Crime Perception and Victimization in Europe: Does Immigration Matter?

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1 Crime Perception and Victimization in Europe: Does Immigration Matter? Luca Nunziata (Department of Economics, University of Padova and IZA) Abstract This paper presents an empirical analysis of the effect of changes in immigration in region of residence on the likelihood of being a crime victim and on the subjective representation of criminality in local area of residence. To this purpose, the analysis exploits the recent immigration waves that took place in western European regions in the 2000s, using European Social Survey data matched with data from Labour Force Survey and other sources. Three alternative research strategies are proposed and discussed to account for possible endogeneity and measurement error of migration penetration: fixed effects by regions and country specific years; IV by instrumenting migration penetration at regional level using a second measurement provided by an alternative data source and an IV specification in differences where changes in immigration are instrumented using exogenous supply-push changes by migration flow areas. All identification strategies suggest no effect of immigration on crime victimization and perception in western Europe. However, simple OLS estimates suggest otherwise, implying that regional unobserved characteristics are partly responsible for inducing the public to wrongly imply a causal relationship between immigration and crime, whereas only a small positive correlation exists. This latter finding helps explaining why crime perception is an important driver of the attitude of European citizens towards immigration. Keywords: Crime, Migration, Crime Perception, Victimization, Safety, Security. JEL Classification Numbers: F22, J15, K42, O15, R23. Copyright CSEA.

2 Crime Perception and Victimization in Europe: Does Immigration Matter? Luca Nunziata Department of Economics, University of Padua and IZA August 3, 2012 Abstract This paper presents an empirical analysis of the effect of changes in immigration in region of residence on the likelihood of being a crime victim and on the subjective representation of criminality in local area of residence. To this purpose, the analysis exploits the recent immigration waves that took place in western European regions in the 2000s, using European Social Survey data matched with data from Labour Force Survey and other sources. Three alternative research strategies are proposed and discussed to account for possible endogeneity and measurement error of migration penetration: fixed effects by regions and country specific years; IV by instrumenting migration penetration at regional level using a second measurement provided by an alternative data source and an IV specification in differences where changes in immigration are instrumented using exogenous supply-push changes by migration flow areas. All identification strategies suggest no effect of immigration on crime victimization and perception in western Europe. However, simple OLS estimates suggest otherwise, implying that regional unobserved characteristics are partly responsible for inducing the public to wrongly imply a causal relationship between immigration and crime, whereas only a small positive correlation exists. This latter finding helps explaining why crime perception is an important driver of the attitude of European citizens towards immigration. Keywords: Crime, Migration, Crime Perception, Victimization, Safety, Security. JEL Classification Numbers: F22, J15, K42, O15, R23. Address: Dept. of Economics, University of Padua, Via del Santo 33, 35121, Padua, Italy, luca.nunziata@unipd.it. The author is grateful to Tommaso Frattini for advice and suggestions, to seminar participants at the Centro Studi Economici Antonveneta (CSEA) workshop on Migration, University College London NORFACE Migration conference, and ESPE conference in Bern for helpful comments and to Veronica Toffolutti for research assistance. Financial support from CSEA is gratefully acknowledged. The usual disclaimer applies. 1

3 1 Introduction One of the issues that dominated the political debate in recent years in most OECD countries pertains to the economic and social implications of increasing immigration intakes in affluent economies. This debate has been particularly relevant in Europe, where immigration flows, have steadily increased over the past few years, especially from less developed areas of the world. Economic theory provides some guidance as regards how immigration affects host countries, with the literature largely focusing on labour markets outcomes and welfare state provision 1. One dimension that has recently attracted more attention attains to the implications of immigration in terms of criminality. As a matter of fact, this aspect is one of the most important when evaluating European natives attitudes towards immigrants (see Boeri, 2010; Card, Dustmann, and Preston, 2009). Indeed, European politics has witnessed various attempts at exploiting a supposed link between immigration and criminality to gain electoral prominence in Europe, sometimes with success 2. According to economic theory, possible channel associating immigration and criminality originates from the different opportunity costs of committing a crime experienced by immigrants and natives (Becker, 1968). Whereas immigrants face reduced economic opportunities in host countries, they may be more prone to engage in criminal activities with respect to natives. In addition, immigration may increase labour supply in specific skill groups, therefore reducing wages and legal employment opportunities and possibly increasing the propensity to commit crimes among the natives characterized by such skills (Borjas, Grogger, and Hanson, 2010). Finally, the age composition of migrants is generally much younger than that of natives whereas the frequency of criminal activity among younger individuals is generally higher (Freeman, 1999). On the other hand, natives and immigrants experience different costs of apprehension and conviction, stemming from unequal access to quality legal defense to more dramatic consequences in case of unfavorable verdict, including possible deportation in the case of migrants. By internalizing these costs, immigrants may be less likely to become criminals. 1 See, for example, Borjas (1994, 1999) 2 See, for example, Mayer and Perrineau (1992); Norris (2005) and the electoral results in France in 2012 where the Front National gained 17.9% of the votes. 2

4 Considering these conflicting factors, economic theory does not provide clear predictions as regards the effective criminality patterns among immigrants with respect to natives. The investigation of the relationship between migration and crime remains then an empirical question. Most of the empirical literature on the relationship between criminality and immigration concentrates on US data and focus on actual crime outcomes only, in terms of crime reported, convictions or victimization, without paying much attention to crime perception. However, crime perception patterns may not necessarily be in line with actual reported crime for a number of reasons. On the one hand citizens may be subject to cognitive bias in their representation of the amount of crime experienced in their local area. On the other hand, the traditional media attention to crime phenomena may lead to an over-representation bias in their portrait of security issues. Crime perception may then provide a useful measure of how citizens internalize social fears about criminality, and whether those fears are actually justified in terms of actual criminality outcomes. This paper provides the first attempt, to my knowledge, to investigate this topic with a European focus, concentrating on crime victimization as well as on crime perception. I exploit the recent years increase in immigration flows into European countries to assess whether immigration affects criminality and the perception of insecurity among European natives. By matching individual crime victimization and crime perception data with immigration penetration in European regions I estimate the effect of changes in immigration patterns on the likelihood of being a crime victim and on the subjective representation of criminality in local area of residence. In order to account for possible endogeneity and measurement issues, I present a set of alternative estimation techniques characterized by different degrees of generalization and derive and discuss the conditions of validity of each model. These include a model with fixed effects by regions and country specific time dummies; an instrumental variables model where the measure of migration penetration by region is instrumented using an alternative regional penetration measure from a different dataset in order to account for potential measurement errors; and a model specified in differences where immigration is instrumented by using exogenous supplypush changes in migration patterns in order to account for both measurement errors and time varying omitted factors. According to the empirical results, no evidence is found of an increase in criminality nor in crime perception induced by the recent immigration waves in Europe. This result is at odds 3

5 with crime perception being a consistent predictor of natives less favorable attitudes towards migrants. However, the existence of a positive cross-sectional correlation between migration and crime may partly explain the wrong public perception of a supposed causal link between the two phenomena. The paper is organized as follows. Section 2 presents a review of the existing empirical literature, section 3 introduces the research design and discusses the different empirical strategies, section 4 provides a description of the data and presents the empirical findings, and finally section 5 concludes. 2 Literature Review Most of the empirical literature analyze US data. For example, Butcher and Piehl (1998) identify a cross sectional correlation between criminality and immigration rates across US cities investigating Uniform Crime Reports and CPS data. However, the correlation disappears after controlling for the cities demographic characteristics. No correlation is found by exploiting within city variation in crimes and immigration. Looking at three US border cities (Miami, El Paso and San Diego) Lee, Martinez, and Rosenfeld (2001) find that immigration does not increase the number of homicides among Latinos and African Americans. Butcher and Piehl (2007) show that immigrants in the US have much lower incarceration rates than natives (around one fifth). This difference increased from 1980 to 2000 and does not appear to be driven by an increase in deportation. The authors suggest instead that the migration process selects individuals who are less prone to be criminal or who respond more to deterrence than the average native born. Moehling and Piehl (2007) use early 20th century US prison data and find analogous crime patterns for natives and immigrants, with minor exceptions. More specifically, in 1904 prison commitment rates for more serious crimes are similar for all ages except ages 18 and 19 which are instead characterized by higher commitment rates for immigrants with respect to natives. Spenkuch (2011) uses a panel of US counties observed from 1980 to 2000 and shows that an increase in immigration induces an increase in crime, especially those that are motivated by a financial gain. However, the effect is originated by immigrants with poor labour market 4

6 opportunities only. Researchers have only recently drawn their attention to European data. Bianchi, Buonanno, and Pinotti (2008) provide an empirical examination of the relationship between crime and immigration across Italian provinces from 1990 to 2003 using police administrative data. Exploiting the within variation across provinces the authors find a positive correlation between the size of immigrant population and the incidence of property crimes and the overall crime rate. However, the relationship disappears when immigration is properly instrumented, suggesting a significant effect on robberies only. The effect on the overall crime rate is therefore negligible since robberies are only a tiny fraction (1.5%) of total criminal offences. Mastrobuoni and Pinotti (2010) look at the implications of immigrants legal status on criminal behavior by using exogenous variation in migration restriction laws in Italy. They exploit the last round of European Union enlargement that took place in 2007 when Romania and Bulgaria entered the European Union to show that obtaining legal status reduces the propensity to commit a crime by raising its opportunity cost. Bell, Machin, and Fasani (2010) analyze two recent large immigration waves in the UK to investigate the implications in terms of changes in crime rates. Considering the large immigration wave of asylum seekers in the late 1990s/early 2000s and the large inflow of workers from EU accession countries from 2004 onward, the authors argue that the opportunity costs of engaging in criminal offenses have been radically different for the two groups of migrants. The asylum wave was associated with low labor force participation, high unemployment and low wages, while the EU enlargement wave was characterized by higher participation rates than natives. The empirical analysis is consistent with the standard economic theory of crime, with non-violent crime rates found to be significantly higher in areas in which asylum seekers are located and not in areas affected by the EU enlargement wave. Bassetti, Corazzini, Cortes, and Nunziata (2012) adopts a broader perspective and investigate the relationship between migration and crime using a two-country labor matching model in which the migration flows and the crime rates are determined by the interaction between crime and the labor market. Their theoretical findings suggest that countries with flexible labor markets are more likely to be characterized by a negative relationship between migration and crime. Overall the existing literature on the US plus selected European countries mostly points 5

7 to a non significant effect of migration on crimes, except minor occurrences. No evidence is generally provided as regards the implications of immigration in terms of crime perception. However, various studies in criminology focus on the fear of crime, defined as the fear to be a crime victim as opposed to the actual probability to be a crime victim (Hale, 1996; Jackson and Stafford, 2009). Fitzgerald, Curtis, and Corliss (2009) suggest that fear of crime is a predictor of the attitude towards immigration in Germany, with a larger effect during election years. It is not clear what are the actual drivers of these fears if the relationship between immigration and crime is not supported by most studies. Possible answers could be cultural factors and, according to the authors, political and media representation of the immigration phenomenon. In what follows we provide further empirical evidence on the relationship between immigration and crime with a comprehensive analysis of Western European data from 2002 to 2008, i.e. a period characterized by large migration inflows to the regions object of our analysis. Our focus will be both on crime victimization and perception. 3 Research Design 3.1 Endogeneity and Measurement Issues I analyze data from the first four waves of the European Social Survey (ESS henceforth) covering a large number of European countries every two years from 2002 to 2008, i.e. a period characterized by fairly homogeneous economic conditions. The time span of the analysis covers a crucial period for immigration in Europe, characterized by a significant increase in immigration penetration in most countries, although with a certain degree of regional heterogeneity within and across countries. In order to estimate the effect of immigration penetration on crime, I adopt three alternative econometric specifications that vary in their degree of generality and in their ability to solve possible endogeneity and measurement error problems. Despite the ESS providing information on both Western and Eastern European countries, I choose to focus on the former only, in order to capture the relationship between immigration and outcomes of interest within an homogeneous economic and social environment. In addition, rather than being typical destination countries, Eastern European countries are often subject 6

8 to migration outflows towards Western Europe, and therefore do not qualify for the empirical test. Alternative sources of information on immigration penetration in Europe are used, both by country and region. These are Census 2001 data, European Labour Force Survey (LFS), and OECD Database on Immigrants in OECD Countries (DIOC). The ESS contains information on crime victimization, i.e. whether the respondent or household member has been a victim of assault or burglary in the last 5 years, and the degree of perceived insecurity (when walking alone after dark in local area) that represents a measure of individual crime perception. In addition, we observe the respondents region of residence, that is classified according to various degrees of geographical aggregation (NUTS I, II or III) according to country. The latter can be matched with a set of measures of immigration penetration by regions, from different sources, and the within regional variation in immigration or some instrumented change in immigration across regions can be exploited to address the questions of interest. The advantage of using survey data consists in relaying on a measure of crime victimization that it is not affected by the problem of under-reporting of crimes that is typical of administrative and judicial sources. In addition, survey data information on regional immigration does not necessarily include legal immigrants only, as in the case of official administrative sources. However, as previously recognized in the literature, both legal and illegal immigrants may have a lower propensity to be interviewed with respect to natives. As a consequence, immigration patterns by regions may be under-estimated and our findings may be biased if the propensity to participate is related to the propensity to commit a crime. Furthermore, the regional figures of migration penetration calculated using ESS and LFS data may be affected by sampling error especially when the survey design devotes less attention to the regional dimension. The estimates may then suffer from an attenuation bias. One of the major difficulties in the analysis is therefore to deal with the problem of measurement errors in immigration penetration numbers across regions. In addition to measurement issues, immigration is not randomly assigned to regions, but may be endogenously driven by factors directly affecting criminality and crime perception, resulting in biased estimates and wrong policy implications. We choose to deal with the problems above using three alternative strategies. A first empir- 7

9 ical strategy is to adopt a fixed effects approach by matching individual level crime victimization and perception with regional variation in immigration penetration. The model investigates whether a relationship exists after controlling for regional time invariant and country specific time varying unobserved confounding factors. The analysis exploits the different patterns in immigration penetration within geographical areas for different levels of regional disaggregation. A second strategy is to allow the measurement error in regional immigration to be random and to adopt a more general fixed effects model where we instrument the potentially mis-measured immigration penetration by region and year using a second measure of regional immigration obtained from a different dataset. In this case, the fixed effects still capture possible omitted variables and the measurement error problem is solved by instrumental variables. Finally, a third strategy consists in adopting a model in differences where unobservable factors are allowed to vary over time in a way that is not captured by country-specific year fixed effects. Immigration is then instrumented using exogenous migration flows to Europe originated by exogenous push factors from world areas of provenience. Each strategy is presented in detail in the next paragraphs. 3.2 Baseline Fixed Effects Model The first and more restrictive empirical strategy consists in adopting a fixed effects model. This amounts to pooling the 4 ESS waves ( ), gathering measures of immigrants across European regions in each wave year, and exploiting the within regional variation of immigration in order to estimate its effect on the likelihood of being a crime victim (or feeling not secure). In other words, we specify a model with regional fixed effects and clustering, as well as country-specific time dummies (plus observable individual and structural regional characteristics), assuming that both unobservable factors influencing migration patterns and measurement errors are constant by region and country-year. The linear probability model is therefore: crime crit = βm crt + λ X it + µ r + µ ct + ε it (1) where crime crit is a dummy variable indicating whether the individual i s household, living in country c and region r at time t, is a crime victim (or whether the individual fears crime), X it is a matrix of individual characteristics, µ r are regional fixed effects and µ ct are country-specific 8

10 time dummies. This approach is successful if no time varying unobservable regional characteristics affect both crime (or perception of insecurity) and immigration, therefore inducing spurious correlation between the two variables (whereas country-level changes in unobserved country characteristics are admitted). In addition, we implicitly assume that region fixed effects and country specific time dummies capture the salient features of measurement errors and therefore we are safe from attenuation bias. If the assumptions above hold, the estimates of model (1) presented in section 4.2 capture the causal impact of immigration on our variables of interest. More specifically, we may think about two sources of error affecting our measure of the share of immigrants by regions. On the one hand, legal migrants may be less inclined to participate to a survey or relatively less likely to be sampled than native born. This aspect is particularly relevant if we consider that immigrants are a small fraction of the total population in many European regions. The immigration shares provided by all our data sources for each region may therefore be wrongly measured due to small sample size, especially if we were to use more disaggregated regional levels. On the other hand illegal immigrants may be less represented in the interviewed sample when considering survey data or not counted altogether as in the case of administrative sources. As regards the first possible source of measurement error, model (1) implicitly assumes that the different propensity to participate to the survey of legal immigrants is either constant, or it changes across regions and\or across time. If it changes across time, the change follows countryspecific motives. All these possible scenarios are accounted by the fixed effects specification in (1) and more specifically by regional and country-specific time dummies. As far as illegal immigration is concerned, previous analysis show that the relative dimension of illegal immigration with respect to legal immigration is fairly stable over time. For example, Bianchi, Buonanno, and Pinotti (2008) use regularization episodes in Italy to show that the ratio of illegal to legal immigrants is very stable within Italian provinces and regularization years. If this is the case, the inclusion of regional and country-specific time dummies should account for this source of error. In analytical terms, we assume that the ESS or LFS measure of the share of immigrants on resident population M survey in each region r of country c, at time t, is proportional to the 9

11 actual migration penetration M total according to the following relationship: M survey crt = M total crt (1 + ν r + ν ct ) (2) where ν r and ν ct are regional and country/time specific proportionality factors. In other words, the proportions vary independently by region and change across time according to country-specific time-paths. Taking logs, equation (2) approximates to: m survey crt m total crt + µ r + µ ct + ε crt (3) which is the rationale for the specification of the fixed effects model (1) estimated in section 4.2. On top of these considerations, we may also rely on alternative data sources of regional immigration patterns in order to check whether our results are robust when we use different sources of information on migration. More importantly, these alternative measures may be exploited to instrument regional migration penetration in order to account for potential measurement errors in a less restrictive fashion. Section 4.1 provides a detailed description of the data and of the consistencies across sources. The next section introduces this alternative instrumental variable approach. 3.3 Instrumental Variables Model with Fixed Effects Using a Second Measurement of Immigration as Instrument The conditions implied by model (1) on the measurement errors of regional immigration may be too restrictive and not supported by the data. As a consequence, our fixed effects estimates may be affected by attenuation bias induced by the measurement error in migration penetration. A possible answer to this limitation is to consider the measurement error in regional immigration to be random. In this case we may solve the measurement error problem by means of an instrumental variables approach (Reiersol, 1941; Durbin, 1954) where potentially mis-measured regional immigration is instrumented using a second measure of regional immigration obtained from a different dataset. 10

12 Under this approach, we assume that omitted variables are still captured by regional and country specific time fixed effects, so that the true unobserved measure of immigration m is uncorrelated with the stochastic error ε, i.e.: E(m ε) = 0 (4) Let us assume our observed measure of immigration m 1 is measured with error e 1, i.e.: m 1 = m + e 1 (5) Let us also suppose that an alternative measure of regional immigration m 2, measured with error e 2, is available, such that: m 2 = m + e 2 (6) Since both m 1 and m 2 are uncorrelated with ε, we just need uncorrelation of e 1 with e 2 to use m 2 as instrument for m 1, since the two measures are correlated through m. ESS and LFS data provide two alternative measures of regional immigration penetration by region/year, each being possibly mis-measured. Given that the two survey designs are independent, we can then use both measures to implement an IV estimation since it is reasonable to assume that the two measurement errors are uncorrelated. The empirical findings under this approach are presented in section 4.3. This research strategy still relies on fixed effects in order to account for potential omitted variable bias. The next section presents a third strategy based on a specification in differences where regional migration penetration is instrumented using exogenous supply-push changes in migration patterns which accounts for both measurement error and omitted variable bias. This is the most general approach, where random measurement error just needs to be uncorrelated with the instrument, and omitted time-varying factors of general form are accounted for. 3.4 Instrumental Variables Model in Differences with Supply Push Factors as Instruments Unobservable factors correlated to immigration patterns may vary over time in a way not captured by country-specific time effects, in which case our fixed effects specification may 11

13 still produce biased estimates. A more general instrumental variable approach may then be advisable in this setting for two order of reasons. On the one hand, in order to account for regional-specific omitted time-varying factors (not therefore captured by regional dummies or by country-specific time-dummies) that may affect both migration patterns and the likelihood of being a crime victim or the perception of insecurity among respondents. On the other hand, we may be able to account for more complex structures of measurement errors in regional immigration. Various instruments have been suggested by the literature. For example, Lemos and Portes (2008) and Bell, Machin, and Fasani (2010) instrument recent migration patterns from eastern Europe towards UK regions by using the availability of flights from Eastern Europe to the UK. Card (2001), Dustmann, Frattini, and Preston (2008) and Bianchi, Buonanno, and Pinotti (2008) consider instead the geographical distribution of previous immigrants flow areas as an instrument for subsequent flows. We follow this second approach, and similarly to what Bianchi, Buonanno, and Pinotti (2008) do with Italian data, we use exogenous migration flows to Europe as instruments in a specification in differences. These are measured by changes in migration flows towards European countries (or regions) from different world areas of provenience, weighted by the predetermined share of immigrants located in each country (or region). These changes in migration patterns account for the exogenous supply-push factors inducing an increase in migration from world areas of provenience. These supply-push factors in areas of origin can be related to wars, political repression, famine, economic stagnation or else, and are therefore exogenous to our outcome of interest. The exogenous changes in migration waves are weighted by the predetermined share of immigrants previously located in that region from each area of origin, since immigrants tend to locate in areas that previously hosted individuals from the same area of provenience (Munshi, 2003). In our specification in differences, the changes in crime victimization are regressed on changes in migration penetration. Assuming we have N possible world flow areas a and that prior to the period under investigation each region r in country c is characterized by a certain share s a cr of immigrants from each area, then the change in migration in that region will be 12

14 approximately equal to: N m crt s a cr ln Mcrt a pop crt (7) a=1 Equation (7) provides a basis for constructing an instrument for m crt aimed at solving possible endogeneity and measurement error problems. To this end, we define the exogenous changes in migration from each flow area of origin a as the change induced by supply-push factors only. These factors pertain to each flow area of origin and are assumed unobservable by the researcher. However, the consequences of these factors may be generally observed by looking at the marginal changes in global migration patterns of individuals from area a. When considering each European region, however, the change in migration from area a to region r may be due to both exogenous supply push and endogenous demand pull factors pertaining to the region of destination. We therefore eliminate any change in regional migration that could be due to demand-pull factors at the local level by considering the changes in migrants from each flow area of origin a in all regions other than r. This amounts to substituting Mcrt a with Mkt a in equation (7), where k cr. Our instrument therefore becomes: z crt = N s a cr ln Mkt a pop crt, k cr (8) a=1 Expression (8) identifies an instrument for the total change in migration in country c, region r and time t by exploiting the changes in migration from flow area a induced by exogenous supply-push factors observed in all regions excluding r weighted by the predetermined share of migrants from area a in region r. The same procedure may be applied by calculating the changes in migration in all countries (instead of regions) other than the one where region r belongs. In order to avoid to incorrectly measure the predetermined shares of migrants s a cr we choose to be not too disaggregated in both our definitions of flow areas a, and in our definition of region r. In our baseline specification we therefore consider N=21 flow areas 3, and we calculate the predetermined share at country level, i.e. s a c. For robustness, we repeat the analysis for 3 These are those indicated by the United Nations classification of composition of macro geographical (subcontinental) regions (M49 code), i.e. North America, South America, Central America, Northern Africa, Western Africa, Eastern Africa, Middle Africa, Southern Africa, Caribbean, Central Asia, Western Asia, Eastern Asia, Southern Asia, South eastern Asia, Australia and New Zealand, Melanesia, Polinesia, Northern Europe, Western Europe, Eastern Europe, Southern Europe. 13

15 N > 21, i.e. introducing a finer geographical classification, and for N < 21 i.e. considering continents instead of subcontinents. In addition, we also use regional predetermined shares instead of country shares. The instrument in the baseline IV model becomes: z crt = N s a c m a kt pop crt (9) a=1 We then estimate the following model of crime victimization (or perception) in differences: c crt = β m crt + γ W crt + ε crt (10) by instrumenting m crt with (9) and including a set of regional controls W. The estimates of this model are reported in section Empirical Analysis 4.1 The Data The only coherent administrative measure of immigration presence across European regions is Census data, which is available for selected years only. Last available Census data for some but not all European countries is dated Accordingly, we rely on a number of alternative data sources on regional immigration presence in European regions. These consists in our main database, i.e. the European Social Survey (ESS), the European Labour Force Survey (LFS), and the OECD Database on Immigrants in OECD Countries (DIOC). In principle, Census data is the most reliable source as regards official figures on legal immigrants. Survey data should instead be able to capture the presence of illegal immigration, if well designed. In practice, as noted above, it is quite likely that both measures underestimate the number of migrants by region. In what follows, we use both definitions of born abroad and non national immigrants, although the preferred definition of immigrant is an individual who is born abroad, in line with most of the literature. The latter avoids the distortions induced by differences in legislation on naturalizations across countries. As noted by Boeri (2010) the born abroad concept may 14

16 induce some bias in countries with former colonies, where nationals born in those colonies and returning to the home country may be wrongly counted as immigrants. However, if we look at the data, the definition based on citizenship seriously underestimate regional immigration, even by comparison with the 2001 Census data. In general, ESS and LFS measures based on whether respondents are born abroad are closer to Census that those based on nationality only. We therefore believe that in our setting the second type of bias is less relevant than the first type 4. In addition to using alternative data sources, we adopt various levels of regional disaggregation, i.e. country, NUTS I and NUTS II level 5. In general, migration measures calculated for geographical areas characterized by a larger number of sampled residents should be less likely to be mis-measured. Figure (1) displays the percentage of immigrants over total residential population in European regions (NUTSII) according to alternative data sources (European Social Survey, Eurostat Census 2001 and European Labour Force Survey) and to alternative definitions of immigrants (born abroad and non-nationals). TABLES 1 AND 2 AROUND HERE The correlation among these measures is pretty high, as shown in Tables 1 and 2, where different definitions of immigrants are considered. LFS figures are generally closer to Census than ESS. The same is true for born-abroad counts versus non-national. Of all born abroad immigrants surveyed by ESS, around 82% are born in non-european countries and 24% do speak a European language most often at home. Figure (2) presents the distribution of born-abroad immigrants across European regions by different individual characteristics and data sources. Individuals born in Non-European countries and those who mostly do not speak a European language at home are also displayed. The figure points to a strong cross-sectional correlation among alternative measures, that is also displayed in Table 2. Tables 3 and 4 provide some summary statistics of the percentage of immigrant population across alternative definitions and data sources, respectively over the whole sample and by country. 4 In this paper we only consider first generation immigrants, as the second generation is counted as native. The implication of second versus first generation immigration on criminality and perception of insecurity may be a topic of a paper on its own. 5 According to the NUTS classification, NUTSI regions are characterized by a population between 3 and 7 millions individuals, whereas NUTSII regions are between 800,000 and 3 millions. 15

17 TABLES 3 AND 4 AROUND HERE FIGURES 1, 2, 3, 4 AND 5 AROUND HERE Figure (3) provides instead a summary of crime victimization rates across European regions as portrayed by ESS data. Here a crime victim is an individual whose household is reported to have been victim of burglary or assault in the last five years. The regions most affected seem to be Central UK, Southern France, Belgium, Luxembourg and Finland. A bit less affected are Southern Spain, Norway, Sweden and Denmark. Overall, Germany appears to be the country affected the least. The ESS measure of fear of crime is a variable depicting the feeling of safety when walking alone in local area after dark, with 4 categories stemming from very safe to very unsafe. Figure 4 gives a picture of crime perception across European regions, measured by individual feeling of safety. This is measured by the percentage of residents who feel safe and very safe when walking alone after dark. Not surprisingly, Germany is a country where citizens feel safe. Less clear is the pattern in other countries such as Norway, Sweden and Finland where citizen feel safe despite the relatively high rate of crime victimization. Table 5 provide some summary statistics of the data including the rate of victimization by country and the proportion of individuals who feel unsafe and very unsafe when walking alone after dark. Finally, Figure 5 provides a summary of the attitude towards immigrants across European regions. This is measured by the average score assigned by natives to the question asking whether immigrants make the host country a worse or a better place to live (from worse to better). Sweden, Denmark, Finland, Poland, Ireland and Eastern Spain (Cataluna) are very pro-immigrant regions. In Germany there is a clear divergence between the West and the East, with Eastern Germany being significantly less positive towards immigration than the West. In the next sections we present the results of our empirical analysis and investigate whether crime victimization and perception have been affected by the recent immigration waves in European regions in the 2000s using our three alternative estimation strategies. We first start with the baseline fixed effects model. TABLE 5 AROUND HERE 16

18 4.2 Baseline Fixed Effects Estimations We estimate the fixed effects model in (1) adopting a linear probability model specification. Since we are interested in the average effect of immigration on crime victimization, this linear estimator is preferable since it allows consistency without requiring a specific distributional assumption of the error term as in the case of Probit and Logit (for a similar approach see Angrist and Evans, 1998). The resulting heteroskedasticity is accounted for by clustering. We use alternative definitions of immigrant and different measures of the proportion of immigrants by region calculated from ESS and LFS data. Since the ESS and the LFS provide different level of regional disaggregation by country, the regions are defined according to different NUTS criteria in each country in order to have a large enough number of observations by geographical unit. We end up having 115 western European regions 6 with a number of observations per region that goes from a minimum of 207 for the German region of Hamburg, to a maximum of 4451 for the Belgian Flemish region. The first row of table 6 reports the effect of immigration penetration in region of residence on the probability of being a crime victim, using alternative definitions of immigrant (born abroad and non-national) and both ESS and LFS data. TABLES 6 AND 7 AROUND HERE Columns (1) to (3) report the effect of immigration on crime victimization using ESS data. Here an immigrant is defined, respectively, as a non-national, a born-abroad and a born outside Europe. In all cases the coefficient of immigration is not significant. Models in columns (4) to (7) use LFS measures of regional immigration. These are, respectively, non-national, non- European national, born abroad and born outside Europe. Here the observations drop by around 10% because of the missing data for Germany, Luxembourg and Italy 7. The only measure which is weakly significant (at the 10% level) is LFS non-national, whose coefficient would suggest that a ten percentage points increase in immigration (i.e. a quite large positive shock) is associated with only around 0.16% increase in the likelihood of being a crime victim. 6 We use NUTSII for AT, CH, DK, FI, IE, NO, PT, SE and NUTSI for BE, DE, ES, FR, IT, LU, NL, GR, GB. We drop two regions, i.e DE5 (Bremen) and DEC (Saarland) because the number of observations are lower than 200. In all models estimated on LFS data we drop Germany, Luxembourg and Italy because of missing data. 7 Note that the estimates on ESS data are robust when we drop Germany, Luxembourg and Italy from the ESS sample. 17

19 In each column we control for educational attainment, degree of urbanization of local area, gender, age, age squared and if the main source of income is financial. The latter is a measure of the respondent s wealth. Standard errors are clustered by regions. Overall, the estimates do not point to a significant effect of immigration on criminality. Males are more likely to be crime victims than females (typically by around 1%), whereas if the main source of income is financial the likelihood of being a crime victim increases by around 7%. As regards the other controls, in a typical regression the further from big city the individual is, the lower the probability of being a crime victim, with rural areas having around a 8% lower probability than big cities. Education level is significant, with higher education being correlated with a higher likelihood of being a crime victim. In addition, crime victimization decreases with age. Table (7) reports the estimation of equation (1) without including regional fixed effects. In this case, all specifications report a significant albeit very small effect of regional immigration on the likelihood of being crime victims. A one percent increase in immigration is found to rise crime victimization in the range spanning from 0.02% to 0.06%. The comparison of the results in tables (6) and (7) may help explaining why some commentators view the increase in immigration as being related to criminality. Indeed, regions with more immigrants are also regions where it is slightly more likely to be crime victims. However, this spurious relationship is explained by unobserved regional characteristics that both attract immigration and affect criminality, rather than pointing to a causal relationship from immigration to crime. Tables 8 and 9 report the effect of immigration on crime perception (or crime fear) where the latter is defined, respectively, as feeling unsafe or very unsafe when walking alone after dark, or just very unsafe. In other words, the tables account for different degrees of crime perception by the respondents. Here we find signs of a significant positive effect of immigration on crime perception, although the effect is not robust across all definitions of immigration. In particular, we find a significant effect of born abroad and born in non-european countries immigration on the very unsafety feeling in table 9, but only when using the LFS measure. The LFS born abroad measure is also significant in table 8. TABLES 8 AND 9 AROUND HERE As regards the controls, in general males feel safer when walking alone after dark in local 18

20 area (around 18% more than females) together with more educated respondents and those living in less urbanized areas. In addition, as expected, crime perception increases with age. 4.3 IV Estimations Using a Second Measurement of Immigration as Instrument In some cases, the assumptions outlined in section 3.2 may be too restrictive and the fixed effects estimates presented above may still be biased. A possible solution is to use instrumental variables. Table 10 present a set of IV models where immigration penetration by region is instrumented by using a second measurement of regional immigration provided by an alternative data source, as discussed in section 3.3. Assuming both measures are affected by measurement error and correlated with the true unobservable measure of regional immigration, when the errors are uncorrelated we may use one measurement as instrument for the other. TABLE 10 AROUND HERE The first three columns of table 10 provide a set of IV estimates with fixed effects, where the definition of immigrants is respectively non-national, born abroad, and born outside Europe. The migration penetration is calculated using ESS data whereas analogous measures from LFS data are used as instruments. In all cases the first stage regressions indicate that the instrument is highly correlated with the potentially mis-measured regressor with t-statistics in the order of 15. In all cases the estimates confirm what found in section 4.2, i.e. no significant effect of migration on crime. In addition, the effect becomes significant when we drop regional fixed effects in column 4 (where immigrants are defined as born abroad), like before. Tables 11 and 12 provide a similar set of estimates considering crime perception instead of crime victimization. As before, the first table consider individuals who feel unsafe and very unsafe when walking alone after dark, and the second table consider individuals who feel very unsafe only. As in the previous section there are some indications of a significant and positive effect of migration on crime perception, but the effects are not robust across alternative definitions of migrants. TABLES 11 AND 12 AROUND HERE 19

21 4.4 IV Estimations Using Supply-Push Factors as Instruments The model in differences discussed in section 3.3 is the most general since it does not require any restrictive assumption as regards omitted variables and measurement errors. Equation (10) is estimated by instrumenting the change in regional immigration with the exogenous supply-push factors given by (9). Immigrants flow areas are defined using the United Nations classification of composition of macro geographical regions (M49 code). We end up having N = 21 possible migrants origins that include the major sub-continental macro areas. The predetermined share of immigrants by geographical areas of origin in each European country is calculated using the Database on Immigrants in OECD Countries (DIOC), provided by the OECD. The DIOC contains information on several demographical dimensions (including country of birth and citizenship) of the population of 28 OECD countries in 2000, i.e. prior to the timeframe of our analysis. Since the DIOC does not contain any information on the distribution of immigrants across regions within each country, we assume that the proportion of immigrants by areas of origin is common to all regions in each country. Table 13 provides a set of regressions in differences where the data has been collapsed at NUTSI regional level. The first column presents a simple OLS regression in differences where the victimization rate by regions is regressed on the log change of immigration on population, considering migrants from outside Europe only (i.e. those affected by supply-push factors). Here the coefficient of the log change in immigration is found positive but not significant. TABLES 13 AND 14 AROUND HERE Column 2 presents the IV estimation where the change in immigration is instrumented by means of (9) defined at country level. In other words, in this case the instrument is constructed by considering all migration changes from a specific flow area taking place in all countries other than the region s. The effect of immigration on crime victimization is still not significant. Column 3 provides an analogous IV estimate but with instrument (9) defined at the regional instead of the country level. In this case the instrument is built by considering all migration changes from a specific flow area taking place in all regions other than the one for which the instrument is constructed. The effect of immigration on crime is again not statistically significant. Columns 4 and 5 display the first stage regressions for, respectively, the models in 20

22 columns 2 and 3. In both cases the instrument is positively and significantly correlated with the migration measure. Table 14 display similar results for the proportion of regional citizens feeling unsafe or veryunsafe when walking alone after dark. Here, again, we do not find any significant effect of regional immigration on crime perception. 4.5 Crime Perception and Attitude Towards Immigration All identification strategies adopted above suggest no significant effect of immigration on crime victimization and perception in Western Europe. However, crime perception measured by feeling of insecurity is a significant predictor of the attitude of respondents towards immigration, controlling for educational attainment and degree of urbanization. Table 15 provides some evidence of the implications of crime victimization and perception on the attitude of natives towards immigrants. Here we consider two different measures of attitude that can be potentially affected by crime victimization and fear. The dependent variable in columns (1) and (3) is whether immigrants make country worse according to respondents (i.e. the variable is a dummy equal to 1 for respondents who answer less than or equal 4, on a scale from 0 to 10, from worse to better). The dependent variable in columns (2) and (4) indicates whether many or some immigrants from poorer countries outside Europe should be allowed to come and live in the country (as opposed to few or none). TABLE 15 AROUND HERE The regressions are estimated both on individual data (columns 1 and 2) and on data collapsed at regional level (columns 3 and 4). The linear probability models in the first two columns have the disadvantage of not controlling for individual fixed effects. This may potentially harm our estimates since, differently from all previous models above, the regressors of interest here are defined at the individual level, rather than at the regional level. The estimates on regional data in the last columns do instead control for regional unobservables, as well as country-specific time dummies as in the previous regressions. All models point to a significant and positive correlation of crime perception on the belief that immigrants make the country a worse place to live. According to model (1), feeling unsafe increases by 12% the probability of seeing immigration as making the country worse. According 21

23 to model (3) instead, a 1% increase of individuals who feel unsafe in the region induces a 0.4% increase in negative attitude towards immigrants. Crime perception also significantly affects the attitude towards the openness of the country for immigrants coming from poor regions outside Europe. Here the number become -8% and -0.3%, respectively. Crime victimization is only significant in the first column, where having been a crime victim increases by almost 2% the probability of seeing immigration as making the country worse. Similar findings are provided when controlling for migration penetration by region. Overall, these results point to a clear and robust effect of crime perception on the attitude of natives versus immigration but they are at odds with the lack of a causal effect of immigration on crime victimization and perception. Nevertheless, these findings may be partly explained by the cross-sectional correlation between the two phenomena displayed in table 7. The public perception of the effect of immigration on crime may be misled by the observation that regions with higher migration penetration are generally those characterized by higher crime victimization, failing to impute such correlation to unobserved third factors and therefore to distinguish correlation from causation. However, the cross sectional correlation is too low to fully explain these findings. A natural question arises as regards the role of the media in such misrepresentation. Previous studies such as Fitzgerald, Curtis, and Corliss (2009) show how the link between fear of crime and worries about immigration is stronger in election years. This suggests that the public perception of the consequences of immigration in host societies may be strongly influenced by its media misrepresentation and by the political debate. 5 Concluding Remarks I presented an empirical analysis of the relationship between immigration and criminality patterns by exploiting the recent immigration waves in western European regions. To my knowledge this is the first attempt to investigate this topic with a European focus, providing some insights on crime victimization as well as crime perception. The idea behind the analysis is to match individual crime victimization and crime perception data with immigration penetration in European regions and to estimate the effect of changes in immigration patterns on the likelihood of being a crime victim and on the subjective representation of criminality in 22

24 local area of residence. The sample is constituted by individual level repeated cross sectional data collected in 17 Western European countries by the four waves of the European Social Survey (ESS) covering a large number of European countries every two years from 2002 to Such time-frame was characterized by large immigration inflows for most western European countries. In addition, we rely on a number of additional data sources on regional immigration i.e. Census data, Eurostat Labour Force Survey and the Database on Immigrants in OECD Countries (DIOC), provided by the OECD. The major difficulty in the analysis attains to the construction of a reliable measure of immigration penetration in each European region\year since regional immigration penetration may be measured with error by using survey data. We use three alternative research strategies to deal with measurement error and omitted variable bias. First we simply control for regional and country specific time fixed effects. This is the most restrictive strategy: under certain assumptions we are able to estimate the causal effect of immigration on crime victimization and perception. Alternatively, assuming a more general random error in the immigration penetration measure, we instrument migration measured with ESS data using a second measurement from Eurostat LFS data. Assuming both measures are correlated with the true unobservable measure of regional immigration and that both are affected by measurement error, in the quite general case in which the two errors are uncorrelated we may use one measurement as instrument for the other. In this case, omitted factors are still accounted for by including fixed effects. Finally, we provide a set of IV estimates in a specification in differences by instrumenting migration using exogenous supply-push changes in migration patterns that accounts both for measurement error and omitted variable bias. This is the most general approach, where both measurement errors and omitted time-varying factors are dealt with. The empirical results consistently point to a statistically not-significant effect of immigration on crime victimization in destination regions when we control for unobservable regional characteristics. Immigration is characterized by a significant although small positive correlation with crime only when we exclude the regional fixed effects from the linear probability model. In other words, the correlation between immigration and crime is likely to be induced by third unobservable factors at the regional level. In addition, the analysis provides some evidence 23

25 of immigration inducing an increase in crime perception (or crime fear), but the result is not robust across alternative definitions of immigration. According to our findings, a supposed relationship between immigration and crime in western Europe may originate from a wrong interpretation of the casual empirical evidence available to the public. The observation by some commentators that criminality is higher where immigration is more frequent is biased by the omission of relevant factors at the regional level that are likely to influence both immigration and crime. This may in part explain the estimated robust positive correlation between crime perception and the attitude of natives versus migration, with higher crime fear found to induce a worse disposition of European citizens even after accounting for unobservable factors at regional level. Such findings call for a better public assessment of the immigration phenomenon in western Europe and for a more transparent approach to the discussion about the costs and benefits of immigration in host countries. 24

26 References Angrist, J., and W. Evans (1998): Children and Their Parents Labor Supply: Evidence from Exogenous Variation in Family Size, American Economic Review, 88, Bassetti, T., L. Corazzini, D. Cortes, and L. Nunziata (2012): Do Immigrants Make Us Safer? A Model on Crime, Immigration and the Labor Market, University of Padua, unpublished. Becker, G. S. (1968): Crime and Punishment: An Economic Approach, Journal of Political Economy, 76, 169. Bell, B., S. Machin, and F. Fasani (2010): Crime and Immigration: Evidence from Large Immigrant Waves, IZA Discussion Papers 4996, Institute for the Study of Labor (IZA). Bianchi, M., P. Buonanno, and P. Pinotti (2008): Immigration and crime: an empirical analysis, Temi di discussione (Economic working papers) 698, Bank of Italy, Economic Research Department. Boeri, T. (2010): Immigration to the Land of Redistribution, Economica, 77(308), Borjas, G. (1999): The economic analysis of immigration, in Handbook of Labor Economics, ed. by O. Ashenfelter, and D. Card, pp Elsevier, Amsterdam. Borjas, G. J. (1994): The economics of immigration, Journal of economic Literature, 32, Borjas, G. J., J. Grogger, and G. H. Hanson (2010): Immigration and the Economic Status of African-American Men, Economica, 77, Butcher, K. F., and A. M. Piehl (1998): Cross-city evidence on the relationship between immigration and crime, Journal of Policy Analysis and Management, 17, (2007): Why are Immigrants Incarceration Rates so Low? Evidence on Selective Immigration, Deterrence, and Deportation, NBER Working Papers 13229, National Bureau of Economic Research, Inc. 25

27 Card, D. (2001): Immigrant Inflows, Native Outflows, and the Local Labor Market Impacts of Higher Immigration, Journal of Labor Economics, 19(1), Card, D., C. Dustmann, and I. Preston (2009): Immigration, Wages, and Compositional Amenities, NBER Working Papers 15521, National Bureau of Economic Research, Inc. Durbin, J. (1954): Errors in Variables, Review of the International Statistical Institute, 22, Dustmann, C., T. Frattini, and I. P. Preston (2008): The Effect of Immigration along the Distribution of Wages, CReAM Discussion Paper Series 0803, Centre for Research and Analysis of Migration (CReAM), Department of Economics, University College London. Fitzgerald, J., K. A. Curtis, and C. L. Corliss (2009): Anxious Publics: Worries over crime and immigration, in Proceedings of the Annual Meeting of the International Society for Political Psychology, Dublin. Trinity College. Freeman, R. (1999): The Economics of Crime, in Handbook of Labor Economics, ed. by O. C. Ashenfelter, and D. Card, vol. 3, pp Elsevier. Hale, C. (1996): Fear of crime: A review of the literature, International Review of Victimology, 4, Jackson, J., and M. Stafford (2009): Public Health and Fear of Crime: A Prospective Cohort Study, British Journal of Criminology, 49, Lee, M. T., R. Martinez, and R. Rosenfeld (2001): Does Immigration Increase Homicide? Negative Evidence From Three border Cities, The Sociological Quarterly, 42(4), Lemos, S., and J. Portes (2008): New Labour? The Impact of Migration from Central and Eastern European Countries on the UK Labour Market, IZA Discussion Papers 3756, Institute for the Study of Labor (IZA). Mastrobuoni, G., and P. Pinotti (2010): Migration Restrictions and Criminal Behavior: Evidence from a Natural Experiment, paper presented at the CSEA 2010 workshop on migration. 26

28 Mayer, N., and P. Perrineau (1992): Why do they vote for Le Pen?, European Journal of Political Research, 22(1), Moehling, C., and A. M. Piehl (2007): Immigration and Crime in Early 20th Century America, NBER Working Papers 13576, National Bureau of Economic Research, Inc. Munshi, K. (2003): Networks In The Modern Economy: Mexican Migrants In The U.S. Labor Market, The Quarterly Journal of Economics, 118(2), Norris, P. (2005): Radical Right: Voters and Parties in the regulated market. Cambridge University Press. Reiersol, O. (1941): Confluence analysis by means of lag moments and other methods of confluence analysis, Econometrica, 9, Spenkuch, J. (2011): Understanding the impact of immigration on crime, University of Chicago, unpublished. 27

29 Figure 1: Immigrants (non-nationals and born abroad) as percentage of resident population in Europe from alternative data sources Note: figures are drawn by the author using, respectively, data from the European Social Survey from 2002 to 2008, the European census in 2001, and Eurostat European Labour Force Survey from 2002 to Regions are NUTS 2 level. 28

30 Figure 2: Immigrants (born abroad) as percentage of resident population in Europe from alternative data sources and using alternative definitions Note: figures are drawn by the author using, respectively, data from the European Social Survey from 2002 to 2008 and Eurostat European Labour Force Survey from 2002 to Regions are NUTS 2 level. 29

31 Figure 3: Victims of burglary or assault in the last five years in Europe as percentage of resident population in Europe Note: the figure is drawn by the author using data from the European Social Survey from 2002 to Regions are NUTS 2 level. 30

32 Figure 4: Percentage of respondents feeling safe or very safe when walking alone after dark in local area Note: the figure is drawn by the author using data from the European Social Survey from 2002 to Regions are NUTS 2 level. 31

33 Figure 5: Average score to question Is country made a worse or better place to live by people coming to live here from other countries?, where answer can go from 0 (worse) to 10 (better) Note: the figure is drawn by the author using data from the European Social Survey from 2002 to Regions are NUTS 2 level. 32

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