Description of the Data

To begin with, although in the empirical analysis I focus on burglaries, personal thefts and violent crime, the distribution of the count form of all dependent variables is presented in Table 3.2. However, the full distribution of the violent crime variables is presented separately in Table 3.22. There are two main issues that deserve a brief discussion. Firstly, the number of zeroes is very large for most of the variables. Thus, for some variables it is hard to obtain precise estimates because of the low variation in the dependent variable, particularly for count data models which are not very robust when the presence of zeroes is very high. Secondly, there are few cases of victims that reported extreme number of crimes. For instance, in variable Personal Theft there is only one person above ten crimes, who actually reported 1_{The ‘do’ files (Stata}r _{format) for the creation of dependent variables from the Victim Forms data set}

157 97 crimes, or, for Inside Burglary there are eight people that reported between 70 and 100 crimes. In this table for ease of exposition we cap the crime count at ten plus more. Count data models are very sensitive to these cases, particularly when the positive counts are too few to identify the parameters assumed to affect the conditional mean, and when the extreme cases are very dispersed from the less extreme cases. Someone would think of dropping these cases because they could be considered as highly unreliable. However, this practice would result in sample selection issues. Therefore, as will be discussed in Section 3.8, we also use several modified count data models that are both (in a sense) more robust under these cases and more appropriate to explain the observed distribution of victimization incidents. Finally, it is also clear that the dispersion of most variables is very high. Therefore, the Negative Binomial distribution that allows for over-dispersion may be more appropriate to fit the observed data.

Moreover, descriptive statistics of the dependent and independent variables are presented in Table 3.3. The mean for native and immigrant groups for all variables is also given in order to have a first indication on the victimization differences between immigrants and natives. In addition, we will be able to observe the aspects in which immigrants differ from natives with regard to their observed characteristics. It must be noted that, the immigration status variable is created as a dummy that takes the value 1 if the respondent or the house reference person is not born in the UK. Moreover, the information of how many years the respondent lives in the UK can be exploited to examine assimilation patterns of the immigration-native victimization differentials. This will be examined in Section 3.6.

A first look at the raw data shows that there are victimization differences between immi- grant and native groups, although they are very small in most cases. Regarding acquisitive crime, both household and personal, we can see that the probability and the mean victimiza- tion are higher for immigrants, apart from Outside Burglary (and Outside Thefts or Other Thefts).1 _{Moreover, Home Criminal Damage is slightly lower but Vehicle Criminal Damage}

is slightly higher for immigrants. Concerning Violent Crime, which is the crime group most discussed in this study, we can see that immigrants are less victimized. However, the picture 1_{Here I do not discuss statistical significance of the differences as these descriptive statistics are used}

158 is different if we break violence into the categories discussed before, as immigrants are much less victimized by acquaintances and family members, but slightly more by strangers.

In addition, in Table 3.3 the independent variables which will be used in the main anal- ysis are also presented. Again, the mean for both immigrants and natives is given. Note that the means for the respondent’s and the household reference person’s characteristic are separately given. This is because the appropriate variables in personal crime are the personal characteristics, but in household crime it is the household characteristics. The main observed differences between immigrants and natives is that immigrants are younger (which can be considered mainly as a measure of exposure) and that they are relatively more concentrated in London, urban and inner city areas, but most importantly that they reside in relatively more deprived areas (which can be thought as proximity measures).1 _{Thus, a first question in}

the main analysis would be: what would be the immigrant-native differences in the likelihood to suffer a crime if immigrants displayed the same basic demographic characteristics?

Moreover, immigrants are more married, more of nonwhite ethnic groups, more renters and they reside relatively more in flats (mainly exposure measures). They also live fewer years at their current home or area (which is a measure of social guardianship) and finally, they possess fewer cars (measure of exposure). There are no strong differences in income and education. Hence, another question would be: if there still are differences, can they be explained by the remaining observed individual and household characteristics?

Finally, notice that for some of the independent variables there are many missing cases. Dropping all these cases would result in losing too much information. Therefore, a dummy is created for each variable that contains a considerable number of missing cases that takes the value one if the particular variable displays a missing value and zero otherwise. Thus, 1_{The Deprivation Index is the “Multiple Deprivation Index of England and Wales” for 2007, constructed}

as a weighted mixture of the individual deprivation indices (Income deprivation, Employment deprivation, Health deprivation and disability, Education, skills and training deprivation, Barriers to housing and services, Living environment deprivation, and Crime deprivation index) provided by the Department of Communities and Local Governments for England and Welsh Assembly Government for Welsh. Very briefly, this index, that takes integer values from 1 to 10, provides a measure of multiple deprivation at the Lower Super Output Areas (LSOAs) level by considering some indicators of deprivation. These values indicate the decile of deprivation in which someone scores. For example, if someone scores at the 7th decile, only 30% of the population resides in more deprived areas. Each respondent, depending on the small level area that he/she resides, is matched by the Home Office with the corresponding decile of this variable. For more information on these indices refer to Noble at al (2008). In the empirical analysis I include this variable as an 1 - 10 integer index that measures the effect of scoring at a one decile higher on the probability of victimization.

159 these dummies intend to absorb the effects of the missing cases of each characteristic on the dependent variables.

In a summary of this subsection, we saw that immigrants suffer in general slightly more property crime and personal theft (apart from outside thefts and home criminal damage) but less violent crime than natives, although they live in more deprived inner city neighbourhoods where violent crime is much higher. However this picture changes if we distinguish crime by strangers from crime by acquaintances and family members. More on these relationships will be discussed in the next two sections.