Journal of Quantitative Criminology

, Volume 32, Issue 1, pp 23–45 | Cite as

Immigration and Crime in the New Destinations, 2000–2007: A Test of the Disorganizing Effect of Migration

  • Vincent Ferraro
Original Paper



Drawing from a social disorganization perspective, this research addresses the effect of immigration on crime within new destinations—places that have experienced significant recent growth in immigration over the last two decades.


Fixed effects regression analyses are run on a sample of n = 1252 places, including 194 new destinations, for the change in crime from 2000 to the 2005–2007 period. Data are drawn from the 2000 Decennial Census, 2005–2007 American Community Survey, and the Uniform Crime Reports. Places included in the sample had a minimum population of 20,000 as of the 2005-07 ACS. New destinations are defined as places where the foreign-born have increased by 150 % or more since 1990 and with a minimum foreign-born population of 1000 in 2007.


Results indicate new destinations experienced greater declines in crime, relative to the rest of the sample. Moreover, new destinations with greater increases in foreign-born experienced greater declines in their rates of crime. Additional predictors of change in crime include change in socioeconomic disadvantage, the adult-child ratio, and population size.


Results fail to support a disorganization view of the effect of immigration on crime in new destinations and are more in line with the emerging community resource perspective. Limitations and suggestions for future directions are discussed.


Crime Immigration Social disorganization New destination Community resource perspective 


In recent years, researchers have highlighted the changing nature of immigrant settlement within the US, as the urban-centric streams that characterized much of nineteenth and twentieth century waves of immigration have begun to shift. According to Massey and Capoferro (2008), from 1901 to 1930, 36 % of all immigrants settled in the nation’s five largest urban areas, with 54 % settling in the states where those cities were located. These trends intensified after the passage of the 1965 Hart-Cellar immigration reform Act, such that from 1971 to 1993, 48 % of the foreign-born were settling in the top five destination cities, and 78 % in the top five destinations states. However, the 1990s precipitated a dramatic shift away from these so-called global cities as settlement destinations. By 2005, the number of immigrants settling in the top five destination states had dropped to 52 % (Massey and Capoferro 2008, 35). Instead, many immigrants have begun settling in states that have either not seen significant numbers of immigrants since the early twentieth century or have never seen them in significant numbers at all. The annual growth rate to the 45 other states is approaching the growth rate to the top five (Alsalam and Smith 2005). States such as Indiana and Wisconsin saw their share of the total US immigrant population double from 1980 to 2005, while states such as Arizona, Georgia, North Carolina and Nevada saw their shares triple over the same period.

Clearly, something significant is at work. And demographers, sociologists and other immigration researchers have begun to question what the effects on these new destinations might be. Several researchers have noted the rejuvenating effect of immigrant entrepreneurialism on local commerce, a finding consistent with the concept of immigrant revitalization in both urban (Nielsen and Martinez 2006) and rural areas (Fennelly 2008; Oberle and Li 2008). Some have focused on the role of racism, and anti-black racism in particular, in structuring the settlement patterns of recent immigrants (Odem 2008; Price and Singer 2008). Others have looked at how newcomers have challenged traditional negative stereotypes and, by extension, weakened some aspects of natives’ prejudice while strengthening others (Griffith 2008; Winders 2008). Still others have focused on the reactions of the native-born and how these vary along lines of social class (Fennelly 2008; Fennelly and Federico 2008).

Yet what remains unclear from the literature on new settlement patterns is whether they exert a significant effect on crime. Despite the recent surge in research on the immigration-crime nexus, at present few studies have investigated whether rates of criminal offending are consistent across both traditional receiving locations and new host locales, net of other factors (MacDonald et al. 2013; Shihadeh and Barranco 2013; Lichter et al. 2010; Shihadeh and Winters 2010; Crowley and Lichter 2009). The emerging body of research on new settlement patterns has already begun to offer some intriguing findings for criminologists. It would seem that recent immigrants who settle in new destination locales far from urban centers tend to rate high on what have historically been viewed as the major criminogenic factors: low occupational skill, low educational attainment, and young adulthood. They also tend to be more geographically mobile, which, according to work by Sampson and colleagues (cf. Sampson et al. 1999), should weaken collective efficacy, a deterrent to crime. Lastly, they appear more resistant to English language acquisition (Donato et al. 2008), an indicator of traditional social disorganization, as well as a constraint on acculturation and a potential impediment to structural assimilation via upward occupational attainment.

Moreover, many of the new areas in which immigrants are settling have limited experience with the process of immigration, certainly when compared to traditional gateways such as New York, Chicago, and Los Angeles, raising questions about the likelihood for immigrants’ successful incorporation. From a structural perspective, it is unlikely that such areas have well-developed mechanisms in place that can ease the incorporation of newcomers. Places which have experienced previous waves of immigrants are better able to absorb successive ones into labor and housing markets.1 With these factors in mind, there is reason to suspect that increased immigration to new destinations may be associated with increased crime.

This research seeks to contribute to the nascent body of knowledge on new destinations by focusing on an alternative unit of analysis—the place-level—and a more recent time period—2000 to 2007. In doing so, it has two aims. First, it attempts to provide insight into the question of whether new destinations differed from other areas in terms of crime over the period of study. Did places that have experienced dramatic growth in immigration experience increases in crime? Second, this research seeks to address whether the effect of immigration on crime differs between new destination places and other areas. Does the relationship between immigration and crime differ in new destination compared to other areas? Little research to this point has specifically addressed this question.

Uncovering low rates of crime for the foreign-born might necessitate an extension of the so-called Latino Paradox, whereby Mexican immigrants, despite severe socioeconomic disadvantage, are involved in crime—either as victims or offenders—at lower rates than African Americans and at lower rates than would be expected from traditional theories of crime (Sampson and Bean 2006; Martinez 2002). Conversely, such a finding would also pose a direct challenge to the idea of social disorganization, which has guided the understanding of immigration and crime for decades.

Theoretical Expectations on the Immigration-Crime Link

Immigration as a Criminogenic Process

According to one leading school of thought, the large scale process of immigration results in a heterogeneous population that is residentially unstable. Heterogeneity and instability undermine an area’s formal and informal sources of social control, leading over time to increases in crime. Communication between neighbors becomes difficult as they lack linguistic, cultural, or historic commonalities. Community members’ familiarity with each other and attachment to local institutions and organizations are consequently weakened. Residents begin to treat one another with indifference, creating an environment wherein informal controls are ineffective, leading to a greater reliance and eventual strain on formal social control in the form of police or other official agents.

This perspective traces to Shaw and McKay’s (1942) social disorganization theory, which posits that the criminogenic effects of immigration stem from structural factors rather than qualities of racial and ethnic groups themselves, as is often posited by theories of the cultural deficiencies of subordinated groups. In their analysis, those authors indicate that no matter from where the immigrant groups travel, their experiences will be similar and crime will be an option for many, again not because of innate dispositions, but rather a lack of viable legitimate alternatives. Most importantly, Shaw and McKay found no direct correlations between immigration status and crime or between poverty and crime. Rather, immigration status and poverty each worked indirectly through residential instability and population turnover to produce a socially disorganized community, no longer able to enforce norms against crime and delinquency. As Ousey and Kubrin note in their city-level analysis of the change in crime from 1980 to 2000, “[w]hile the logic of social disorganization theory, at least as traditionally conceptualized, has long provided a scientific basis for the expectation that immigration causes crime, empirical assessment of that hypothesis as well as other theories of the immigration-crime nexus has been limited” (2009, 465). This study seeks to address this limitation by extending testing of social disorganization theory over a more recent time period and for differential effects within new destination places. It does so also with a longitudinal design, which is key for identifying social disorganization, a process that plays out over time.

Immigration as a Crime-Inhibiting Process and the Challenge of New Settlement Destinations

Much recent research on the immigration-crime link has challenged the notion that contemporary immigration is a disorganizing force. A number of recent studies at aggregate levels have tended to find either no effect of immigration on crime or an inverse relationship, whereby more immigration leads to less crime (Martinez and Stowell 2012; Wadsworth 2010; Crowley and Lichter 2009; Ousey and Kubrin 2009; Ousey and Lee 2007; Reid et al. 2005; Butcher and Piehl 1998). In fact, scholarly research in a variety of fields offers evidence that an increasing concentration of immigrants in an area is associated not with negative outcomes but with a range of positive ones, including economic stimulation (Parrado and Kandel 2008; Portes and Rumbaut 2006; Reid et al. 2005; Kotkin 2000; Portes and Zhou 1993), mortality rates (LeClere et al. 1997), and reduced crime and violence (Shihadeh and Barranco 2010; Desmond and Kubrin 2009; Feldmeyer 2009; Morenoff and Astor 2006; Lee et al. 2001).

However, the studies finding these organizing effects have tended to focus on areas with long histories of immigration—that is, areas in which new immigrants may find a welcoming context of reception and existing mechanisms to ease their incorporation. Because many of the new locations to which today’s immigrants are moving have a limited history in dealing with and incorporating disparate groups, it is likely that the reception and process of incorporation will vary for newcomers, relative to traditional settlement destinations. This raises the question of whether immigration in certain areas and under certain conditions might operate as a disorganizing force, thereby contributing to rates of crime.

Recent research by Donato et al. (2008) buoys this point. Their findings suggest that not only are the settlement destinations for immigrants changing, but immigrants themselves are also changing on a host of key demographic and socioeconomic factors. From 1990 to 2000, as the overall native US population aged, the overall foreign-born population grew younger. Moreover, the foreign-born in 2000 reported fewer years of formal education and fewer high school graduates than in 1990, despite the fact that in 2000 the foreign-born were less likely than in 1990 to live in poverty. They were also less likely to speak English well or to speak it in the home in 2000 than in 1990. In concert, these factors paint an image of recent immigrants that resembles earlier waves of European immigrants in the nineteenth and twentieth centuries—precisely those studied by Shaw and McKay in their research on social disorganization.

Research is beginning to address the effects of immigration within new destination areas. At the place level, Lichter et al. (2010) find that Latinos experience greater segregation within new destinations than traditional areas and find greater difficulty in either structural or cultural assimilation, which offers reason to suspect the potential for increasing crime. With specific regard to crime, at the county level, research by Shihadeh and colleagues finds that while in traditional receiving areas, Latino communities provide a buffer between economic deprivation and violence, the same buffer is absent within new destinations (Shihadeh and Barranco 2013; Shihadeh and Winters 2010). These results suggest that the re-organizing effects of contemporary immigration highlighted by much recent research may not be consistent features of the process within new destinations, and that for these new settlement areas, the contemporary process may harken back to that experienced by earlier waves of immigrants.

Conversely, however, research by Crowley and Lichter on social disorganization and immigration in rural counties finds that from 1990 to 2000 new Latino destination counties “experienced significantly larger reductions in overall arrest rates, and in rates of arrest for violent and liquor-related crimes in particular, than other nonmetropolitan counties” (2009, 597). Similarly, in a cross-sectional analysis of US county homicides from 1997 to 2001, Martinez and Stowell (2012) find that counties with the greatest concentration of Latinos have more homicides than counties with lower concentrations of Latinos, leading those authors to conclude that “most Latino homicides are in counties where Latinos reside, not in new destination counties with a low Latino population base” (Martinez and Stowell 2012, 188). Finally, in a longitudinal study of the effect of immigration on crime within Los Angeles city census tracts, MacDonald et al. (2013) find that increasing immigrant concentration is associated with significant reductions in overall and violent crime, net of other factors.

Taken together, these recent studies on new destinations and crime offer conflicting results. Some suggest a picture that resembles the traditional social disorganization view played out in new destinations (measured at the county- and place-levels) (Shihadeh and Barranco 2013; Lichter et al. 2010; Shihadeh and Winters 2010) while others provide evidence that runs counter to the expectations offered by the theory (MacDonald et al. 2013; Martinez and Stowell 2012; Crowley and Lichter 2009). The present research seeks to add to this discussion by testing social disorganization theory over a more recent time period (2000–2007), at an alternative level of analysis, and by testing for effects both between new destinations and other places and within new destinations themselves.

Hypotheses on the Relationship Between Immigration and Crime

Given that new destinations very likely lack mechanisms by which to aid in the incorporation of newcomers, social disorganization theory would predict their arrival is likely to manifest as a more disruptive force. Moreover, given the current cultural context of opposition to immigration and research indicating greater marginalization of ethnic minorities within new destinations (Lichter et al. 2010), it is likely that the foreign-born in these areas may experience blocked economic and employment opportunities. Consequently, drawing from social disorganization theory, I expect to find that change in the concentration of immigrants will be positively associated with change in the rate of crime within new destinations. Second, I expect that within new destinations, greater increases in the foreign-born population will be associated with greater increases in crime.


To test these hypotheses, I perform fixed effects regression analyses on a sample of 1252 cities and towns over the period 2000–2007. Official crime data are drawn from the Federal Bureau of Investigation’s Uniform Crime Reports (UCR). Sociodemographic data are drawn from the 2000 decennial census and the 2005–2007 American Community Survey (ACS) 3-year product.2 Relying on the 2005–2007 data eliminates a potentially confounding variable: the global economic crisis that began in earnest in 2008. From 2007 to 2008, for the first time in nearly four decades, the rate of immigration to the US held steady (US Census Bureau 2009; see also Frey 2009), perhaps signaling that in the midst of the economic downturn, the US had lost some luster as a settlement destination, or perhaps signaling that the global movement of people was put on hold, as belts were tightened around the world. In any case, the post-2007 changes would pose significant challenges to interpreting a change-over-time analysis, and so the study is limited to the period 2000–2007.

Unit of Analysis

This analysis focuses on large and medium-sized US cities and towns for the period 2000–2007. I include places with a minimum population of 20,000 individuals in 2007.3 While 2065 cities and towns meet this criterion, missing data—largely from the UCR—effectively reduce the number of useable locations to 1252. The choice to focus on places is driven by a desire to capture the effects of new destinations. While much research on crime and immigration has focused on the metropolitan statistical area (Stowell et al. 2009; Reid et al. 2005; Butcher and Piehl 1998), that level of analysis is too broad for the purposes of this research as it includes not only major central cities, but many satellite cities that could be destinations for recent immigrants as well. While much research has also focused on the neighborhood level, the lack of nationally representative neighborhood data available in annual form would preclude any change-over-time analysis outside of the decennial census.4 Additionally, while crime is very much experienced at a local level, focusing on the neighborhood or census block might present too narrow a view, missing some of the larger forces that impact crime and extend beyond neighborhoods, including employment and opportunity structures. By focusing on the place-level, this study is able to isolate the effects of change over time for a unit smaller than counties and metropolitan areas. Because of the availability of the data in 3-year waves, it is also able to avoid the confounding effect of the economic crisis and presents results for a fairly recent time period. Finally, looking at the place-level allows this analysis to capture social and economic effects on crime that extend beyond individual neighborhoods.

There is also the important question of the appropriate unit of analysis for testing social disorganization theory.5 Although the construct of “social disorganization” is often viewed as more applicable to neighborhoods than cities, there is reason to expect that the process may apply to larger aggregations as well. As Chamlin (1989, 283) notes with regard to cities, “changes in the ecological structure of cities [measured by population size and residential mobility], as well as ongoing social processes, weaken and disrupt informal mechanisms of control and. in turn, increase the rate of crime.” This finding suggests that disorganization at the neighborhood level may also play out at larger units as well. Indeed, a number of analyses of higher-level aggregations—cities, metropolitan statistical areas, and counties—have found that predictors of social disorganization, including residential instability and population heterogeneity, exert significant effects on crime (Shihadeh and Barranco 2010, 2013; Stowell et al. 2009; Mosher 2001; Liska et al. 1998; Miethe et al. 1991; Chamlin 1989), thereby offering tests of the theory at a variety of geographical units. This study follows in the tradition of these and other leading scholars in the field (Beaulieu and Messner 2010; Ousey and Kubrin 2009; Velez et al. 2003; Steffensmeier and Haynie 2000; Peterson and Krivo 1993; Sampson 1985, 1986, 1987) by testing tenets of social disorganization theory at the city-level.

Dependent Variables

Much research on immigration and crime has tended to focus on violent crime, and to a lesser extent, property crime (Martinez and Nielsen 2006; Sampson et al. 2005; Lee et al. 2001). Following that previous research, the dependent variables analyzed are the rates of overall, violent, and property crime, drawn from the FBI’s Uniform Crime Reports. To strengthen the data against the effects of any single-year anomalies in crime rates, 3 years of crime data were pooled for each of the two time periods. For the first time period, crime data were combined for the years 1999, 2000, and 2001. For the second time period, data were combined for the years 2005, 2006, and 2007.6 The violent crime rate was calculated by averaging the yearly counts of murder, robbery, and aggravated assault, dividing by the population, and standardizing per 100,000. Similarly, the property crime rate was calculated by averaging the yearly counts of burglary, larceny-theft, and motor vehicle theft.7 These two indices are summed to create the overall crime index.

Explanatory Variables

Immigrant Concentration

To measure immigrant concentration, an index was created from the summed z-scores of the percent of the population that is recent foreign-born and the percent Latino (cf. Ousey and Kubrin 2009; Stowell et al. 2009).8 The recent foreign-born are defined as foreign-born who entered the US in the previous 5 years.9 The choice to focus on the recent foreign-born rather than the total foreign-born population is driven both by the public focus on new immigrants as particularly crime-prone and the theoretical focus of the paper. The total foreign-born population would capture persons who may long ago have entered the US and perhaps even become naturalized—that is, somewhat stable sections of the immigrant population. As a test of social disorganization, I focus instead on the most recent arrivals for whom the process of incorporation may be most tumultuous.10

New Destination Places

In recent years, considerable academic interest has developed around the concept of the “new gateway,” a term coined and a concept delineated in the work of Singer and colleagues (cf. Oberle and Li 2008; Price and Singer 2008; Singer 2008). Conceptually, “new gateway” refers to an immigrant settlement destination that has only recently become so. It contrasts with traditional gateways, such as New York, Chicago, and other major urban centers of the northeast, which have experienced inflows of migrants at a fairly constant, and high, rate for decades. The structural differences between new and traditional receiving areas are potentially key in understanding the link between immigration and crime. Places wherein immigration has a long history are likely to have developed mechanisms that aid in immigrants’ successful incorporation. Conversely, places to which immigration is something new may lack the means to successfully incorporate newcomers, and so immigration may operate as a more disorganizing process.

To capture the potential differing effect of immigration across new and traditional destinations, I operationalize “new destinations” as places wherein the number of foreign-born has increased by at least 150 % since 1990, with a minimum of 1000 foreign-born in the 2005–2007 time period. Places matching these criteria are coded “1”; all others are coded “0,” resulting in a total of 194 new destination places.11 , 12 This method follows Crowley and Lichter’s (2009) approach to defining high-growth Latino counties in that it establishes both a sizeable growth in immigration and a minimum threshold for the immigrant population in each place.13 Such growth would also be consistent with social disorganization theory, which posits a large influx of residents as a destabilizing force, which can undermine social cohesion and control.

Additionally, as this research is interested in whether the effects of immigration on crime vary within new destinations, I create an interaction term, which is calculated by multiplying the recent foreign-born index by the new destination dummy variable for each time point. Incorporating this measure into the fixed effects analyses produces estimates for the effect of change in immigration within new destinations.

Socioeconomic Disadvantage

A hallmark of a disorganized area is socioeconomic disadvantage (Shaw and McKay 1942). To account for disadvantage, I incorporate measures for median family income (in 2013-inflation adjusted dollars, log transformed); the percentage of the population with less than a high school education; the percentage of families living below the poverty line; the percentage of households headed by single females; and the percentage of the population age 15 and over that is unemployed. To avoid collinearity in the models, I combine these measures into an index composed of their summed z-scores (cf. Ousey and Kubrin 2009; Morenoff et al. 2001; see also Stowell et al. 2009 for an inverse representation measuring resources, rather than disadvantage).14

Residential Stability

Socially disorganized communities are residentially mobile, marked by significant population turnover, which makes the development of shared norms, and ultimately social control, difficult. Following previous research on the link between crime and population turnover (Martinez et al. 2008; Sampson et al. 2005; Lee et al. 2001; Sampson et al. 1999), residential stability is controlled for using an index composed of the summed z-scores for the percentage of households that are owner occupied and the percentage of households occupied for 2 years or more (Stowell et al. 2009; Sampson et al. 2005; Morenoff et al. 2001; see also Lee et al. 2001 for a similar measure of residential instability).15

Employment Structure

Recent research has suggested that the type of employment predominant in an area also has an effect on crime. Consistent with notions of social organization, Sampson et al. (2005) find that the presence of managerial positions in a community has a depressive effect on African American crime rates (see also Alaniz et al. 1998). Additionally, Lee and Slack (2008) find a significant negative effect of seasonal employment, which may draw immigrants to an area, on crime, suggesting that even short-term work may structure time and buffer against involvement in criminal activity. To capture the effect of employment structure, measures of both the percentage of the population employed in management and professional occupations and the percentage of the population aged 15 and over employed in seasonal work are included.

Population Structure

Social disorganization theory suggests that as the size and heterogeneity of a community increase, crime will increase as well. This implies an absolute increase in overall crime rates. Moreover, urbanization theory suggests that as the size of a community increases, there should be greater reliance on agents of formal control, thereby increasing the official rates of crime in a given area (Schulenberg 2003). To account for the effect of population structure, I incorporate the measure of population size.

The presence of youth and young adults is also conceptually connected to crime, particularly in disorganized areas, where the breakdown of social control leaves a vacuum wherein youth gangs are more likely to flourish (Hagedorn 2008). Research has consistently shown crime to be perpetrated by the young, and that crime, over the life course, increases as the individual approaches the teenage years and peters out as he or she approaches adulthood (Agnew 2001; Sampson and Laub 1993; Hirschi and Gottfredson 1983). Additionally, research on recent immigration indicates that the majority of newcomers from Latin America tend to be young males (Donato et al. 2008). I control for age using the percentage of the population who are male aged 15–29.

Finally, following recent research, and consistent with social disorganization theory, I include a proxy measure for informal social control, defined as the ratio of adults to children in the population (Martinez and Stowell 2012; Martinez et al. 2008, 2010; Stowell et al. 2009). The higher the ratio, the greater is the number of adults, and the fewer the number of children. One implication is that places with extremely high ratios would be less likely to be populated by families, which would be key instillers of social control. Another implication is that places with high ratios of adults to children should be more likely to also have high rates of official crime—a consequence of having a greater number of potential adult offenders.

Analytical Technique

Generally, there is no singly agreed upon technique for a cross-sectional analysis of immigration and crime data, nor for a repeated cross-sectional testing change-over-time. Recent studies have tended to employ one of a few alternatives, including random effects models (Stowell et al. 2009), fixed-effects models (Ousey and Kubrin 2009), or structural equation models (Feldmeyer 2009). Each is preferred over Ordinary Least Squares (OLS) analyses, given the potential for heteroskedasticity. Following Ousey and Kubrin (2009, 460), I employ a fixed-effects model because it focuses only on variation within units, effectively controlling for all other unmeasured time-invariant effects.16 This last point is especially important given the well-established concern that official UCR data are subject to reporting differences across departments.17 By testing only within units, rather than within and between, as is the case with traditional regression analyses, this method essentially controls for any unmeasured time-invariant effects (Allison 2005). The model employed is as follows:
$$y_{it} = \mu_{t} + \beta x_{it} + \alpha_{it} + \varepsilon_{it}$$
where y is the rate of crime, μ is the intercept allowed to vary with time, i refers to the various places (i = 1…n), t refers to the different time points (t = 1, 2), α i refers to the set of fixed, unmeasured time-invariant parameters, and ε is the error term.


Univariate Results

Table 1 presents18 the means and standard deviations for the 1252 cities and towns included in the analysis. The first two panels of the table offer the sample mean scores and standard deviations for the year 2000 and the 2005–2007 period, respectively. The third panel offers the average within unit change. In general, while mean scores on a number of key variables changed very little, others changed quite a bit, despite the relatively short period under study.
Table 1

Profile of places included in analyses




Within-place change







Total population







Percent foreign-born







Percent recent foreign-born







Percent Latino







Percent male aged 15–29







Adult-child ratio







Percent less than high school







Percent SFHH







Median HH income







Pct of HH in poverty







Pct unemployed







Pct of HH owner-occupied







Pct of HH occupied 2 years or more







Pct Mgmt/professional occupations







Percent seasonal occupations







Overall crime rateb







Violent crime rateb







Property crime rateb








n = 1252

n = 1252

n = 1252

aWithin-place change is calculated by subtracting the value for Time 1 (2000) from the value for Time 2 (2005–2007) for each place and then averaging the differences across the sample. As such, a negative value indicates a decline over time

bRate calculated as number of offenses per population of 100,000

Over the period of study, places included in the sample experienced increases in their total and foreign-born populations, and in their Latino populations especially. While educational attainment appears to have increased, as evidenced by a decline in the share of the population with less than a high school degree, other indicators suggest an overall increase in disadvantage. Median household income declined over the period and single-female-headed households and households in poverty increased. However, despite these signs of increasing disadvantage, crime overall declined within the sample. This change was driven largely by a decline in property crime, as there was a slight increase in the average rate of violent crime.

For comparison, Table 2 presents the mean scores for the 194 new destination places included in the sample. As expected, these places saw on average greater increases in their total, foreign-born, recent foreign-born, and Latino populations. Given that the recent foreign-born in new destinations increased only a little less than a 1 % from Time 1 to Time 2, the greater increase in overall foreign-born populations may point to the effects of intranational population shifts, due both to labor market forces and state and local level policy enactments (cf. Massey and Capoferro 2008). The patterns observed for the full sample are similar for new destinations. These places also saw a general increase in educational attainment, along with increases in several measures of disadvantage, to levels slightly higher than the overall sample. While new destination places saw even greater declines in overall and property crime rates, they also experienced a greater increase in violent crime over the period.
Table 2

Statistical profile of new destinations included in analyses




Within-place change







Total population







Percent foreign-born







Percent recent foreign-born







Percent Latino







Percent male aged 15–29







Adult-child ratio







Percent less than high school







Percent SFHH







Median HH income







Pct of HH in poverty







Pct unemployed







Pct of HH owner-occupied







Pct of HH occupied 2 years or more







Pct Mgmt/professional occupations







Percent seasonal occupations







Overall crime rateb







Violent crime rateb







Property crime rateb








n = 194

n = 194

n = 194

aWithin-place change is calculated by subtracting the value for Time 1 (2000) from the value for Time 2 (2005–2007) for each place and then averaging the differences across the sample. As such, a negative value indicates a decline over time

bRate calculated as number of offenses per population of 100,000

In sum, across the period 2000–2007 on average, places included in the full sample experienced a surge in population growth coupled with declines in overall crime. At the same time, measures of socioeconomic well-being either declined slightly or remained stable, on the whole. The trends are mirrored, if somewhat intensified within the new destinations included in the sample, where increases in foreign-born were higher.

Multivariate Results

Table 3 presents the results of the fixed effects regression analyses for change in rates of overall, violent, and property crime. As the first panel shows, change in the recent foreign-born index exerts a negative, marginally significant effect (p ≤ 0.10) on change in overall crime, net of the controls included and all unmeasured time-invariant factors. Two of the measures of population structure exert significant effects on the change in overall crime. Change in population is negatively correlated (p ≤ 0.01); each one unit increase over time in population (i.e. one additional person) is associated with a decline in the rate of crime of 0.006 (per 100,000 persons). Alternatively, this can be read as an increase over time of 1000 persons is associated with a decline of 6 crimes per hundred thousand persons. Change in the ratio of adults to children is positively associated with the change in crime. A one unit increase over time in the ratio of adults to children results in an increase of roughly 315 crimes (per 100,000). Similarly, change in socioeconomic disadvantage is positively and significantly correlated with change in overall crime, with a one unit increase over time associated with an increase in overall crime of 163 per 100,000 persons. The effect of the new destination dummy measure is negative and marginally significant (p ≤ 0.10), offering some tentative evidence that in terms of overall crime, new destinations differed from places that did not experience as much growth in immigration. Specifically, compared to the rest of the sample, new destinations experienced a decline in overall crime of approximately 189 crimes per 100,000 persons.
Table 3

Fixed effects regression models predicting change in crime rates, 2000–2007


Overall crime

Violent crime

Property crime



Change in recent foreign-born index







Change in city/town population







Change in pct male aged 15–29







Change in adult-child ratio







Change in disadvantage index







Change in residential stability







Change in pct managerial/professional work







Change in pct seasonal work







New destination places







Intercept (change in time)







Model summary information

Model SS




Corrected SS




F value




Between-unit variation (R-square)




Total number of observations (N × T)




Total number of cities/towns (N)




Standard error in parentheses

p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001

The results for change in violent crime presented in Table 3 are similar to those for overall crime with regard to the change in population and change in disadvantage, though the effects are smaller as a result of the relative rarity of violent crime, compared to property crime. For each additional increase in population from 2000 to 2007, the rate of violent crime declined by 0.003 crimes per 100,000 persons. For each one unit increase in disadvantage, the rate of violence increased by roughly 63 crimes per 100,000. As with overall crime, the effect of change in immigration is marginally significant (p ≤ 0.10) and negative. A one unit increase in the recent foreign-born index over time is associated with a decrease in the rate of violence by 51. For change in violence, there is no effect of the adult-child ratio, nor any differential effect within new destinations.

The same cannot be said for property crime. Relative to other places in the sample, new destinations experienced a decline of 228 crimes per 100,000 persons (p ≤ 0.001). As with overall crime, change in property crime over the period is most heavily affected by change in the adult-child ratio and change in disadvantage. While the main effect of change in immigration is null, the results suggest that changes in crime over the period operate differentially in areas of greatest growth in immigration. New destination places experienced greater declines in property crime than did places that did not experience as large a growth in immigration.

Overall, the results presented in Table 3 offer mixed support for social disorganization theory. As expected, change in disadvantage is significantly positively related to changes in all three rates of crime. The ratio of adults to children was positively associated with increases in overall and property crime. This finding may indicate the effect of the presence of large numbers of adults in non-family arrangements. When the analyses were performed with the adult-child ratio removed (results not shown), the effect of the change in males aged 15–29 was positive and significant for both overall crime (β = 59.78, p ≤ 0.05) and property crime (β = 50.38, p ≤ 0.05), suggesting that the adult-child ratio may be capturing the effect of increasing young adults in the population who have not yet started families. As social disorganization would suggest, the presence of large numbers of young people would be associated with increasing crime.

The results for the measures of immigration, however, contrast with hypotheses drawn from social disorganization theory. While the theory would suggest that changes in immigration should exert positive or null effects—working through disadvantage and instability–on crime, the results here indicate the direction of the effect on all three forms of crime is negative. Moreover, compared to other places in the sample, new destinations experienced significantly greater reductions in property crime (β = −228.22, p ≤ 0.01). The finding that places that have experienced large increases in their foreign-born population also experienced greater declines in crime suggests that contemporary immigration may not be the disorganizing force predicted by the theory.

The question remains as to whether the effect of immigration in new destinations differed from the effect within other places. To address this question, fixed effects regression analyses were run in which the dummy variable for new destinations was replaced with an interaction between new destinations and change in the recent foreign-born index. Table 4 presents the results of these analyses for the three rates of crime.
Table 4

Fixed effects regression models predicting change in crime, 2000–2007, controlling for effect of immigration within new destinations


Overall crime

Violent crime

Property crime



Change in recent foreign-born index







Change in city/town population







Change in pct male aged 15–29







Change in adult-child ratio







Change in disadvantage index







Change in residential stability







Change in pct managerial/professional work







Change in pct seasonal work







New destination × recent FB index







Intercept (change in time)







Model summary information

Model SS




Corrected SS




F value




Between-unit variation (R-square)




Total number of observations (N × T)




Total number of cities/towns (N)




Standard error in parentheses

p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001

The results for change in overall crime indicate that, controlling for the factors included in the model and all unmeasured time-invariant predictors, the effect of change in immigration within new destinations was both stronger and more robust than for the overall sample. Within new destinations, a one unit increase in the recent foreign-born index is associated with a decrease of 178 crimes per 100,000 persons (p ≤ 0.001), compared to a main effect within the overall sample of a decline of 134 crimes per 100,000 (p ≤ 0.10). The finding is similar with respect to change in violent crime, where a one unit increase in the recent foreign-born index is associated with a decline of 93 crimes per 100,000 (p ≤ 0.001), compared to a null effect within the sample as a whole. For property crime, both the interaction term and the main effect are marginally significant and relatively close (−84.98 compared to −94.54). Within new destinations, immigration was associated with significant and greater declines in both overall and violent crime, compared to the rest of the sample. While the results of Table 3 indicated new destinations experienced less crime than other places in the sample, the results of Table 4 indicate that recent immigration contributed to greater declines in new destinations than in other places. In short, greater increases in immigration meant greater declines in crime within new destinations.

Discussion and Conclusions

Social disorganization theory suggests that immigration is one of several large-scale social processes that can operate to foster crime by increasing social heterogeneity and residential instability and working through socioeconomic disadvantage. Consequently, analyses of immigration’s effect on crime would be expected to find either positive effects or null effects when working through disadvantage. It stands to reason these effects would be heightened in places where the influx of immigrants has been greatest (Shihadeh and Barranco 2013; Lichter et al. 2010; Shihadeh and Winters 2010). This research has sought to address two key issues: first, to determine whether new destinations experienced more or less crime than areas that did not experience dramatic growth in immigration; and second, to determine whether the effect of immigration on crime differed between new destinations and other places. To address these issues, analyses of change-over-time were conducted on rates of overall, violent, and property crime.

With regard to the first issue and whether new destinations, which experienced dramatic growth in immigration, experienced an increase in crime relative to other places, the results presented in Table 3 suggest otherwise. Despite surges in immigration of at least 150 %, with a minimum threshold of 1000 foreign-born in 2007, new destinations appear to have experienced greater declines in crime than places that did not experience such growth. This was especially the case for property crime, as new destinations saw a reduction of 228 crimes (per 100,000) over the period (p ≤ 0.001). The effect on violent crime was null while the effect on overall crime was marginally significant (p ≤ 0.10). Overall, in places wherein immigration might reasonably be expected to be most disorganizing, the results presented here instead build upon the growing body of literature finding null or negative effects of immigration on crime in new settlement areas (Martinez and Stowell 2012; Crowley and Lichter 2009). Despite their growth in immigration, new destination analyzed here experienced less crime than the rest of the sample.

With regard to the issue of whether the effect of immigration differs within new destinations, compared to other places, the results of Table 4 suggest it does. Findings indicate that in new destinations, more immigration in fact meant less crime over the period. Within these places, a one unit increase in the recent foreign-born index over time was associated with a decrease of nearly 180 crimes per 100,000 (p ≤ 0.001). This decrease contrasted with the main effect of the change in recent foreign-born, which was both smaller in magnitude and less robust (β = −134.5, p ≤ 0.10). The finding for violent crime was similar, with a one unit increase in the recent foreign-born index associated with a decline of 93 crimes per 100,000 (p ≤ 0.001). Taken together, the results presented contrast sharply with hypotheses drawn from social disorganization theory, falling more line with the findings of recent research on new destinations at the county-level (Crowley and Lichter 2009). Results demonstrate that changes in immigration vary systematically with changes in crime for cities and towns experiencing dramatic increases in immigration—and with little-to-no history of immigrant incorporation. Moreover, more immigration in these areas translates to less crime over time.

There are four key limitations of this study that should be addressed by future research. First, as the nature of a fixed effects analysis prevents generalizing beyond the sample of study, additional research on alternative samples is needed. Further, while these analyses eschewed the potentially confounding variable of the global economic crisis, which has dramatically impacted the immigration landscape, future research should begin to investigate whether and how patterns of migration and their effects may have changed in recent years.

Second, while the use of the city-level as a test of social disorganization has been established in the literature as discussed above, there remain key questions as to whether and how the primary factors of population heterogeneity and residential instability operate at different aggregations. While the results above suggest that immigration may not be a disorganizing force at the place-level, the reverse finding would pose difficult to interpret, as the mechanisms of social disorganization at the city-level require further elaboration. Recent research has begun to test the linkages between neighborhood and city aggregations (Weijters et al. 2007, 2009) but more work is needed in this important area.

Third, the operationalization of new destinations warrants future consideration and analysis. While the method employed in the present is consistent with recent research and provides a means to compare places experiencing significant growth in their immigrant populations to those which have experienced stability or decline, the threshold applied may obscure settlement differences in terms of the timing and degree of population change. It is unclear whether the lack of a disorganizing effect of immigration would hold across both places that have experienced steady accretions of foreign-born since the 1990s and those that have experienced a more rapid influx over a shorter period of time. Similarly, an absolute threshold may mask effects at different levels of population change. There is reason to suspect that the potential for disorganizing effects of immigration may differ, for example, between places experiencing a three- or fourfold increase in their share of foreign-born and those experiencing a 150 or 200 % increase. Additional research is needed to control for both the relative increases in foreign-born populations and the timing of those increases.

Fourth, the city-level data employed here are limited in their ability to capture immigrants’ ethnic and national origins, which raises the issue of confounding settlement effects, as highlighted by MacDonald et al. (2013). Immigrants may be drawn to particular areas by proximity to co-ethnics and those who share similar views toward law-abiding behavior. As those authors note, the result would be reductions in crime that are “simply an artifact of segmented assimilation” (MacDonald et al. 2013, 193). In a related manner, increasing immigration may be a result of newcomers’ being drawn to areas of emerging economic development. In this way, reductions in crime may be the result of local, and differential, growth in particular employment sectors that favor foreign-born workers. While the results presented here contribute to the literature by demonstrating differential effects between new destinations and other areas and within new destinations themselves, no controls are included to account for immigrants’ selection of settlement locations. Future research should incorporate controls for co-ethnic residential concentration and local economic growth to further isolate potential disorganizing effects of immigration on crime.

That change in immigration exerts either null or negative effects on change in crime within places for which immigrant settlement is a new phenomenon poses a firm challenge to the social disorganization view. Given the current cultural context of opposition and research indicating greater marginalization of ethnic minorities within new destinations (Lichter et al. 2010), the findings are somewhat surprising and may offer support for the emergent community resource view (Desmond and Kubrin 2009; Feldmeyer 2009; Logan et al. 2002; Portes and Jensen 1992), which suggests that immigrants who settle in nontraditional areas may in many ways be different from their counterparts in more traditional areas, highlighting the notion of immigrant “pioneers,” whose distinct personality traits that buffer from criminal involvement (Portes and Rumbaut 2006). The findings may also point to the effect of network ties and the existence of pull factors, such as the availability of employment, that draw immigrants to these new places and provide a buffer (Lee and Slack 2008; Parrado and Kandel 2008). Moreover, results give credence to the notion that emergent immigrant communities are able to provide their members with support systems by which to manage the stressors of social, cultural, and economic marginalization (Smith and Furseth 2008). Future research is needed in the area to continue to uncover the variations across types of settlement areas and the specific mechanisms at work.


  1. 1.

    For example, Card (1990) finds that the influx of Mariel Cubans into Miami had virtually no effect on the wages of Miami's existing workforce precisely because previous waves of immigrants resulted in large numbers of immigrant employers who were able to absorb the newcomers.

  2. 2.

    The ACS provides much of the same information as the Census (in most cases the wording of questions is identical), the major differences being that the ACS is conducted in 1-, 3-, and 5-year waves and is extrapolated from a sample of the population, as opposed to the counts offered by the Census. Though few studies have used the ACS to date, there are several reasons to do so. First, because it is conducted yearly, the ACS provides some of the most recent data available on demographic and economic characteristics for the nation’s population. Second, the ACS offers data products in 1-, 3-, and 5-year forms, with each increase in the number of years corresponding to a smaller population threshold for inclusion, essentially allowing researchers the option of choosing between increased data currency or heightened stability. Third, in contrast to a point-in-time survey such as the decennial census, wherein data are tied to a specific date, ACS data represent the average of any given characteristic over a 1-, 3-, or 5-year period.

  3. 3.

    Twenty-thousand is the minimum population size for places included in the ACS 3-year product.

  4. 4.

    The ACS 5-year product is generalizable to all-places in the US, with no minimum size requirement. However, as the ACS began in 2005, the first available 5-year wave is 2009.

  5. 5.

    I thank the anonymous reviewer for raising this important question, which is also discussed further in the limitations section.

  6. 6.

    In each case, a minimum of two years of reporting was required for inclusion in the analysis. Any place for which two years of data in each period could not be obtained was excluded from the analysis.

  7. 7.

    For sake of brevity, only the analyses of the three index measures of crime are presented here. Additional analyses were also run on each type of crime for each time period, for a total of 12 more models, all of which are substantively similar to the ones presented here. They are available upon request from the author.

  8. 8.

    It was hoped that this index would also include the z-score for the percent of the population that speaks English less than very well, capturing variations in human capital. However, given the extent of missing values on that measure, as drawn from the ACS 2005–2007, it was excluded to preserve sample size.

  9. 9.

    For the first time period, recent immigrants are those who arrived between January 1995 and March 2000; for the second time period, those who arrived between January 2000 and the average of 2005–2007. Because the ACS data are essentially averaged across the period and because growth in immigration began to slow from 2006 to 2007 (before leveling off from 2007 to 2008), the total number of recent foreign-born drawn from the data set most closely resembles the total from 2006, effectively adding only one additional year’s worth of immigrants.

  10. 10.

    Analyses were also run with a total-foreign-born index in place of the recent foreign-born index presented here. Results do not differ substantively and are available for interested readers upon request.

  11. 11.

    Pre-1990 immigration counts are calculated from the measure on foreign-born year-of-entry provided in both the 2000 decennial Census and the 2005–2007 ACS.

  12. 12.

    Additional analyses (not shown but available upon request) were run wherein new destinations were operationalized as (1) places whose recent foreign-born population increased by 150 % with a minimum of 500 of foreign-born in the 2005–2007 time period (n = 200). The results obtained using this alternative specification did not significantly differ from what is presented here.

  13. 13.

    Since the unit of analysis here is cities, rather than counties, it may be argued that the above operationalization undercounts new destination places. However, as few studies on new destinations and crime exist at the place-level, an approach modeled on the established literature seems warranted.

  14. 14.

    The analyses were re-run (not shown) with a disadvantage index containing the percent of the population that is non-Hispanic black, in addition to the items mentioned above, and a third time with the NH Black measure included as its own predictor, with no substantive differences to the results discussed here. The only considerable effect of the measure’s inclusion was to reduce the overall sample size by approximately 60 cases, due to missing data from the ACS.

  15. 15.

    The decision to use two years as the measure of stability is entirely data-driven, as appears to be the case for other researchers. The 2000 census specifically asks whether householders have resided in the same place for at least the last five years, while the ACS asks only whether residence has been continuous since the previous year. Such a disparity is unacceptable. Fortunately, each data set also includes a variable reporting when the householder moved into the home. As a result, the stability variable incorporated here from the ACS measures the percentage of householders who moved in prior to 2004, two years from the median time point of the data set, while the decennial census variable measures the percentage of the population who moved in prior to 1998, approximately two years prior to the decennial census.

  16. 16.

    To confirm the fit of a fixed effects model over a random effects one, I performed a centered scores test. The results suggest that the random effect is in fact correlated with the measured predictor variables, warranting the use of a fixed effects model.

  17. 17.

    An additional benefit is that, because there are only two time points, the within-unit differences for all measures are approximately normally distributed, precluding the need to modify measures and easing interpretation of results.

  18. 18.

    See “Appendix” Table 5 for correlation coefficients among variables.



The author would like to thank the anonymous reviewers for their detailed and constructive comments, which have significantly improved the final paper. The author also wishes to thank the Editor for his helpful comments and support during the review process.

Conflict of interest

The author declares that he has no conflict of interest.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of SociologyFramingham State UniversityFraminghamUSA

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