Victimization and Social Structure : The Case of Spain 2 in 1999 and 2016 3

After brings about a brief review of the theoretical explanations and researches on the 11 reasons for being a victim, this article is organized into two sections. The first presents a comparative 12 analysis of the data for 1999 and 2016 in terms of perceptions, profiles and most significant 13 sociodemographic and socioeconomic variables. The second one shows an explanatory analysis 14 based on a multivariate logistical regression model using as an independent variable lifestyle of the 15 population and socioeconomic variables, and as dependent variables individual’s susceptibility to 16 becoming a victim of certain crimes. The results points towards an explanatory model of 17 victimization in which sociodemographic variables play an increasingly less important role while 18 variables related to lifestyle and subjective perceptions make a significant contribution to greater 19 understanding of the nature of being the victim of a crime. 20


Introduction
A major focus of research in the area of victimization relates to how lifestyles shape victimization (von Henting 1948;Hindelang et al. 1978;Joutsen 1987).This "scientific victimology" approach differs from others such as those of "humanistic victimology" (Fattah 1992) or even of socalled "radical victimology", which is mainly concerned with human rights and societal power structures (Elias 1986(Elias , 1993;;Dignan 2005;Dixon et al. 2006;Walklate 2015).In this sense, while is it believed that the connection between victimology and human rights through the analysis of victims' movements and power structures in society is a very important perspective, this is essentially unviable to explore given the state of available data.One limitation of the field concerns the lack of proposals made from a feminist perspective (Genn 1988;Crawford et al. 1990;Walklate 2007;Sharp 2009).
While initially the tendencies of such research may be referred to as those of "positivist victimology" (Miers 1989), "conservative victimology" (Karmen 1990) or "conventional victimology" (Walklate 1989), the methodology utilized in national surveys rules out other options.In this realm, the objectives of victimology coincide with the classical proposal (Mendelsohn: 1976) to prevent 3 of 13 This article is divided in two sections.The first presents a comparative analysis of the data for 1999 and 2016 in terms of perceptions, profiles and most significant sociodemographic and socioeconomic variables; the second presents an explanatory analysis based on a multivariate logistical regression model using the lifestyle of the population and socioeconomic variables as independent variables, and individual's susceptibility to becoming a victim of certain crimes as dependent variable.Theoretical framework The applied approach is used as part of the broader framework of lifestyle exposure theory, which focuses on the actions and behaviours of potential victims that increase their susceptibility to  the need to obtain large samples via panel sampling (six months over three years); victimless crimes are excluded; and no reference is made to crimes related to fraud, health, the possession of drugs and white-collar crimes in general (Walklate 2007;Spalek 2006).In international surveys, there are also problems of a comparative nature such as those related to translation, the standardization of questions, etc. (Nelken 2009).In questions on victimization, 11 forms of victimization ranging from domestic crime to consumer fraud and street corruption have been considered (del Frate and van

Kesteren 2004).
This survey is sponsored by the Dutch Ministry of Justice, and subsequent surveys were held in 1992, 1996 and 2000.Telephone interviews were used in developed countries, and face-to-face interviews were employed in non-industrialized countries.However, the problem lies in the reliability of questions given through telephonic surveys and particularly in the most complex ones such as those related to crimes of a sexual nature.As indicated by Walklate (2007) in her general critique of such surveys, "the assumptions of a white, middle-class, westernized, male became the domain assumptions bounding what can be asked, how it can be asked, and what sense might be made of the data in relation to the experience of crime".Another survey that has used the same methodology is that of the United Nations Interregional Crime and Justice Research Institute (UNICRI).Such surveys have been carried out at the urban level to determine problems affecting large urban areas.Even so, these surveys establish consistent patterns that should be taken into account and that are logically taken into account in our analysis of the case of Spain.
Traditionally, as previously noted, the most significant variables that predict who is most likely to become a victim of crime are: social class, age, gender, and ethnicity or race.In general terms, the most significant conclusions regarding the propensity to be a victim of crime are as follows: 1.In terms of habitat, those who live in cities with more than 100,000 inhabitants; 2. The highest income households; However, lifestyles can also be determined subjectively from individuals' perceptions and opinions about citizens' insecurity or from perceptions they have of being victims of a crime.

Methods and Data
To summarize, our hypothesis points towards an explanatory model of victimization in which sociodemographic variables play an increasingly less important role while variables related to lifestyle and subjective perceptions make a significant contribution to greater understanding of the nature of being the victim of a crime.

Methodology
As stated above, this article on one hand is based on a comparative analysis of the 1999 and 2016 surveys conducted in Spain and on the other hand on an explanatory analysis of victimization based on the second survey.The two surveys were carried out by the Centre for Sociological Research (CIS, for its initials in Spanish, find more information at http://www.cis.es) with a similar methodology (see Table 1).Both studies focus on the national environment and on the population All methodological information drawn from the two surveys such as data sheets, questionnaires, data tables and descriptive results are available for download from the corresponding link (see Table 1).The instrument or questionnaire of each survey presented a set of items measuring different types of crimes experienced within in the last year (12 for the 1999 survey and 19 for the 2016 survey), making it possible to construct the dependent or victimization variable.The list of items of victimization derived from the 2016 questionnaire was reduced to 17, as there had been no cases of "terrorism" in the last year and "rape" was added to the category "sexual aggression", as it appeared on the questionnaire during the 1990s.The independent variables cover traditional sociodemographic issues (sex, age, subjective social class, ideology, education and habitat), and other questions used to explain victimization (e.g., lifestyles or free time spent outside the home covering four items, perceptions of the safety of one's neighbourhood or locality and the probability of being the victim of a crime) of the same crimes were measured on a scale of 0-10.For a confidence level of 95.5% (two sigma) and where P = Q, the error is ±0.88% for the whole sample.
For a confidence level of 95.5% (two sigma) and where P = Q, the real error is ±1.4% for the whole sample and for the simple random sampling association.Source: The authors.

Date
On one hand, the analysis of the data applied describes the evolution of victimization and of the reporting of different crimes experienced through the two surveys described, which address a period of 17 years, and on the other hand, it is used to explain or construct a model with which to understand which variables cause one to become or not become a victim of a crime based on the 2016 survey alone.For the descriptive analysis, the victimization rate or the percentage of victims of each type of crime who have reported a crime is used.Meanwhile, for the explanatory study, a logistic regression analysis is carried out with a dichotomous dependent variable (having been a victim of a crime in the last 12 months or having never experienced any crime) and with the independent variables described above.

Results
A comparison of the victimization rates derived from the 1999 and 2016 surveys offers several results of interest to us.First, the most frequent crimes reported by both are the same: the theft of objects from vehicles, the theft of a purse or wallet and home and shop robberies.Second, crimes varying most significantly are muggings (-87.8%),vehicle thefts (-84.4%) and thefts of objects from vehicles (-67.1%).Finally, total aggregate victimization levels decline from 15.5% to 9.6%, reflecting a reduction of nearly six percentage points or a negative variation of 38.2% (see Table 2).
According to both studies, crimes most frequently reported are identity crimes and with the same percentage, thefts of vehicles (reporting rates of 86.4% for 1999 and of 83.2% for 2016) and home robberies (reporting rates of 74.7% for 1999 and of 79.7% for 2016).While a percentage increase is observed in the rate of reporting for more crimes, of note are crimes involving thefts of objects from vehicles, cases of abuse or coercion and cases of fraud and scams, which show increases of over 40%.
Thus, for the aggregate set, the rate of reporting increased significantly by 43.2% or nearly 20 percentage points.
In sum, a comparison of the survey for the end of the 1990s and the more recent one most notably shows that along with a reduction in the victimization rate (-38.3%)there has been an increase in the rate of reporting (43.2%).Thus, from these data and the surveys, we can conclude that while the statistics denote a similar percentage of reported crimes over the two years (1999 and 2016), behind this lies a reduction in the number of crime victims and an increase in reporting by victims, each compensating for the other.The evolution of the sociodemographic profiles of those who have experienced a crime in recent months between one survey and the other allows us on one hand to confirm which variables are significantly associated with victimization and to on the other determine whether this has varied over the long term.Thus, the profile of a victim according to the 1999 survey is a man of 18 to 30 years of age, of high social standing, of leftist ideology, with higher education and residing in a city with more than 100,000 inhabitants.All sociodemographic variables were found to be significant (p<.01) with the exception of ideology.The same profile is derived from the 2016 survey, though in addition to the variables of sex and social class, ideology in this case is statistically significant.
In this way, the sociodemographic variables lose an association or significant link to victimization.
Thus, from the 1999 survey, five of the six classification variables analysed were found to be significant (sex, age, social class, education and living area), while for 2016, only three were found to be significant (age, education and living area).Additionally, when we analyse differences between the differences (see Table 3), values of the sociodemographic variables highlighted decline with respect to the total for the 2016 survey (male, -0.5; between 18-30 years of age, -1.8; etc.). 1 Difference in the difference = (v2016-total2016) -(v1999-total1999) 2 Chi-square is significant for *p-value<.05or **p-value<.01.
The binary multivariate logistic regression allows us to evaluate how the dependent variable "having been or not been a victim of a crime in the last 12 months" is presumably related to the independent variables and to build a model or equation for predictive purposes.The independent variables are those used in the descriptive analysis and are considered continual variables except for sex, which is treated as categorical (male=1; female=0), and three variables are added regarding lifestyles or free time and regarding perceptions of the safety of one's environment and of the probability of becoming the victim of a crime.The variable on lifestyle and leisure covers four items regarding the frequency of a series of free time activities occurring outside of the home ("I'm going to read you a series of leisure activities, and I would like you to tell me, for each one, if you often engage in them: go out to meet friends; go to the movies, to the theatre or the concerts; go to museums or exhibits; and attend conferences or colloquia").Perceptions of safe neighbourhoods were measured with a question rated on a scale of 0 to 10 (In thinking about these types of problems or situations, on a scale from 0 to 10 where 0 denotes "I feel very unsafe" and where 10 denotes "I feel very safe", how would you characterize the feelings that you experience when you are in your neighbourhood/locality?").Finally, the probability of being the victim of a crime is determined by adding the scores from 0 to 10 from the interviewees for the 17 types of crimes ("to what extent do you think that it is possible for you to be a victim of the following crimes in the neighbourhood/locality in which you live?").The results demonstrate that six of the nine variables contribute significantly to the explanatory model on being the victim of a crime over the last 12 months.The six variables correspond to three sociodemographic variables and another three are related to lifestyles, to free time spent outside of the home, to subjective perceptions of the safety of one's neighbourhood and to the probability of being a victim.The latter variable generates the highest Wald test statistic (35.204) followed by sex (9.350) and free or leisure time spent outside of the home (9.185).In any case, the Omnibus test is highly statistically significant (p< 0.000), although the proportion of the explained variability of being a victim of a crime according to our model is not excessively high (between 12.9% for Cox and Snell's R-squared and 21.2% for Nagelkerke's R-squared).

Conclusions
The first conclusion drawn from the descriptive and comparative study verifies the evolution of the sociodemographic variables profiling or relating to victimization from the 1999 and 2016 surveys for Spain.While sex (being male), age (young), education (higher education or university) and living area (urban) maintain associations with being a victim of crime for both surveys, it has been demonstrated that the sociodemographic or classificatory variables have declined in intensity, and some have even lost their associations as in the case of social class while others were never associated as in the case of ideology.
Second, the explanatory model obtained again demonstrates that besides sex, education and habitat, the other sociodemographic variables of age, social class and ideology did not achieve statistical significance.By contrast, variables related to lifestyle or leisure, free time spent outside of the home, and subjective perceptions of the safety of one's neighbourhood and the probability of being a victim of crime do grow in importance in the explanatory model and particularly in the case of the latter.
This study also shows that there is a need for future work to expand on the role of the probability of being a victim or of an awareness or fears of experiencing a crime.Thus, research on perceptions of fear and their relationship to victimization is salient as confirmed by recent studies neighbourhood locations and social contexts.From this work future studies may also examine the evolution of victimization and its relationship to reporting crimes, as the data demonstrate a reduction in the victimization rate and an increase in the rate of reporting.In this sense, researching the causes of this process and potential hypotheses (increased civic responsibility, social pressures, a loss of fear, increased ease of reporting, etc.) may be an area of interest for the social sciences.

3 . 4 .
becoming the victim of a crime.As stated by Clevenger et al. (2018), "LET was proposed by Hindelang, Gottfredson and Garofalo in 1978 and is very similar to the routine activity theory (RAT) by Cohen and Felson…".In fact, routine activity theory is viewed as an extension of lifestyle exposure theory (Choi 2008).Lifestyle exposure theory states that the risk of becoming a victim varies in societies depending on the positioning of individuals in the social structure depending on their age, sex, race or ethnicity or social class.Meanwhile, individuals and their activities and lifestyles are interwoven with roles and their expectations.More recently, the theory has been successfully applied to study victimization in cases of cybercrime (Reyns et al. 2011).Recent studies (Navarro and Jasinski 2015) have demonstrated the vulnerability of becoming a victim through exposure to social networks such as Facebook and susceptibility to being exposed to cyberbullying through the daily use of such networks.In a similar vein, evidence of the harassment of young women using online videogames has been observed (Breuer et al. 2015; Tang and Fox 2016).Using a classical approach Von Henting (1948) has established a typology of characteristics of crime victims based on, for example, the emotional and physical vulnerabilities of young people, the physical vulnerabilities of women, challenges facing immigrants with regard to their assimilation to a new culture, etc. (Clevenger et al. 2018).From other classical perspective, Hindelang et al. (1987) argue that a link exists between the routine activities of individuals and their susceptibility to becoming the victim of a crime.They establish eight factors shaping risks of victimization: 1.The probability of becoming a victim is directly related to the amount of time a person spends in certain public spaces at night.2. The probability of being in a public space at night varies according to the lifestyle of the individual.Contact and social interactions disproportionately occur among individuals who share similar lifestyles.The likelihood of victimization depends to a large extent on an individual sharing demographic characteristic with delinquents.5.The amount of time an individual spends with individuals who are not family members varies by lifestyle.6.The probability of experiencing personal victimization and specifically robberies increases depending on the amount of time an individual spends with persons who are not family members.7. Variations in lifestyle are associated with changes in the capacity for individuals to isolate themselves from persons with delinquent characteristics.
stratified by clusters with the random proportional selection of primary sampling units (municipalities) and secondary units (sections) and with the selection of final units (individuals) by random routes and sex and age quotas.Two stages and stratified by cluster.The selection of primary sampling units (sections) is proportional to the resident population, and the selection of final units (individuals) is carried out through a systematic selection of individuals residing in the area by house number.Sampling error:

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Walklate (2007)er the age of 55 at a rate twice that of older people; 4. Those who go out the most as well as singles; 5.In relation to robberies, assaults and threats, men are 20% more likely than women to be involved; and 6.The most widespread crimes are car thefts, representing more than 40%, and bicycle and motorcycle thefts, representing 10% (van Kesteren et al. 2002; Goodey 2005).National surveys normally include information that is both consistent and discordant.For example, in the case of Spain (Alvira et al. 1982), consistent data are used for living area, age and sex (men between the ages of 26 and 35), and there is a positive relationship between victimization and higher rent and higher professional level.Alvira et al.(1982)establishes that behaviour in Spain is different from that observed in the Anglo-Saxon world.In this sense, the notion is consistent withWalklate (2007).For example, in the United States, homes in urban areas with incomes of less than $7,500 are more likely to be subjected to robberies and assaults within and outside of the home Preprints (www.preprints.org)| NOT PEER-REVIEWED | Posted: 9 November 2018 doi:10.20944/preprints201811.0238.v1Peer-reviewed version available at Soc. Sci.2019, 8, 102; doi:10.3390/socsci8030102(Catalona 2005).In the case of England and Wales (Nicholas et al. 2005), those most susceptible to home robberies are persons between the ages of 16 and 24, single parent families, low income families (£5,000), and homes left unoccupied for more than five hours a day and without private security.Groups most vulnerable to victimization over the last decade (Mawby and Walklate 1994; Walklate 2007) include the following: low-income people, ethnic minorities, renters, the elderly, young people, single parent families and women (especially older women).However, the picture becomes more complex when we take into account people and homes.Pease and Tseloni (2014) argue that the two differ based on risk factors such as location, visible goods, a lack of employment and lifestyles, but a greater risk of victimization is borne by both reiterative subjects and homes.Several projects, such as that led by Kikholt in Rochdale, Lancashire, have proven predictive vigilance to be successful (Forrester et al. 1988).Pease and Tseloni (2014) consider the frequency of domestic crimes and their predictions based on the following traits: three or more cars; single parents; inner city location; social housing; duplex or townhouse; between five and 15 years of age; and urban areas.In short, citizens' lifestyles are objective facts that we can determine from different variables such as habitats, neighbourhoods of residence, locales frequented, night-time outings, age, social class, race or ethnicity, belonging to unstructured families and socially disadvantaged groups, etc.

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Peer-reviewed version available at Soc. Sci.2019, 8, 102; doi:10.3390/socsci80301026 of 13 over 18 years of age with large samples in Spain, although the 1999 sample is especially large, with nearly 13,000 interviewees.Sample selection with a broad network of sampling points by municipality and with systematic sampling conducted over several phases culminated with face-toface interviews.The sampling error was measured at roughly 1%, with that for the 1999 survey being ±0.88% and that for the 2016 survey being ±1.4% for the entirety of the corresponding sample.

Table 2 .
Comparison of the victimization and reporting rates by type of crime derived from the 1999 and 2016 surveys for Spain.

Table 3 .
Comparison of the sociodemographic profiles of people who have experienced a crime in the last 12 months in Spain (1999 and 2016).