 convention compromises accuracy. The writers’ analy- 1 ses utilising the Crime survey for England and Wales 0 2000 (CSEW) with unlimited incident reporting show many 91 92 93 94 95 96 97 98 99 100 Population Percentiles victimisation repeats above the cut-off point of five. The Year 1989 1992 1996 2000 underrepresentation of chronic victims in crime surveys Total Sample 31017 20217 19892 33072 Victimised Sample 806 1822925 1641 (Property) 6 1989 Total Crimes (Property)640 1298676 1235 5 Fig. 3 Mean property victimisations per household by year and percentile, ICVS Sweeps 1989–2000 4 1992 3 Mean 2 1996 is controversial (Farrell and Pease 2007; Lauritsen et al. 1 2012). The pre-imposed ICVS five-count threshold was 0 2000 by necessity retained. The reader should be aware that by doing so the extent of chronic victimisation, and hence Population Percentiles the inequality of victimisation, is understated. Year 1989 1992 1996 2000 When considering measurement of victimisation ine- Total Sample 31017 20217 19892 33072 quality, a slightly amended approach from that of Lorenz Victimised Sample 4013 3521 3033 4165 (Vehicle) (1905) was adopted. Households were ranked by number Total Crimes (Vehicle) 6224 5619 4596 6464 of victimisations suffered, the ranked households divided into percentiles, and number of victimisations per per- Fig. 1 Mean vehicle victimisations per household by year and per- centile for each year calculated. Each percentile’s crime centile, ICVS Sweeps 1989–2000 could then be expressed as a proportion of the year’s total Pease and Ignatans Crime Sci (2016) 5:11 Page 3 of 6 60 40 1989 35 1989 50 30 40 25 1992 1992 30 20 Proportion Proportion 15 20 1996 1996 10 10 5 0 2000 0 2000 91 92 93 94 95 96 97 98 99 100 91 92 93 94 95 96 97 98 99 100 Population Percentiles Population Percentiles Year 1989 1992 1996 2000 Year 1989 1992 1996 2000 Total Sample 31017 20217 19892 33072 Total Sample 31017 20217 19892 33072 Victimised Sample Victimised Sample 806 1822925 1641 4249 2983 3132 4917 (Property) (Personal) Total Crimes (Property)640 1298676 1235 Total Crimes (Personal)2364 1768 1850 2944 Fig. 4 Proportion of property victimisations by year and percentile, Fig. 6 Proportion of personal victimisations by year and percentile, ICVS Sweeps 1989–2000 ICVS Sweeps 1989–2000 6 • The proportion of all victimisations of that type suf- 1989 5 fered by the most victimised percentile, next most victimised, and so on. 4 1992 3 Results Mean In all the figures the scales should be noted. They differ, 2 1996 being chosen to provide the clearest representation of 1 the key part of the victimisation distribution. Offences were categorised in the appropriate categories in a fash- 0 2000 91 92 93 94 95 96 97 98 99 100 ion consistent with previous papers (Ignatans and Pease Population Percentiles 2015a, 2015b) attributing all crimes that involve direct Year 1989 1992 1996 2000 contact with the victim to the personal crime category, Total Sample 31017 20217 19892 33072 even where property was taken. Categories were con- Victimised Sample 4249 2983 3132 4917 (Personal) structed in the following fashion. Vehicle crimes: car Total Crimes (Personal) 2364 1768 1850 2944 theft, theft from car, damage to vehicle, motor vehicle theft, bicycle theft. Property crimes: burglary, attempted Fig. 5 Mean personal victimisations per household by year and percentile, ICVS Sweeps 1989–2000 burglary, theft from garage. Personal crimes: robbery, personal theft, sexual offences, assault. Figure 1 depicts mean vehicle crimes by year. 82 % of households suffered no vehicle crime so the abscissa starts at the 81st per- victimisations.
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