Immigration and Law Enforcement Preferences Immigration ... · greater government spending on law enforcement speak to popular demand for policing, punishment and social control.1

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Immigration and Law Enforcement Preferences

Immigration and Preferences for Greater LawEnforcement Spending in Rich Democracies

Joshua J. Fink, San Antonio SpursDavid Brady, University of California, Riverside

Immigration to rich democracies grew substantially in the 1990s and 2000s. Weinvestigate whether the rise of immigration influenced the novel and salient out-come of preferences for greater law enforcement spending. We propose that

these preferences are consequential for policymaking, reflect popular demand forpunitive social control, and represent micro-level preferences underlying the politicsof criminal justice. Motivated by literatures on criminal justice politics, minoritythreat, and the fear of crime, we examine whether stocks and flows of immigrationinfluence individual-level preferences for greater law enforcement spending. UsingInternational Social Survey Programme (ISSP) data, we analyze between-countryvariation with multi-level models of 25 countries in 2006, and within-country variationwith differences-in-differences (DD) models of 16 countries with available data inboth 1996 and 2006. Both multilevel and DD models show that flows of immigrationincrease preferences for greater law enforcement spending. Indeed, the coefficientsfor immigration flows are larger than or comparable in magnitude to the coefficientsfor any other variable, and are robust net of homicide rates and police officers per100,000. By contrast, the stock of immigrants is not robustly associated with prefer-ences. The results demonstrate that rising immigration contributed to increasing pub-lic support for greater law enforcement spending.

Immigration to rich democracies increased substantially in the 1990s and 2000s.Indeed, for many rich democracies, this period featured the largest annual increasesin immigration since the availability of cross-nationally reliable immigration data(WDI 2016). Yet, while immigration grew in most rich democracies, includingboth the U.S. and Western Europe, a few (e.g., Japan and Poland) continued to

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Direct correspondence to David Brady, School of Public Policy, University of California, INTS4133, 900 University Ave., Riverside, CA 92521; email: [email protected]. The authors are listedreverse alphabetically, each contributed equally. We thank Social Forces reviewers, Ryan Finnigan,Dave Jacobs, Joscha Legewie, Ruud Koopmans, Ken Land, Clyde Schechter, Fink’s fellow Dukegraduate students, the WZB Rule of Law Colloquium, and the WZB conference on Contexts ofInequality attendees for suggestions and assistance. This research was partially supported byNational Science Foundation grant 1059959/1061007 (principal investigators: David Brady,Evelyne Huber and John D. Stephens).. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

© The Author(s) 2019. Published by Oxford University Press on behalf of the University ofNorth Carolina at Chapel Hill. All rights reserved. For permissions, please e-mail: [email protected].

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have small immigrant populations. As a result, rich democracies exhibit tremen-dous variation in immigration across countries and over recent decades.

In response to rising immigration to Western Europe, animosity toward for-eign born residents rose to historically high levels (McLaren 2012; Semyonovet al. 2006), which contributed to the rise of extreme Right parties (Arzheimer2009; Kitschelt 1997; Rydgren 2005). Lively debate has occurred over whetherimmigration and ethnic heterogeneity erode trust and solidarity (Abascal andBaldassarri 2015; McLaren 2012), and undermine public support for social pol-icy (Brady and Finnigan 2014; Catran-Schmidt and Spies 2016; Steele 2016).Many studies focus on the consequences of immigration stocks, or percent of thepopulation foreign born. However, there is also growing interest in immigrationflows, or changes and surges in immigration (Ceobanu and Escandell 2010;Hopkins 2010; Newman and Velez 2014). Some propose that immigration flowsmay have even larger effects than stocks (Meuleman et al. 2009; Newman andVelez 2014). Therefore, the recent increase in immigration, combined withcross-national survey data, provides a unique opportunity to study the politicaland social consequences of immigration.

Despite growing interest in the consequences of immigration, the politics oflaw enforcement has been neglected as a potential outcome. This is unfortunateas a relationship between rising immigration and the politics of law enforcementis plausible. Past research suggests rising immigration could trigger minoritythreat and increase the fear of crime, both of which could influence the politicsof law enforcement. Indeed, cross-national research shows ethnic heterogeneityis associated with higher imprisonment and harsher punishments (Jacobs andKleban 2003; Ruddell and Urbina 2004). Across French communities for exam-ple, Pager (2008) finds a greater percent of North Africans increases the use ofpretrial detentions and judicial control, and rates of criminal convictions. Thereis even evidence that countries with open and multicultural policies to immi-grants have higher incarceration rates (Koopmans 2010).

Building on these insights, we examine the relationship between immigrationand preferences for greater government spending on police and law enforce-ment. Using International Social Survey Programme (ISSP) data, we analyzebetween-country variation with multi-level models of 25 countries in 2006. Wealso analyze within-country variation with differences-in-differences models of16 countries with available data in both 1996 and 2006. By studying individualpreferences for law enforcement spending, we explore a novel social conse-quence of immigration and an understudied element in the politics of criminaljustice. In the next section, we justify this focus on preferences for greater lawenforcement spending. Then, drawing on literatures on the politics of criminaljustice, minority threat, and the fear of crime, we develop expectations for therelationship between immigration and these preferences.

The Salience of Preferences for Law EnforcementLike other aspects of public policy (Manza and Brooks 2012), public opinion isconsequential to the politics of law enforcement (Enns 2016; Garland 2002;

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Jennings et al. 2016). Criminal justice depends on public resources, and politi-cians making decisions on law enforcement spending are highly responsive topublic opinion (Barker 2009; Garland 1990; Jacobs and Helms 1999). In hisclassic account, Garland (2002) contends that the recent expansion of punitivesystems of control was partly driven by popular demand for policing and proac-tive crime prevention. Even though there is likely a reciprocal relationshipbetween politicians/policy and public opinion, the dramatic increase in incarcer-ation in the United States was at least partly driven by public opinion and thegrowing popularity of punitiveness (Beckett and Sasson 2003; Campbell andSchoenfeld 2013; Enns 2016). For instance, Enns (2014) estimates that had thepopularity of punishment stopped rising in the 1970s, there would be a 20 per-centage lower incarceration rate in the United States. Others show that publicopinion on the death penalty influences the election of judges and how judgesvote (Brace and Boyea 2008). Scholars have noted that historically public opin-ion was less influential on criminal justice outside the United States. (Savelsberg1994). However, recent studies show incarceration rates responded to publicsupport throughout Western Europe and North America (Jennings et al. 2016;Mayhew and van Kestern 2013), and punishment practices were at least partlyshaped by public opinion across rich democracies (Kury et al. 2013).

Therefore, preferences for greater spending on law enforcement plausiblyserve as a micro-level mechanism in the politics of criminal justice (Enns 2016).In addition to being consequential for policy, public opinion concerning lawenforcement is interesting as a manifestation of public and cultural sentiment(Manza and Brooks 2012). Several scholars have shown that public opinionregarding law enforcement is driven more by social anxiety, fear and perceptionsof insecurity and malice toward a perceived threat rather than actual crime(Beckett 1997; Elchardus et al. 2008; Savelsberg 1994). As Garland (2002: 194,199) observes, “the desire for security, orderliness and control” has “socialroots,” including especially social and ethnic divisions. Hence, preferences forgreater government spending on law enforcement speak to popular demand forpolicing, punishment and social control.1

Despite our claim that public opinion on law enforcement is salient, there isrelatively little research on individual preferences for government spending onlaw enforcement (Barkan and Cohn 2005; Soroka and Wlezien 2010). Althoughthere is research on attitudes toward criminal punishment (Baumer et al. 2003;Bobo and Johnson 2004; Enns 2014; King and Wheelock 2007), there has beena lack of research on the specific ISSP/GSS question on preferences for greaterspending on police and law enforcement.2

Criminal Justice Politics, Minority Threat, and Fear of CrimeThe literature on the politics of criminal justice demonstrates how political ac-tors mobilize the fear of crime, racial and ethnic divisions, and insecurity withsocial change to expand law enforcement and punishment (Barkan and Cohn2005; Barker 2009; Beckett 1997; Garland 2002; Jacobs and Carmichael 2001;Jacobs and Helms 1999). This literature often concentrates on the American

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case, chronicling the politics behind America’s swift and dramatic rise to becom-ing the world’s most punitive country (Beckett 1997; Beckett and Sasson 2003;Campbell and Schoenfeld 2013). However, while the United States may be thecardinal case of a heavily politicized punitive system, politics and public opinioninfluence law enforcement and punishment in other settings as well (Garland2002; Ruddell and Urbina 2004). Indeed, Jacobs and Kleban (2003) claim thatpolitics better explain variation between affluent democracies in punishment andlaw enforcement practices than other potential explanations.

Underlying much of the literature on the politics of criminal justice is minoritythreat theory. Following Blumer’s (1958) account, the dominant group feels asense of superiority and an entitlement to privilege and advantage over an aliensubordinate group. Minority threat is triggered by the fear and suspicion thatthe subordinate group intends to undermine the dominant group’s privilege andadvantage. Thus, minority threat theory emphasizes the subjective perception ofa dominant group identity, their difference from and superiority over an alienminority, and their “felt challenge” to protect against a perceived threat (Blumer1958: 5). In minority threat theory, politicians, entrepreneurs and the mediaplay a key role in constructing the dominant group’s identity and the perceptionof minority threat. These subjective perceptions are then often linked to behaviorand politics that reflect the majority group’s desire to maintain and protect theirdominance over the subordinate minority (Blalock 1973).

Minority threat theory is particularly relevant to our study for at least tworeasons. First, minority threat has been utilized to explain responses to immigra-tion (Crowder et al. 2011; Quillian 1995; Wang 2012). For instance, scholarshave used immigrant threat to explain how increasing foreign born populationsin the 1990s and 2000s triggered opposition to immigration and anti-foreignersentiment (Kaya and Karakoç 2012; Meuleman et al. 2009; Semyonov et al.2006). Indeed, scholars have shown that Mexican immigrants are perceived asequally or even more threatening as African-Americans to native-born Whites(Eitle and Taylor 2008). Second, minority threat should encourage preferencesfor greater law enforcement spending. Many show that racial/ethnic heterogene-ity is associated with greater punitiveness, harsher sentences, more aggressivelaw enforcement, and increased resources for criminal justice (e.g., Beckett1997; Light 2015; Muller 2012; Pager 2008; Ruddell and Urbina 2004). Indeed,areas in the United States with a greater share of African Americans have morepolice officers per capita (Jacobs and Carmichael 2001; Kent and Jacobs 2005;Stults and Baumer 2007).

Much research on minority threat concentrates on competitive threats fromout-groups to the majority group’s power, interests, and especially economic orpolitical resources (Ceobanu and Escandell 2010). This implies that the majoritygroup is aware of an economic and political threat, and alters its behavior andbeliefs to protect its dominance (Blalock 1973; Blumer 1958; Muller 2012). Forinstance, competitive threat has been used to explain why the economically mar-ginalized (e.g., less educated) are more prone to anti-immigrant sentiments, par-ticularly in a context of high unemployment (Kaya and Karakoç 2012; McLaren2003; Semyonov et al. 2006). Nevertheless, competitive threats over resources


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are unlikely to be the sole source of preferences for greater law enforcementspending (Stults and Baumer 2007). Rather, immigration must influence abroader set of social-psychological processes beyond solely economic or politicalinterests. For example, even though King and Wheelock (2007) find that the per-ception that African Americans are economic threats matters to punitive atti-tudes, the perception that African Americans are a threat to safety also matters.

Beyond competitive threat, one plausible reason that minority threat shouldincrease preferences for greater law enforcement spending is the fear of crime.When individuals are surrounded by culturally different out-groups, anxietyabout and fear of crime are higher (Elchardus et al. 2008; Liska et al. 1982).Socially marginalized out-groups are more likely to be constructed and labeledas delinquent and deviant (Calavita 2003; Hagan and Palloni 1999; Wacquant1999). Indeed, immigrants are an out-group often socially constructed as crimi-nal (Calavita 2003; Wacquant 1999), seen as problem populations even in theabsence of deviance (Spitzer 1975), and perceived as a criminal threat (Wang2012).

Several studies demonstrate that the presence of racial/ethnic minorities in-creases the perception of crime in a neighborhood, regardless of actual levels ofcrime (Quillian and Pager 2001; Wang 2012). For example, the size of theLatino population predicts Whites’ fear of crime (Eitle and Taylor 2008), andthe perceived proximity of Hispanics increases the perceived risk of being a vic-tim of crime (Chiricos et al. 2001). Also, the changing racial/ethnic compositionresulting from immigration provokes a perception of unstable social change andinsecurity (Hopkins 2010). Therefore, even if rising immigration does not pro-vide a competitive threat to the dominant majority’s resources, the fear of crimeis likely to manifest from a subjectively perceived threat to security and stability(King and Wheelock 2007).

To the extent that immigration increases the fear of crime, the fear of crimeshould then increase preferences for greater law enforcement spending. There isevidence that the fear of crime influences law enforcement strategies (Terrill andReisig 2003). There is also evidence of a connection between immigration, thefear of crime, and incarceration rates (Crutchfield and Pettinicchio 2009).However, there is a paucity of scholarship linking the fear of crime specificallyto preferences regarding law enforcement. In a rare exception, King andWheelock (2007) show that the perception that African Americans are a threatto public safety predicts punitive attitudes. However, they stress that there isneed for further research connecting changing racial/ethnic composition, percep-tions of crime, and punitiveness. Therefore, while the fear of crime is one plausi-ble mechanism between immigration, minority threat, and preferences for lawenforcement spending, research showing immigration influences such prefer-ences could enhance understanding of these processes.

Empirical ImplicationsWe expect a positive association between immigration and preferences forgreater law enforcement spending. However, it is important to distinguish

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between stocks and flows of immigration. First, many studies built on minoritythreat theory focus on immigration stock, or percent foreign born (Eitle andTaylor 2008; Kaya and Karakoç 2012; Quillian 1995; Semyonov et al. 2006).Following this literature’s use of objective measures of immigration stock, weexpect that percent foreign born will be positively related to preferences forgreater law enforcement spending.

Beyond objective levels of immigrant stock, it is important to recall thatminority threat was originally focused on subjective perceptions, felt challenge,fear, and the threat constructed through the media, by politicians, and throughcollective identity formation (Blumer 1958). These perceptions might not bedriven as much by actual experiences interacting with a larger immigrant popu-lation, but by constructed subjective feelings, fears, and images.3 Therefore,political reactions and changes in public opinion may be even more pronouncedwith sudden influxes of immigrants (Ceobanu and Escandell 2010; Hopkins2010; Meuleman et al. 2009). Indeed, citizens’ perception of immigration intotheir community is more responsive to the rapid growth of immigration than thesize of the immigrant population (Newman and Velez 2014). Indeed, that per-ceptions are driven more by such seemingly unstable, rapid increases in immigra-tion rather than stable high levels is consistent with Blumer’s (1958: 7) argumentthat prejudice and threat “usually become pronounced only as a consequence ofgrave disorganizing events that allow for the formation of a scapegoat.” As aresult, we expect immigration flows will be positively related to preferences forgreater law enforcement spending.

Third, we expect that the relationship between immigration and preferencesfor greater law enforcement spending will exist independently of crime rates.Although there is often a perception of an immigrant-crime link, the idea of thecriminal migrant is formed more by prejudice and stigma than by actual crime(Hagan and Palloni 1999; Kubrin 2013). Many studies demonstrate that immi-gration does not cause crime and that immigrants are less likely to engage incriminal and delinquent behavior than natives (e.g., Ousey and Kubrin 2009).4

Therefore, rising immigration should influence preferences regardless of actualcrime.

Before proceeding, it is important to acknowledge plausible counterargumentsto our expectations that immigration directly affects preferences. Several studiessuggest immigration might only affect beliefs when interacted with measures ofeconomic development (e.g., GDP per capita) or performance (e.g., unemploy-ment rates) (e.g., Quillian 1995). As noted above, immigration might only haveeffects in a context of media and politicians’ mobilization and social construc-tion of immigrants as criminals and subordinate others. This might mean thatany direct effect of immigration on preferences is small, especially after control-ling for the mediating role of affiliation with extreme right political parties.While studies show respondents can generally perceive the immigrant share intheir local communities (Newman et al. 2013), perceptions of nationwide immi-gration levels tend to be biased upwards (Gallagher 2014; Sides and Citrin2007). Because these biases are more pronounced among certain groups, it ispossible that immigration will only be associated with preferences among, for


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example, the less educated. All these factors make it less likely we will find a pos-itive direct association between immigration and preferences for greater lawenforcement spending. If we find a significant positive association, it is despitethese factors.

Data and MethodsThe analyses are based on data from the 1996 and 2006 International SocialSurvey Programme (ISSP). The ISSP is an annual, internationally-standardizedset of nationally representative surveys. We use the 1996 and 2006 role of gov-ernment modules, which contain the question on preferences for greater govern-ment spending on the police and law enforcement. The World Bank WorldDevelopment Indicators Database (WDI) provides the data on immigration.Police and crime data are from the United Nations Survey on Crime Trends(CTS).

The analyses include all 25 rich democracies with ISSP data: Australia, Canada,Croatia, Czech Republic, Denmark, Finland, France, Germany, Hungary, Ireland,Israel, Japan, Latvia, the Netherlands, New Zealand, Norway, Poland, Portugal,Spain, Slovenia, South Korea, Sweden, Switzerland, the United Kingdom, and theUnited States. In the 2006 cross-section, data are available for all 25 countries.5 Inthe pooled 1996–2006 sample, data are available in both waves for 16 countries(omitting Croatia, Czech Republic, Denmark, Finland, Latvia, the Netherlands,Portugal, Slovenia, and South Korea).

Dependent VariableOur dependent variable is based on the ISSP question: “Please show whetheryou would like to see more or less government spending in: the police and lawenforcement.” Each respondent was given a scale of five response options:“Spend much more,” “Spend more,” “Spend the same as now,” “Spend less,”and “Spend much less.” In our variable, preference for greater law enforcementspending, “Spend much more” and “Spend more” are coded as a 1 and all otherresponses are coded as a 0.

In addition to following research on welfare attitudes (Brady and Finnigan2014; Svallfors 2006), we dichotomized the dependent variable for four reasons.First, because the study spans 25 countries, the differences between precise cate-gories such as “Spend less” and “Spend much less” likely vary due to cultural,historical and linguistic differences. Second, relatively few respondents selected“Spend much less.” Third, respondents were also informed: “Remember that ifyou say “much more,” it might require a tax increase to pay for it.” The mean-ing of this also likely varies due to cultural, historical and linguistic differences,which makes the measurement of preferring “much more” spending less cross-nationally reliable. Fourth, we also estimated the models with the underlyingordinal values and using linear, ordinal logistic, and ordinal generalized linearregression. These alternatives are presented in Appendix III and are consistentwith the presented results.

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Key Independent VariablesThe immigration data are available for all countries in 1985, 1995 and 2005,meaning the immigration variables are lagged one year (WDI 2016). We exam-ine both the stock and flow of immigration. For stock, we use the percent foreignborn. For flows, we analyze net migration and the rate of change in percent for-eign born. Net migration measures the number of immigrants minus the numberof emigrants as a percentage of the population. The rate of change is calculatedby subtracting the 2006 value minus the 1996 value, dividing by the 1996 valueand multiplying times 100.6

Though the three measures are related, each is distinct. At the macro-level in2006 (N = 25), percent foreign born correlates 0.30 with net migration, and−0.38 with the change in percent foreign born. Net migration correlates 0.39with the change. Unfortunately, it is not possible to examine immigration bycountry of origin. Still, we discuss sensitivity analyses with one measure below.7

Control VariablesAt the country level, we include the homicide rate per 100,000 and the numberof police officers per 100,000. Homicide rates are the most conventional andreliable cross-national crime indicator available (Fajnzylber et al. 2002). Thenearly universal use of death certificates across these countries reduces the prob-lem of under-reporting. Other types of crime, including theft and assault, are lessreliable cross-nationally because they rely on the willingness of the residents toreport the crime. This willingness systematically varies across countries (Soares2004). Also, the definition of homicide does not vary across countries nearly asmuch as other crimes such as assault and theft (Bennett and Lynch 1990). Still,as a robustness check, we estimated all models with other measures of crime.Using any combination of theft, assault, homicide and overall crime rates, the re-sults were consistent. We control for police officers to adjust for baselineshortages or surpluses of police and law enforcement spending in a country.8

Following prior research on law enforcement spending in the United States(e.g., Jacobs and Helms 1999), we also analyzed other country-level indicatorsincluding the Gini coefficient, economic growth, the employment rate, and GDPper capita. Appendices IV–V show the main results while controlling for GDPper capita. However, we ultimately omit these variables in order to keep themodels parsimonious at level-2, and because their inclusion does not alter ourconclusions.9

We also include a set of individual-level variables from the ISSP. We includetwo dummy variables for how the respondent voted in the last election: extremeright party and left party (using ISSP coding of parties).10 The analyses incorpo-rate four measures of religion. Binary measures for each of Protestant, Catholic,and other religion are in reference to no religion. Religious attendance is an ordi-nal measure such that 0 = never, 1 = less frequently, 2 = once a year, 3 = severaltimes a year, 4 = once a month, 5 = 2 or 3 times a month, 6 = once a week, and7 = several times a week. We include age and age-squared (both converted to


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z-scores within individual-level samples), and an indicator variable for female.For marital status, we include binary measures for each of divorced, widowed,and never married, with married as the reference. Binary variables for each ofsuburb/town and rural measure geographic context with urban as the reference.For education, binary variables for each of less than secondary and university orabove are used with secondary degree as the reference. To measure employment,we include dummies for each of not in the labor force, part-time employment,self-employment, public employment and unemployed with private, full-timeemployment as the reference. Finally, we include relative income, which weobtain by calculating country-year-specific z-scores.11

For the 1996–2006 models, data availability prevents us from including someindividual-level variables present in 2006 but not all 1996 surveys: party vote,religion, marital status, rural/suburb/urban, part-time employment, and publicemployment. The 2006 models were estimated without these variables and theresults were consistent. Descriptive statistics for all variables are included inAppendix I.

Analytical StrategyTwo sets of analyses are used. The first are multi-level models with 30,096 re-spondents from the 2006 ISSP in 25 countries. These models analyze between-country differences to assess the cross-sectional relationship between immigra-tion and preferences. The second set pools the 1996 and 2006 ISSP surveysincluding 45,122 respondents from the 16 countries participating in both waves.These models analyze within-country over time variation. Within both sets, wefirst include one immigration variable at a time. We then include each immigra-tion variable while adjusting for the homicide rate and police officers. Finally,we include both percent foreign born and each measure of immigration flows. InAppendices VI–VII, we display the results while omitting the individual-levelvariables as a robustness check.

The dependent variable is binary. For the 2006 cross-sectional models, we usemulti-level logistic regression with random intercepts for countries (Raudenbushand Bryk 2001). These models can be expressed as two equations. The firstequation estimates the log odds of preferring greater law enforcement spendingfor the ith individual in the jth country. This is a function of a random interceptfor each country β0j, and a set of fixed individual-level coefficients βxij.

β β(−

) = = +p

py xIn

1ij

ijij j ij0

The second equation predicts the intercept for each country as a function of aset of country-level variables γCj, an intercept γ00, and an error term u0j.

β γ γ= + +C uj j j0 00 0

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Some suggest that multilevel models can be fragile when too many level-2variables are included or cross-level interactions are estimated without a largenumber of level-2 units. (Bryan and Jenkins 2016; Maas and Hox 2005;Stegmueller 2013). Therefore, because we only have 25 countries, it is essentialto keep the models parsimonious at level-2. As a result, we concentrate on ran-dom intercepts in the multi-level models.

For the pooled 1996–2006 sample of 16 countries, we employ differences-in-differences (DD) logistic regression models. With continuous treatments, noclear “pre/post” periods, and no control group receiving zero treatment, a DDmodel includes fixed-effects for the 16 countries and a fixed effect for year(Angrist and Pischke 2009). The log odds of preferring greater law enforcementspending (yijt) is a function of an intercept (β0), individual-level variables (βxXijt),country-level variables (βzZjt), fixed-effects for the 16 countries (βcCj), and afixed-effect for 2006 (βwWt).

β β β β β(−

) = = + + + +p

py X Z C WIn

1ijt

ijtijt x ijt z jt C j W t0

The country and year dummies correct for the non-independence of observa-tions within-country and year.12 Country dummies adjust for any stable unob-served characteristics of countries with stable effects. There are likely to bestable unobserved characteristics of countries—for example, criminal justice in-stitutions—that are correlated with immigration and preferences for greater lawenforcement spending (Savelsberg 1994). Such unobserved characteristics couldintroduce omitted variable bias, and may influence any effect of immigration weobserve in the multi-level logit models. The country dummies difference out anysuch stable characteristics, and estimate the effect of changes in immigration onwithin-country temporal variation in preferences for greater spending. Thefixed-effect for 2006 adjusts for any generic time trend across countries. We alsorobust cluster the errors by country (Angrist and Pischke 2009).

RESULTSMulti-Level Models 2006We begin with the bivariate correlations between the country-level aggregate ofpreferences for greater law enforcement spending and the immigration measuresin 2006. Figure 1 plots the correlation for each immigration measure with 95percent confidence intervals. The percent foreign born is positively correlatedwith the proportion with a preference for greater law enforcement spending (r =0.10). But, this correlation is relatively trivial. Japan, France, and Switzerland allexhibit lower support for greater spending, but have low, medium and high per-cent foreign born. Indeed, both the lowest (South Korea) and the highest percentforeign born country (Israel) have similar preferences.

Despite the weak association with percent foreign born, the proportion sup-porting greater law enforcement spending is more strongly, positively correlated


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with the two measures of immigration flows: net migration (r = 0.47) and thechange in percent foreign born (r = 0.39). Countries that experienced high orincreasing migrant flows (e.g., Ireland, Spain) tend to have high support forgreater spending. Countries with stagnant migration flows (e.g., Croatia, Japan,and Latvia), had relatively low support for greater spending. Of course, these as-sociations are partly driven by the high migrant flows in Spain and Ireland.However, if we omit Spain and Ireland, the correlations between preferences forgreater spending and all three measures of immigration flows remain positive.

Table 1 presents the 2006 multi-level models. We present odds ratios andstandardized odds ratios for non-binary variables. For country-level variables,standardized odds are based on based on macro-level means and standard devia-tions within each sample.

Several individual-level variables are robustly significant. Preferences forgreater spending are positively associated with extreme right party vote, being aProtestant or Catholic, being female or a public employee, and relative income.Preferences are negatively associated with left party vote, religious attendance,living in a rural setting, having a university education, and being part-time em-ployed, unemployed, or self-employed. There is also an inverse u-shaped rela-tionship between preferences and age. The largest odds ratios or inverse oddsare for extreme right party vote, Protestant, and university education.

The first three models include one immigration variable at a time. The mea-sure of stock, percent foreign born, is insignificant in model 1. In models 2–3,the two measures of immigration flows are each significantly positive. Holdingall other variables constant at their means, a standard deviation increase in

Figure 1. Bivariate correlations between proportion with a preference for greater lawenforcement spending (y-axis) and three immigration measures in 2006

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Table1.

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(4)

(5)

(6)

(7)

(8)

%Fo

reignbo

rn1.00

91.01

10.99

61.02

9*

1.07

31.08

70.97

01.23

6*

(0.533

)(0.651

)(−

0.25

6)(1.730

)

Net

migration

1.18

6***

1.21

3***

1.19

2***

1.39

3***

1.45

4***

1.40

6***

(2.878

)(3.148

)(2.828

)

Cha

nge%

foreignbo

rn1.00

4**

1.00

4**

1.00

5***

1.33

2**

1.30

2**

1.44

5***

(2.399

)(2.227

)(3.008

)

Hom

iciderate

1.03

61.15

01.04

1

1.04

81.20

61.05

5

(0.364

)(1.594

)(0.451

)

Policeofficers

0.99

90.99

90.99

9

0.82

70.88

80.87

3

(−1.47

3)(−1.06

0)(−1.12

7)

Extremerigh

t1.35

6***

1.35

9***

1.35

7***

1.35

6***

1.36

0***

1.35

6***

1.35

8***

1.35

9***

(3.473

)(3.495

)(3.478

)(3.470

)(3.506

)(3.474

)(3.493

)(3.501

)

Left

0.83

2***

0.83

2***

0.83

2***

0.83

2***

0.83

3***

0.83

2***

0.83

2***

0.83

2***

(−6.15

5)(−

6.15

7)(−

6.16

7)(−

6.15

6)(−6.15

3)(−6.16

9)(−

6.15

9)(−

6.15

6)


ownloaded from



Protestant

1.23

0***

1.23

1***

1.23

1***

1.22

8***

1.23

0***

1.23

0***

1.23

1***

1.23

1***

(4.962

)(4.989

)(4.992

)(4.938

)(4.973

)(4.971

)(4.992

)(4.993

)

Catho

lic1.10

3**

1.10

1**

1.10

3**

1.10

4**

1.10

2**

1.10

4**

1.10

1**

1.10

3**

(2.305

)(2.256

)(2.309

)(2.336

)(2.285

)(2.336

)(2.255

)(2.311

)

Other

relig

ion

0.99

50.99

50.99

70.99

50.99

30.99

70.99

50.99

5

(−0.09

4)(−

0.10

7)(−

0.05

5)(−

0.09

9)(−0.13

0)(−0.05

9)(−

0.09

9)(−

0.10

6)

Religious

attend

ance

0.98

3**

0.98

3**

0.98

3**

0.98

3**

0.98

3**

0.98

3**

0.98

3**

0.98

3**

(−2.52

4)(−

2.52

4)(−

2.52

3)(−

2.52

9)(−2.53

9)(−2.52

9)(−

2.52

5)(−

2.51

3)

Age

1.25

4***

1.25

4***

1.25

4***

1.25

4***

1.25

4***

1.25

4***

1.25

4***

1.25

4***

(12.90

0)(12.89

0)(12.91

0)(12.89

0)(12.89

0)(12.90

0)(12.89

0)(12.90

0)

Age

20.96

6**

0.96

6**

0.96

6**

0.96

6**

0.96

6**

0.96

6**

0.96

6**

0.96

6**

(−2.41

9)(−

2.42

4)(−

2.41

3)(−

2.41

5)(−2.42

2)(−2.41

0)(−

2.42

3)(−

2.41

7)

Female

1.04

5*1.04

5*1.04

5*1.04

6*1.04

5*1.04

5*1.04

5*1.04

5*

(1.684

)(1.683

)(1.675

)(1.688

)(1.686

)(1.678

)(1.682

)(1.676

)

Never

married

0.96

70.96

60.96

70.96

70.96

60.96

70.96

60.96

7

(−0.90

0)(−

0.91

1)(−

0.89

7)(−

0.90

2)(−0.90

7)(−0.89

7)(−

0.91

1)(−

0.90

2)

Divorced

0.97

40.97

40.97

40.97

40.97

30.97

40.97

40.97

4

(−0.58

3)(−

0.58

5)(−

0.57

2)(−

0.58

3)(−0.59

4)(−0.57

3)(−

0.58

4)(−

0.57

9)

Widow

0.92

20.92

30.92

20.92

30.92

30.92

20.92

30.92

2

(−1.52

0)(−

1.51

2)(−

1.52

0)(−

1.51

3)(−1.51

2)(−1.51

6)(−

1.51

2)(−

1.52

0)

Rural

0.93

6*0.93

5*0.93

5*0.93

6*0.93

6*0.93

6*0.93

5*0.93

5*

(−1.88

6)(−

1.90

7)(−

1.89

9)(−

1.88

0)(−1.87

9)(−1.89

0)(−

1.90

7)(−

1.90

6)

(Con

tinu

ed)

1086 Social ForcesD

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Table1.

continued

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Subu

rb1.02

71.02

61.02

61.02

61.02

61.02

61.02

61.02

6

(0.835

)(0.817

)(0.823

)(0.822

)(0.819

)(0.814

)(0.817

)(0.814

)

<Second

aryeducation

1.02

21.02

11.02

11.02

31.02

21.02

21.02

01.02

2

(0.686

)(0.648

)(0.676

)(0.714

)(0.687

)(0.702

)(0.646

)(0.679

)

Universityeducation

0.73

6***

0.73

5***

0.73

6***

0.73

5***

0.73

5***

0.73

6***

0.73

5***

0.73

5***

(−8.39

7)(−

8.40

8)(−

8.39

5)(−

8.40

1)(−8.41

1)(−8.39

8)(−

8.40

7)(−

8.40

4)

Part-tim

eem

ploy

ment

0.90

1**

0.90

0**

0.90

1**

0.90

0**

0.90

0**

0.90

1**

0.90

0**

0.90

0**

(−2.40

7)(−

2.41

3)(−

2.40

0)(−

2.41

8)(−2.42

0)(−2.40

8)(−

2.41

1)(−

2.41

8)

Unemploy

ment

0.89

9*0.89

9*0.89

9*0.89

9*0.90

0*0.89

9*0.89

9*0.89

9*

(−1.68

7)(−

1.68

4)(−

1.68

7)(−

1.67

9)(−1.67

4)(−1.68

1)(−

1.68

4)(−

1.68

0)

Not

inthelabo

rforce

0.99

20.99

20.99

20.99

20.99

20.99

20.99

20.99

2

(−0.23

2)(−

0.23

0)(−

0.23

9)(−

0.23

0)(−0.22

6)(−0.23

7)(−

0.23

1)(−

0.23

5)

Self-employ

ment

0.88

2***

0.88

2***

0.88

2***

0.88

2***

0.88

2***

0.88

1***

0.88

2***

0.88

2***

(−3.04

1)(−

3.03

8)(−

3.05

9)(−

3.04

5)(−3.04

3)(−3.06

3)(−

3.04

0)(−

3.05

1)

Publicem

ploy

ment

1.05

11.05

11.05

2*1.05

11.05

2*1.05

2*1.05

11.05

2*

(1.630

)(1.643

)(1.650

)(1.635

)(1.656

)(1.654

)(1.643

)(1.669

)

Relativeincome

1.03

1**

1.03

0*1.03

1**

1.03

1**

1.03

1**

1.03

1**

1.03

0*1.03

1**

(1.961

)(1.953

)(1.962

)(1.971

)(1.967

)(1.971

)(1.953

)(1.962

)

***p<0.01,**p<0.05,*

p<0.1.


ownloaded from



immigration flows is expected to increase preferences by a factor of 1.33–1.39.Compared to the individual-level variables, these standardized odds ratios aresubstantively large. The standardized odds are comparable to the odds forextreme right party vote and university education. Also, the two measures offlows have larger standardized odds than being Protestant or any other variable.

Models 4–6 include one immigration variable at a time, while adjusting forthe homicide rate and the number of police officers per 100,000 people. Thehomicide rate and police officers are insignificant in all three models. By con-trast, the results for the immigration variables are consistent with models 1–3. Inmodel 4, percent foreign born is insignificant. In model 5–6, the two measures ofimmigration flows are significantly positively associated with preferences (stan-dardized odds = 1.30–1.45). For net migration, the standardized odds are evenlarger than before controlling for the homicide rate and police officers.

Models 7–8 include the measure of immigration stock (percent foreign born)along with each measure of flows.13 The percent foreign born is not significantin model 7, but is positive and significant in model 8 (p < 0.10). Despite model8, we conclude that immigration stock is not robustly associated with prefer-ences for greater law enforcement spending. Like models 1–6, both measures offlows are significantly positive.14 Holding other variables constant at theirmeans, a standard deviation increase in immigration flows is associated with in-creases in preferences for greater law enforcement spending by a factor of1.41–1.45. In models 7–8, the standardized odds for immigration flows arelarger than the odds or standardized odds for any other independent variable.

To substantively illustrate the effects of the three immigration measures,Figure 2 displays the marginal predicted probabilities of preferences for greaterspending on police and law enforcement from models 1–3. As the upper leftpanel shows, preferences increase modestly from roughly 0.53 at the lowest per-cent foreign born country (South Korea) to about 0.59 at the highest percent for-eign born country (Israel). However, the confidence intervals are quite wide andthe differences in preferences are not significant. The upper right panel, featuringnet migration, exhibits more substantial variation. Preferences increase fromabout 0.45 at the minimum net migration country (Latvia) to about 0.68 at themaximum country (Spain).

The bottom panel displays similarly pronounced variation in preferencesacross changes in percent foreign born. At the minimum in the change in percentforeign born (Poland), preferences are expected to be 0.44, while at the maxi-mum (South Korea), preferences are expected to be 0.81. Thus, preferences forgreater law enforcement spending significantly increase across the range of bothmeasures of immigration flows.

DD Models 1996–2006We begin with the correlations in the macro-level 1996–2006 changes in thesample of 16 countries with ISSP data in both waves. Figure 3 shows the1996–2006 changes in the proportion preferring greater law enforcement spend-ing on the y-axis. The x-axis displays the 1996–2006 changes in the immigration

1088 Social ForcesD

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stock and flows measures. Like the DD models, this plots within-country over-time change in aggregate preferences and the country-level immigrationvariables.

Figure 2. Marginal predicted probabilities of preferences for greater law enforcementspending (y-axis) from multi-level logit. Models 1–3 in Table 1

.4.5

.6.7

.8

–1.28 –.53 .22 .97 1.72 2.47% Foreign Born (z-scores)

.4.5

.6.7

.8

–1.35 –.6 .15 .9 1.65Net Migration (z-scores)

.4.6

.81

–1.1 –.35 .4 1.15 1.9 2.65 3.4 4.15 4.9Change % Foreign Born (z-scores)

Figure 3. Bivariate correlations between changes in proportion with a preference for greaterlaw enforcement spending (y-axis) and changes in three immigration measures 1996–2006

AUL

CAN

FRA

GERHUN

IREISRJPN

NZL

NOR

POL

SPA

SWE

SWZ

UKM

USA

–.2

–.1

0.1

.2

–5 0 5 10Change in % Foreign Born

r = .07

AUL

CAN

FRA

GER HUN

IREISRJPN

NZL

NOR

POL

SPA

SWE

SWZ

UKM

USA

–.2

–.1

0.1

.2.3

–2 0 2 4 6Change in Net Migration

r = .42

AUL

CAN

FRA

GER HUN

IREISRJPN

NZL

NOR

POL

SPASWE

SWZ

UKM

USA

–.2

0.2

.4

–100 0 100 200 300Change in Change % Foreign Born

r = .31


ownloaded from



Figure 3 shows changes in preferences correlate positively but weakly withchanges in percent foreign born (r = 0.07). By contrast, changes in preferencescorrelate more strongly with changes in the two measures of immigration flows(r = 0.42 with net migration, and 0.31 with relative change in percent foreignborn). Net migration has the strongest association. Countries with declining netmigration (e.g., Germany and New Zealand) had declining preferences forgreater law enforcement spending. By contrast, countries like Canada, Spainand Ireland had increasing net migration and increasing preferences. That said,the correlations are weaker and have larger confidence intervals in Figure 3 com-pared to Figure 1.

Table 2 presents the results for the DD models. Age, secondary education,and relative income are significantly positively associated with preferences.University education and self-employment are significantly negatively associatedwith preferences. Having a university education has the largest odds ratio.Holding other variables constant at their means, a university education reducespreferences by a factor of 1.38. Like Table 1, the homicide rate is not significantin models 4–6. However, police officers per 100,000 is significantly negativelyassociated with preferences in models 4–6. For a standard deviation increase inpolice officers, preferences are expected to decline by a factor of 1.12–1.19.

The conclusions from Table 1 are confirmed by Table 2. Immigration stock(i.e., percent foreign born) is insignificant in all models. Both measures of immi-gration flows are robustly significant and positive.15 Holding other variablesconstant at their means, a standard deviation increase in net migration or thechange in percent foreign born should increase preferences by a factor of1.26–1.37 or 1.17–1.23. These effects are substantively large. Net migration hasa standardized odds ratio that is nearly as large as university education, andlarger than the effect of police officers or other independent variables. Thechange in percent foreign born has a slightly smaller standard odds ratio, butthis effect is still larger than any variable except university education or netmigration.

Figure 4 illustrates the effects of immigration with marginal predicted proba-bilities of preferences for greater spending on police and law enforcement (basedon models 1–3). The differences in predicted probabilities are not significant forpercent foreign born in the upper left panel. In the upper right panel, across therange of net migration, preferences increase from 0.49 to 0.66. In the bottomright panel, across the range of the change in percent foreign born, preferencesincrease from 0.52 to 0.75. Both of these effects are substantively large.

Figure 5 counterfactually simulates what would have happened to preferencesin 2006 if net migration had remained at 1996 levels. Using the predicted proba-bilities from model 7, preferences for greater law enforcement spending were0.56 in 1996 (blue hollow circles) and 0.59 in 2006 (black hollow triangles).However, if net migration had remained at 1996 levels, preferences would havebeen 0.55 in 2006 (gray filled diamonds). Thus, if net migration had notincreased 1996–2006, preferences would have declined. This pattern of lowersimulated preferences in 2006 than preferences in 2006 or even 1996 occurs inseveral countries (e.g., Australia, Canada, Norway, Sweden, and the UK). In

1090 Social ForcesD

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Table2.

Diffe

rences-in

-DifferencesLogitM

odelsof

PreferencesforG

reater

Law

Enforcem

entS

pendingon

Country

-and

Individual-Level

Varia

bles

in16

Rich

Democracies

1996–2006

(N=

45,122):Od

dsRa

tiosandStandardize

dOd

dsRa

tios(ro

bustz-statistic

sin

parentheses)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

%Fo

reignbo

rn1.02

51.05

50.95

40.97

1

1.23

01.57

30.67

30.77

9

(0.518

)(1.331

)(−

1.64

2)(−

0.99

3)

Net

migration

1.17

11.15

31.21

1

1.29

3***

1.26

2***

1.36

5***

(4.221

)(3.439

)(5.329

)

Cha

nge%

foreignbo

rn1.00

31.00

31.00

4

1.19

9***

1.16

8***

1.23

4***

(4.097

)(3.300

)(3.858

)

Hom

icide

1.25

51.11

21.12

9

1.39

91.17

01.19

7

(1.421

)(0.724

)(0.777

)

Policeofficers

0.99

80.99

90.99

9

0.83

9**

0.89

2***

0.89

0**

(−2.09

6)(−2.71

3)(−2.53

1)

Age

1.27

2***

1.27

2***

1.27

1***

1.27

0***

1.27

0***

1.26

9***

1.27

0***

1.27

0***

(8.752

)(8.475

)(8.446

)(8.660

)(8.506

)(8.455

)(8.585

)(8.516

)

Age

20.97

50.97

50.97

50.97

30.97

40.97

40.97

50.97

5

(−1.40

6)(−

1.34

3)(−

1.42

3)(−

1.56

8)(−1.42

0)(−1.50

9)(−

1.30

9)(−

1.41

1)


ownloaded from



Female

1.05

01.04

81.05

01.04

91.04

81.05

01.04

91.05

1

(1.337

)(1.261

)(1.306

)(1.280

)(1.270

)(1.305

)(1.286

)(1.327

)

<Second

aryeducation

1.14

4***

1.15

5**

1.13

1**

1.14

4***

1.15

6***

1.13

6**

1.16

0**

1.13

1**

(2.713

)(2.505

)(2.492

)(2.648

)(2.609

)(2.474

)(2.504

)(2.445

)

Universityeducation

0.72

5***

0.72

6***

0.72

1***

0.72

4***

0.72

4***

0.72

0***

0.72

6***

0.72

0***

(−5.60

3)(−

5.99

9)(−

5.78

0)(−

5.63

7)(−5.99

2)(−5.73

0)(−

6.09

8)(−

5.79

4)

Unemploy

ment

0.88

6**

0.92

20.91

60.90

2*0.92

10.91

40.91

70.91

3

(−2.22

8)(−

1.23

2)(−

1.27

9)(−

1.68

9)(−1.26

4)(−1.32

3)(−

1.31

4)(−

1.34

2)

Not

inlabo

rforce

0.96

60.98

60.98

50.97

90.98

90.98

80.98

70.98

6

(−0.93

6)(−

0.43

1)(−

0.41

1)(−

0.58

0)(−0.32

5)(−0.32

2)(−

0.37

9)(−

0.36

6)

Self-employ

ment

0.84

0***

0.85

5***

0.85

5***

0.84

5***

0.85

5***

0.85

5***

0.85

6***

0.85

7***

(−4.76

0)(−

3.99

7)(−

4.05

5)(−

4.69

6)(−4.10

4)(−4.11

2)(−

4.07

7)(−

4.04

4)

Relativeincome

1.02

5*1.03

0**

1.02

7*1.02

7*1.03

0**

1.02

8*1.03

0**

1.02

8*

(1.706

)(2.065

)(1.856

)(1.816

)(2.082

)(1.900

)(2.099

)(1.872

)

Notes:S

tandarderrors

areclusteredby

country

;**p<0.01,*

p<0.05.

1092 Social ForcesD

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Spain, preferences would have been 0.59 in 2006, below predicted preferencesof 0.66 in 1996, and far below predicted preferences of 0.78 in 2006. Had netmigration not increased in Ireland, preferences would have been 0.65 in 2006instead of the predicted 0.84 in 2006 and the predicted 0.74 in 1996. In the fewcountries that had declining net migration, preferences would have remained sta-ble. If net migration remained at 1996 levels, 2006 preferences would have been0.57 in Germany (vs. predicted 0.47 in 2006 and actual 0.61 in 1996) and 0.75

Figure 4. Marginal predicted probabilities of preferences for greater law enforcementspending (y-axis) from DD models 1–4 in Table 2

.2.4

.6.8

1

–1.33 –.58 .17 .92 1.67 2.42% Foreign Born (z-scores)

.45

.5.5

5.6

.65

.7

–1.35 –.6 .15 .9 1.65Net Migration (z-scores)

.5.6

.7.8

–1.1 –.35 .4 1.15 1.9 2.65 3.4 4.15Change % Foreign Born (z-scores)

Figure 5. Predicted preferences for greater law enforcement spending in 1996 and 2006, andsimulated preferences in 2006 with 1996 levels of net migration (see Model 9 in Table 2)

.2 .4 .6 .8

USAUKMSWZSWESPAPOLNZL

NORJPNISRIRE

HUNGERFRACANAUL

*Total

Predicted 1996 Predicted 2006Simulated 2006


ownloaded from



in New Zealand (vs. predicted 0.70 in 2006 and actual 0.79 in 1996). In sum,net migration substantially altered preferences in many rich democracies relativeto what those preferences would have been in the absence of increasingimmigration.

DISCUSSIONScholars have examined a variety of consequences for rich democracies of therecent dramatic increase in immigration and the ensuing increased ethnic hetero-geneity. Building on literatures on the politics of criminal justice, minority threat,and the fear of crime, we propose that preferences for greater law enforcementspending are influenced by rising immigration. In turn, these preferences warrantattention alongside well-studied outcomes of immigration like trust, solidarity,social policy attitudes, and anti-immigrant sentiments. In addition, we proposethat preferences for greater law enforcement spending are consequential for pol-icymaking, reflect popular demand for punitive social control, and representmicro-level preferences in the politics of criminal justice. Using InternationalSocial Survey Programme (ISSP) data on 25 rich democracies in 1996–2006, weexamine both immigration stocks and flows. We use multi-level models of 25countries in 2006 to analyze between-country variation, and DD models of 16countries in 1996–2006 to analyze within-country variation. We also considerthe potentially conflating country-level variables of crime rates and police forcesize.

In both analyses, we find immigration is positively associated with preferencesfor greater police and law enforcement spending. Across both multilevel and DDmodels, the two measures of immigration flows are robustly significant. Themeasures of immigration flows have standardized odds ratios that rival the mag-nitude of any other individual- or country-level independent variable. As well,we observe sizable increases in the marginal predicted probabilities of prefer-ences for greater spending across the observed ranges of the immigration flowsmeasures. Further, counterfactual simulations suggest that had net migrationnot increased 1996–2006, preferences for greater law enforcement spendingwould have declined. The effects of the immigration flows are robust when ad-justing for the homicide rate and police officers, in models simultaneouslyincluding stock, and in a variety of sensitivity analyses.

In all models, percent foreign born fails to have a significant association withpreferences for greater law enforcement spending. Therefore, the effects of immi-gration stock do not appear to be nearly as consequential as the effects of immi-gration flows. Preferences for greater law enforcement spending are influencedby flows, but not stock, of immigration.

Our analyses contribute to several literatures. Broadly, the literatures we dis-cuss have traditionally focused on static comparisons across the stable ethniccomposition of places (e.g., the percent African American in a place). By con-trast, our study contributes to growing interest in the changing dynamics of eth-nic heterogeneity (Ceobanu and Escandell 2010; Hopkins 2010; Meulemanet al. 2009; Newman and Velez 2014). Our DD models demonstrate the

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powerful effects of changes over time when controlling for stable unobservedcharacteristics of places.

Regarding the literatures on minority threat and the consequences of immi-gration, we demonstrate how accelerating flows of immigration are more conse-quential than the stock of immigrants. Thus, the sudden increase in immigrationappears to be more influential than the level of immigration. As we explainedabove, the rapid change and instability of increasing immigration is arguablyconsistent with the subjective perception emphasized by minority threat theory.Moreover, we emphasize the salience of other aspects of minority threat besidesthe well-studied competitive threat. Although scholars previously studied howimmigration affects trust, solidarity, social policy attitudes and anti-immigrantsentiments, we identify law enforcement preferences (and the politics of criminaljustice more broadly) as a novel and salient outcome.

Our study also informs the fear of crime literature. As discussed above, thereis no clear relationship between immigration and increased crime. Further, ourmodels adjust for homicide rates, which are not significantly associated with pre-ferences. Therefore, the effects of immigration are not likely due to real changesin crime and security. Rather, our analyses suggest the fear of crime dependsmore on unfounded fears and prejudices resulting from changing ethnic hetero-geneity than from reasonable concerns about safety. Though we do not observethe fear of crime directly, we posit it as a plausible mechanism between immigra-tion, minority threat, and the politics of criminal justice. In the process, weencourage research on the consequences of the fear of crime alongside the well-studied causes (King and Wheelock 2007).

For the politics of criminal justice, we contribute to the study of micro-levelpreferences. Micro-level preferences are often implicit in political explanationsof criminal justice, and many contend public opinion and the popularity of puni-tiveness encourage increased incarceration and policing (e.g., Enns 2014, 2016).We also contribute to the prominent theme in this literature that race and ethnic-ity are powerful drivers of the politics of criminal justice. Because immigrationincreased considerably in recent decades, and because immigration is a principalsource of ethnic heterogeneity, this suggests one source of changes in criminaljustice politics in recent decades. Going forward, it would be worthwhile toinvestigate if the well-documented turn Republican politicians took towardmobilizing criminal justice politics in the 1980s and 1990s and the ensuingincarceration boom (Beckett and Sasson 2003; Campbell and Schoenfeld 2013;Enns 2016) were linked to the simultaneous increase in immigration. Finally,there is considerable research on the political consequences of immigration, andon how race/ethnicity influences criminal justice politics. Nevertheless, beyondthe emerging literature on the criminalization of immigration (Calavita 2003;Kubrin 2013; Wacquant 1999), our investigation into how immigration affectscriminal justice politics is still relatively unique. Therefore, we encourage othersto incorporate immigration alongside more well-studied factors driving criminaljustice.

Beyond these points, and beyond the scope of our study, we encourage futureresearch in three directions. First, one concern with the lack of significant effects


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for immigrant stock is that our measures are unable to distinguish country oforigin. It could be that the presence of foreign born immigrants from developingregions like Africa have larger effects (Semyonov et al. 2006). As noted above,unfortunately data on immigrants’ country of origin are not available (see foot-note 7). However, the OECD (2008) has a circa-2000 estimate of the percent ofthe over-15-year-old population that was born in Africa, Asia or LatinAmerica.16 In Appendix II, we estimate multi-level logit models in 2006 substi-tuting this measure for the overall percent foreign born. One should be cautiousas this measure is lagged six years before the ISSP, when immigration is increas-ing in most countries, and we only have this measure for 17 of the 25 countries.The percent foreign born from developing countries is not significant in the firstthree models. In model 4, when adjusting for the change in percent foreign born,the percent foreign born from Africa, Asia and Latin America becomes signifi-cantly positive. Therefore, there is some evidence that the stock of immigrantsfrom developing countries does have a significant association with preferences.Still, the effects are not robust, and when significant, are modest in magnitude.Importantly, the measures of flows remain significantly positive. Therefore, ourmain conclusions are similar even if we measure stock as immigrants from devel-oping countries. Nevertheless, research with more precise measures of immigra-tion would be valuable.

Second, we have conceptually argued that immigration is linked to minoritythreat, these are linked to the fear of crime, and the fear of crime is linked to pre-ferences for greater law enforcement spending, which is linked to criminal justicepolitics. However, we concentrate on the reduced, direct relationship betweenimmigration and preferences for greater law enforcement spending. Whilebeyond the scope of this paper, future research should investigate the mecha-nisms in this causal chain more fully. It would be valuable to have surveys incor-porating questions about immigration and immigrants, the fear of crime,preferences for law enforcement spending, and partisan affiliation and electoralbehavior. To the best of our knowledge, unfortunately, no survey currently of-fers this combination of questions.

Third, it would be valuable to have more studies integrating micro-level pre-ferences and macro-level changes in studies of the politics of criminal justice(Baumer et al. 2003; King and Wheelock 2007). For instance, given the strikingregional patterns in both criminal justice and immigration across U.S. states(Barker 2009; Campbell and Schoenfeld 2013; Jacobs and Carmichael 2001),studies should explore how rising immigration and state-level politics interactand shape the beliefs and preferences on individuals nested in such contexts.

In sum, this study presents evidence that rising flows of immigration encour-age preferences for greater spending on police and law enforcement. The effectsof immigration flows are generally robust, and are substantively large relative toother variables. We underline that crime rates do not have robust effects on pre-ferences. Therefore, we conclude that without rapid increases in immigration,public preferences for greater law enforcement would not have increased, andlikely would have declined, especially as crime rates in most countries declinedor stagnated. Rising immigration is among the most prominent social changes to

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rich democracies in recent decades, and for most countries, the trends do notseem to be abating. As a result, it is reasonable to expect immigration to con-tinue to be a central influence on the politics of criminal justice and other publicpolicies in the future.

Notes1. As validation of this point, analyses of the 2006 ISSP (data and methods described

below) these preferences significantly negatively predict the perception that lawenforcement is successfully controlling crime (z = −10.82).

2. Probably the closest to our study are Soroka and Wlezien’s (2010) and Barkan andCohn’s (2005) analyses of the GSS question on “spending on halting the rising crimerate” with response categories “too little,” “about right” and “too much.” The lackof research on our question is also notable given so many analyze similar questionsabout social policy (e.g., Catran-Schmidt and Spies 2016; Steele 2016; Svallfors2006).

3. As Blumer remarks (1958: 6), “It is not the experience with concrete individuals indaily association that gives rise to the definitions of the extended, abstract groups. . .The collective image of the abstract groups grows up not by generalizing from ex-periences gained in close, first-hand contacts but through the transcending character-izations that are made of the group as an entity. . .in the ‘public arena.’”

4. Victimization perceptions/experiences by self or peers could cause preferences forgreater law enforcement spending. If victimization is correlated with immigration ora variable correlated with immigration, there could be confounding between immi-gration and victimization. Unfortunately, the ISSP data we use below does notinclude any questions on victimization. Hence, we can only control for crime rates—which proxy the average person’s probability of victimization. As we show below,crime rates do not predict preferences and the literature shows that immigration doesnot cause rising crime.

5. In analyses available upon request, we reestimated all analyses while omitting thecountries that have not been stable democracies since 1980 (Croatia, CzechRepublic, Hungary, Latvia, Poland, Slovenia, and South Korea) and Israel. The re-sults were consistent with those presented.

6. For example, in 1995, 2.57 percent of Spain was foreign born, and in 2005 it was9.37 percent. Spain’s rate of change is 265.09 as ((9.37–2.57)/2.57)*100. In otheranalyses, we also measured the change in percent foreign born as absolute difference(2005–1995) and relative change (2005/1995). The results were consistent withthose reported.

7. The OECD immigration database purports to supply data by country of origin, butthe data are mostly missing or incomplete (Brady and Finnigan 2014). The sensitivityanalysis uses an OECD (2008) estimate of the circa-2000 measure of the percent ofthe over-15 population foreign born from Africa, Asia or Latin America. As dis-cussed below, the results from this sensitivity analysis were consistent with the resultsin the main tables (see Appendix II).

8. This is similar to how studies of welfare attitudes often control for current levels ofwelfare generosity/spending (Brady and Finnigan 2014; Svallfors 2006) and howthermostatic models of public opinion control for government spending (Soroka andWlezien 2010). Of course, we appreciate preferences may also cause higher police of-ficers per 100,000 (Kent and Jacobs 2005). Because this could bias the coefficient for


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police officers upwards, we show multiple models with and without this control. Theresults are robust to including/omitting this control.

9. These additional country-level controls are mostly insignificant. However, the Ginicoefficient was significantly positive in 1 of 8 cross-sectional models and 6 of 8 DDmodels. These results are available upon request, and including the Gini coefficientdid not alter the conclusions for the immigration variables. We also tested for inter-actions between our immigration measures and GDP per capita or the unemploy-ment rate. All interactions were insignificant. This contradicts the view thatimmigration only affects preferences when interacted with economic development orperformance.

10. The results for immigration were consistent if we omit the party vote variables. Thiscontradicts the view that immigration’s effects are fully mediated by partypreferences.

11. This income measure advantageously enables international and over-time compari-son without currency conversion or inflation-adjustment. Income has greater missingthan other independent variables. In other analyses, multiple imputation was imple-mented using linear regression with preferences for greater law enforcement spend-ing, country, year, age, age squared, sex, dummies for less than secondary anduniversity education, and dummies for employment status all as predictors. Usingthe dependent variable in this imputation helps prevent the models from assumingthat income and the dependent variable are unrelated (Allison 2000; Little 1992;Schafer and Olsen 1998). Models without these cases and with nine alternative im-putations of income produced results consistent with those presented.

12. Given concerns about the consistency of logit with fixed effects (i.e., the incidentalparameters problem), we estimated all models in Table 2 as linear probability modelswith fixed effects for country and year (and robust clustered errors by country). Theresults are consistent with those presented.

13. We restimated model 7 while dropping one country at a time. In all 25 models, netmigration remained significantly negative.

14. In analyses available upon request, we included the % foreign born, the homiciderate, the number of police officers, plus one measure of flows in one model. Weencourage caution with these models given we only have 25 level-2 units. However,both measures of flows are significantly positive and the % foreign born is notsignificant.

15. We reestimated model 7 while dropping one country at a time. In all 16 models, netmigration remained significantly negative. Also, we estimated models including thepercent foreign born, the homicide rate, the number of police officers plus one flowsmeasure at a time. Net migration remains significantly positive in this model as well.

16. The % foreign born from developing countries correlates strongly with the % foreignborn (r = 0.72, N = 17), which suggests that the % foreign born proxies the % for-eign born from developing regions. This is likely because immigrants from develop-ing regions are mainly present in the context of higher overall immigration.

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Appendix

Appendix I. Descriptive Statistics for 25 Rich Democracies in 2006 (N = 30,096) and 16 RichDemocracies 1996–2006 (N = 45,122)

2006Mean

2006SD

1996Mean

1996SD

Preferences for greater spending on police andlaw enforcement

0.565 0.496 0.575 0.494

% Foreign born (z-scores) 0.029 1.010 0.043 0.988

Net migration (z-scores) 0.079 0.977 0.115 0.997

Change % foreign born (z-scores) 0.095 1.111 0.171 1.204

Homicide rate (z-scores) 0.014 0.995 −0.016 0.958

Police officers (z-scores) 0.024 0.988 0.028 1.023

Extreme right 0.023 0.151

Left 0.249 0.432

Protestant 0.267 0.443

Catholic 0.375 0.484

Other religion 0.129 0.335

Religious attendance 2.386 2.210

Age (z-scores) 0.000 1.000 0.000 1.000

Age2 (z-scores) 1.000 1.084 1.000 1.098

Female 0.530 0.499 0.512 0.500

Never married 0.232 0.422

Divorced 0.088 0.284

Widowed 0.079 0.269

Rural 0.303 0.459

Suburb 0.430 0.495

<Secondary education 0.420 0.493 0.436 0.496

University education 0.175 0.380 0.158 0.365

Part-time employment 0.106 0.308

Unemployment 0.043 0.204 0.048 0.214

Not in the labor force 0.377 0.485 0.365 0.482

Self-employment 0.116 0.320 0.113 0.317

Public employment 0.271 0.445

Relative income 0.004 0.933 0.007 0.928


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Appendix II. Multi-Level Logit Models of Preferences for Greater Law Enforcement Spendingon Country- and Individual-Level Variables with % Over-15 Population Foreign Born fromAfrica, Asia and Latin America in 17 Rich

(1) (2) (3) (4)

% Foreign born from Africa,Asia & Latin America

1.014 1.013 1.005 1.081**

(0.369) (0.329) (0.177) (2.522)

Homicide rate 0.997

(−0.015)Police officers 0.999

(−0.100)Net migration 1.348***

(3.818)

Change % foreign born 1.009***

(3.974)

Extreme right 1.604*** 1.604*** 1.609*** 1.605***

(3.655) (3.655) (3.683) (3.661)

Left 0.747*** 0.747*** 0.748*** 0.748***

(−8.501) (−8.500) (−8.476) (−8.486)Protestant 1.330*** 1.330*** 1.334*** 1.334***

(6.055) (6.053) (6.126) (6.120)

Catholic 1.218*** 1.218*** 1.208*** 1.212***

(3.734) (3.736) (3.574) (3.648)

Other religion 1.057 1.057 1.054 1.053

(0.867) (0.866) (0.826) (0.817)

Religious attendance 0.989 0.989 0.989 0.989

(−1.317) (−1.316) (−1.355) (−1.314)Age 1.032*** 1.032*** 1.032*** 1.032***

(5.429) (5.429) (5.438) (5.431)

Age2 0.999*** 0.999*** 0.999*** 0.999***

(−3.275) (−3.274) (−3.286) (−3.280)Female 1.072** 1.072** 1.072** 1.072**

(2.162) (2.162) (2.160) (2.164)

Never married 0.971 0.971 0.970 0.971

(−0.666) (−0.666) (−0.686) (−0.670)Divorced 0.982 0.982 0.981 0.982

(−0.339) (−0.339) (−0.357) (−0.332)(Continued)

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Appendix II. continued

(1) (2) (3) (4)

Widowed 0.994 0.994 0.993 0.994

(−0.084) (−0.084) (−0.097) (−0.081)Rural 0.900** 0.900** 0.899** 0.900**

(−2.391) (−2.390) (−2.438) (−2.414)Suburb 1.032 1.032 1.032 1.031

(0.802) (0.801) (0.804) (0.785)

<Secondary education 1.056 1.056 1.054 1.056

(1.377) (1.379) (1.335) (1.383)

University education 0.680*** 0.680*** 0.679*** 0.680***

(−8.845) (−8.844) (−8.851) (−8.841)Part-time employment 0.893** 0.893** 0.892** 0.893**

(−2.254) (−2.255) (−2.268) (−2.261)Unemployment 0.949 0.949 0.949 0.948

(−0.630) (−0.630) (−0.629) (−0.637)Not in the labor force 0.989 0.989 0.989 0.989

(−0.268) (−0.268) (−0.263) (−0.266)Self-employment 0.866*** 0.866*** 0.867*** 0.866***

(−2.933) (−2.933) (−2.928) (−2.933)Public employment 1.032 1.032 1.034 1.034

(0.852) (0.852) (0.883) (0.885)

Relative income 1.010 1.010 1.010 1.010

(0.524) (0.525) (0.509) (0.525)

Notes: Z-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.


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Appendix

III.Pa

nelA

.X-StandardizedCo

efficients(and

z-statistic

sin

parentheses)forImmigratio

nVa

riables

forF

inal

TwoMUL

TI-LEV

ELModelsfor

Table1with

AlternativeTechniques

andOrdinalV

ersionsof

theDe

pendentV

ariablein

25Rich

Democracies

in2006

(N=30,096)

Log

itLog

itLinear

Linear

Ordered

logit

Ordered

logit

OGLM

OGLM

%Fo

reignbo

rn−0.06

60.21

4−0.00

70.10

5−0.01

60.24

9*−0.04

20.22

5

(−0.56

)(1.78)

(−0.12

)(1.54)

(−0.14

)(2.09)

(−0.33

)(1.67)

Net

migration

0.40

3**

0.15

9**

0.38

5**

0.36

0**

(3.12)

(2.78)

(3.02)

(2.77)

Cha

ngein

%foreignbo

rn0.34

5**

0.14

0**

0.32

6**

0.34

4**

(2.86)

(3.09)

(2.73)

(3.65)

**p<0.01,*

p<0.05.

1102 Social ForcesD

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PanelB

.X-Standardized

Coefficients(and

z-statistic

sin

parentheses)forImmigratio

nVa

riables

forF

inal

TwoDD

ModelsforT

able

1with

Alternative

Techniques

andOrdinalV

ersionsof

theDe

pendentV

ariablein

16Rich

Democracies

1996–2006

(N=

45,122)

Log

itLog

itLinear

Linear

Ordered

Log

itOrdered

Log

itOGLM

OGLM

%Fo

reignbo

rn−0.39

6−0.25

0−0.18

0−0.14

2−0.42

8−0.34

3−0.21

50.01

6

(−1.64

2)(−

0.99

3)(−

1.67

9)(−1.37

0)(−

1.64

9)(−

1.28

0)(−

1.17

)(0.09)

Net

migration

0.31

1**

0.11

0**

0.26

2**

0.32

9**

(5.329

)(4.159

)(4.351

)(2.98)

Cha

ngein

%foreignbo

rn0.21

1**

0.08

2**

0.19

3**

0.17

0*

(3.858

)(3.598

)(3.558

)(2.31)

Note:Stataom

itscountry

FE’sinOG

LM.**p<0.01,*

p<0.05.


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Appendix

IV.Multi-LevelLogitModels,INCLUD

INGGD

PP.C.,ofP

references

forG

reater

Law

Enforcem

entS

pendingon

Country

-and

Individual-Level

Varia

bles

in25

Rich

Democracies

in2006

(N=

30,096):Standardized

Odds

Ratio

s(z-statistic

sin

parentheses)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

%Fo

reignbo

rn1.05

01.07

90.96

91.21

7

(0.37)

(0.57)

(−0.26

)(1.57)

Net

migration

1.39

0**

1.46

9**

1.40

1**

(2.69)

(3.15)

(2.67)

Cha

nge%

foreignbo

rn1.32

5*1.30

5*1.43

2**

(2.39)

(2.26)

(2.92)

Hom

iciderate

1.05

21.20

51.06

2

(0.39)

(1.59)

(0.51)

Policeofficers

0.83

80.87

20.90

1

(−1.18

)(−

1.11

)(−0.77

)

GDPP.C.

1.11

61.00

71.11

11.02

80.95

71.06

81.01

11.06

5

(0.83)

(0.06)

(0.89)

(0.39)

(−0.35

)(0.49)

(0.09)

(0.54)

Note:Allvariables

included

inTable1arealso

included

here,but

notshown.

**p<0.01,*

p<0.05.

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Appendix

V.Diffe

rences-in

-DifferencesLogitM

odels,INCLUD

INGGD

PP.C.,ofP

references

forG

reater

Law

Enforcem

entS

pendingon

Country

-and

Individual-Level

Varia

bles

in16

Rich

Democracies

1996–2006

(N=


Odds

Ratio

s(ro

bustz-statistic

sin

parentheses)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

%Fo

reignbo

rn0.85

80.94

20.60

60.57

6*

(−0.25

)(−

0.08

)(−

1.85

)(−1.99

)

Net

migration

1.26

3**

1.23

6*1.32

2**

(3.43)

(2.21)

(3.85)

Cha

nge%

foreignbo

rn1.16

3**

1.12

41.21

9**

(2.91)

(1.70)

(3.73)

Hom

icide

1.52

21.20

51.30

3

(1.91)

(0.74)

(1.10)

Policeofficers

0.93

90.90

1*0.92

0

(−0.40

)(−2.43

)(−1.58

)

GDPP.C.

2.27

31.20

61.63

12.17

81.15

71.64

21.46

22.09

8

(1.43)

(0.40)

(1.15)

(0.90)

(0.27)

(1.05)

(0.77)

(1.65)

Notes:A

llvaria

bles

included

inTable1arealso

included

here,but

notshown.Standard

errors

areclusteredby

country

.**

p<0.01,*

p<0.05.


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Appendix

VI.Multi-LevelLogitModels,OM

ITTINGINDIVIDU

AL-LEV

ELVA

RIAB

LES,

ofPreferencesforG

reater

Law

Enforcem

entS

pendingon

Country

-LevelV

ariables

in25

Rich

Democracies

in2006

(N=


Odds

Ratio

s(z-statistic

sin

parentheses)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

%Fo

reignbo

rn1.06

51.08

10.96

51.21

2

(0.49)

(0.63)

(−0.31

)(1.57)

Net

migration

1.37

9**

1.42

2**

1.39

4**

(2.85)

(3.01)

(2.82)

Cha

nge%

foreignbo

rn1.30

2*1.26

9*1.40

1**

(2.23)

(2.04)

(2.76)

Hom

iciderate

1.02

81.17

31.03

5

(0.22)

(1.38)

(0.29)

Policeofficers

0.82

40.88

20.86

6

(−1.54

)(−

1.15

)(−1.21

)

**p<0.01,*

p<0.05.

1106 Social ForcesD

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Append

ixVII.Diffe

renc

es-in

-Differen

cesLogitM

odels,OM

ITTINGINDIVIDU

AL-LEV

ELVA

RIAB

LES,

ofPreferencesforG

reater

Law

Enforcem

ent

Spending

onCo

untry

-Level

Varia

bles

in16

Rich

Democracies

1996–2006

(N=


Odds

Ratio

s(ro

bustz-statistic

sin

parentheses)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

%Fo

reignbo

rn1.22

31.53

60.69

10.76

7

(0.49)

(1.24)

(−1.35

)(−1.04

)

Net

migration

1.27

5**

1.24

0*1.34

1**

(3.80)

(2.95)

(4.29)

Cha

nge%

foreignbo

rn1.19

9**

1.16

6**

1.23

6**

(4.25)

(3.42)

(4.04)

Hom

icide

1.43

41.21

51.22

5

(1.52)

(0.88)

(0.88)

Policeofficers

0.84

80.89

9*0.89

7*

(−1.91

)(−2.48

)(−

2.44

)

Note:Standard

errors

areclusteredby

country

.**

p<0.01,*

p<0.05.


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ABOUT THE AUTHORSJoshua Fink is Director of Data Science for the San Antonio Spurs. He earned aPh.D. in sociology and Masters of Science in statistical science at DukeUniversity. He has published articles in Sociology of Race and Ethnicity andHomicide Studies. His interests include using modern Bayesian statistics andcomputational methods to better understand sports, social structure, and crime.

David Brady is Professor of Public Policy and Director of the Blum Initiativeon Global and Regional Poverty at UC Riverside, and Fellow at the WZB BerlinSocial Science Center. His publications include Rich Democracies, Poor Peopleand The Oxford Handbook of the Social Science of Poverty. He is presentlystudying the measurement and causes of poverty, how long term economic re-sources shape racial and health inequalities, and how immigration and racial/ethnic heterogeneity influence politics.

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