Connection between Immigration and Violent Crime Rates

Social Problems, Vol. 56, Issue 3, pp. 447–473, ISSN 0037-7791, electronic ISSN 1533-8533. © 2009 by Society for the Study of
Social Problems, Inc. All rights reserved. Please direct all requests for permission to photocopy or reproduce article content
through the University of California Press’s Rights and Permissions website at www.ucpressjournals.com/reprintinfo/asp.
DOI: 10.1525/sp.2009.56.3.447.
Exploring the Connection between
Immigration and Violent Crime Rates
in U.S. Cities, 1980–2000
Graham C. Ousey, College of William & Mary
Charis E. Kubrin, George Washington University
A popular perception is that immigration causes higher crime rates. Yet, historical and contemporary
research finds that at the individual level, immigrants are not more inclined to commit crime than the native
born. Knowledge of the macro-level relationship between immigration and crime, however, is characterized by
important gaps. Most notably, despite the fact that immigration is a macro-level social process that unfolds over
time, longitudinal macro-level research on the immigration-crime nexus is virtually nonexistent. Moreover, while
several theoretical perspectives posit sound reasons why over-time changes in immigration could result in higher
or lower crime rates, we currently know little about the veracity of these arguments. To address these issues, this
study investigates the longitudinal relationship between immigration and violent crime across U.S. cities and
provides the first empirical assessment of theoretical perspectives that offer explanations of that relationship. Findings support the argument that immigration lowers violent crime rates by bolstering intact (two-parent) family
structures. Keywords: immigration, violent crime, demographic transitions, family structure, drug markets.
Nearly 80 years ago, criminologist Edwin Sutherland (1924, 1934) highlighted immigration and crime as an area of popular misconception and policy distortion. Today, not much
has changed as both public opinion about immigration and immigration policy appear to be
driven more by stereotype than by empirical fact (Martinez and Lee 2000). Ruben Rumbaut
and Walter Ewing (2007) note: “The misperception that the foreign born, especially illegal
immigrants, are responsible for higher crime rates is deeply rooted in American public opinion and is sustained by media anecdote and popular myth” (p. 3; see also Hagan, Levi, and
Dinovitzer 2008:96).
In contrast to common perception, a rapidly expanding literature reports that immigrants
are less criminally involved than their native-born counterparts (Hagan and Palloni 1999;
Martinez 2002). Based on an extensive review of the literature, Ramiro Martinez and Matthew Lee (2000) conclude: “The major finding of a century of research on immigration and
crime is that . . . immigrants nearly always exhibit lower crime rates than native groups”
(p. 496). This finding of lower immigrant criminality is evident across studies that focus on
several outcomes including crime and incarceration (e.g., Butcher and Piehl 1998b; Sampson,
Morenoff, and Raudenbush 2005). Thus, the salient question of whether immigrants have a
greater propensity to commit crime than the native born appears to be no, although by no
means is this answer definitive.
But the immigration-crime research picture remains incomplete. First, unlike the abundance of research on the individual-level association between immigrant status and criminal
offending, there exists a comparative shortage of research on the macro-level relationship
An earlier version of this manuscript was presented at the 2008 meeting of the American Society of Criminology,
St. Louis, MO. The authors would like to thank the anonymous reviewers and the editor of Social Problems for their insightful comments on a prior draft of this paper. Direct correspondence to: Graham C. Ousey, Department of Sociology,
College of William & Mary, PO Box 8795, Williamsburg, VA 23187. E-mail: [email protected]

448 Ousey/ Kubrin
between immigration and crime rates (Mears 2001; Reid et al. 2005). This is problematic because immigration is an aggregate-level phenomenon whose effects may extend far beyond
the simple thesis that immigrants are more crime prone than nonimmigrants. Indeed, there
are good reasons to suspect that immigration affects demographic, economic, and social structures in ways that will impact overall crime rates, net of any differences in the individual-level
offending of immigrants and natives (Reid et al. 2005).
Second, the relative scarcity of macro-level research is compounded by a near absence
of longitudinal research on the immigration-crime nexus. In fact, although immigration is
fundamentally a process of social change that unfolds over time, most prior aggregate-level
studies on immigration and crime are cross-sectional. This is a serious limitation because
cross-sectional analysis is best suited for analyzing whether stable features of aggregate social
units are correlated with one another (i.e., stock effects) not how temporal change in one
social process affects change in another (i.e., flow effects). Moreover, ample evidence suggests
there are differences in the cross-sectional and longitudinal effects of macro-social predictors
on crime (Cantor and Land 1985, 2001; Chiricos 1987; Marvell and Moody, Jr. 1991; Phillips
2006a), which makes it clear that we cannot make generalizations about the longitudinal
immigration-crime relationship from extant cross-sectional research.
Third, prior macro-level research is limited by the fact that theories of the immigrationcrime relationship have not been sufficiently evaluated. Indeed, although there are multiple
explanatory frameworks that posit plausible mechanisms by which immigration may affect
change in crime, indicators representing those frameworks often have not been collectively
included within macro-level studies (Mears 2002:284). Perhaps more importantly, prior aggregate studies have not assessed whether salient social factors mediate the immigration-crime
relationship in the manner predicted by prominent theoretical arguments.
In the present study, we seek to advance the literature on the connection between immigration and crime by attending to these limitations. Specifically, we investigate the impact of
change in measures of immigration on change in serious crime for 159 U.S. cities from 1980
through 2000. We also address important theoretical limitations by empirically testing the
efficacy of several alternative explanations of the temporal nexus between immigration and
crime. In particular, we examine the extent to which within-city, over-time change in measures drawn from these explanatory models can account for (i.e., mediate) the longitudinal
relationship between immigration and violent crime. Before describing our research design
and discussing the results, we begin by outlining potential explanations for the immigrationcrime relationship. We follow that with a summary of what has been learned from prior research on the immigration-crime nexus.
Conceptual Framework
Despite popular perception that immigration and crime go hand in hand, there are sound
reasons to believe that immigration can impact social life in ways that either increase or decrease crime rates. We first review those perspectives that suggest immigration leads to more
crime. Then we review those that suggest the opposite—that increased immigration results in
less crime.
Perspectives Positing a Positive Immigration-Crime Relationship
Demographic Transition and Population Instability One framework theorizing a positive longitudinal relationship highlights the fact that immigration leads to demographic transitions that
affect crime rates. There are two variants of the demographic transition framework, with each
emphasizing somewhat different causal mechanisms. The first argument is compositional. It
suggests that immigration increases crime rates by raising the share of the population with a

Immigration and Violent Crime Rates in U.S. Cities 449
“crime-prone” demographic profile. A firmly established criminological finding is that crime
follows a distinctive age pattern with offending rates being highest among teens and young
adults (Hirschi and Gottfredson 1983). Another well-established “crime-fact” is that males are
involved in crime, especially violent crime, at significantly higher rates than females (Pastore
and Maguire 2008). Thus, to the extent that immigration increases the percentage of the
population that is young and male, crime rates will increase. Consistent with that logic, evidence from the 2000 Census suggests that compared to the native-born population, a higher
proportion of recent immigrants are male (51.4 percent versus 48.9 percent) and in the 15 to
34 year age range (53.7 percent versus 26.8 percent) (U.S. Census Bureau 2001).
The second demographic transition argument is contextual and it draws from the social disorganization framework, particularly early formulations of that theoretical perspective
(e.g., Shaw and McKay 1969). As traditionally conceptualized, social disorganization theory
contends that crime rates will rise when rapid social change breaks down the social networks
and institutions necessary for effective socialization and behavioral regulation. One change
believed to contribute to social disorganization is population instability. As a major driver of
population change and residential instability, immigration may thus be regarded as a critical
factor behind the breakdown of informal social control and concomitant increases in crime
rates (Bankston 1998; Lee, Martinez and Rosenfed 2001; Lee and Martinez 2002; Mears 2002;
Reid et al. 2005). In essence, this argument asserts that immigration creates population turnover and instability, which lead to more crime. It should be noted that a key distinction between the two demographic transition hypotheses is the latter suggests that increased immigration will lead to higher crime rates among all population groups, while the former suggests
the increase will be confined to the recent foreign born.
Labor Market Structure and Economic Deprivation A second set of perspectives supporting a
positive association between changes in immigration and crime highlight the role of economic
opportunities and economic deprivation. One strand of this viewpoint posits that immigration
elevates crime by increasing the share of the population with low educational attainment,
marginal labor market skills, and poor employment prospects. Research documents that recent waves of immigrants are less skilled than both earlier immigrants and natives (Butcher
and Piehl 1998a:461; see, for example, Borjas 1990). This lack of human capital dampens
their job prospects and also may narrow their residential options. Consequently, many immigrants are channeled into neighborhoods located in or around urban ghettos (Hagan and Palloni 1999; Shaw and McKay 1969; Thomas and Znaniecki 1920) where they are more likely
exposed to unemployment, poverty, and sundry social ills associated with contexts of severe
economic deprivation. Immigrants may thus come to the realization that opportunities for attaining economic success via legitimate avenues are bleak. According to opportunity structure
theory, that realization can lead to strain and frustration, which will heighten the probability
of adaptive responses that involve alternative economic pursuits, such as crime (Lee et al.
2001:561; Mears 2002:284; Reid et al. 2005:759).
While an influx of low-skilled immigrants may, in general, contribute to rising unemployment and poverty rates, more recent waves of immigration may be particularly likely
to produce such outcomes. This is because the urban labor market structure encountered by
immigrants in recent decades is distinct from that faced by immigrants during other periods of
mass immigration in American history:
Whereas earlier European immigrants entered American cities at a time when manufacturing jobs
were plentiful and provided a means of upward mobility, new immigrants must confront an “hourglass economy” that bifurcates opportunities for employment between menial low-wage jobs at the
bottom and high-skill professional and technical jobs at the top and provides very limited opportunities for immigrants to advance beyond the bottom rung of the economic ladder without substantial investments in human capital and acquisition of requisite social networks (Morenoff and Astor
2006:38).

450 Ousey/ Kubrin
The implication of this newer economic order is that the “Americanization” experience of recent arrivals, in some cases, parallels the experience of similarly situated African Americans or
Latinos (Martinez, Lee, and Nielsen 2004:135). That is, assimilation into American life may not
involve the desired trajectory of upward mobility but instead may be “downward,” involving
sustained exposure to economic deprivation and a deviant lifestyle (Portes and Rumbaut 2001;
Rumbaut et al. 2006:73). In short, while immigrants generally face tough economic hurdles in
the assimilation process, immigrants entering during the most recent waves may be especially
vulnerable to the types of economic deprivation associated with greater crime and violence.
The above argument focuses on how a lack of human capital among immigrants creates
disadvantages in the labor market that ultimately lead to their own involvement in crime. A
broader related thesis suggests that immigration may affect economic deprivation and crime
among nonimmigrants as well. This viewpoint contends that immigration increases crime by
changing the overall structure of local labor markets (Reid et al. 2005). For example, research
suggests that increased immigration produces a new pool of low-skill, low-wage labor that
competes with and may displace existing low-skill workers (Beck 1996; Waldinger 1997). As
a result, displaced groups face greater deprivation, which may elevate their involvement in
crime (Wilson 1996). Immigration also may increase overall levels of economic deprivation by
driving up the supply of low-skill labor and driving down the base wage among all low-skill
workers. Meanwhile, the surplus of low-skill workers rendered by immigration may simultaneously increase unemployment rates. These deleterious effects of immigration on wages and
employment rates may increase the demand for public welfare services beyond the capacity
of existing resources, which would only exacerbate further the experience of economic deprivation. In sum, this latter argument suggests that by increasing unemployment, depressing
wages, and straining public welfare resources, temporal increases in immigration may contribute to higher crime rates among both low-skilled native-born workers as well as among
immigrants themselves (Butcher and Piehl 1998a).1
Illegal Drug Markets Several studies document that recent trends in crime are linked to
changes in illicit drug markets (Baumer et al. 1998; Fryer et al. 2005; Levitt 2004; Ousey and
Lee 2002, 2004, 2007). Researchers have argued the proliferation of crack-cocaine markets
during the 1980s produced a volatile marketplace that contributed to higher rates of violent
crime. The volatility of these drug markets emanated from several sources. First, the rush to
capitalize on this emergent economic opportunity was substantial and competition-related
conflict was fairly prevalent. Second, due to the illegality of the enterprise, drug market conflicts were less likely to be resolved through legal dispute resolution mechanisms and more
likely to be handled with personal aggression than conflicts that occurred within legitimate
businesses. Third, drug market activities commonly took place in open-air street markets that
exposed sellers and buyers to high risks of robbery and assault, which contributed to an “onguard” posture and the frequent carrying of firearms (Blumstein 1995; Jacobs 2000). And
finally, these markets developed in economically disadvantaged areas where attenuated informal social controls exacerbated their tendency toward violence (Ousey and Lee 2002, 2004).
There are numerous reasons to suspect the proliferation of drug markets may be a salient
intervening mechanism linking immigration to changes in crime rates. Considering long-standing stereotypes, Martinez (2002) notes a popular perception is that Latinos are heavily involved
in drug trafficking and the concomitant violence it generates. He claims this stereotype is both
reflected in, and reinforced by, blockbuster Hollywood films such as Scarface (1983), Carlito’s Way
1. We acknowledge there are other possible links between immigration and economic outcomes. In addition to
research showing deleterious outcomes (Borjas 2003; Borjas, Freeman, and Katz 1997), there is also evidence that immigration may produce beneficial impacts for some labor market sectors and native-born groups (Pedace 2006). However,
given these alternative arguments are still debated in the labor economics literature and given the nascent state of
longitudinal immigration-crime research, we believe it prudent at this point to limit our focus to the most prominent
immigration-labor market thesis discussed above. We leave it to future work to extend our efforts in this regard.

Immigration and Violent Crime Rates in U.S. Cities 451
(1993), and Traffic (2001), which depict the drug trade and gang activity as pervasive in Latino
communities. Despite common stereotypes, empirical research in this area is not well developed.
In one of the few studies that does exist, Martinez (2002) reports evidence that Latinos are not
over-involved in drug- and gang-related violence (see also Martinez, Nielsen, and Lee 2003).
While acknowledging that the disproportionate involvement of immigrants in the drug
trade may be more myth than fact, we also argue there are structural reasons supporting the
notion that immigration, drug markets, and violence may be connected. As explained earlier,
many immigrants enter the United States with relatively low levels of human capital, which
exposes them to tough sledding in the post-industrial labor market. Hence, it is plausible that
illegitimate opportunity structures such as the illegal drug trade are particularly appealing avenues of economic success for immigrants who encounter difficulties locating work in legitimate
industries. Moreover, given that immigrants disproportionately settle in economically disadvantaged neighborhoods, it is likely they face greater exposure to the promises (and pitfalls) of
open-air drug markets. Finally, because new immigrants are disproportionately young and male,
they fit the demographic profile of individuals recruited to participate in crack-cocaine markets
(Blumstein 1995). Indeed, some scholars contend that Latino gangs in California heavily recruit
drug market participants from recently arrived immigrant pools, particularly illegal immigrants
from Mexico and Central America (Mac Donald 2004). It has also been reported that service in
the drug trade is one way that illegal immigrants pay off debts to the Latino gangs that helped
arrange their transit from Mexico to the United States (Mac Donald 2004).
In sum, there are several conceptual arguments that proffer reasons why immigration will
produce increases in rates of crime and violence over time. Some of these perspectives focus
on the behavior of immigrants themselves, while others suggest that rising crime rates reflect
the behavior of both immigrants and the native born. Although each explanation provides a
rationale consistent with the popular perception of a positive relationship between immigration and crime, much empirical evidence—particularly at the individual level—contradicts
that perception. This conflicting evidence calls for conceptual arguments that explain the possibility of a negative association between immigration and crime. In the following section, we
discuss such arguments.
Perspectives Positing a Negative Immigration-Crime Relationship
Immigrant Selection Effects Several explanatory frameworks posit that over-time increases
in immigration will contribute to less crime and violence. The first argument suggests that
immigrants are not necessarily a random cross-section of the sending population but are a
self-selected group with relatively high levels of achievement ambition and low criminal propensity (Butcher and Piehl 2005). As Michael Tonry (1997:21) argues, many immigrants are
highly motivated to come to the United States to pursue economic and educational opportunities that are not available in their home countries. They seek to build better lives, are willing to
work hard, defer short-term gratification in the interest of longer-term advancement, and are
likely to avoid actions that put them in opposition to mainstream norms and values of American society. Moreover, some immigrant groups (e.g., Koreans) arrive in the United States
better educated than the average native-born American and are therefore more qualified to
find jobs in the primary labor market (Alba and Nee 1997). In essence, because immigration
is often an arduous process that takes considerable planning and resources, those who immigrate are more likely to be selected from the low end of the criminal propensity distribution
and therefore, rising immigration levels should bolster the low-criminality segment of the U.S.
population, leading to less crime over time.
Formal Social Control A second argument that increased immigration will result in lower
crime rates focuses on the formal social control response to immigration flows. This viewpoint
posits that because of stereotypes regarding immigrant criminality, an increase in immigration

452 Ousey/ Kubrin
will propagate fear and concern about a worsening “crime problem.” Fear and concern among
the general public will, in turn, put pressure on elected officials, local governments, and law enforcement leaders to respond to this perceived problem. One common response is to bolster the
local formal social control apparatus. The most straightforward and visible way to do this may
be to hire additional police officers to patrol the streets and deter the crimes that immigration is
believed to engender. Some longitudinal research evidence suggests that increasing the police
force size contributes to lower crime rates (Levitt 2004; Marvell and Moody, Jr. 1996). Thus, a
plausible hypothesis is that temporal increases in immigration contribute to decreases in crime
and violence by expanding the formal social control capacity (i.e., police officers per capita).
Social Capital and Family Structure A final perspective that posits a negative relationship
between immigration and crime focuses on the levels of social capital and informal social control that tend to characterize communities populated by immigrants. Decades ago, Donald Taft
(1933:72) argued that immigrant “ghettos” can serve a protective function by dampening culture conflict and preserving “old world” mechanisms of informal social control. More recently,
this argument has been made with respect to ethnic enclaves. Ethnic enclaves may encourage
cultural preservation, promote or maintain family ties and social networks, provide employment and entrepreneurial opportunities, and bolster informal social control, all of which help
curb crime (see Desmond and Kubrin forthcoming for a detailed discussion). In their study
of a New Orleans Vietnamese enclave, Min Zhou and Carl Banskton (2006) provide empirical evidence in support of this argument: “We found that although Vietnamese young people
lived in a socially marginal local environment they were shielded from the negative influences
of that environment by being tightly bound up in a system of ethnic social relations providing
both control and direction” (pp.119–20). Along these lines, it has been argued that illegal immigrants’ relatively limited involvement in crime can be explained, in part, by social support
in ethnic communities (Engbersen and van der Leun 2001:51).
An extension of this argument, reflecting more recent formulations of social disorganization theory, claims the positive benefits of immigration are not confined to the immigrants
residing in enclaves. Recall that early versions of social disorganization theory suggested an
influx of immigrants into an area weakens informal social control and increases crime. In line
with more contemporary versions of the theory, Lee and Martinez (2002) advance an immigrant revitalization thesis suggesting that an increase in immigration instead fosters social
control, thereby reducing crime. Martinez (2006) notes:
Contemporary scholars are now more open to the possibility that an influx of immigrants into disadvantaged and high-crime communities may encourage new forms of social organization and adaptive
social structures. Such adaptations may mediate the negative effects of economic deprivation and
various forms of demographic heterogeneity (ethnic, cultural, social) on formal and informal social
control, thereby decreasing crime (p. 10; see also Lee and Martinez 2002:366; Lee et al. 2001:564).
Empirical support for this argument is documented by Alejandro Portes and Alex Stepick
(1993), who find that rather than causing community disorganization, immigrants stabilized
and revitalized Miami’s economic and cultural institutions.
An unresolved issue for the revitalization thesis focuses on identifying which salient
changes brought on by immigration ultimately contribute to declining rates of crime and violence. One possibility is that immigration itself contributes to a revitalization of the economy
in places where immigrants settle. In contrast to the view that immigrants increase the size of
the economically marginal population and add strain to existing public services, this argument
suggests that immigrants bring new energy, skills, and entrepreneurial spirit into their communities. As a result, they may work to lower unemployment and poverty rates and improve
the vitality of economic institutions.
Another strand of this argument suggests that immigration alters aggregate family and
household structures in ways that strengthen informal social control and impede crime. The

Immigration and Violent Crime Rates in U.S. Cities 453
segmented assimilation model, for example, suggests that contemporary immigrant communities erect important social networks that fortify traditional intact (two-parent) family structures and support the legitimacy of parental authority norms (Martinez et al. 2004). Related to
this, scholars argue that many immigrant groups have a more familistic and pro-nuptial cultural orientation than the native born (e.g., Fukuyama 1993; Oropesa 1996; Oropesa, Lichter
and Anderson 1994; Vega 1990, 1995; Wildsmith 2004). According to David Brooks (2006),
“[I]mmigrants themselves are like a booster shot of traditional morality injected into the body
politic . . . They have traditional ideas about family structure, and they work heroically to
make them a reality.” In accordance with this logic, research finds that despite experiencing
higher rates of the types of economic deprivation that impede marriage, immigrant groups
such as Mexican Americans have comparable marriage rates to non-Hispanic whites (Oropesa
and Landale 2004; Oropesa et al. 1994; Sampson et al. 2005) and place greater value on
marriage than do non-Hispanic whites (Oropesa and Gorman 2000). To the extent that immigrants have greater intact family structures and pro-family cultural orientations, it is likely
that increasing immigration will lead to less crime. Indeed, criminologists have long documented that areas with higher rates of single-parent families and higher divorce rates experience more crime, presumably because the breakdown in traditional family structures deplete
social capital and attenuate socialization and informal social control processes (Land, McCall
and Cohen 1990; Ousey 2000; Sampson 1987; Sampson and Groves 1989; Shihadeh and
Steffensmeier 1994). To summarize, the above discussion leads to a hypothesis that temporal
increases in immigration result in lower crime rates, in part by reducing family disruption.
Prior Research
The Immigration-Crime Relationship
Documented scholarly interest in the connection between immigration and crime goes
back more than a century but attention devoted to the issue has been intermittent. Indeed,
investigations of immigration and crime have varied along with immigration itself. When immigration flows have been high, scholarship has flourished; when immigration flows have
been low, scholarship has waned (Stowell 2007). Reviewing research from the past century,
several observations are particularly noteworthy.
First, the vast majority of research focuses on the question of whether immigrants have
higher crime, arrest, and incarceration rates than native-born individuals. In general, the answer to this individual-level question is no. Going back over seven decades, the National Commission on Law Observance and Enforcement—commonly known as the Wickersham Commission—reported that in proportion to their numbers, the foreign born commit considerably
fewer crimes than the native born. Contemporary empirical studies as well as comprehensive
literature reviews continue to find that crime, arrest, and incarceration levels are lower among
immigrants (Butcher and Piehl 1998b:654; Hagan and Palloni 1999:629; Martinez and Lee
2000; McCord 1995; Tonry 1997).
A related observation from prior research is that the individual level link between immigrants
and crime appears to wane across generations. That is, the children of immigrants who are born in
the United States exhibit much higher crime rates than their parents (Morenoff and Astor 2006:36;
Rumbaut et al. 2006:72; Sampson et al. 2005; Taft 1933; Zhou and Bankston 1998), suggesting
one part of the “Americanization” process involves increased crime and incarceration levels.
Second, and shifting focus from the individual-level immigrant-crime question to the
central concern of this research—the macro-level immigration-crime relationship—it is apparent that findings from extant studies are more inconsistent (see Table 1 for a summary of
aggregate-level studies on the immigration-crime relationship). For example, in their analysis
of metropolitan areas, Lesley Williams Reid and colleagues (2005) report that the bivariate

454 Ousey/ Kubrin Table 1 • Summary of Aggregate-Level Studies on the Immigration-Crime Relationship Study Unit of Analysis a Sample Size Immigration Independent Variable(s) Dependent Variable(s) Relationship Controls Butcher and Piehl (1998)b Metropolitan areas 43 % recent immigrants Total and violent crime rates No sig. effects Mean age, % female, % black, % Hispanic, mean education, % central city,
total population size, mean
wage, wage dispersion,
% employed
Desmond and
Kubrin
(forthcoming)b
Individuals nested
within block
groups
9,500 youth
in block
groups
Immigrant concentration index:
(1) % foreign born
(2) % persons who
speak English not
well or not at all
Self-reported
juvenile violence
Sig. (-) effect Individual-level controls
include gender, age,
race/ethnicity, parental
SES, immigration status,
residential mobility, family
structure, attachment to
child, parental attachment, parental supervision,
grades in school, school
attachment, delinquent
peers, prior delinquency,
urban location, region.
Neighborhood-level controls
include disadvantage,
residential mobility, racial
heterogeneity.
Lee and Martinez
(2002)
Census tracts 12 % recent immigrants Black homicide rates (-) effect c N/A
Lee et al. (2001) Census tracts 352 % new immigrants Latino and black
homicide rates
Sig. (-) effect for El
Paso tracts in Latino
models; sig. (-)
effects for Miami
and San Diego
tracts in black
models
Race-specific measures of
population size:
% poverty, instability
% female-headed families
% male joblessness
% young males, spatial lag

Immigration and Violent Crime Rates in U.S. Cities 455
Martinez (2000) Cities 111 Latino immigration
index:
(1) Foreign-born
Latinos
(2) Latinos residing
in foreign-country
5 yrs. before 1980
Census
Total and motivedisaggregated
Latino homicide
rates
No sig. effect for total
homicide rates; sig.
(+) effect for Latino
felony rates; sig.
(-) effect for Latino
acquaintance rates
% Latino poverty, Latino
income inequality, % Latino
high school grads, total population size, % Latino males
15 to 24, % Latino divorced,
% Latino population, region
Martinez et al.
(2008)
Census tracts 532 % recent immigrants Homicide victims Sig. (-) effectd Disadvantage index, stability
index, % professional, adult
to child ratio, spatial lag
Martinez et al.
(2004)
Census tracts 266 (1) % immigrants who
arrived in U.S. in
1980s
(2) % immigrants who
arrived in U.S.
in 1970s
(3) % immigrants who
arrived in U.S.
in 1960s e
Drug-related
homicides
Sig. (-) effect of %
immigrated 1960s;
sig. (+) effect of %
immigrated 1980s
in San Diego tracts
only
% young males, % low-skill
workers, % vacant, economic deprivation
Nielsen et al.
(2005)
Census tracts 266 % recent immigrants Race- and motivedisaggregated
homicide rates
Too many findings
to report here but
generally null or
(-) effects
Disadvantage index, residential instability, % young
males, population size,
spatial lag
Reid et al. (2005) Metropolitan
statistical areas
150 (1) % recent foreignborn
(2) % born in Asian
country
(3) % born in Latin
American country
(4) % foreign-born
who speak English
not well or not at allf
Murder, robbery,
burglary, and
theft rates
Sig. (-) effect for
recent foreign born
on homicide rate;
sig. (-) effect for
Asian foreign born
on theft rate; other
measures nonsignificant for all
crime rates
Total population size,
population density, median
family income, % families
living below poverty, %
black, % children < 18 yrs.
not living with both parents,
income inequality,
unemployment rate,
% employed in low-skill
service sector industries,
% employed manufacturing
industries, divorce rate,
% pop. aged 15 to 29, region
(continued)

456 Ousey/ Kubrin Table 1 • (continued) Study Unit of Analysisa Sample Size Immigration Independent Variable(s) Dependent Variable(s) Relationship Controls Sampson et al. (2005)g Individuals nested within census tracts 2,925 persons in 180 tracts % 1st generation immigrant Self-reported violence Sig. (-) effect Individual-level controls include race/ethnicity, gender, immigrant status,
family structure, SES,
individual differences (e.g.,
verbal/reading ability)
measures
Neighborhood-level controls include % black, %
managerial occupation,
concentrated disadvantage,
residential stability, moral/
legal cynicism, collective
efficacy, friend/kin ties, organizations/youth services,
prior violent crime rate
Stowell and
Martinez
(2007)
Census tracts 592 % recent immigrants Total and motivedisaggregated violent crime rates Sig. (-) effect only for tracts located in Miami h % poverty, racial diversity, % residential instability, % unemployment, % males
aged 18 to 24, spatial lag
aA description of data years included in each study would be beneficial here. Unfortunately, for the studies listed, the use of diverse data sets covering different time periods in
conjunction with running multiple subanalyses over several periods of time preclude effective representation of data years for each study.
bThis study measures the contextual effect of neighborhood immigrant concentration on self-reported juvenile violence.
cFindings are not based on regression results. Lee and Martinez (2002) conduct a version of the single case study method known as the critical case as well as conduct spatial analysis.
dEffect is negative and significant only in San Antonio neighborhoods in subanalysis of Latino neighborhoods.
eImmigration measures are individually included in analyses.
fImmigration measures are individually included in analyses.
gThis study measures the contextual effects of neighborhood immigrant concentration on residents’ self-reported violence.
hSignificant negative effects were found for all outcomes excluding Haitian violent crime rates in Miami. No significant effects were reported for Houston tracts.

Immigration and Violent Crime Rates in U.S. Cities 457
association between immigration and crime varies from negative to positive to nil depending
on which measures of immigration and crime are used. And in their multivariate analyses, 14
of the 16 immigration-crime regression coefficients are not statistically significant. Of the two
that are, one suggests that metropolitan areas with a higher percentage of recent immigrants
have lower homicide rates while the other indicates that a greater percentage of Asian foreign
born is linked to lower larceny rates. Likewise, in their analysis of metropolitan areas, Kristin
Butcher and Anne Morrison Piehl (1998a) find that the flow of recent immigrants is positively
associated with the level of crime but that it has no effect on changes in the crime rate. Finally,
in a study of 111 cities, Martinez (2000) reports that Latino immigration has no relationship
with overall Latino homicide rates, a positive association with Latino felony-murder rates, and
a negative relationship with Latino acquaintance-murder rates.
Results from research on neighborhoods also exhibit inconsistency, particularly when the
results are compared across cities. In their analysis of drug-related homicides in Miami and
San Diego, Martinez and colleagues (2004) find the share of the population that immigrated
in the 1980s is unrelated to drug homicides in Miami but is positively related to such offenses
in San Diego. In addition, the percentage of immigrants who entered the United States in the
1960s is negatively related to drug homicides in both cities, and the percentage of immigrants
who entered in the 1970s is unrelated to drug homicides in either city. In a related study
that focuses on the immigration-homicide link in the subsection of Miami disproportionately
populated by African Americans and Haitians, Lee and Martinez (2002:374) discover that the
magnitude of the negative immigration-homicide relationship is even stronger than the moderate relationship observed in the city-wide regression model. In another related study focusing on motive-disaggregated homicides (e.g., escalation, intimate, robbery, and drug-related)
among Latinos and blacks in Miami and San Diego, Nielsen and associates (2005) report additional evidence of variability in the direction, magnitude, and statistical significance level of
a measure of the prevalence of recent immigrants. Interestingly, they find that the impact of
immigration on homicide varies across cities for the same racial/ethnic group or across racial/
ethnic groups within the same city. And in yet one more related study where the city of El
Paso is added to the analysis, Lee and colleagues (2001) document that the presence of recent
immigrants in neighborhoods is significantly negatively associated with Latino homicide victimization in only one (e.g., El Paso) of the three cities examined. Lee and colleagues (2001)
also find that recent immigration has a significant negative association with black homicide
victimization in Miami but a significant positive association with black homicide victimization
in San Diego. Most recently, Jacob Stowell (2007), Stowell and Martinez (2007), and Martinez, Stowell, and Jeffrey Cancino (2008) all report between-city differences in the censustract level relationship between immigration and violence.
Two recent contextual studies, which examine the impact of immigration on violence net
of individual characteristics (including immigrant status), support the thesis that immigration
is negatively related to crime. Using data from Chicago, Robert Sampson, Jeffrey Morenoff,
and Stephen Raudenbush (2005) report neighborhood-level immigrant concentration has a
significant negative relationship with violence, controlling for individual-level characteristics.
Likewise, using national-level data from the Census and the Add Health study, Scott Desmond
and Charis Kubrin (forthcoming) find that communities with greater immigrant concentration generally have lower average levels of violence, but that contextual effect appears most
salient for Hispanics, Asians, and the foreign born.
In sum, a substantial literature at the individual level indicates that, contrary to public
opinion, immigrants are no more likely to engage in crime and violence than their native-born
counterparts. A smaller but expanding literature at the aggregate level suggests less certainty
with some studies documenting no relationship, some documenting a negative relationship,
and a handful documenting a positive immigration-crime relationship. While this body of
work provides a solid foundation for research in this area, we believe major questions and
unresolved issues remain. Below we highlight two of the most critical.

458 Ousey/ Kubrin
Limitations of Immigration-Crime Research
In our view, research on the macro-level immigration-crime nexus contains two major shortcomings that have hampered our understanding of this issue. First, theories of the connection between immigration and crime have not, to date, been adequately tested empirically. Daniel Mears
(2002) notes: “Although several criminological theories suggest certain hypotheses about criminal
behavior among immigrants, these remain largely undeveloped and untested” (p. 287). As a consequence, Mears claims it remains unknown whether and to what extent immigration and crime
are truly associated once covariates such as poverty, inequality, racial and ethnic composition,
and drug and gun markets are controlled. Along these lines, we argue that while some aggregatelevel studies do include theoretically salient covariates in their models, they are typically added as
control variables without any systematic attempt to determine whether, and to what extent, they
mediate or help to explain the relationship between immigration and crime. This is crucial because
the theories delineated above primarily predict that the immigration-crime relationship is indirect,
operating through changes in demographic, economic, and family structures.
Second, past research has left unanswered the exceptionally important question of the
longitudinal relationship between immigration and crime. As far back as the 1930s researchers have called for proper assessment of the dynamic relationship between immigration and
crime (Taft 1933:69), but research in this area is overwhelmingly cross-sectional. In fact, our
review uncovered only one published multivariate study that has examined this relationship
longitudinally (Butcher and Piehl 1998a). In that study, Butcher and Piehl (1998a) assessed
the relationship between changes in immigration and changes in total (index) and violent
crime rates for a sample of metropolitan areas. They find that change in immigration is not
related to change in crime rates. However, their analysis was restricted to only 43 cities, and
therefore, may miss important effects of immigration occurring across a broader sample of
urban locales. Equally important, Butcher and Piehl assessed the longitudinal immigrationcrime relationship only for the decade of the 1980s. This limitation seems particularly acute
because the heavy influx of the foreign born in the 1990s coincided with another important
trend—the large and unexpected drop in crime rates (Blumstein and Wallman 2000). While
recent scholarly speculation (Sampson 2006) has proffered a link between the two temporal
trends, rigorous theoretical and empirical analysis of that link remains in short supply.
In summary, although several additional shortcomings in the immigration and crime literature remain (see Mears 2001 for a discussion), we believe the most pressing include: (1) accurately
investigating the direction and magnitude of the longitudinal relationship between immigration
and crime; and (2) testing the various explanatory frameworks that posit intervening mechanisms
by which change in immigration affects change in crime rates at the macro-level. In response to
these limitations, the current study investigates the nature of, and theoretical explanation(s) for,
the longitudinal immigration-crime relationship. We choose to examine the effects of immigration
on violent crime (although, as noted below, we perform similar analyses for property crime rates
for comparison) because public opinion on immigration and crime overwhelmingly centers on
the idea that immigrants are violent and that increasing immigration into an area increases rates
of violence. In particular, public perception is that immigrants are heavily involved in criminal
gangs that frequently perpetrate assault and homicide. Immigrants are also commonly believed
to be regular participants in the drug trade and sponsors of the violent interactions thought to be
concomitant of illegal drug markets. Our focus on violent crime is thus intended to directly assess
whether immigration and violence are positively associated, as popular perception suggests.
Data and Methods
Units of Analysis
Our analysis focuses on large U.S. cities observed during the 1980 to 2000 period. We include
cities with a minimum population of 100,000 persons in 1980, 1990, and 2000. While 173 cities

Immigration and Violent Crime Rates in U.S. Cities 459
meet these criteria, missing data reduce the number of available cities to 159. When we pool
available 1980, 1990, and 2000 observations for each city, the total number of city-year observations in our multivariate models is 463.2
Dependent Variable
The dependent variable in the analysis is the violent crime rate. This rate is computed
by summing counts of homicides, robberies, aggravated assaults, and rapes and dividing that
sum by the city population (expressed in units of 100,000).3
Data on violent offenses are
obtained from the Uniform Crime Report (UCR) compiled by the FBI and made available by
the Inter-University Consortium for Political and Social Research (ICPSR) at the University of
Michigan. To create more stable estimates, violent crime rates for each time point are based
on the sum of three consecutive years of UCR data. In other words, the 1980 rate is computed
on the basis of summed violent crime and population counts from the 1980 to 1982 data files,
the 1990 rate is based on 1990 to 1992 data, and the 2000 rate is calculated with 2000 to
2002 data.
Independent Variables
Following prior research (e.g., Lee et al. 2001; Reid et al. 2003; Sampson et al. 2005), we
initially selected two variables as proxies for immigration. First, the percentage of the population
made up of foreign-born persons who immigrated in the past ten years. Second, as a measure of
linguistic isolation, we include the percentage of the population that speaks English “not well”
or “not at all.” As one might expect, these two measures are highly correlated (r = .84). Moreover, they are highly collinear with a third variable, the percent Latino (r = .68 and r = .88, respectively). We initially conceived of this latter item as an essential covariate of aggregate crime
rates, as indicated in prior research (Butcher and Piehl 1998a; Butcher and Piehl 1998b). Yet
high levels of covariance between the percent Latino and the two immigration items suggests
that estimating their unique effects on crime would be difficult. Moreover, since Latinos have
constituted the largest immigrant group entering the United States in recent decades (Martinez
2006:9; Rumbaut and Ewing 2007:3), it is apparent that our measure of within-city change in the
percentage Latino is, in fact, reflective of an important dimension of recent immigration. For
these reasons, we created a three-item immigration index by summing the z-scores of these
measures.4
The Cronbach’s alpha for the immigration index is .94.5
2. Each of the 159 cities has nonmissing data for at least one time point but a few are missing information for one
or two decennial census years. Therefore, the total number of observations (463) is slightly less than the total number of
cities multiplied by the total number of time points (i.e., N = 159, T = 3, N × T = 477).
3. In preliminary analyses we modeled each violent crime outcome individually. Results from those analyses are
very similar to one another and also closely resemble the results reported in Table 2. Thus, to eliminate redundancy,
we present results only for the summary index of violent crime. Results from models predicting specific violent crime
measures are available on request.
4. Here we wish to note the substantive findings reported below do not hinge upon the use of the three-item
index. If we replace the immigration index with any one of the three items, the substantive conclusions remain unchanged. Likewise, if we substitute any combination of two of the three items, we reach the same set of conclusions.
Those points aside, we recognize that if the single-item measures were used separately, the interpretation of results
would vary somewhat depending upon the particular measure utilized. For example, interpretation of the results for
the percent Latino measure would differ to some degree from the interpretation of results based on the percent recent
foreign-born measure. Still, given the strong correlations among the items, we believe the three-item index is tapping
an underlying immigration component and that it is not feasible to parse out and interpret each item’s unique effect. For
these reasons, we have chosen to utilize the three-item index in the analysis reported below. Results from analyses that
use the single-item measures are available upon request.
5. The data for these items come from Summary File 3 of the 1980, 1990, and 2000 Censuses of Population and
Housing (U.S. Census Bureau 1982, 1992, 2002). The lone exception is for the item measuring recent foreign born for
the year 1980, which was obtained online from the National Historical Geographic Information System (Minnesota
Population Center 2004).

460 Ousey/ Kubrin
To the extent possible, the analyses include measures of each theoretical perspective discussed above.6
To measure the demographic transition explanations, we include three proxies
obtained from Summary File 3 of each of the past three decennial censuses. The first is simply
a measure of the overall city population (change). Second, we gauge residential instability
with an item that reflects the percentage of persons (aged 5 and older) not living in the same
house as five years ago. The third measure, percent of the population comprised of males aged
15 to 34, is included to account for the thesis that changes in the gender and age structure of
the population may account for changes in violent crime rates.
Four items drawn from the Census serve as proxies for the argument that changes in economic deprivation and labor market structure are key intervening factors that link changes in
immigration to changes in crime rates. First, we measure the percentage of persons living below the government-designated poverty line. Second, we tap employment difficulties by computing the percentage of civilian persons aged 16 and over who are unemployed. And third,
to represent the decline in the industrial base and the increase in high-skill sector jobs, we
incorporate a measure of the percent of persons working in the manufacturing industry and a
measure of the percent of persons employed in professional or managerial occupations.
To account for the explanation that immigration may affect serious crime rates by first increasing the scope of the illegal drug trade, we include a measure of the rate of arrests for the
sale/manufacture/distribution of cocaine and opiates. Although this measure reflects law enforcement as well as drug market activity, past research suggests it is correlated with other measures of drug activity and serious crime, including homicide and robbery (Baumer et al. 1998;
Fryer et al. 2005; Ousey and Lee 2002, 2004, 2007). The data for the 1980 and 1990 measurements of this variable are obtained by summing three years of data (i.e., 1980 to 1982 and 1990
to 1992) from the UCR extract file compiled by Roland Chilton and Dee Weber (2000), while
data for the year 2000 measurement is computed from the 2000 to 2002 versions of the Uniform
Crime Reporting Program Data: Arrests by Age, Sex and Race (FBI 2006a, 2006b, 2007).
Finally, we measure changes in family structure with two items, which are combined into
a family instability index by taking the sum of their z-scores. The first is the percent of the
population (aged 15+) that is divorced. The second is the percent of family households not
headed by married couples. The within-city, over-time correlation between these measures is
r = .88. Both items are obtained from the Census Summary File 3 (U.S. Census Bureau 1982,
1992, 2002).
The means and standard deviations for all variables used in the analysis are presented in
Appendix A.
Analytic Strategy
Since our interest centers on investigating the nature and explanation of the longitudinal
relationship between immigration and violent crime, we estimate fixed-effects linear regression models via the xtreg procedure in STATA version 10. Fixed-effects (FE) models constitute
one of several alternatives to analyzing panel data, with others including random-effects (RE)
and generalized estimating equation (GEE) models. Generally speaking, all of these models are
preferable estimators of longitudinal data over ordinary least squares (OLS) regression because
their standard error estimates adjust for the fact that repeated observations on the dependent
variable for a particular city are likely to be correlated. Moreover, all methods yield consistent
estimates of parameters if model assumptions hold. Yet, we prefer the FE model because it
focuses solely on the within-unit (change) variation in the variables and requires less restrictive assumptions than the alternative models. In our case, the RE and GEE models assume that
6. One exception is we have no direct measure of the “self-selection” thesis. Thus, our analyses do not directly
investigate that hypothesis. However, to the extent that the immigration index has a direct negative effect on violent
crime rates after controls for the other theoretical models are accounted for, that result could be interpreted as consistent
with a self-selection effect.

Immigration and Violent Crime Rates in U.S. Cities 461
time-varying explanatory variables (e.g., the immigration index) are uncorrelated with unmeasured city-specific, time-invariant factors (i.e., the “random-effect”), whereas the FE makes no
such assumption. In fact, the FE method actually controls for the influence of all time-invariant
predictors whose effects are time-stable while RE and GEE models do not.7
This means that the
FE model controls for a key source of omitted variable bias that the RE and GEE models essentially assume is not present.8
In addition to accounting for the city-specific fixed-effects, we
also include in our models dummy variables for the years 1990 and 2000 (1980 is reference),
which helps to account for the influence of unmeasured city-invariant, period-specific effects
on violent crime rates. Finally, we employ the Huber/White/Sandwich or “robust” variancecovariance matrix in our computation of standard errors. This adjustment helps to account for
other deviations from usual “normal error” linear regression assumptions.
Results
Results from the regression models examining whether within-city, over-time change in
immigration (and other covariates) affects within-city over-time change in violent crime rates
are reported in Table 2. This table contains results from a series of seven regression models. The
first column reports a baseline regression model in which change in violent crime is predicted
only by the immigration index and time-period dummy variables. In each subsequent model,
we progressively expand on that initial model by adding measures reflecting the theoretical
perspectives discussed above. Consistent with our objectives, this model-building strategy allows us to gauge the extent to which the observed relationship between change in immigration
and change in violent crime is mediated—or explained—by proxies for those theories.
In the first model of Table 2 we find evidence of a statistically significant relationship
between change in immigration and change in violent crime. Specifically, a one unit increase
over time in the immigration index is associated with a decrease of 253 violent crimes (per
100,000 persons). In standardized terms, these results indicate that a one standard deviation
increase in the immigration index corresponds with a .3 standard deviation decrease in the
violent crime rate.9
Interestingly, the direction of this coefficient contradicts popular perception
that immigration is a major contributor of increased crime rates, but is consistent with theoretical models proffering rationales for why immigration has an inhibiting impact on violence.
In the second model, we introduce measures representing the various demographic transition explanations. Recall these arguments suggest that immigration leads to greater residential instability and a larger young male population, both of which should raise crime rates. The
results of the second model provide very little support for that expectation. Although changes
in residential mobility affect changes in the violent crime rate, neither changes in total population nor the percent of males aged 15 to 34 are significantly associated with the dependent
variable. Moreover, controlling for all three measures only marginally affects the magnitude
of the immigration coefficient.
The third model introduces variables that tap changes in labor market structure and economic deprivation. Generally speaking, the economic deprivation/labor market structure hypothesis posits that the inflow of immigrants into the shrinking low-skill job base of the postindustrial labor market will produce increases in poverty and unemployment rates, which
in turn, will yield a rise in crime rates. Of the four economic deprivation and labor market
7. Thus, important time-invariant correlates of crime—such as region—are automatically controlled in the fixedeffects framework.
8. For an excellent discussion of the merits and potential biases of RE models for analyzing panel data, see Brame,
Bushway, and Paternoster (1999).
9. For interested readers, supplemental analyses that include the percent recent foreign born in place of the immigration index indicate that cities experiencing a one percentage point increase in the percent recent foreign born exhibited, on average, a decrease of 74 violent crimes per 100,000 persons. The associated standardized coefficient is -.21.

462 Ousey/ Kubrin Table 2 • Fixed-Effects (Within-City) Linear Regression Models Predicting Violent Crime Rates, 1980–2000 Predictors Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Immigration index -253.09*
(-.30)
-254.24*
(-.30)
-253.49*
(-.30)
-256.96*
(-.31)
-256.75*
(-.31)
-128.83†
(-.15)
-129.86†
(-.15)
Percent males, 15 to 34 3.68
(.01)
4.33
(.01)
-11.57
(-.02)
-12.27
(-.02)
-10.29
(-.02)
-9.59
(-.02)
City population (Ln) 95.39
(.02)
20.54
(.003)
-25.55
(-.004)
2.40
(.0004)
352.60
(.06)
370.64
(.06)
Residential instability 8.93*
(.08)
9.91*
(.09)
9.14*
(.08)
9.17*
(.08)
7.74†
(.07)
7.83†
(.07)
Percent below poverty -3.08
(-.01)
-9.55
(-.04)
-8.85
(-.03)
-1.15
(-.004)
-1.76
(-.007)
Unemployment rate 13.31
(.02)
26.07
(.04)
25.02
(.04)
16.62
(.02)
17.46
(.02)
Percent manufacturing 5.83
(.03)
5.69
(.03)
5.80
(.03)
15.04
(.08)
15.00
(.08)
Percent professional/managerial -30.17†
(-.18)
-32.31*
(-.19)
-32.00*
(-.19)
11.76
(.07)
11.55
(.07)
Drug market arrests (Ln) 114.96*
(.20)
114.61*
(.20)
106.75*
(.18)
105.68*
(.18)
Police officers per capita 31.00
(.01)
-13.46
(-.01)
-21.40
(-.01)
Family instability index 432.75*
(.62)
414.47*
(.60)
Percent black 6.58
(.02)
Time trend dummy variables
Year 1990 1299.11* 1321.95* 1465.01* 1256.47* 1248.07* 379.02† 398.41†
Year 2000 1185.33* 1181.47* 1535.72* 1418.48* 1398.47* -231.13 -200.09
Model summary information
R2 (within-unit) .428 .434 .440 .464 .464 .501 .501
Corr (ui, XB) -.377 -.362 -.302 -.290 -.274 -.078 -.140
Total number observations (
N × T) 463 463 463 463 463 463 463
Total Number Cities (
N) 159 159 159 159 159 159 159
*p < .05 †
p < .10 (two-tailed tests)
Standardized coefficients reported in parentheses.

Immigration and Violent Crime Rates in U.S. Cities 463
structure variables entered into the equation in Model 3, only the percent employed in professional and managerial occupations shows evidence of a statistically discernible relationship
with the change in violent crime rates. Specifically, a unit increase in professional/managerial occupation employment is associated with a drop of 30.17 (per 100,000 persons) in the
violent crime rate. In contrast, change in manufacturing employment, poverty rates, and unemployment rates are not significantly associated with within-city changes in violent crime
rates. Moreover, controlling for these economic deprivation/labor market structure variables
does not materially affect the magnitude of the association between immigration and violent
crime. Thus, these results offer little support for the argument that immigration affects crime
by altering economic deprivation or labor market opportunities in large U.S. cities.10
In the fourth model, we examine the thesis that immigration may contribute to changes
in the prevalence of the illegal drug trade, with consequences for violent crime rates. Consistent with the viewpoint that within-city changes in drug markets are factors in over-time
changes in homicide (see Ousey and Lee 2002, 2004, 2007), the results suggest that increases
in drug arrests are significantly associated with increases in the overall violent crime rate as
well. However, despite this finding, there is little evidence that drug markets serve as a main
process behind the longitudinal immigration-violent crime association. Indeed, the direct effect of immigration on violent crime changes very little between Models 3 and 4. Thus, although changes in drug markets may be an important piece of the puzzle regarding violent
crime trends since 1980, they do not appear to be major intervening mechanisms that link
within-city changes in immigration to changes in violent crime rates.
Controlling for the impact of within-city changes in formal social control capacity—as
measured by police officers per capita—also appears to have little efficacy in explaining the
immigration-violent crime relationship. As shown in Model 5, the results indicate that withincity over-time variation in police officers per capita has little association with temporal change
in violent crime rates and no impact on the immigration-violent crime coefficient. Thus, our
models do not support the thesis that immigration has influenced the number of police officers on the street, which in turn drove inter-decade changes in city-level violent crime rates
(but see Levitt 2004; Marvell and Moody 1996).
In the sixth model, we introduce the family instability measure into the equation to assess
the hypothesis that immigration contributes to lower crime rates by decreasing the prevalence
of family breakdown and increasing two-parent, married households and the concomitant
social capital that such family structures bring to communities. Consistent with this thesis, the
results indicate that family instability has a positive relationship with the violent crime rate.
Specifically, a one unit change in the family instability index is associated with an increase
of nearly 433 violent crimes (per 100,000 persons). Both the t-ratio (5.54) and the standardized coefficient (.62) for this variable are far and away the largest in the model, indicating a
statistically significant and fairly strong effect of within-city over-time changes in family structure on within-city changes in violent crime rates. Equally important, the results of Model 6
suggest that family structure is an important mediator of the effect of immigration on the
violent crime rate. With the inclusion of the family instability index, the negative coefficient
for the immigration index decreases by nearly 50 percent and the t-ratio drops from 3.74 to
1.73, indicating the direct effect of immigration on violent crime is no longer significant at
the .05 level. In sum, the results from the sixth model are consistent with the hypothesis that
increases in the immigrant population lead to less violent crime in large part by altering family
10. While the absence of significant effects for variables like age structure and poverty may strike some readers as
unusual, we note that much of the macro-level empirical evidence regarding links between these variables and crime is
drawn from cross-sectional studies of homicide rates. Longitudinal evidence of the effects on broader measures of violent
crime is actually quite scarce. Moreover, results from longitudinal research examining the effects of similar age structure
and poverty measures on measures of crime are decidedly mixed (cf. LaFree and Drass 1996; Marvell and Moody 1991;
McCall, Parker, and McDonald 2008; Ousey and Lee 2002; Phillips 2006b; Worrall 2005).

464 Ousey/ Kubrin
structure.11 More specifically, immigration appears to have a dampening influence on family
instability, which in turn, lowers violent crime rates.12
Supplemental Analyses
We conducted several supplemental analyses to ensure the robustness of our findings.
First, because it is commonly found to be a correlate of macro-level violent crime rates, we
examined whether our findings were affected by the inclusion of a measure of change in the
percent black population. We note the initial exclusion of this variable was rooted in theoretical considerations. In our view, the theoretical models we assess do not make a compelling case for its inclusion. More importantly, if percent black were to be included, it is not
obvious where within the theoretical elaboration/model-building process it would best fit.
Indeed, while percent black is sometimes used as one indicator of economic disadvantage, it
could arguably also proxy for immigration—to the extent immigration processes affect racial
composition—or simply as another measure of demographic transitions. Likewise, because
percent black has long been the main measure of racial threat processes, it can be argued that
it should be included either with the drug arrests variable (e.g., the drug war focus on black
communities) or with the measure of formal social control (police per capita). In short, the
lack of theoretical specificity of percent black suggests that its inclusion with any particular
set of explanatory variables may weaken, rather than strengthen, the measurement properties of the theoretical models that are the focus of our analysis. Nevertheless, to determine if
our results are contingent on its inclusion, we added it to a final regression model (Model 7),
presented in Table 2. Two results are noteworthy. First, both the direct effect of the immigration index and the effect of the family instability index remain significant after percent black
is controlled. And second, there is no direct effect of percent black on violent crime. Thus, our
results are generally unaffected by whether or not percent black is controlled.13
Second, we examined whether our results were sensitive to other specific modeling issues. For instance, one reviewer suggested that because many cities may have very small
immigrant populations, our analyses may be unduly affected by the inclusion or exclusion
of “low immigration” cities. To examine this issue, we repeated our analyses after alternately
imposing a minimum immigrant population criterion of: (1) 2,000 recent immigrants; and
(2) 5,000 recent immigrants. Results from each set of supplemental models are substantively
identical to those presented in Table 2.
On the advice of another reviewer, we also considered whether there was nonlinearity
in the effect of immigration on violent crime. The logic here is that in cities with relatively
few immigrants, the impact of a unit increase in the immigration measure may be more
dramatic than it would be in cities where the immigrant population base is already substantial. We probed this possibility in two alternative ways. First, we estimated a model that
included an interaction between a measure of within-city change in the immigration index
11. Although our primary substantive interest centers on violence, we also investigated whether the immigrationfamily structure-crime linkage was evident for property crime (measured as an index comprised of burglary, larceny,
and motor-vehicle theft). As demonstrated in Appendix B, the immigration index has a significant negative effect on
property crime rates, which becomes attenuated and—in this case—completely nonsignificant after controlling for family instability.
12. A reviewer of an early draft of this paper argued that because “family breakdown” increased between 1980 and
2000 (see Appendix A), the notion that immigration was working to bolster intact family structures was not supported.
However, it should be noted that changes in the means of the family variables reflect overall trends, not immigrationspecific trends. In general, divorce rates and single-parent family households did increase over time in large cities in the
United States. Yet, as illustrated in Appendix C, our analyses suggest that immigration countered those upward trends to
some extent. Stated another way, if the influence of immigration was removed, the increase in divorce and single-parent
families across the three time points would have been even more substantial than what is observed in Appendix A.
13. As shown in Appendix B, percent black does have a positive association with the property crime rate that is
significant at the .10 level. However, other substantive results in the property models are unchanged by its inclusion.

Immigration and Violent Crime Rates in U.S. Cities 465
(deviations from the city-level mean) and a measure of the average level of immigration
(the city-specific over-time mean of the immigration index). Second, we computed a model
that included the immigration index and its quadratic. Not surprisingly, the results from
both models are similar and they suggest that the immigration index has an additive, rather
than multiplicative, effect on violent crime. In other words, the effect of a unit change in
immigration appears to be fairly constant regardless of whether a city has a relatively small
or large immigrant population base.14
Finally, because multicollinearity is commonly troublesome in macro-level research,
we examined the degree to which high levels of collinearity are evident in our set of explanatory variables. While some explanatory variables are moderately correlated with each
other, no correlations are strong enough to suggest a near linear dependency. This conclusion is supported by our analysis of the variance inflation factors (VIFs) computed for the
models estimated. For example, in the final model reported as Model 7 in Table 2, the average VIF is 2.35 and all substantive variables have VIFs below 3.4. Only the VIF for the year
2000 dummy variable is above 4 (5.13), and that finding only underscores the importance
of controlling for the time-period fixed-effects. Interestingly, once the city- and time-specific
fixed effects are partialled out, the VIFs for the substantive explanatory variables are quite
low, with an average of 1.51 and a maximum of 2.28. In short, there is little indication of
problematic multicollinearity levels that would threaten the stability or interpretability of
the results reported above.
Summary and Conclusion
Common belief holds that immigration creates more crime and violence. This belief is
rooted in the notion that either individual immigrants have a greater propensity for violent
criminal behavior than natives or that an influx of foreigners disrupts existing mechanisms
of social regulation. While this belief has held firm in the public conscience, an accumulated
body of research has tested the idea that the foreign born and/or their offspring are more
involved in criminal behavior than natives. Findings on this question of “immigrant criminality” generally contradict the popular belief that immigrants are particularly crime prone.
In fact, much work suggests that first-generation immigrants engage in less criminal activity
than natives.
In contrast to the relative clarity of findings from research at the individual level, there
exists more uncertainty on the question of the macro-level impact of immigration on crime.
While 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. Indeed, despite a surge in immigration-crime research since 2000, the extant
body of empirical work remains relatively small and to date has produced a somewhat inconsistent pattern of results. Moreover, while much theorizing about the macro-level connection
between immigration and crime is founded on the notion that immigration is a process of
change that affects crime rates by altering the demographic, economic, and social organization
of society, virtually all empirical findings are based on cross-sectional analyses that do not
measure over-time change in immigration, crime, or other relevant social factors.
To begin addressing these limitations, the current study pooled 1980, 1990, and 2000
Census data on crime, immigration, and various demographic, economic, and social factors
for 159 large U.S. cities to assess the nature of the longitudinal relationship between immigration and violent crime. Using a fixed-effects panel data regression approach, we first
14. All supplemental analyses described herein are available from the first author.

466 Ousey/ Kubrin
investigated whether within-city, over-time change in immigration was associated with within-city change in a violent crime index. We then examined the efficacy of several alternative
theories on the link between immigration and crime by assessing whether changes in factors
such as demographic structure, economic deprivation, labor markets, illegal drug markets,
police force capacity, and family structure could account for the observed longitudinal immigration-crime association.
Our analyses yielded a number of key findings. First, unlike the long-held popular view
that immigration is a major factor contributing to higher crime rates, our results suggest the
opposite. The baseline regression models indicate that within-city change in immigration has
a significant negative association with within-city change in violent crime. In other words,
on average, cities that experienced increases in immigration from 1980 to 2000 experienced
a decrease in violent crime rates. Second, while our results show the measure of illicit drug
market arrests has a positive association with changes in crime rates, that consistent pattern of
results is not repeated for most of the measures of demographic transitions, economic deprivation and labor market structure, or formal social control. Indeed, among the variables reflecting those arguments, we find that only the percent employed in professional and managerial
occupations shows any consistent association with within-city changes in violent crime rates.
Third, our analyses indicate that the city-level longitudinal immigration-crime relationship is
not explained by the demographic transition, economic deprivation, drug market, or formal
social control theoretical arguments evaluated in our analysis. We continue to find evidence
of a moderate negative relationship between within-city change in the immigration index and
within-city change in violent crime after controlling for percent young males, population size,
residential instability, economic deprivation, labor market characteristics, illegal drug market
activity/arrests, and the relative size of the police force.
Our analyses do, however, suggest that the family structure/social capital theoretical
framework offers an important clue to the longitudinal immigration-crime relationship. As
predicted by that framework, we find evidence that changes in family structure are an important factor linked to changes in violent crime. Equally important, controlling for changes
in family structure substantially mediates the within-city immigration-crime relationship.
That is, it appears the negative relationship between immigration and violent crime is due,
in part, to the fact that immigration is negatively associated with divorce and single-parent
families, which in turn, are positively related to violent crime rates.We believe these results
buttress “immigrant revitalization” arguments that have appeared in recent scholarship on
the impacts of immigration (Lee and Martinez 2002). At the same time, we acknowledge
there is a plausible alternative interpretation of the results we’ve presented. Namely, it can
be argued that our analyses show simply that after all relevant controls are included there
is a modest and marginally significant negative effect on violent crime, with no direct effect
observed for property crime. Although our interpretation of the data is intuitively appealing because it identifies a logical mechanism by which immigration may affect crime rates
at the macro level, this alternative interpretation is also viable and cannot be rejected on
empirical grounds.
On face value these findings support some tentative conclusions. One is that violent
crime is not a deleterious consequence of increased immigration. Rather the results are
consistent with Sampson’s (2006) recent speculation that immigration may be a key factor
contributing to the crime drop of the 1990s. Thus, in line with the individual level finding
that immigrants are less inclined to commit crime than the native born, our work suggests
that the macro-level process of immigration may have notable protective effects with regard to crime. A second conclusion is that immigration also may have beneficial impacts on
important social institutions. While our findings do not indicate any clear influence of immigration on city economies, there certainly is evidence to support the notion that immigration may bolster the family by increasing two-parent families and lowering divorce rates in
U.S. cities. Indeed, in a supplemental analysis shown in Appendix C, we find evidence that

Immigration and Violent Crime Rates in U.S. Cities 467
net of controls for time trends, changes in the immigration index are negatively related to
family instability in our sample of cities. These results are somewhat consistent with findings
reported in other studies. For instance, in their multi-level analysis of data from Chicago,
Sampson and colleagues (2005) report that having married parents is one of the key protective factors that help explain black/white and Mexican American/white gaps in violent
offending—although they do not find that having married parents mediates the effect of
immigrant status on offending.
While these conclusions suggest a positive outlook with regard to the impacts of immigration, we point out that additional research on the connections among immigration, family
structure/family values, and crime is needed. Some scholars, in fact, are sharply critical of
the notion that immigration is driving a revitalization of the kinds of traditional family structures and values that otherwise have been steadily declining in the United States (Mac Donald 2006). The crux of this counterargument is that due to Americanization, out-of-wedlock
childbirth is quickly becoming normative among immigrants. Thus, one speculation is that as
recent immigrants grow older and produce successive generations, pro-family cultural elements will be eroded by their sustained exposure to relatively high levels of deprivation, with
the end result being lower marriage rates and higher rates of single parenthood. Along these
lines, some studies find that later-generation Mexican Americans are less likely to be married
than comparable generation non-Hispanic whites (Oropesa and Landale 2004). Simply put,
immigration is a complex issue and though our results support the view that immigration
has beneficial consequences in the immediate term, it remains an open question as to what
the longer-term results will be once recent immigrants become increasingly socialized into
American culture.
While we believe our study has begun to fill important gaps in the literature, there are
several directions that future studies could take to expand upon our efforts. First, our work
suggests that immigration has a negative influence on the change in violent crime rates but
our analysis was unable to determine if those effects are general, experienced equally by the
entire population, or are limited to certain population subgroups. Although our measures
were computed on the basis of the total population, much insight may be gained if future
studies are able to investigate whether there are differences in the effects of immigration on
family structure and crime rates between different ethnic/racial groups or between immigrants and nonimmigrants. Likewise, an important extension to the current research involves
attempting to isolate if the apparent beneficial impact of immigration differs by sending country or immigrant group. Clearly, there are cultural and skill differences between immigrants
coming from, for example, the Caribbean, South America, Asia, and Eastern Europe. The
extent to which those differences translate into varied impacts on crime as well as intervening
demographic, economic, and family structures remains unclear.
Future research should also investigate the degree to which the longitudinal macro-level
relationship between immigration and crime is affected by the “immigrant generation” issue discussed earlier. As individual-level studies have suggested, second- and later-generation
immigrants become more Americanized in terms of their involvement in crime than firstgeneration immigrants. Thus, research that attempts to dissect the unique effects of over-time
changes in first- and later-generation immigrant population bases on social organization and
crime would be an essential next step in the development of the macro-level literature on the
immigration-crime nexus.
Finally, given that past work suggests the protective effects of immigrant communities
often are a result of the existence of enclaves, an important extension of the current research
would be to investigate whether the impact of immigration on the change in crime rates is
contingent upon the extent to which enclave characteristics are evident in a city. Do cities that
have immigrant enclaves have especially lower crime rates? If so, what aspects of enclaves
inhibit criminal activity? Answering these questions will go a long way towards empirically
testing a key argument theorized for the negative immigration-crime relationship.

468 Ousey/ KubrinTable A1 • Means and Standard Deviations of Variables in the Analysis
1980 1990 2000 Within-City Change a
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Violent crime rate 1705.73 880.43 2804.60 1458.72 2478.94 1198.17 -3.14 751.20
Immigration index -1.01 2.06 -.28 2.78 .61 1.17 .004 .895
Percent recent foreign born 3.53 4.31 5.23 6.15 6.84 5.48 .011 2.14
Percent speak English poorly 2.87 4.94 4.16 6.08 6.04 6.55 .006 1.91
Percent Latino 9.25 13.75 12.14 15.92 17.39 18.19 .019 4.63
Percent males aged 15 to 34 18.85 2.53 17.63 2.32 15.89 2.25 -.011 1.43
City population 333,173 639,026 361,498 670,951 393,244 729,661 .001b .120 b
Residential instability 50.90 8.25 48.10 7.23 51.31 5.96 -.016 6.92
Percent poverty 13.65 5.13 16.45 6.21 16.55 5.88 -.005 2.90
Unemployment rate 6.79 2.75 7.38 2.64 7.29 2.71 -.002 1.10
Percent employed manufacturing 19.92 9.27 15.49 6.66 12.42 5.75 -.022 3.84
Percent professional/managerial occupations 23.65 5.43 27.28 6.36 33.53 8.31 .018 4.47
Sale cocaine/opiates arrest rate 15.52 27.06 140.28 186.24 88.05 179.44 -.004 b 1.28 b
Police officers per capita 2.04 .82 2.15 .86 2.33 .90 -.0003 .276
Percent black 19.22 17.08 21.03 18.24 22.03 19.19 .0095 2.53
Family instability index -1.18 1.12 .28 1.30 1.17 1.46 -.005 1.08
Percent divorced 8.20 1.71 10.20 1.94 11.10 2.08 .011 1.40
Percent families single-parent headed 23.30 6.84 28.34 8.17 32.49 9.02 -.006 4.20
aRefers to variables expressed as deviations from their city-specific, over-time mean (i.e., “group-mean centered”).
bThese values refer to the log transformed measures.
Appendix A

Immigration and Violent Crime Rates in U.S. Cities 469
Table B1 • Fixed-Effects (Within-City) Linear Regression Models Predicting Property Crime Rates, 1980–2000
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Predictors
Immigration index -470.57* -537.67* 394.48* -399.71* -399.47* -181.08 -188.87
Percent males aged 15 to 34 110.16 39.59 15.60 14.79 18.18 23.45
City population (Ln) 427.57 -614.06 -683.61 -651.45 -53.55 82.36
Residential instability 12.10 13.84† 12.67 12.71 10.28 10.28
Percent below poverty -107.44* -117.22* -116.41* -103.26* -107.91*
Unemployment rate 72.37 91.62 90.41 76.06 82.43
Percent manufacturing 10.97 10.77 10.89 26.67 26.38
Percent professional/managerial -41.11 -44.35 -43.98 30.72 29.16
Drug market arrests (Ln) 173.45* 173.05* 159.63* 151.58*
Police officers per capita 35.67 -40.23 -100.09
Family instability index 1477.69* 601.10*
Percent black 49.51†
Time Trend Dummy Variables
Year 1990 208.83† 384.52* 763.38* 448.73 439.06 -1044.67* -898.55*
Year 2000 -1528.51* -1163.36* -658.20 -835.10 -858.12 -3640.35* -3406.45*
Model Summary Information
R2 (Within-unit) .520 .526 .550 .569 .569 .595 .595
Corr (ui, XB) -.356 -.396 -.496 -.507 -.498 -.123 -.123
Total number observations (
N × T) 463 463 463 463 463 463 463
Total number cities (
N) 159 159 159 159 159 159 159
*p < .05 †
p < .10 (two-tailed tests)
Appendix B

470 Ousey/ Kubrin
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Whichever your reason is, it is valid! You can get professional academic help from our service at affordable rates. We have a team of professional academic writers who can handle all your assignments.

Why Choose Our Academic Writing Service?

  • Plagiarism free papers
  • Timely delivery
  • Any deadline
  • Skilled, Experienced Native English Writers
  • Subject-relevant academic writer
  • Adherence to paper instructions
  • Ability to tackle bulk assignments
  • Reasonable prices
  • 24/7 Customer Support
  • Get superb grades consistently

Online Academic Help With Different Subjects

Literature

Students barely have time to read. We got you! Have your literature essay or book review written without having the hassle of reading the book. You can get your literature paper custom-written for you by our literature specialists.

Finance

Do you struggle with finance? No need to torture yourself if finance is not your cup of tea. You can order your finance paper from our academic writing service and get 100% original work from competent finance experts.

Computer science

Computer science is a tough subject. Fortunately, our computer science experts are up to the match. No need to stress and have sleepless nights. Our academic writers will tackle all your computer science assignments and deliver them on time. Let us handle all your python, java, ruby, JavaScript, php , C+ assignments!

Psychology

While psychology may be an interesting subject, you may lack sufficient time to handle your assignments. Don’t despair; by using our academic writing service, you can be assured of perfect grades. Moreover, your grades will be consistent.

Engineering

Engineering is quite a demanding subject. Students face a lot of pressure and barely have enough time to do what they love to do. Our academic writing service got you covered! Our engineering specialists follow the paper instructions and ensure timely delivery of the paper.

Nursing

In the nursing course, you may have difficulties with literature reviews, annotated bibliographies, critical essays, and other assignments. Our nursing assignment writers will offer you professional nursing paper help at low prices.

Sociology

Truth be told, sociology papers can be quite exhausting. Our academic writing service relieves you of fatigue, pressure, and stress. You can relax and have peace of mind as our academic writers handle your sociology assignment.

Business

We take pride in having some of the best business writers in the industry. Our business writers have a lot of experience in the field. They are reliable, and you can be assured of a high-grade paper. They are able to handle business papers of any subject, length, deadline, and difficulty!

Statistics

We boast of having some of the most experienced statistics experts in the industry. Our statistics experts have diverse skills, expertise, and knowledge to handle any kind of assignment. They have access to all kinds of software to get your assignment done.

Law

Writing a law essay may prove to be an insurmountable obstacle, especially when you need to know the peculiarities of the legislative framework. Take advantage of our top-notch law specialists and get superb grades and 100% satisfaction.

What discipline/subjects do you deal in?

We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.

Are your writers competent enough to handle my paper?

Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.

What if I don’t like the paper?

There is a very low likelihood that you won’t like the paper.

Reasons being:

  • When assigning your order, we match the paper’s discipline with the writer’s field/specialization. Since all our writers are graduates, we match the paper’s subject with the field the writer studied. For instance, if it’s a nursing paper, only a nursing graduate and writer will handle it. Furthermore, all our writers have academic writing experience and top-notch research skills.
  • We have a quality assurance that reviews the paper before it gets to you. As such, we ensure that you get a paper that meets the required standard and will most definitely make the grade.

In the event that you don’t like your paper:

  • The writer will revise the paper up to your pleasing. You have unlimited revisions. You simply need to highlight what specifically you don’t like about the paper, and the writer will make the amendments. The paper will be revised until you are satisfied. Revisions are free of charge
  • We will have a different writer write the paper from scratch.
  • Last resort, if the above does not work, we will refund your money.

Will the professor find out I didn’t write the paper myself?

Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.

What if the paper is plagiarized?

We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.

When will I get my paper?

You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.

Will anyone find out that I used your services?

We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.

How our Assignment Help Service Works

1. Place an order

You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.

2. Pay for the order

Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.

3. Track the progress

You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.

4. Download the paper

The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.

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Place your order
(550 words)

Approximate price: $22

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
$26
The price is based on these factors:
Academic level
Number of pages
Urgency
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

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Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

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Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

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Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

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By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

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