Ethnic and immigrant residential concentration,

and crime rates

Sergio Herzog

Institute of Criminology

The Hebrew University of Jerusalem

Suggested Running Head: Immigrant concentration and crime

* The study was financed by a grant from the Haifa University Research Authority.

Address for correspondence:

Sergio Herzog, Ph.D.

Institute of Criminology, Faculty of Law

The Hebrew University of Jerusalem

91905 Mount Scopus, Jerusalem, Israel

Voice: (972)-2-588-3791

Fax: (972)-2-588-1725

2

Ethnic and immigrant residential concentration,

and crime rates

Abstract

One of the main arguments of social disorganization theory is that ethnic

heterogenity, influenced by immigrant residential concentration, is highly disruptive

for community organization and, therefore, highly criminogenic. The effect of

immigrant residential concentration on crime rates is, however, generally masked by

the general effect of the broader category of ethnic heterogeneity. Some recent studies

even suggested a negative relationship between immigrant residential concentration

and crime. The present study, conducted in the city of Haifa, Israel, used

neighborhood level data to test the specific relationship between immigrant residential

concentration and crime rates among recent immigrants from the former Soviet

Union. The results showed that the decomposition of ethnic heterogeneity into its two

main components – immigrant residential concentration and ethnic residential

concentration – served to qualify the predicted effects of social disorganization

theory.

3

Introduction

Sociological research on neighborhood social problems, such as high crime rates,

generally emphasizes the role of community, and social and organizational factors. In

the framework of social disorganization theory, neighborhoods characterized by

negative structural factors, such as economic disadvantage, residential instability,

ethnic heterogeneity, and family disruption, are expected to be more vulnerable to a

wide range of social problems, including elevated crime rates (e.g., Bursik, 1988;

Miethe, Hughes, & McDowall, 1991; Rountree, Land, & Miethe, 1994; Sampson &

Groves, 1989; Smith & Jarjoura, 1989).

Recent studies on neighborhood crime refined and expanded the understanding of

the role of structural factors, such as the role of poverty (Krivo & Peterson, 1996;

Warner, 1999) and residential instability (Ross, Reynolds & Geis, 2000). Empirical

research, however, has yet to focus on the specific role of ethnic and immigrant

concentration. In attempting to fill this gap, the present study extended previous

research investigating the separate role of ethnic and immigrant residential

concentration – and analyzing whether they contribute similarly or differentially to

neighborhood crime rates.

Literature review

Social disorganization theory has traditionally emphasized the geographical

distribution of crime and the structural characteristics of neighborhoods related to this

distribution. Among the latter, neighborhood ethnic (or racial) heterogeneity,

influenced by factors such as residential concentration of ethnic and foreign-born

minorities, has traditionally been perceived by social disorganization theory as

disruptive for community organization, and consequently highly criminogenic; thus a

4

positive relationship between both ethnic and immigrant residential concentrations

and neighborhood crime rates has been predictable. Within this theoretical frame, the

thesis that ethnic and immigration concentrations are linked with high crime rates is

widespread and well known. It was established by the early research of Shaw and

McKay (1969), who already in the 1930s reported that urban neighborhoods with high

concentrations of African American and immigrant families were also places with the

highest rates of urban juvenile crime.

According to social disorganization theory, areas with large proportions of

immigrants are culturally complex; they are more likely to be characterized by a

plurality of value systems, norms, and experiences, which limit the development of

effective social control structures (e.g., Smith & Jarjoura, 1989). In fact, Shaw and

McKay (1969, p. 172) argued, for example, that “… unlike children from middle-

class living in relatively homogeneous neighborhoods, who are usually insulated from

value systems that favor law violation and are more easily socialized into

conventional values and behaviors, children living in lower class areas of ethnic

heterogeneity are exposed to a variety of contradictory standards and forms of

behavior rather than to a relatively consistent and conventional pattern …” (see also

Warner, 2003, p. 74). Accordingly, ethnic heterogeneity has been thought to thwart

the ability of neighborhood residents to achieve consensus, realize common goals, and

reduce crime (e.g., Miethe et al., 1991; Sampson & Groves, 1989).

Note that the proposition that neighborhood crime is caused, at least partially, by a

great presence of immigrants seems quite logical not only to social disorganization

theory but also to other theoretical viewpoints (for a recent review, see Martinez &

Lee, 2000). Cultural models have asserted that unlike natives, immigrants face

acculturation and assimilation difficulties in the host country, which may be

5

criminogenic (Sellin, 1938). Moreover, new immigrants have tended to settle in urban

areas where co-ethnic communities have been established by past immigration.

Research revealed that this immigrant residential concentration has tended to occur in

poor inner-city neighborhoods, characterized by structural characteristics often

associated with crime, such as low socio-economic status and high unemployment

rates (e.g., Logan, Alba & Zhang, 2002; Martinez & Lee, 2000).

Recent formulations of social disorganization theory have also supported this view.

In them, the aforementioned negative community features have also traditionally been

perceived as having deleterious consequences for community social organization,

mainly weakening and limiting formal and informal networks, which in turn account

for variations in crime and delinquency (e.g., Sampson & Groves, 1989; Sampson,

Morenoff & Earls, 1999; Sampson, Raudenbush & Earls, 1997; Warner, 1999;

Warner & Pierce, 1993). In this framework, “collective efficacy” is a relatively new

concept, used to describe the differential ability of neighborhoods to realize common

values. Variations in collective efficacy has been perceived as the result of a

neighborhood’s social characteristics such as socio-economic status, residential

stability, family disruption, and also social heterogeneity (see Sampson et al., 1997).

In line with these propositions, ethnic differences among people have been also

perceived as both increasing the likelihood that neighbors would be strangers and

diminishing and restricting the development of informal community ties and

friendship networks with the other residents in their neighborhood. These

circumstances have lead to limited development of effective social control structures

and, consequently, to higher rates of crime (e.g., Ross et al, 2000; Sampson, 1991;

Sampson et al., 1997; Warner, 1999; Warner & Rountree, 1997). Although it could be

argued that various ethnic groups residing in the same neighborhood shared

6

conventional values (e.g., reducing crime), ethnic heterogeneity has also been

perceived by social disorganization theory as impeding communication and patterns

of interaction between neighborhood residents, thus obstructing the quest to solve

common problems and reach common goals (e.g., Bursik, 1988; Miethe et al., 1991;

Rose, 2000; Sampson et al., 1997). According to Sampson et al (1997, p. 920) “…

immigrant concentration describes neighborhoods of ethnic and linguistic

heterogeneity, and there is reason to believe that immigrant concentration decreases

the ability of residents to realize common values and to achieve social controls which

in turn explains an increased risk of violence.”

Inmigrant residential concentration

Conceptually, ethnic homogeneity and heterogeneity form two ends of a continuum

describing the ethnic composition of neighborhoods at a given time. Ethnic

heterogeneity has represented the probability that two randomly chosen persons have

not belonged to the same ethnic group. In concrete terms, because most studies based

on ecological theories, particularly social disorganization theory, have been conducted

in the U.S. (except for a few in England: Sampson, 1988, 1991; Sampson & Groves,

1989), neighborhood ethnic heterogeneity have referred theoretically to the existence

of considerable proportions of native-born Blacks and immigrants versus native-born

Whites in the chosen units of analysis (Ross et al., 2000; Sampson et al., 1997).

Despite these two theoretical components of ethnic heterogeneity, however, a

review of studies has revealed that most of them dealt mainly with ethnic

heterogeneity in terms of the proportion of Black in relation to White residents in the

analyzed geographical units, thus masking or even ignoring the specific, and perhaps

different effect of immigrant residential concentration on crime rates.

7

THE PRESENT STUDY

Basically, both this dichotomized conceptualization of ethnic heterogeneity between

Whites and Blacks and the lack of specific reference to immigrants should not be

problematic for social disorganization theory: both early and recent formulations of

this theory have consistently considered any combination of different ethnic groups in

a given neighborhood as disruptive for community organization (e.g., Morenoff &

Sampson, 1997; Sampson et al., 1997). A major limitation of this approach, however,

has been the tacit assumption that the effects of ethnic (or racial) and immigrant

residential concentrations were similar and in the same direction. There is reason to

question this claim, as the immigrant experience has changed in recent years and

immigrant concentration has not occured necessarily in the inner cities and has been

sometimes a choice rather than a constraint (Logan et al., 2002). Accordingly, the

separate effects of ethnic and immigrant heterogeneity in the framework of social

disorganization theory need to be reconsidered.

In this context, recent urban perspectives, such as the “ethnic community” approach,

have suggested that a high proportion of immigrants residing in a neighborhood might

not be disruptive for community organization; on the contrary, it might even

constitute a positive organizing force in many cities, for both the immigrants and the

neighborhood as a whole (e.g., Breton, 1964; Logan et al., 2002; Majka & Mullan,

2002).

THE “ETHNIC COMMUNITY” APPROACH

According to the “ethnic community” approach, immigrant residential concentration

has often provided the basis for important social functions that facilitate immigrants’

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social and economic integration to the host society. For new immigrants recently

settled, neighborhoods with a high proportion of immigrants already living there have

provided emotional, social, and cultural support, as well as other resources including

information, housing, initial entry into the labor market, and social capital, which has

helped them adapt to the new environment (Aldrich, Cater, Jones, McEvoy &

Velleman, 1985; Bailey & Waldinger, 1991; Hagan, 1998; Logan et al., 2002). In

addition, when public attitudes to immigrants are negative, the ethnic neighborhood

provides a shelter from discrimination and abuse (Zhou & Logan, 1989). In some

cases, immigrant residential concentration is the first step in the formation of an

ethnic community, that is, areas in which members of a particular population group

congregate as a means of enhancing their social and cultural traditions (Marcuse,

1997).

Apart from these advantages for new immigrants, studies on ethnic communities

have also emphasized the positive impact of immigrant residential concentration for

the non-immigrant population in the neighborhood (e.g., Carmon, 1998; Smith,

Tarallo & Kagiwada, 1991; Winnick, 1990). For example, immigrants may serve as a

catalyst for urban revitalization, bringing new life to the streets where they live and

work. The feeling of activity and vitality generated by the presence of large numbers

of people for many hours of the day has been likely to instill a sense of security in

neighborhoods that may previously have been considered unsafe and dangerous.

Immigrant concentration has also increased the demand for housing and possibilities

for employment in the neighborhood. It has been suggested that immigrant residential

concentration facilitates collective mobilization and the achievement of collective

goals. Togeby (1999) employed the term “double integration” to denote the notion

that immigrants integrated more easily in the new country if they were well integrated

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in their own ethnic group. Indications of the latter may be seen in the immigrants’

membership in ethnic associations and the development of an “institutional

completedness,” a formal structure containing organizations of various sorts,

including educational, political, recreational, and mutual aid organizations (Breton,

1964). These social networks have played an important role in fostering social and

political involvement in neighborhood issues.

Within a criminological context, an important implication of the ethnic community

approach has been that contrary to the propositions of social disorganization theory,

immigrant residential concentration is not necessarily related to high neighborhood

crime rates. Basically, the rationale underlying the ethnic approach is that immigrants

living in areas with high concentrations of co-ethnics are embedded in a web of social

relationships with family, friends, neighbors, and co-workers. Within these circles,

people convey expectations to others about the kinds of behaviors that are appropriate

and desirable (Ramakrishnan & Espenshade, 2001). These social networks can

identify those who comply with the social expectations and those who do not, and

they can reward the participants and withhold reward from others (Rosenstone &

Hansen, 1993).

The main aim of this study was to explore empirically the specific effects of urban

concentration of immigrants on neighborhood crime rates: in this study it was

analyzed whether the specific relationship between immigrant residential

concentration in a given neighborhood and crime rates was similar to or different

from the broader relationship between ethnic heterogeneity, usually measured only in

terms of ethnic residential concentration, and crime rates.

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THE RESEARCH CONTEXT

The choice of Israel for the investigation of the relationship between ethnic and

immigrant residential concentrations and crime rates was particularly appropriate in

view of the existence of a large (only native-born) Arab ethnic minority (constituting

18 percent of the total population) 1 and large numbers of recent new immigrants

(arriving during the 1990s), mainly from the former Soviet Union, who since then

have formed the largest single country-of-origin group in the Jewish population of

Israel (CBS, 2004).

There has been increasing concern in Israel regarding the involvement of new

immigrants from the former Soviet Union in criminal activities. Such an increase has

been, indeed, reflected in the criminal statistics of the Israel Police with regard to both

property and violent crime (see Rattner, 1997). The police and the media have seemed

to be particularly concerned about the active involvement of immigrants from the

former Soviet Union, adults and juveniles, males and females, in organized crime,

especially blackmail, extortion, illegal and black-market trading, smuggling, and

prostitution (Horowitz, 1998; Lemish, 2000; Tartakovsky & Mirsky, 2001).

In contrast to this negative image, however, other features pertaining to new

immigrants from the former Soviet Union seem to support the assertions of the ethnic

community approach. These immigrants have developed a highly organized

community, on the local and national levels alike. This trend may be exemplified by a

number of developments. First, with the approach of the 1996 general election,

leaders of immigrants from the former Soviet Union established their own national

political party, winning seven (out of 120) parliamentary seats, entering the

government coalition, with two of their leaders becoming cabinet ministers. In 1998

the party decided to run in the local elections as well. Second, these immigrants’ large

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numbers created a demand for mass communication media (newspapers, journals, and

a television station) in Russian, which in turn contributed to the development of the

community by supporting its economic activities and advertising immigrant

establishments. Third, a policy shift by the Israeli authorities from bureaucratic

absorption to individual absorption resulted in less governmental involvement in

absorption and reduced services for immigrants. As a result, the importance of social

networks and immigrant voluntary organizations in helping the immigrant gain access

to housing, employment, language classes, and health became more salient (Leshem

& Lissak, 2000).

The present research was conducted in Haifa, the third largest city in Israel (with a

population of about 250,000 residents). Haifa is a culturally diverse city, its urban

Jewish secular majority sharing space with both new immigrants from the former

Soviet Union and Israeli-Arabs. The Arab native population constitutes 13 percent of

the city’s population, the remnant of a much larger pre-1948 Palestinian population.

At the end of the 1948 war, almost half of the Arab population of Haifa was

concentrated in a limited geographical area (known as Wadi an-Nisnas). Over the

years, however, adjacent areas have been added to this core area, and segregation

indexes calculated for the last forty years show that residential segregation of the

Arabs from the Jewish population has increased over time. Given the high levels of

residential segregation, it is not surprising that social interaction between the two

groups is generally low. On average, the Arab population of Haifa enjoys lower

socioeconomic status than that of the Jewish population, is residentially segregated,

and faces barriers to residential mobility (Falah, 1996; Lewin-Epstein & Semyonov,

1994).

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Since the early 1990s, about 53,000 new immigrants from the former Soviet Union

have settled in Haifa. Although new immigrants from other countries have also

reached the city, more than 90 percent of new immigrants in Haifa came from the

former Soviet Union (Israeli Ministry of Absorption, 2003). Hence, during the

nineties the immigrant sector from the former Soviet Union accounted for more than

one-fifth of the city’s population.

Similar to the situation of Arabs in the city, one of the main features of the

settlement of immigrants from the former Soviet Union in Haifa is their high

residential concentration. Their spatial distribution among Haifa’s neighborhoods is

uneven, and a large number of immigrants and immigrants’ businesses has been

located in relatively less advantaged, specific areas (see Mesch, 2002). The spatial

distribution of the immigrant population in the city is uneven. While the immigrant

population represents 20 percent of the city population, immigrants represent almost

41 percent of the population of Hadar Hacarmel, 31 percent of the population of

Western Carmel, and 28 percent of the population of Neve Shaanan. This has led to a

dramatic demographic change in the city and some of its traditional neighborhoods.

A relatively large percentage of the new immigrants are residentially concentrated

in the Hadar Hacarmel area. Before the arrival of the immigrants, Hadar Hacarmel

was an inner city area rapidly declining on every urban indicator. Between 1972 and

1983 the population declined by 17 percent, the average income and average

education were below the city average. The rate of the elderly population in the area

was higher that the proportion generally in the city, the rate of minorities groups in the

total population was increasing, housing density was higher than in other areas of the

city, and the proportion of homeowners was lower (Kipnis, 1998). In fact, not only

was Hadar Hacarmel declining before the arrival of the new immigrants, it was losing

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its attraction as a residential area. Residents of other areas perceived the Hadar

Hacarmel as a low status area, with a poor reputation in terms of physical appearance

and social composition (Kipnis, 1998). The arrival of immigrants caused a dramatic

change in this area.

The demographic change in the city had an impact on the housing stock, in

particular during the first year after the immigrants' arrival. New immigrants occupied

a large number of empty dwellings. The increased demand for housing raised the

purchase price of apartments and monthly rents. Whereas in 1988 the price of a two-

bedroom apartment was $22-27,000, three years later in 1991, the price for the same

apartment was $70-85,000. The increase in rents was from $250 to $400 a month for a

two-bedroom apartment (Carmon, 1998).

Moreover, in line with the ethnic community approach, some studies (e.g., Carmon

1998) showed that unlike the Arab residential areas, the settlement of immigrants

from the former Soviet Union in the city has halted the deterioration of these areas

and initiated a process of social and physical revitalization. In addition, the

immigrants succeeded in establishing educational, social, and political frameworks for

negotiating with the local government over the allocation of financial as well as

political resources for the former Soviet Union's community. For example, in January

1995, a non-governmental organization (NGO) was set up whose goal was to provide

housing, employment, and educational services to the immigrant population. The

NGO has a board of directors, headed by a former immigrant, representatives of

different immigrant organizations, and representatives of local government. Only 40

percent of the operating budget comes from the local government. The NGO has

supported institutional structures, such as recreation and community centers, in which

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cultural activities have been conducted in Russian and directed to this population

(Mesch, 2002).

METHOD

Much of the research examining the effect of structural social features on crime

rates have used neighborhoods as units of analysis (e.g., Krivo & Peterson, 1996;

Logan & Stults, 1999; Miethe et al., 1991; Peterson, Krivo & Harris, 2000; Warner,

2003; Warner & Rountree, 1997). Similar to these studies, the empirical analysis of

this research combined data on ecological characteristics of all neighborhoods in the

Haifa metropolitan area (n = seventy three) and crime rates. The ecological data,

serving as independent and control variables, were from the most recent Census of

Population and Housing conducted in Israel, in 1995. The data were published by the

Strategic Planning Unit of the Haifa Municipality. The Israeli National Police

provided geo-coded counts for reported crime statistics in this year for all tracts in the

city. These data constituted the dependent variables in this study. Thus, both census

track and crime data referred to the same year - 1995.

In this study census tracts were used as the best objective approximation of

neighborhoods. It could be argued that census tracts do not necessarily correspond to

neighborhoods in a socially meaningful sense (see Peterson et al., 2000, pp. 38).

Tracts are the smallest unit for which Haifa neighborhood and crime data are

available, however, and they have been used in many prior analyses of urban crime.2

On the other hand, the crime data in this study were for officially reported

victimizations only. As similarly stated in other studies (e.g., Bursik, 1988, pp. 533-4;

Logan & Stults, 1999, pp. 258; Miethe et al., 1991, pp. 170; Warner & Pierce, 1993,

pp. 494), the controversy over the use of reported crime data as measures of the

15

incidence of crime has been well known: the number of reported crimes has been

shown to be considerably lower that the actual number of incidents. Alternative

sources such as victimization data, however, were not available for 1995 at the

geographic level of this study (Logan & Stults, 1999, pp. 258).

DEPENDENT VARIABLES

The dependent variables of this study were given by the overall rates in the census

tract of total, violent (homicide, assault, attempted assault, rape, and threats), and

property (theft, larceny, robbery, and burglary) crime (see Krivo & Peterson, 1996;

Logan & Stults, 1999; Peterson et al., 2000) reported to the Israeli police in 1995.3

In this study violent and property crimes were examined for a variety of reasons.

First, it was decided to examine these offenses separately to determine whether the

model works differently for different categories of crime. As known, violent and

property offenses are committed by different reasons – both expressive (for express

some internal state or mood) and instrumental (for gaining a personal advantage, such

as money, valuable tools, etc.). This decision was intended to increase both the

validity and the generalization of the research findings. It could not be hypothesized

here, at least at this step of the research, which offenses would be more predicted and

which less, by the dependent variables. Second, these crimes are frequent and serious

enough to warrant empirical investigation. Third, some studies (e.g., Rountree et al.,

1994; Smith & Jarjoura, 1989; Warner & Pierce, 1993; Warner & Rountree, 1997)

have analyzed rates for specific offenses (e.g., assault, burglary). In this study, the

tract’s overall rates of burglaries (private and commercial) were analyzed separately.

This was due both to their relatively high absolute numbers, and to their relatively

reliable, formal statistics, including the fact that their formal recording generally is not

16

influenced at all by differential police activities (the police task in this context is

exclusively reactive, regardless of the characteristics of the offender involved, mostly

unknown to the victim) or by public reporting patterns (the main motivation of the

reporting concerns insurance compensation). For all crime measures the average

number of crimes per 1,000 population were calculated. Accordingly, the total

number of reported events in each crime category within each tract was divided by the

total tract population and multiplied by 1,000 to obtain an offense rate per 1,000

populations for each tract.

INDEPENDENT AND CONTROL VARIABLES

Consistent with social disorganization theory, the independent variables in this

study were given by ethnic heterogeneity, socioeconomic status, and residential

stability. As previously stated, studies on social disorganization theory have

traditionally masked immigrant residential concentration behind the broader concept

of ethnic heterogeneity. Accordingly, ethnic heterogeneity was applied in two ways in

this study: (1) in the “classic” way, as a unified variable: both Arab and former Soviet

Union immigrant residential concentrations calculated together; (2) in order to

overcome the methodological limitation of many studies on social disorganization

theory, this variable was also calculated as a decomposed variable: Arab and former

Soviet Union immigrant residential concentrations calculated separately.

Operationally, the unified variable was calculated as the percentage of (post-1989)

former Soviet Union immigrants and non-Jews (Arabs) in each census tract, and in the

decomposed variable both residential concentrations were calculated separately.

Socioeconomic status was a composite variable. Following previous work (see

Sampson, 1991; Sampson & Groves, 1989), three items were included in this

17

variable: percentage of residents employed in academic and managerial positions,

percentage of residents with college education, and percentage of residents with

graduate education. The items were subjected to a principal component factor analysis

resulting in one factor (Eigenvalue = 2.355). Internal reliability was acceptable with

Cronbach’s alpha at .852. In the multivariate analysis a regression factor score was

used. Residential stability referred to the percentage of residents living five or more

years in the same tract (see Logan & Stults, 1999; Miethe et al., 1991; Rose, 2000;

Ross et al., 2000; Warner, 2003; Warner & Rountree, 1997).

According to social disorganization theory, family disruption is viewed as

undermining traditional forms of informal control within the community. Studies

using a measure of family disruption (generally, percentage divorced/separated) have

reported a positive relationship with crime measures (see Sampson & Groves, 1989;

Warner & Pierce, 1993). Due to the fact that the Israeli Census unfortunately did not

provide measures of single-parent families, here the percentage of divorced women

was used as a proxy of family disruption by dividing their numbers by the total

number of females aged sixteen and over in each census tract. This measure was

similar to the census track divorce rate that had been used in a number of previous

studies (Martinez & Lee, 2000; Warner & Pierce, 1993). Finally, as control variables

the percentage of adolescents aged fifteen to nineteen in each tract was also calculated

(see Krivo & Peterson, 1996; Peterson et al., 2000), and density was measured as the

relationship between the number of neighborhood residents in each tract and its size

(see Smith & Jarjoura, 1989).

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Data analysis

Multivariate data analysis was conducted using ordinary least squares multiple

regressions. Due to the fact that the number of census tracts was relatively small (n =

seventy three), special attention was paid to multicollinearity and heteroscedasticity.

Multicollinearity was tested using variance inflation factors. In the models, no

variable was found with a factor higher than three. Standardized residuals were

examined using the Breusch-Pagan global test for the possibility of high correlation

between standardized residuals and the predictor variables. No heteroscedasticity was

found.

RESULTS

The analysis was started by presenting descriptive statistics of the socioeconomic,

demographic, and crime characteristics of the city’s neighborhoods (n = seventy

three) and the correlations for all variables.

[INSERT TABLE ONE ABOUT HERE]

From Table 1 it is possible to see that the components of ethnic heterogeneity –

(post-1989) former Soviet Union's immigrant and Arab residential concentrations

(variables one and two) – shared similar means (about 20 percent), although the

dispersion of the latter was somewhat wider.

[INSERT TABLE TWO ABOUT HERE]

This finding was further investigated and in Table Two the distribution of the

immigrant and Arab Israeli population according to the concentration of the group in

neighborhoods was presented. One salient finding of the Table was that most of the

FSU immigrants and Arab Israelis lived in heterogeneous neighborhoods. As shown

53 percent of the immigrants and 57.8 percent of the Arab Israelies resided in a

19

neighborhood in which each group was less than 10 percent of the population.

Regarding residing in homogeneous neighborhoods, 10.5 percent of the city residents

were Arab Israelis, but only 5 percent of the new immigrants resided in

neighborhoods in which they were more than 50 percent of the population.

[INSERT TABLE THREE ABOUT HERE]

When inspecting the correlation matrix, the results for the three socioeconomic

measures (variables three to five) and the density variable (variable ten) were of

interest: Increased proportions of former Soviet Union new immigrants and Arabs in a

tract was associated with increased social disadvantage and density. Note, however,

that although both groups lived in economically disadvantaged neighborhoods, the

correlation coefficients were stronger for the Arab population than for the former

Soviet Union immigrants. The percentage of former Soviet Union new immigrants in

a tract was positively correlated with residential instability (variables six and seven),

family disruption (variable eight), and three measures of crime rates (variables eleven

to thirteen); the percentage of Arabs in a tract was positively correlated with

residential stability (variable six) and percentage of adolescents (variable nine).

Unlike the situation for percentage of Arab Israelis, no significant correlation was

found between the percentage of immigrants and the four crime measures.

Table four presented the results of the regression models for the four crime rates

(dependent variables), the measures of ethnic heterogeneity (analyzed as a unified

variable for former Soviet Union new immigrant and Arab residential concentrations

taken together), neighborhood socioeconomic status, residential stability, family

disruption, and two control variables: percentage of adolescents and density.

[INSERT TABLE FOUR ABOUT HERE]

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The findings presented in Table four were largely consistent along the four crime

measures: family disruption was associated with higher crime rates; population

density was also significantly related to all crime measures. The remaining

coefficients for ethnic heterogeneity, socioeconomic status, residential stability, and

percentage of youth were not significant in any of the models.

Table five presented the same regression models analyzed in Table four but with the

ethnic heterogeneity variable separated into its two main components, former Soviet

Union new immigrant and Arab residential concentrations (i.e., the two components

were treated as separate variables).

[INSERT TABLE FIVE ABOUT HERE]

Similar to the findings of Table four, the regression findings presented in Table five

were highly consistent along the four crime measures for almost all variables. Again,

the family disruption variable showed significant, strong and positive coefficients in

the four models. Unlike Table four, however, a substantial negative relationship was

obtained between percentage of former Soviet Union new immigrant residents in the

tract and the four crime variables. For Arab residential concentration – the second

component of ethnic heterogeneity – a significant positive relationship, weaker than

the former, was found only with regard to total and violent crime rates. For property

crime and burglary taken separately, this relationship, although also positive, was not

significant.

Also unlike Table four, Table five showed significant, strong and negative

coefficients for socioeconomic status and residential stability in the four analyzed

models; the higher the population density in the tract, the higher the rates of total,

violent, and property crime. Similar to Arab residential concentration, a non-

significant coefficient was obtained in the burglary model. Note also that compared

21

with the foregoing table, Table three showed substantially increased R2s in the four

models.

DISCUSSION

The aim of the present study was to investigate the specific relationship between

neighborhood ethnic concentration and crime rates. According to social

disorganization theory, variations in crime rates among neighborhoods are the result

of neighborhood differences in social and organizational characteristics. Both earlier

and recent versions of this perspective have stipulated that high concentration of

immigrants in a neighborhood, as a component of ethnic heterogeneity, diminished

the capacity of the community to regulate its members and to mobilize them for the

achievement of collective goals (Morenoff & Sampson, 1997; Sampson et al., 1997).

A central goal of any community is to provide its members a safe and orderly

environment. Accordingly, social disorganization theory has suggested that negative

neighborhood characteristics, such as low socioeconomic status, residential

instability, family disruption, and ethnic heterogeneity, influenced by high residential

concentration of immigrants, were disruptive for social organization, leading to higher

crime rates (e.g., Bursik, 1988; Miethe et al., 1991; Sampson, 1991; Sampson et al.,

1997; Sampson & Groves, 1989; Smith & Jarjoura, 1989; Warner, 1999; Warner &

Rountree, 1997).

This research's findings are partially consistent with social disorganization theory,

as it was found that low socioeconomic status, residential instability, and family

disruption were positively related to neighborhood crime rates (Tables two and three).

Poverty and social disadvantage are usually associated with negative community

outcomes, such as higher levels of crime. Generally, poverty has impeded the ability

22

of communities to sustain basic institutional structures and various sources of social

control that normally serve to discourage and fight crime effectively (Krivo &

Peterson, 1996). In addition, long-term residents are usually acquainted with one

another, participate in local voluntary associations, develop rules that govern social

behavior, and enforce them through informal social control (e.g., Sampson, 1988;

Sampson & Groves, 1989; Smith & Jarjoura, 1989). Family disruption has been often

negatively related to social organization and positively related to crime rates. Informal

social control has been more difficult to achieve in areas in which the percentage of

single female families was high (e.g., Krivo & Peterson, 1996; Sampson et al., 1997;

Warner & Pierce, 1993).

This research's results with regard to former Soviet Union immigrant residential are

consistent with expectations from the ethnic community approach. A negative

relationship was obtained between immigrants residential concentration and the four

measures of crime, and a positive relationship was obtained between Arab residential

concentration and crime, especially total and violent crime. These findings emphasize

the importance of separating ethnic heterogeneity into its components. Research has

consistently found relatively lower crime rates for immigrants than for their native-

born counterparts (for reviews see Martinez & Lee, 2000; Yaeger, 1997).

This study's findings are consistent with the ethnic community approach (Majka &

Mullan, 2002). According to this approach, the existence of ethnic communities with

large concentrations of immigrants serves to facilitate immigrant adjustment and

neighborhood organization. Such communities have the capacity to achieve

institutional completeness, that is, the development of formal structures in which

educational, political, recreational, and mutual aid immigrant organizations operate

and control social relations within the community (Breton, 1964).

23

As stated, the pattern of settlement of immigrants from the former Soviet Union in

Haifa was mainly in inner city neighborhoods, which before their arrival were

declining in population and homeownership and increasing in Arab Israeli

concentration. The arrival of these immigrants in these areas apparently served as a

catalyst for neighborhood revitalization and stability. Their presence reduced the

percentage of unoccupied apartments, increased the rate of homeownership and

created a feeling of business and community activity that had a positive effect on

neighborhood social organization. Note that neither of these relationships are uniquely

expected by social disorganization theory, but by others theories, among them broken

windows, routine activities, and political economy.

As also stated, the former Soviet Union immigrants of the recent wave have

apparently succeeded in establishing highly organized communities based on mutual

aid and voluntary association. Previous research has revealed the existence of social

networks that facilitate the mobilization of resources in Haifa’s immigrant

neighborhoods as reflected, for instance, in the strong positive relationship between

immigrant residential concentration and voting for a Russian party in local elections

(Mesch, 2002).4

The higher exposure of the Arab population to crime can be explained in terms of

residential segregation in which the Arabs, because of discrimination in the housing

market, face barriers that constrain their residential mobility, channelling Arabs into

the worst neighborhoods and preventing them from moving out of the worst

neighborhoods; these barriers do not exist for the immigrants from the former Soviet

Union (see Falah, 1996). Furthermore, the correlation matrix indicates that while both

groups tend to be residents of disadvantaged neighborhoods, the Arab population is

concentrated in the most disadvantaged ones. This pattern of residence in

24

disadvantaged areas is very likely a possible explanation, even more plausible than

the explanation provided by social disorganization theory, for the higher exposure of

Arabs to crime.

Concerning the conclusions of this study, it would be added that other research

questions could be formulated and added with similar, but not exactly the same, future

data. For example, it will be interesting to determine whether the effect of the

percentage of FSU immigrant concentration is mediated by the percentages of both

vacant and owned apartments. Ideally these questions could be answered using panel

data, and this possibility is considered by the article's author.

In sum, the findings of this study point to the need to address the assumed effect of

ethnic heterogeneity in social disorganization theory studies with more caution. The

effects of disadvantaged minorities and immigrant residential concentration on crime

do indeed need to be analyzed separately. Consequently, it would be suggested that

further analyses of social disorganization theory would take into account other social

theories, such as the ethnic community approach, while analyzing their findings; such

expansion of the social disorganization theory would only benefit its analyses, while

analyzing whether its main findings and hypotheses would be still relevant while

applying other theoretical frames.

25

NOTES

1. For those unfamiliar with some demographic characteristics of Israel it is

important to clarify that the origin of the Arab population of Haifa is in the Arab

families that were residents of the city before the creation of the State of Israel.

2. The average population size of census tracks in Haifa is 2,770 individuals. The

smallest census track has a population of 600 and the biggest a population of 5,810.

3. It needs to be emphasized that terrorist, military, and criminal acts directly

related to the Israeli-Palestinian conflict were excluded from the study. The study

focused only on criminal acts typically committed both in Israel and abroad, hence

enhancing the opportunity to generalize this research's findings to other national

contexts.

4. In this regard another explanation for the research's findings may be related to

less significative levels of crime reporting in areas with higher concentrations of

Russian immigrants.

26

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31

Table 1: Descriptive Statistics and Correlations for all Variables for all Neighborhoods in Haifa (n = 73).

Variable

category

Variable Mea

n

SD Min. Max.

A. Ethnic 1. Percent FSU

immigrants

20.36 16.15 0.00 66.67

heterogeneity 2. Percent Arabs 18.21 25.26 0.00 96.44

B. Socioeconomic 3. Percent acad./manag.

Positions

32.95 12.66 8.1 58.5

Status 4. Percent college

education

21.40 7.58 2.9 57.2

5. Percent graduate

education

17.64 9.95 2.9 38.7

C. Residential 6. Percent 5-year

residence

59.77 13.68 5.4 89.4

Stability 7. Percent homeowners 20.62 11.20 8.8 89.4

D. Fam.

Disruption

8. Percent divorced

women

6.57 2.79 0.06 13.7

E. Control 9. Percent adolescents

15-19

6.57 11.20 0.0 52.40

Variables 10. Density 7.96 6.74 0.0 3.10

Dependent 11. Rate of total crime 46.89 38.58 3.48 49.31

Variables 12. Rate of violent crime 4.07 8.44 0.0 53.60

13. Rate of property

crime

42.82 40.48 2.61 247.8

14. Rate of burglary 12.73 19.54 0.0 26.96

+ p < .10 ; * p < .05 ; ** p < .01

32

Table 2: Concentration of FSU immigrants and Arab Population in City Neighborhoods (n = 73).

Neighborhood

composition

Percentage of

immigrants

Percentage of

Arabs

0-10 percent 53% 57.9%

11-30 percent 26% 23.7%

31-50 percent 16% 6.6%

50 or more 5% 10.5%

33

Table 3 Correlation Matrix of the Variables in the Analysis

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Percent FSU immigrants 1.00

2. Percent Arabs .068 1.00

3. Percent acad./manag. positions -.50* -.60* 1.00

4. Percent college education -.13 -.49* .59* 1.00

5. Percent graduate education -.48* -.58* .89* .54* 1.00

6. Percent 5-year residence -.53* .22* .12 -.13 .14 1.00

7. Percent homeowners -.31* .61* .37* .10 .38* .70* 1.00

8. Percent divorced women .58* -.04 -.42* -.20 -.39* -.36* -.20 1.00

9. Percent adolescents 15-19 -.14 .23** -.07 -.50* -.17 .01 .68* -.19 1.00

10. Density .43* .17 -.33* -.09 -.32* -.16 .37* .31* .39* 1.00

11. Rate of total crime .22 .26* -.29* -.31* -.28* -.48* -.52* .37* .17 -.19 1.00

12. Rate of violent crime .21 .34** -.37* -.32* -.34* -.42* -.52* .36* .14 -.11 .96* 1.00

13. Rate of property crime .22 .25* -.28* -.31* -.27* -.49* -.52* .37* .17 -.20 .97* .95* 1.00

14. Rate of burglary .01 -.04 -.02 -.001 .02 -.03 -.08* .31* -.37* .05 .39* .36* .39 1.00

34

Table 4: Typical Social Disorganization - OLS Regression Analysis

Dependent

variables

Total crime Violent crime Property crime Burglary

Independent and

control variables

Parameter

estimate

(S.E.)

Standard

coefficien

t

Paramete

r estimate

(S.E.)

Standard

coefficien

t

Parameter

estimate

(S.E.)

Standard

coefficient

Parameter

estimate

(S.E.)

Standard

coefficien

t

A. Ethnic

heterogeneity

.367

(.451)

.141 .065

(.046)

.242 .302

(.406)

.129 .002

(.119)

.031

B. Socioeconomic

status

-17.579

(16.077)

-.228 -1.947

(1.649)

-.238 -15.663

(14.462)

-.226 -4.408

(4.242)

-.223

C. Residential stability -.608

(.892)

-.096 -.079

9.092)

-.120 -.529

(.803)

-.093 -.112

(.235)

-.069

D. Family disruption 8.643

(4.111)

.309* .781

(.422)

.268* 7.861

(3.698)

.313* 2.351

(1.085)

.329*

E. Control - Percent

youth

-.193

(1.563)

-.015 .068

(.160)

.052 -.261

(1.406)

-.023 -.180

(.412)

-.057

F. Control – density .005

(.002)

.400** .004

(.001)

.377** .004

(.001)

.402** .009

(.001)

.303*

Intercept 62.422 4.182 58.240 17.304

Adj. R2 .185 .212 .183 .135

N 73 73 73 73

+ p < .10

* p < .05

** p < .01

35

Table 5: Decomposed Ethnic Heterogeneity in Social Disorganization - OLS Regression Analysis

Dependent

variables

Total crime Violent crime Property crime Burglary

Independent and

control variables

Parameter

estimate

(S.E.)

Standard

coefficien

t

Paramete

r estimate

(S.E.)

Standard

coefficien

t

Parameter

estimate

(S.E.)

Standard

coefficient

Parameter

estimate

(S.E.)

Standard

coefficien

t

A. Percent FSU

immigrants

-3.580

(1.038)

-.711** -.295

(.109)

-.562** -3.284

(.931)

-.726** -1.158

(.264)

-.899**

A. Percent Arabs .810

(.419)

.265* .106

(.044)

.332** .704

(.376)

.257+ .153

(.107)

.195

B. Socioeconomic

status

-30.937

(14.796)

-.401* -3.137

(1.558)

-.389* -27.80

(13.275)

-.401* -8.396

(3.760)

-.425*

C. Residential stability -2.423

(.914)

-.382* -.245

(.096)

-.371** -2.177

(.820)

-.383* -.654

(.232)

-.403**

D. Family disruption 19.099

(4.477)

.684** 1.737

(.471)

.596** 17.363

(4.016)

.692** 5.473

(1.137)

.765**

E. Control - Percent

youth

-1.873

(1.462)

-.150 -.008

(.154)

-.066 -1.788

(1.311)

-.160 -.682

(.371)

-.214+

F. Control – density .034

(.001)

.271* .003

(.0001)

.264* .003

(.001)

.271* .004

(.001)

.153

Intercept 199.961 16.747 183.214 58.370

Adj. R2 .343 .330 .344 .353

N 73 73 73 73

+ p < .10 * p < .05 ** p < .01