do immigrants cause crime?

Do Immigrants Cause Crime?

Milo Bianchi1 Paolo Buonanno2 Paolo Pinotti3

1Paris School of Economics

2University of Bergamo

3Bank of Italy

2008 NASM, June 19, 2008

Paolo Buonanno (University of Bergamo) Do Immigrants Cause Crime? 2008 NASM, Pittsburgh, June 19, 2008 1 / 39

Introduction Motivation

Motivation

Two main concerns on the impact of immigration in receiving countries:I competition to low-skilled workersI widespread concerns that immigrants may increase crime

Economics literature has devoted much attention to effect of migration onnatives labor outcomes (surveys: Borjas, 1994; Card, 2005 and Freeman,2006)

very scant attention on the effect of migration on crime

however, crime in the main issue in the political debate on migrationrestrictions



Crime vs. labor market concerns in OECD countries

AUS

DEU−WDEU−E

GBR

USA

AUT

IRL

NOR

SWE

NZL

CAN

JPN

ESP

FRA

POR

DNK

CHE

FIN

ITA

020

4060

8010

0%

con

cern

ed w

ith c

rime

0 20 40 60 80 100% concerned with jobs



Motivation (cont’d)

From a more theoretical viewpoint, there are several reasons to expect asystematic relation between immigration and crime

I Immigrants and natives may have different crime propensity (due differentearnings opportunity, cost of crime, attitude towards risk..)

I Immigrants are very concentrated spatially

I Immigrants have different cultural background

I Immigrants may compete on the labor market and induce natives to commitcrime (Borjas et al. 2006)

But the direction of these effects is a priori unclear


Introduction Contibution of this paper

Contribution of this paper

1 We document patterns of migration & crime across Italian provinces during1990-2003

I crime is the issue for migration restrictions

I very recent but very fast migration trend

I mainly determined by shocks in neighboring countries (fall of Soviet Union andBalkan Wars) origin

I frequent regularizations allow snapshots of illegal migration

2 Address causality from migration to crime

I High mobility of immigrants within the destination country ⇒ migration andcrime simultaneously determined in equilibrium

I Solution: use IV that predicts migration to Italy based on migration towardother destinations



Review of the literature

There exist only a few studies about the US

Butcher and Piehl 1998: across cities, no significant effect of immigrationinflows

Butcher and Piehl 2005: at the individual level immigrants commit less crimethan natives (ceteris paribus)

Moehling and Piehl 2007: in the early 1900s, immigrants were more criminalthan natives



Overview of the main results

Strong and significant correlation between immigration and total crimes, aswell as for most specific types of crime

I Unaffected by controlling for other determinants of crime

However, after taking into account the endogeneity of immigrant population:

I The causal effect of migration on total crimes is not different from zero

I The effect is still significant for some specific types of crime: murders androbberies (approx 1% of total crimes)


Data Immigration

Measuring immigration

Total immigrants = regular + irregular

Regular: use residence permits (source: Police administrative records;Ministero dell’Interno)

Irregular: use applications for regularization in 1995, 1998 and 2002 (source:Police administrative records) trends

I clear incentives for immigrants to declare their irregular status (almost 100%acceptance rate)

I strongly correlated with apprehension rate across provinces (64-81% in allyears)

However, available only during regularization years


Data Immigration

Measuring immigration (cont’d)

The ratio ν̃it = IRREGULARit

REGULARitis approximately constant across provinces in all

regularization years hist

Then, log-changes of total immigrants may be measured by log-changes ofregular immigrants

TOTALit = REGULARit + IRREGULARit

TOTALit = REGULARit(1 + eνit)

ln TOTALit = ln REGULARit + νit

ols scatter

In addition, regressions will always include province- and year- FEs, so it is

enough that E (X it ; ν it) = 0, with ν it = ν it − ν i� − ν�t


Data Crime

Measuring crime

Crimes reported by the police to the judiciary authority (source: ISTAT)

Actual crime remains unobserved ⇒ assume it is proportional to observedcrime

Way to deal with under-reporting: take logs with province- and year-fixedeffects (e.g. Ehrlich, 1996; Levitt, 1996)

ln CRIME∗it = Ci + Ct + ln CRIMEit

Distinguish among different types of crime: TOTAL, violent (MURDER,ASSAULT, RAPE ), property (THEFT, CAR, ROBBERY ) and DRUG


Data Descriptive stat

Migration and crime across provinces

[0.53,1.58](1.58,3.51](3.51,4.75](4.75,7.87]

% immigrants over population

[2.22,3.12](3.12,3.63](3.63,4.16](4.16,7.26]

% crimes over population


Data Descriptive stat

Migration and crime over time

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

total number of reported crimes, thousands (left scale)total number of permits, thousands (right scale)


OLS OLS

OLS regressions

We keep logs and fixed effects to reduce measurement error in migration andcrime

We estimate

ln CRIMEit = β ln IMMIGRANTSit + β1 ln POPit + γ′Xit + FEi + FEt + εit ,

where Xit = log of income, unemployment, % of young males, urbanization,clear-up rate, partisanship of local government

desc corr


OLS OLS

OLS results: baseline

Total Crime Murder Assault Rape Robbery Theft Car Theft DruglnIMMIGRANTS 0.0997∗∗∗ 0.2377∗∗∗ -0.0736∗ -0.0211 0.1753∗∗∗ 0.1234∗∗∗ 0.0427∗ -0.1683∗∗∗

(0.0220) (0.0672) (0.0386) (0.0422) (0.0333) (0.0198) (0.0232) (0.0347)

lnPOP 1.2548∗∗∗ 2.7254∗∗∗ 2.2977∗∗∗ 1.3101 2.8672∗∗∗ 2.4929∗∗∗ 1.1871∗∗∗ -0.7717(0.3434) (1.0541) (0.7686) (0.8779) (0.5952) (0.3285) (0.4353) (0.7174)

Obs. 1,330 1,330 1,330 1,330 1,330 1,330 1,330 1,330Provinces 95 95 95 95 95 95 95 95Prov. FE yes yes yes yes yes yes yes yesYear FE yes yes yes yes yes yes yes yesR-squared 0.89 0.47 0.59 0.69 0.83 0.87 0.83 0.62F-stat. 16.52 10.12 26.18 107.4 20.98 17.98 30.91 17.52


OLS OLS

OLS results: controls

Total Crime Murder Assault Rape Robbery Theft Car Theft DruglnIMMIGRANTS 0.1017∗∗∗ 0.1895∗∗ -0.0082 0.0161 0.0806∗∗ 0.0926∗∗∗ 0.0636∗∗ -0.0919∗

(0.0266) (0.0784) (0.0500) (0.0597) (0.0346) (0.0188) (0.0282) (0.0487)

lnPOP 1.3738∗∗∗ 3.9104∗∗∗ 1.3055 1.2818 4.5099∗∗∗ 2.2345∗∗∗ 1.7186∗∗∗ -0.2177(0.3855) (1.2629) (0.9335) (1.1071) (0.6677) (0.3563) (0.5536) (0.9061)

CLEAR -0.3446 0.0033 -0.8560∗∗∗ -0.0196 -0.4613∗∗∗ -2.9944∗∗∗ -0.5322∗∗ -0.0286(0.3336) (0.0590) (0.1380) (0.1026) (0.1159) (0.3619) (0.2403) (0.2894)

lnGDP 0.1431 -0.1220 -0.1308 -0.5313 -0.0940 0.1100 0.5697∗∗∗ 0.3420(0.1234) (0.3873) (0.2610) (0.3631) (0.2241) (0.1310) (0.1831) (0.2789)

UNEMP -0.0045 -0.0120 0.0132∗ -0.0013 -0.0180∗∗∗ -0.0063∗∗ -0.0053 0.0138∗(0.0030) (0.0078) (0.0073) (0.0088) (0.0053) (0.0025) (0.0039) (0.0071)

PARTISAN 0.0045 -0.0038 0.0441∗∗∗ 0.0212 0.0057 0.0065 -0.0026 0.0207∗(0.0052) (0.0125) (0.0112) (0.0136) (0.0081) (0.0047) (0.0064) (0.0108)

URBAN 0.2710∗ -0.0231 -0.3230 0.3495 0.0956 0.3561∗∗ 0.4199∗ -0.9826∗∗∗(0.1513) (0.5357) (0.2428) (0.5200) (0.2791) (0.1564) (0.2281) (0.3604)

MALE1529 8.0979∗∗∗ -19.2557∗∗∗ 18.2318∗∗∗ -6.4616 -17.0009∗∗∗ 3.1706 11.2214∗∗∗ 27.8597∗∗∗(2.5463) (6.6419) (5.2524) (6.5273) (4.1689) (2.2295) (3.0954) (5.2653)

Obs. 1,045 1,045 1,045 1,045 1,045 1,045 1,045 1,045Provinces 95 95 95 95 95 95 95 95Prov. FE yes yes yes yes yes yes yes yesYear FE yes yes yes yes yes yes yes yesR-squared 0.91 0.43 0.53 0.68 0.83 0.90 0.80 0.26F-stat. 16.11 4.18 20.78 59.21 20.98 19.48 27.33 13.17


Causality IV

Problems with OLS

High mobility of immigrants within the destination country

Observed distribution is equilibrium between supply-push factors incountries of origin and demand-pull factors in provinces of destination

I Push factors = crises, wars, weather, etc.: exogenous to province ofdestination

I Pull factors = labor mkt, housing mkt, etc.: correlated with crime

Therefore, even after controlling for other factors, it is likely that

E (ln IMMIGRANTSit , εit) 6= 0


Causality IV

Solution

We build a measure of exogenous component of immigrants’ growth based on

1 supply-push factors in origin country

2 beginning-of-period distribution by nationality in destination province

Taking first differences of estimating equation:

∆ ln IMMIGRANTSit ≈∑n∈Nit

IMMIGRANTSnit−1

IMMIGRANTSit−1︸︷︷︸predetermined

×∆ ln IMMIGRANTSnit︸︷︷︸

endogenous

For endogenous term use outcome based measure of supply-push factors


Causality IV

Solution (cont’d)

Previous papers use total immigrants by origin country (e.g. Card 2001,Ottaviano & Peri 2006)

Problem:

I if distribution by nationality is homogeneous across provinces, no instrumentfit in the first stage

I if concentrated, total inflows could still depend on province-specificdemand-pull factors maps herf

Alternative solution: use migration toward other European countries


Causality IV

Immigration to Italy and to the rest of Europe, 1991-2001

ALB

BRA

CHNDZA

EGY

FYR

IND

MOR

NGA

PHL

PKT

ROM

TUN

0.5

11.

52

2.5

log−

chan

ge o

f im

mig

rant

s in

Ital

y

−.5 0 .5 1 1.5 2 2.5 3 3.5

log−change of immigrants in the rest of Europe


Causality IV

Instrument for log-changes of immigrants, 1991-2001

Data: 1991 and 2001 rounds of country Census

Availability: 11 destination countries × 13 origin countries (approx 50% ofmigration to Italy during 1991-2001) share

Use

∆ ln IMMIGRANTS IVit =∑n∈Nit

IMMIGRANTSnit−1

IMMIGRANTSit−1×∆ ln IMMIGRANTSn

EUROPE


Causality IV

Predicted and actual log-changes of immigrants

−1

01

23

actu

al lo

g−ch

ange

of p

erm

its

−.5 0 .5 1 1.5

predicted log−change of permits

∆ ln IMMIGRANTS = 0.665(0.100)

+ 0.687(0.181)

∆ ln IMMIGRANTS IV + ε

F-stat=14.41


Causality IV

Ten-year differences regressions: OLS

Total Crime Murder Assault Rape Robbery Theft Car Theft Drug∆ ln IMMIGRANTS 0.1972∗∗∗ 0.5063∗∗∗ -0.1009 0.1770 0.3771∗∗∗ 0.2012∗∗∗ 0.0036 -0.3332∗∗∗

(0.0500) (0.1619) (0.0979) (0.1106) (0.0812) (0.0486) (0.0724) (0.0840)

∆ ln POP 0.3912 -0.0166 1.8729 -2.5019 1.0991 1.9953∗∗ 1.6573 -.0272(.8572) (2.5428) (2.0755) (2.3786) (1.4885) (0.7946) (1.2313) (1.7445)

obs. 95 95 95 95 95 95 95 95R-squared 0.14 0.12 0.01 0.03 0.18 0.23 0.02 0.10F-stat. 9.83 5.47 0.62 1.42 14.57 18.38 1.03 8.85


Causality IV

Second-stage results, 1991-2001

Total Crime Murder Assault Rape Robbery Theft Car Theft Drug∆lnIMMIGRANTS 0.1617 1.4588∗∗∗ -0.2156 0.0767 0.9478∗∗∗ 0.2132 -0.0875 -0.3251

(0.1576) (0.4551) (0.3242) (0.3547) (0.2999) (0.1325) (0.2134) (0.2385)

∆lnPOP 0.5796 -5.0825 2.4830 -1.9688 -1.9366 1.9316∗ 2.1421 -0.0703(1.1147) (3.2705) (2.4899) (2.7404) (1.8984) (0.9928) (1.6243) (1.8270)

Obs. 95 95 95 95 95 95 95 95F-stat. 9.83 5.47 0.62 1.42 14.57 18.38 1.03 8.851st stage F-stat. 13.75 13.75 13.75 13.75 13.75 13.75 13.75 13.75


Causality IV

Magnitudes

For murders and robberies, IV coefficients are much larger than OLS

I Unobserved demand factors which have opposite effects on immigration andon these types of crime (?)

I If underreporting of crime is positively correlated with underreporting ofimmigration, we may expect the difference IV-OLS to be larger for murdersand robberies since these are the categories of crime with least underreporting

Consider the effect of st.dev. ↑ in the log-change of immigrant pop. during1991-2001 (+54%)

I murders: +75% ⇒ +15 out of 20 in 1991

I robberies: +49% ⇒ +202 out of 413 in 1991


Causality IV

Magnitudes

However, murders and robberies represent only 1% of all criminal offenses inour sample.

I Given that migration does not significantly affect other types of crime, thetotal number of criminal offenses is also unaffected.

Offender nationalitymurder robbery

italian foreign italian foreignVictim italian 86.8% 10.2% 63.9% 30.9%

nationality foreign 24.7% 74.5% 24.6% 71.8%


Discussion Discussion

Discussion

Irregular component of migration is extremely important especially in Italy

I Italian migration policy = rationing of permits + weak enforcement +frequent regularizations ⇒ large irregular component

I Irregulars account for 30%-40% of immigrant population

Irregular commit approx 80% of all immigrants’ crime

I Crime rate of regular immigrants is similar to that of natives



Discussion (cont’d)

Strong correlation between regulars and irregulars:

I Allows to proxy variation of total immigrants using only the regular ones

I Prevents from disentangling the separate effect of regular and irregularimmigrants (collinearity)

Our estimates are conditional on the current ratio of regulars/irregularsI any change to migration restriction would likely affect also the distribution by

legal status

I difficult to do policy comparative statics without distinguishing the effect ofregulars and irregulars

I nice topic for future research..



Future work

Decompose residence permits by nationality (causality?)

Individual data on incarceration (using recent collective pardon immigrantsappear less recidivist than natives)

Media bias


Immigration by area of origin

01

23

4gr

owth

rat

e of

per

mits

sin

ce 1

991

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

East Europe AsiaNorth Africa Total

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Regular and irregular migration to Italy, 1990-2003

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

% of resident population

0

200

400

600

800

1000

1200

1400

1600

1800

2000

thousands of permits

legals (left scale) illegals (left scale) total permits

Dini

Turco - Napolitano

Bossi - Fini

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Irregular to regular ratio

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Total and irregular immigrants

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

total number of reported crimes, thousands (left scale)total number of permits, thousands (right scale)

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Total and regular immigrants: OLS

The dependent variable is log of total immigrants (residence permits + applications for regularization)

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

ln REGULAR 1.013∗∗∗ 1.025∗∗∗ 0.990∗∗∗(0.017) (0.013) (0.016)

∆ ln REGULAR 0.973∗∗∗(0.038)

R-squared 0.97 0.98 0.97 0.87year 1995 1998 2002 1995-2002

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Descriptive statistics

Variable Mean Std. dev. Min Maxln IMMIGRANTS 8.36 1.16 5.35 12.59ln POP 13.01 0.70 11.41 15.18URBAN 14.62 20.15 0 88.11MALE 1529 10.55 1.39 6.71 13.81UNEMP 10.54 7.16 1.70 33.30ln GDP 9.55 0.26 8.94 10.11ln TOTAL 9.56 0.94 6.88 12.74ln MURDER 1.75 1.10 0 5.56ln ASSAULT 5.15 0.89 1.10 7.96ln RAPE 2.41 0.89 0 5.69ln ROBBERY 4.80 1.31 1.10 9.35ln THEFT 8.97 1.02 5.69 12.33ln CAR 6.80 1.41 3.71 10.93ln DRUG 5.50 0.96 2.40 8.39CLEAR (total) 30.54 10.47 9.20 82.75CLEAR (murder) 64.53 31.62 0 100CLEAR (assault) 83.61 12.80 21.58 100CLEAR (rape) 83.56 17.03 0 100CLEAR (robbery) 31.10 13.10 0 96.46CLEAR (theft) 6.78 3.15 1.51 30.06CLEAR (car) 6.59 5.35 0 56.05CLEAR (drug) 95.77 5.61 37.71 100

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Correlation matrix

ln IMMIGRANTS ln POP ln TOTAL ln MURDER ln ASSAULT ln RAPE ln ROBBERY ln THEFT ln CAR ln DRUG MALE 1529 ln GDP UNEMP URBANln IMMIGRANTS 1.000ln POP 0.708 1.000ln TOTAL 0.748 0.943 1.000ln MURDER 0.352 0.724 0.691 1.000ln ASSAULT 0.679 0.763 0.815 0.525 1.000ln RAPE 0.704 0.700 0.742 0.498 0.716 1.000ln ROBBERY 0.671 0.915 0.925 0.768 0.729 0.706 1.000ln THEFT 0.751 0.931 0.983 0.662 0.781 0.712 0.920 1.000ln CAR 0.542 0.910 0.908 0.797 0.697 0.630 0.930 0.901 1.000ln DRUG 0.688 0.820 0.860 0.507 0.708 0.641 0.769 0.848 0.759 1.000MALE 1529 -0.329 0.256 0.133 0.430 0.037 -0.159 0.200 0.118 0.365 0.054 1.000lnGDP 0.549 0.072 0.179 -0.321 0.254 0.205 0.007 0.225 -0.120 0.220 -0.631 1.000UNEMP -0.348 0.137 0.073 0.492 -0.034 0.028 0.221 0.022 0.325 0.003 0.598 -0.859 1.000URBAN 0.442 0.475 0.572 0.330 0.437 0.425 0.524 0.581 0.490 0.528 -0.056 0.216 0.058 1.000

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Distribution by nationality, 1990

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Inverse Herfindal of distribution by nationality

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Immigration to Italy and to the rest of Europe, 1991-2001

−1

01

23

actu

al lo

g−ch

ange

of p

erm

its

−.5 0 .5 1 1.5

predicted log−change of permits

destination countries: Austria, Belgium, Denmark, Finland, France, Greece,Netherlands,Norway, Spain, Sweden and Switzerland

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Migration trends by nationality

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do immigrants cause crime?

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