The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
DOI: 10.1111/j.1468-2311.2011.00697.x
Looking for a Fair Country: Features
and Determinants of Immigrants’
Involvement in Crime in Europe
LUIGI M. SOLIVETTI
Professor of Sociology, Sapienza University of Rome
Abstract: A rise in crime among immigrants allegedly occurred in Europe over the last
decades. The origin of this phenomenon is obscure, and traditional theories offer conflicting explanations. The present article addresses these problems by using data regarding the 18 main countries in Western Europe. The results show that the immigrant share
in crime figures varies greatly from country to country. This suggests that the nonnational contribution to crime is not associated with immigration per se, but with the
contexts in which immigration occurs and features of the immigration inflow. The
cross-national analysis shows, in particular, that ‘culture, respect for rights and universalism’ in the host countries are associated with lower immigrant crime.
Keywords: Europe; migration; crime
The Debate on Migration and Crime: Past and Present
The migration-crime link has long been affected by stereotypical misperceptions and has fed very emotional debates. In countries of large-scale
immigration, such as France and the US, crime rise was blamed on the
foreigners already in the late 19th to early 20th Centuries. However,
analyses conducted in the US, Canada and Australia negated a larger
incidence of crime in the immigrant population. For the US, for example,
data showed largely varying crime rates in different groups of foreignborns. Their average rates, however, were lower than those for white
natives and much lower than those for coloured natives. In Europe,
studies conducted in large immigration countries – Germany, Switzerland,
France, Belgium and England – during the 1950s to 1960s showed that
immigrant crime was lower or similar to native rates (on the entire subject,
see Marshall (1997); Tonry (1997a); Solivetti (2010)).
However, since the 1980s a new scenario has emerged. Fresh studies in
several European countries (Andersson 1984; Junger-Tas 1985; Natale
1988; Junger 1989; Albrecht 1993; Hebberecht 1997; Martens 1997;
Tournier 1997; Killias 1997; Suisse DFJP 2001; Lagrange 2010; O’Nolan
2011) agreed on one basic point. They showed immigrant crime rates
133
© 2011 The Author
The Howard Journal of Criminal Justice © 2011 The Howard League and Blackwell Publishing Ltd
Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK
The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
much higher (two to four times) than those observed in society at large.
Nothing similar was recorded over the ocean, since in the US – and in
Canada and Australia as well – the foreign-born crime rate remained on
average no higher than that for the natives (Yeager 1996; Reid et al. 2005;
Rumbaut et al. 2006).
Reasons behind all this are unclear. Various hypotheses have been used
to explain immigrant crime. The coarse hypothesis that all immigrants, or
at least some ethnic groups, are prone to crime, is not consistent with
current research, even though it still hovers over the man in the street.
The anomic strain theory goes back to the end of the 19th Century, when
Émile Durkheim emphasised how social change could generate excessive
expectations, followed by frustration and deviance. Recent applications of
anomie have been drawn from Robert Merton’s relative deprivation: a typical
situation for societies like the US, where socio-economic success is presented as a goal for everybody – regardless of his/her social class – whereas
chances of obtaining it are limited and dependent on class structure. To
this, Albert Cohen, Richard Cloward and Lloyd Ohlin and Peter and Judith
Blau in the US added that relative deprivation may explain not only acquisitive crime (for example, theft) but also violence, as a reaction to frustration,
not only in individuals but also in gangs of people with common ethnic and
deprivation traits. Anomie and relative deprivation have been used by
authors dealing with immigrant crime in Europe: Basdevant (1983); Killias
(1989); Segre (1993); von Hofer, Sarnecki and Tham (1997).
The hypothesis of culture conflict, first elaborated by Thorstein Sellin in
the 1930s, considers crime as the direct result of conflict between the
immigrant and native cultural codes. Immigrant crime would consist of
behaviour tolerated in the immigrant code, but punished as criminal
offences in the host country code. The hypothesis of cultural conflict – as
the cause of immigrant crime – has been used for both the European and
North-American societies by several authors: Ferracuti (1968); Samuel
and Faustino-Santos (1991); Yesilgöz (1995); Suisse DFJP (2001); Kühne
(2002).
The more recent theory of control, by Travis Hirschi, moved on from
premises contesting both anomie and culture conflict. Crime is not
ascribed to poverty, under-privileged conditions, or differences between
cultural codes. People would become deviants, instead, because they are
less restrained by social controls: like internal controls, attachment to moral
values, commitment to conventional goals (education, economic achievement), and external controls, the individual’s bonds to institutions, family,
neighbourhood, fellow workers, etc. This approach has been used by other
authors (Kaiser 1988; Villmow 1993; Albrecht 1995; Shen 2002; Francis,
Armstrong and Totikidis 2006) to explain, in particular, crime by immigrants, who often lack social and labour bonds in their host country and,
therefore, do not develop the obligations to society (external controls) that
would restrain them from committing crime.
With regard to the hypotheses explaining non-national crime, something must be said about the labelling approach. If we accept the radical
point of view of Kai Erikson, Howard Becker, and Edwin Lemert, crime
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© 2011 The Author
The Howard Journal of Criminal Justice © 2011 The Howard League and Blackwell Publishing Ltd
The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
ceases to be a quality of peculiar behaviour: it becomes a label applied by
those in power, especially agencies for social control, to powerless people.
So, non-national crime would only represent the effect of negative discrimination, and the search for crime determinants would be unsound.
Biases were, indeed, detected in the police behaviour towards immigrants
and minorities (Skogan 1990; Casman et al. 1992). Are, then, immigrant
crime rates simply the product of a police attitude in line with that made
famous in Casablanca (director M. Curtiz, 1942) by Captain Renault’s
formula: ‘Round up the usual suspects!’? It does not seem so: several
studies showed that arrests by the police are the result of concrete offending behaviour, not of ethnic biases (Walker 1987; Aalberts 1990; JungerTas 1997). Despite the dark figure, at least for serious offences, data from
victim surveys and official crime figures are correlated (Aebi, Killias and
Tavares 2002) and this shows that the latter are not arbitrary representations of crime. In addition, the distribution of offenders by ethnic group,
as perceived by the victim, corresponds to arrest data (Home Office 1989;
US Department of Justice 2006). All this seems to suggest that stereotypes
in police attitudes should not obfuscate differences in offending rates
between nationals and non-nationals.
Besides, the great majority (often 90% or more) of offences are reported
by the public and not by the police. And ethnic discrimination by the
public in reporting incidents to the police is not supported by research
(Shah and Pease 1992; Hart and Rennison 2003). Few exceptions are
recorded (Killias 1988). Moreover, any negative discrimination of nonnationals by the general public would be, at least, counterbalanced by
another phenomenon. Namely, that a substantial share of non-national
crime is committed against other non-nationals (Albrecht 1987; Kammhuber 1997) – as ethnic minority crime is usually intra-minority (Junger-Tas
1997; Smith 1997; US Department of Justice 2006). Now, people’s propensity for reporting offences to the police is negatively correlated with
their social marginality, illegal status, and fear of retaliation. Many nonnationals are characterised by these traits, and their propensity for reporting offences should be even less when offenders belong to the victim’s
ethnic group (on the entire subject, see Kidd and Chayet (1984); Clancy
and Aust (2001); Horowitz (2001)).
Discrimination against non-nationals with recourse to detention may be
more significant. There is evidence of a more extended use of pre-trial
detention in the case of non-nationals in both Europe and the US (Tonry
1997b; Hagan and Palloni 1999). This unfavourable treatment of nonnationals is usually ascribed to the higher probability that some of them
would abscond if not detained in prison. This judicial policy could result in
a distortion of the non-national share of prison populations. However, it is
possible to foresee and contain the consequences of this distortion. First, by
comparing the non-nationals’ share of the prison population with their
share of people charged with criminal offences, which, as we have seen,
seems less exposed to distortions. Second, by calculating the non-national
share of the prison population in terms of presences in prison rather than
admissions. Presences are less responsive than admissions to short periods
135
© 2011 The Author
The Howard Journal of Criminal Justice © 2011 The Howard League and Blackwell Publishing Ltd
The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
of imprisonment, that is, the measures adopted when non-nationals’ conditions advise against alternative measures.
Summing up the state of migration and crime literature, a few shortcomings emerge. In general, theoretical works have produced conflicting
explanations. In particular, the reasons for the wave of immigrant crime,
which allegedly occurred in Europe, pose an open question. And it is not
clear whether the immigrant over-representation in crime figures
regards all countries or only some of them; and in this case, why. Studies
dealing with immigrant crime determinants have analysed single countries (almost always the US). A few multi-authored volumes have investigated the situation of limited sets of western countries. However, they
neither used homogeneous indicators to measure immigrant crime in the
various countries nor conducted cross-national analyses on crime determinants even where – as in Europe – there are common sources of
criminal statistics.
Migration and Integration in Western Europe: What has Changed
Some changes that occurred over the last decades can help us to understand
the present migration-crime link. Migration towards Western Europe has
reached a magnitude that only a few people, even in Europe, are aware of.
In the 1990s, Western Europe received, on average, over 1,650,000 immigrants per year; from 2001, about 2,500,000. In the same period, the US –
the land of migration, in the world’s collective imagination – received an inflow
of about 1,000,000 per year. The two phenomena may, in some measure, be
connected. The relatively stable flow towards the US since the 1990s – which
followed a period of steady growth since the end of World War Two – has
also been the consequence of stricter controls, thus increasing the migratory
pressure on Western Europe. However, it is also true that for people coming
from, for example, Albania, Romania or the Maghreb, Western Europe is
the most obvious destination for geo-cultural reasons. What is indisputable
is that, in the second half of the 20th and early 21st centuries, Western
Europe’s non-national population steadily grew, from about four million to
about 25 million. The non-national share in all the resident population
proportionally increased, from about 1% to 6.4% (Table 1). This increase,
moreover, does not take into account those who acquired host citizenship
TABLE 1
Total Non-national Population Resident in the West European Countries – Absolute Values
(,000), and Percentages of Resident Population, at 31/12 – Years 1950–2005
1950 1960
1970
1980
1985
1990
1995
2000
2005
Absolute value 4,113 5,403 10,151 13,704 14,179 16,350 19,267 21,006 25,470
Percentage
1.4
1.7
2.9
3.8
3.8
4.3
5.0
5.4
6.4
(Source: Eurostat 2006, Eurostat data available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/
population/data/database (accessed 17 October 2011).)
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The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
disappearing from non-national figures. However, non-national shares
have varied hugely in Europe, from about 2%–3% (Finland, Portugal,
Spain) to about 10% (Austria), to 20% (Switzerland) and more (Luxembourg) around the turn of the century. Also the increase in the non-national
population varied hugely from country to country. The total increase
during, for example, the period 1990–2000 was 25%, but in Greece, Spain,
Portugal and Finland it was over 100%.
Some facts can explain this increase in the non-national population.
Between 1950 and the early 21st Century, the population of the main 18
West European countries rose from 304 million to about 400 million: an
increase of 32%. In the same period, the world population rose by over
140%, Africa by 300%, Latin America, 250%, Asia, 180%. A demographic
differential emerged between Europe and most of the world. To this, an
economic differential must be added. With an income per capita of over
$30,000, Western Europe is the world’s richest cluster of countries. This
richer and richer cluster is close to the poorest one: sub-Saharan Africa,
with an income per capita equivalent to 1/30th of that in Europe. Western
Europe is also close to other poor and troubled regions, such as the Middle
East, North Africa – both with an income equivalent to 1/10th of that in
Western Europe – and Eastern Europe’s former Communist countries.
The latter display three aspects affecting migration: relatively low incomes,
worsening standards of living (greater inequality), and the breaking up of
previous strict migration controls.
Migration in Western Europe has shown three discrete phases. Between
1950 and 1960, the number of non-nationals was limited and particularly
low in the Mediterranean countries, which were characterised by substantial emigration flows. Exceptions were represented by a few more developed countries – Switzerland, Luxembourg, France, Belgium and, more
limitedly, Sweden – that attracted migration flows from Southern Europe.
Pull factors – high labour demand and low unemployment in the host
countries – prevailed. Over those post-war years, France, Great Britain,
and the Netherlands, also received immigrants from their former and
present colonies: a phenomenon that would become far more developed
over the years following.
A rapid increase in immigration was recorded in the 1960s: the number
of non-nationals doubled (Table 1) and an immigration boom was registered in West Germany. South European countries – such as Italy and
Spain – kept supplying emigrants; Yugoslavia, Turkey and Algeria joined
them. Most of these emigrants were migrant workers, who were interested in
only a temporary stay in the host country, and usually found a job in the
great industrial factories.
This first phase ended with the 1973 oil crisis. The crisis caused a
decline in the host countries’ labour demand and limitations on immigration, with quota restrictions by country of origin. The economic deterioration caused the surfacing of conflicts between immigrants and,
especially, native, unskilled blue-collar workers (Fassmann and Münz
1994). Anti-immigrant movements arose, and migration and crime
became the hottest issue in the political debate.
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© 2011 The Author
The Howard Journal of Criminal Justice © 2011 The Howard League and Blackwell Publishing Ltd
The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
TABLE 2
Non-nationals from EU (or EFTA) Pre-2004 Membership, from Non-EU European Countries,
and from Non-European Countries, as Percentages of the Total Non-national Population of
West European Countries – Percentages by Year – Years 1980, 1990, 2000 (or Closest
Available Year)
1980
Percentage of
non-nat. pop.
1990
2000
EU +
EFTA
NonEU
NonEuropean
EU +
EFTA
NonEU
NonEuropean
EU +
EFTA
NonEU
NonEuropean
47
24
29
38
29
33
34
33
33
(Source: Various national censuses for the relevant countries, Sopemi for the relevant years.)
Restrictions in the 1970s, however, did not stop the migration inflow.
Family reunions in the host countries increased. The country-fellow networks in the host country became an asset for new immigrants and for
illegal immigrants as well. The latter represented a fresh and growing
phenomenon.
A third phase is evident since the 1980s. The flow from European
Mediterranean countries faded away. Migration from both European
countries outside Western Europe and non-European countries increased
(Table 2). The further growth of non-nationals in Europe, therefore, has
been associated with a transformation of the migration flows. Pull factors
have changed, too, since in Western Europe, jobs available for immigrants
have increasingly become marginal, off-the-book jobs: that is, jobs now
looked down on by most natives. Immigrant employment in the hidden
economy has developed in countries such as Spain, Italy, Greece and
Belgium (Cornelius, Martin and Hollifield 1994). In this new phase, immigration has been mostly fed by push factors, such as rapid demographic
growth associated with limited economic advance in the country of origin;
negative human rights situations; ethnic and political conflicts in the Third
World, and the disintegration of Communist systems in Eastern Europe.
Despite these general changes, the immigrant population composition is
still quite different in the various host countries. Some of them show a
prevalence of the European Union’s (EU’s) citizens in their foreign populations (for example, Luxembourg), others a prevalence of non-Europeans
(for example, Greece, Italy).
Non-nationals and Crime in Europe: Some Figures
Moving on from the previous premises, we collected data on migration
and crime in all the main West European countries (18). We focused on
‘non-nationals’, that is, those who are not citizens of the host country.
Non-nationals (also called ‘aliens’) represent the only category considered
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© 2011 The Author
The Howard Journal of Criminal Justice © 2011 The Howard League and Blackwell Publishing Ltd
The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
TABLE 3
Percentages of Non-national Detainees in the Prison Populations of West European
Countries1 – Years 1985–2005
Country
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
B
DK
D
EL
E
F
IRL
I
L
NL
A
P
FIN
S
UK
ISL
NOR
CH
27.6
7.2
14.5
16.3
10.6
26.4
1.8
8.9
43.3
15.3
8.1
4.9
0.3
21.1
2.9
1.1
8.1
34.6
27.4
10.7
14.5
18.7
13.0
26.6
1.1
8.7
38.5
18.8
8.8
7.3
0.3
21.6
3.8
1.3
10.7
35.4
31.1
14.1
14.5
26.6
15.2
27.8
1.1
8.6
41.2
24.2
14.2
7.6
0.3
21.6
4.7
1.8
11.9
41.2
33.7
11.7
14.5
21.8
16.3
29.8
1.3
15.2
39.7
25.2
22.3
7.7
0.9
19.5
7.1
2.2
11.0
43.9
40.6
13.9
22.0
25.6
16.0
29.8
4.9
14.9
49.2
29.0
26.1
8.4
1.6
25.4
6.0
2.0
13.1
47.1
41.0
13.7
29.4
32.3
15.5
28.5
6.4
17.4
53.9
31.1
26.9
10.7
2.4
25.6
7.8
1.6
14.1
57.5
38.2
13.6
33.6
38.6
17.8
26.0
8.3
22.1
54.6
31.9
26.9
12.4
4.5
26.1
7.8
2.9
14.6
60.3
38.4
15.9
33.1
46.8
18.3
23.7
7.5
26.4
57.6
31.8
29.2
13.8
5.5
24.0
8.2
4.0
12.7
62.0
40.7
16.7
31.0
47.2
22.1
21.6
7.8
29.3
61.5
30.0
31.6
15.2
7.4
21.4
9.8
6.2
14.0
66.7
41.0
16.4
29.1
42.3
26.5
21.7
8.8
30.8
69.3
28.2
39.0
16.6
8.1
20.7
11.7
8.3
17.1
70.7
41.2
18.2
28.0
41.6
30.1
20.5
9.5
33.0
71.4
32.9
45.4
18.5
7.5
20.9
12.7
11.8
17.8
70.5
Total
Average
12.5
14.1
14.0
14.8
15.1
17.1
17.3
18.0
19.3
20.9
21.5
23.1
23.2
24.5
23.7
25.5
24.3
26.6
24.9
28.1
26.2
29.5
(Source: Council of Europe (2007) and previous years.
Note: Germany: data for 1986–90 are estimates. Sweden: data regard sentenced detainees. United
Kingdom: data for 1986–90 by interpolation. Iceland: all data were treated with smoothing
techniques, since there were strong oscillations due to the small number of detainees. Switzerland:
data regard sentenced detainees.)
in the main sources of criminal statistics in Western Europe (Council of
Europe 2006). Data relating to the foreign-born population – which
includes immigrants who acquired the host country’s citizenship – are
abundant in the US but scarce in Europe. In any case, non-nationals
everywhere constitute the relatively more marginal part of the foreignborn population and, therefore, they represent a most interesting group
for a crime analysis. Data regarding non-nationals in prison are the most
comprehensive among those relating to the immigration-crime link,
though they have been available only since the mid-1980s. It should be
noticed that these data do not include immigrants kept in immigration
removal centres and other administrative detention units for illegal entry,
etc. Notwithstanding this, the imprisonment rate for all 18 countries
grew – between 1985 and 2005 – from 73 to 108 detainees (of any
nationality) per 100,000 population. This general increase is partly due
to the growing presence of non-nationals in prison (Table 3). However,
variations in imprisonment rates are not correlated with variations in the
non-national shares of the prison population. Some countries showing
huge increases in their non-national shares of the prison population –
139
© 2011 The Author
The Howard Journal of Criminal Justice © 2011 The Howard League and Blackwell Publishing Ltd
The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
Iceland, Austria, Finland – registered limited increases in their prison
population. In some countries with massive increases in the prison population – the Netherlands, Sweden and the United Kingdom – the nonnational contribution to the prison population remained relatively
limited. More generally, variations in imprisonment rates are not associated with variations in the non-national share of the resident population.
For instance, the Netherlands, Luxembourg, Sweden and the United
Kingdom – the countries with the biggest increases in the prison population – had relatively contained variations in their non-national shares of
the resident population. The popular feeling that prisons in Europe are
‘bursting at the seams’ because of immigrants is, therefore, at least an
oversimplification.
Remarkable increases in the non-national share in the prison population were registered in Finland, Ireland, Iceland, Austria, Greece,
Italy (and in the UK, where pre-1990 figures are estimates by interpolation). The non-national share in all the West European prison population more than doubled between 1985 and 2005, reaching the figure of
more than one-quarter. The average value for all the countries is even
higher: close to 30% over the last few years. Non-national shares of the
prison populations are out of proportion when compared with their
shares in Western Europe’s resident population. Over the last few
years, in Switzerland, Luxembourg, Greece and Austria, non-nationals
came to represent a figure close to, or even higher than, 50% of total
detainees. In half of the countries their share is not less than 30%. Differences between rates recorded in the various countries, however, are
impressive.
Table 4 shows further important data, relating to non-nationals charged
with criminal offences. Unfortunately, Ireland does not collect this type of
information. The UK regularly records race, whereas nationality has been
recorded only for intentional homicide cases. In some other countries,
data collected do not cover some types of offences. Besides, data on people
charged must be treated cautiously, because criminal charge rates for
various types of offences and the behaviour designated as offences may
differ from country to country. It is opportune, therefore, to privilege: (i)
non-national shares of all people charged in the various countries, rather
than the non-national criminal charge rates per population; (ii) standard
serious offences (intentional homicide, rape and robbery), which are more
homogeneously treated in the various societies.
Having said that, we can see that high non-national percentages among
people charged are recorded in Luxembourg, Switzerland, Germany,
Belgium, and the Netherlands. With regard to offences, one can see high
non-national percentages among people charged with homicide, rape,
theft and robbery.
All in all, the non-national share among people charged confirms what
has emerged from prison population statistics: (i) the non-national share in
crime figures differs markedly from country to country, even when their
different shares in the resident population are taken into account; (ii)
non-nationals are largely over-represented in official crime figures as they
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The Howard Journal of Criminal Justice © 2011 The Howard League and Blackwell Publishing Ltd
27.6
22.1
100.0
29.4
25.5
8.2
5
27
20
10.0
13
59.7
40.0
12.5
34.0
21.9
18.3
15.6
Intent.
homicide
21.0
7.4
6
37.0
15
30.4
34.0
20.5
22.5
20.9
28.0
18.6
11.1
Sex
offences
27.5
7.7
14
65.9
25.5
39.5
27.0
28.8
10.6
28
32
12.9
24.6
41.2
33.2
21.5
Rape
21.1
5.3
9
54.6
1
18
24.7
46.2
27.3
23.4
27.3
3.7
18.0
16.4
Serious
assault
24.0
4.2
16
59.8
31.9
52.1
25.9
25.1
4.5
7
22
42.2
17.2
22.3
26.0
12.5
14.7
Theft
(all)
20.6
3.0
15
45.5
29.1
34.1
3.1
5
25.9
37.3
16.0
12.2
Aggr.
theft
27.4
7.5
13
53.4
29.5
42.7
32.5
42.8
6.0
12
20
49.1
30.5
31.5
35.2
17.2
15.0
Robbery
24.6
63.2
2.8
13
42.6
25.6
29.6
11.2
23.5
33.1
15.3
10.7
Breaking
& entering
26.5
9
54.4
11
64.0
34.1
9.8
11.6
22.0
22.8
Motor
car theft
25.1
18
34.0
11
20.2
58.1
21.5
32.9
13.9
16.2
Other
theft
22.4
4.8
8
39.5
7
77.4
22.9
20.2
30.4
12.3
22.8
7.7
23.1
14.7
Fraud
19.2
3.7
11
29.5
35.3
53.5
25.9
15.4
6.1
4
16
40.9
17.7
23.5
8.1
7.0
9.9
Drug
offences
21.0
5.6
11
43.7
19.0
52.0
27.1
19.4
6.4
4
20
27.3
16.6
27.1
4.9
33.1
18.4
All
offences
(Source: Council of Europe 2006 and some national statistics. Belgium, Denmark, Portugal, the Netherlands, and Iceland: data provided by their Ministries of Justice
or of Interior, at our request.
Note: Belgium: data regard subjects already sentenced. Denmark: data regard cases opened and closed during the same year. UK: data are police estimates for
England and Wales figures, which represent about 9/10 of UK homicides. Portugal and Sweden: ‘All offences’ is the average for the various types of offences.)
Average
B
DK
D
EL
E
F
IRL
I
L
NL
A
P
FIN
S
UK
ISL
NOR
CH
Country
TABLE 4
Percentages of Non-nationals among All People Charged, by Type of Offences, in West European Countries – Year 2000 (or Closest Available Year)
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The Howard Journal Vol 51 No 2. May 2012
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are in the prison population. With regard to this second point, virtually
everywhere the non-national share of people charged with serious offences
is higher than their share of the resident population. In some countries, it
can be six to eight times higher, or more.
The similarity of non-national over-representation among people
charged and the population in prison is noteworthy. Let us consider the
most serious offences (Table 4): the average non-national share – for all the
countries with available data – is, for intentional homicide, 28%; for rape,
28%; for robbery, 27%; for drug trafficking, 19%; for theft, according to
type, 21% to 27%. It should be noted that these are the offences responsible for most prison admissions. Now, these percentages are close, actually
even higher, than those of: (i) the non-national share of all the prison
population in Western Europe – 23%–24% at the end of the 20th Century;
and (ii) the average for all the countries of the non-national share of the
prison populations – 25%–26% in the same years (Table 3).
The fact that all these figures match bears witness to the reliability of the
measures used here. In the light of data concerning non-nationals
charged, the problem of a possible discrimination against non-nationals –
which could affect non-national shares in prison populations – is cut down
to size. Data presented here do not exclude a greater recourse to imprisonment, especially in the pre-trial phase, in the case of non-nationals.
These data show, however, that the non-national over-representation in
prison (Table 3) – calculated on prison population, not on prison admissions, and, therefore, less sensitive to short custody periods – coincides
with the criminal charge percentages. And, as we mentioned above, criminal charges are hardly attributable to bias.
Data regarding people charged help reappraise another hypothesis:
that non-national offences are more noticeable and, therefore, fall less
frequently in the dark number of unreported crime. Table 4, instead, shows
how non-nationals charged spread over a range of offences: from those
against the person (for example, rape) to those against property (robbery);
from crimes ‘without victims’ (for example, drug offences) to their opposite ones (homicide).
Last, data concerning people charged question the culture conflict
hypothesis. This would suggest a non-national crime made up of a few
‘special’ offences. On the contrary, from Table 4 one draws the conclusion
that the non-national share among people charged is high for all the
standard types of crime – from car theft to robbery – that do not imply any
cultural peculiarity. The main offence realistically associable with culture
conflict might be rape. The non-national share among people charged
with rape is high, and this offence is particularly susceptible to conflicting
sex-role stereotypes.
Relative Index of Non-national Imprisonment
Non-national over-representation in official crime figures in Europe is a
fact, but it promotes no particular hypothesis on the causes of non-national
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crime. More useful, to this end, could be another significant fact: the very
dissimilar non-national contribution to criminal justice figures in the
various countries.
To further investigate this aspect, we need an indicator suitable for a
cross-national comparison. Data relating to non-nationals in prison
offer some advantages. First, imprisonment implies offences of some
seriousness, whose handling ought to be more homogeneous in the
various countries. Second, an indicator based on imprisonment deals
with an aspect more relevant than criminal charges or sentences, since
imprisonment implies depriving individuals of some of their fundamental rights. To have a dependable imprisonment index, however, it is necessary to modify the basic figures by taking into account also the
dissimilar non-national share of the resident population in the various
countries. Hence, we built a ‘relative index of imprisonment’, which corresponds to the non-national share of the prison population divided by their
share of the resident population. So, for example, a country with an index
value of 2 presents a non-national share of the prison population that is
twice their share of the resident population. Such an index has been
calculated for various periods of time (Table 5). There is an overall
increase in the late 1990s. In all periods, differences between countries
are huge.
Non-national over-representation in the indices of most of the countries
cannot be explained by the non-national population structure. An overrepresentation of males in the non-national population could distort the
index. In some communities of non-nationals (for example, those of recent
immigrants from Islamic countries), the male population share can be very
high. However, this is balanced out by other communities where women
are more numerous. All in all, in Western Europe, the male share in the
immigrant population (about 48%) is slightly lower than that recorded in
all the resident population and, in general, differences between the
various countries are not substantial. However, particular cases such as
that of Iceland, where the male share is lower, have been taken into
consideration.
Age-group structure represents a more critical problem. In nonnational populations, adults and young adults, that is, the age groups
contributing most to crime, are over-represented. If we consider only
the critical age group, 18–39 years, we find that the non-national share
of the prison population grows, but the non-national share of the resident population grows much more. So, when age groups are taken into
account, the crime indices of non-nationals are closer to those of nationals. Precise calculations for each country are difficult, since information
on the age of non-nationals in prison is definitely poor. However, data
and estimates on the age groups of the non-national population residing
in the various European countries are available – even though in some
cases they are not complete. All in all, an increase of between 20% and
40%, depending on case, of the non-national share of the resident population ought to compensate for the age-group differences (see Hagan
and Palloni 1999). As a result, the imprisonment index values would
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TABLE 5
Relative Index of Imprisonment for Non-nationals in West European Countries – Average
Values for the Periods 1985–1995; 1990–2000; 1995–2005; 1985–2005;
the Years 1990–2000 Adjusted for Gender, Age Groups and Illegal Immigration
Country
B
DK
D
EL
E
F
IRL
I
L
NL
A
P
FIN
S
UK
ISL
NOR
CH
Average
Relative index of non-national imprisonment
1985–1995
1990–2000
1995–2005
1985–2005
1990–2000
adjusted for
gender, age
& ill. immigr.
3.8
3.9
2.5
13.0
15.3
4.5
1.1
9.5
1.5
5.5
2.7
8.4
1.1
4.3
1.5
0.9
3.5
2.5
4.3
3.5
3.1
12.2
12.8
4.7
2.0
10.1
1.5
6.5
3.1
7.0
2.1
4.2
2.0
1.2
3.6
2.8
4.7
3.2
3.5
9.9
8.8
4.2
2.1
10.1
1.7
7.1
3.5
5.6
3.2
4.2
2.3
1.8
3.7
3.3
4.2
3.6
3.0
11.4
12.1
4.3
1.6
9.8
1.6
6.3
3.1
7.0
2.2
4.2
1.9
1.4
3.6
2.9
2.9
2.4
2.1
7.4
7.8
3.1
1.4
6.1
1.0
4.3
2.1
4.5
1.5
2.9
1.3
0.6
2.5
1.9
4.7
4.8
4.6
4.7
3.1
decrease, but the non-national over-representation in prison would not
disappear.
Such over-representation in prison and the differences between countries cannot be dismissed as the simple effect of the underestimation of
non-national resident population figures, which do not record illegal
immigrants. Illegal immigrants have reached, in some countries, a considerable level. European governments, however, regard their reabsorption
as a priority (Sopemi 2000), and several regularisations of illegal immigrants, by the countries most affected by the phenomenon, confirm this. In
addition, such reabsorption is in the interest of almost all illegal immigrants. Therefore, the status of the illegal immigrant is of a transitory
nature (Garson and Loizillon 2003). Ultimately, the estimates for the illegal
immigrant population over a long period of time – such as that considered
here by the relative index of imprisonment – would be definitely lower
than those at peak times, also due to the siphon effect of regularisations.
Besides, countries with a higher quota of illegal immigrants are, by
common consent, Spain, Italy and Greece. These very countries are also
those where the relative index of non-national imprisonment reaches its
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highest values. If we recalculate this index taking into account the estimates for illegal immigrant populations, these countries would keep
showing values very dissimilar to those from other countries. However, to
leave no stone unturned, we further adjusted the index of imprisonment
by taking into account illegal immigration. We used international estimates
(Wanner 2002; Jandl 2003; Sopemi 2004) and increased non-national
population figures by 5%, 10% and 20% respectively, according to their
illegal immigration level. The relative index of imprisonment values
further decreased (Table 5); but they remain very high for many countries,
whereas for others they are substantially proportional to the immigrant
share of the resident population.
Other possible peculiarities of the non-national population have been
considered. Non-nationals – especially in the period immediately following their arrival in the host country – tend to gather in urban areas and
this could increase the risk of crime. However, as time goes on, their
distribution over the host country’s territory becomes more homogeneous.
In addition, whereas some types of crime (for example, theft, vandalism)
are definitely more frequent in urban areas, other ones are not. For
instance, this is the case with intentional homicide, whose distribution in
Western Europe is not associated with urbanisation.
What is certainly important is that countries with a high non-national
imprisonment index also show a quite different profile with regard to both
their socio-economic-cultural characteristics and the profile of their immigration flows. It is among these features that one might seek the causes of
dissimilar non-national shares – in the various countries – of the prison
population and people charged.
Determinants of Dissimilarities in Non-national Contribution to
Criminal Justice Figures in the Various Countries
What might be these causes? To identify them, we shall take advantage of
the theories presented above, empirically checking them, ultimately, in the
light of the present picture of migration and crime in Europe. Our
approach will be a macro, cross-national comparison. Micro analyses
would get closer to social actors and qualitative interviews, in turn, would
provide invaluable information about individual routes to deviance and
crime. However, both these methods would be less suitable for identifying
the wider social contexts in which crime emerges and drawing comparisons between these social contexts. Although aware of the inevitable limitations of the macro approach, we still regard it as capable of throwing
light on the differentials in immigrant crime between countries. For
greater clarity, we have distinguished three groups of determinants of
these differentials.
Socio-economic and Cultural Characteristics of the Host Countries
According to anomic strain theory – and to common sense – where the
non-national integration is bad, chances of criminal behaviour increase.
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Countries with a higher level of socio-economic well-being are expected to
offer better chances of integration. Where there is more well-being, usually
there is more for non-nationals too. The higher the well-being, the lower
the crime rate – at least for the European countries (Guajardo and O’Hara
1998). Besides, where there is more, and well-being reaches the lower layers
of the native population, non-nationals stand fewer chances of being
perceived as an economic threat to the native well-being. The economic
threat by non-nationals generates hostility against them (Espenshade and
Hempstead 1996), and hostility affects their integration. To gauge economic well-being, we used classical indicators of context, such as: gross
national product (GNP) per capita, percentage of gross domestic product
(GDP) spent on food, and inflation rates, which measure the economic
framework stability.
Then we considered indicators of equity or social cohesion: GNP for the
poorest 10% of the population, which embraces many immigrants, especially the recent ones, income ratio of the richest 20% to the poorest
20%, and public expenditure on social protection. These indicators are
expected to affect non-national tendency to crime (Martens 1997; Holmberg and Kyvsgaard 2003). They can be regarded as empirical measures of the concept of relative deprivation, which the anomic strain
approach holds so dear. Also indicators of self-sufficiency, such as the
unemployment rate for the resident population, have been considered.
Their validity, however, is impaired by facts such as the hidden economy
diffusion.
We have also considered indicators of education and knowledge. Education is correlated with respect for human rights, tolerance for immigrants (Eurobarometer 2001), and negatively with hate crime (Kühne
2002). Therefore, we have probed indicators such as the population with
at least secondary school diplomas, and number of newspapers sold.
Newspapers are regarded as an indicator capable of measuring social
capital (Putnam 1993), and especially the interest in anything beyond
oneself and one’s own family. Personal computer use is expected to be
similar to the sale of newspapers and to measure also the greater availability of the world made possible by the Internet. Both newspaper and
computer diffusion could act as a limitation on corruption (Adserà, Boix
and Payne 2001).
The index of transparency (Transparency International 1995) measures corruption and, indirectly, solidarity, confidence and ultimately particularism, that is, the prevalence of private, over collective, interests.
Particularism tramples on the rights of the weakest ones, among whom
non-nationals are over-represented. Particularism and corruption are
rampant where the rule of law (that is, reliability of the justice system:
World Bank (2005)) is wanting. Non-nationals – who are socially weaker
and need the law’s protection – are negatively affected by an inadequate
rule of law. Vocational integration, in turn, is hindered by business regulations that restrict entry into markets (Gwartney, Lawson and Easterly
2006). Demanding business regulations are associated with powerful
bureaucracies, which encourage particularism and corruption. Non-
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nationals’ social marginality represents a particular handicap when it is
necessary to deal with these bureaucracies. Hidden economy diffusion
(Friedman et al. 2000; World Bank 2005) could be another relevant variable. Hidden economy – which exploits, first of all, outsiders such as
immigrants – is expected to generate further illegalities by them and to
pave the way for common crime. This set of variables (from corruption
control to hidden economy) could be regarded as institutionalised social
capital: a vertical linkage between State and immigrants.
Non-national Integration in the Host Countries
Host countries’ characteristics merely provide the framework expected to
affect non-national integration. Other indicators could specifically gauge
this integration. Non-national unemployment rates could be a good
measure of integration. Its validity, however, is impaired by the vast
number of immigrants employed in the hidden economy. The percentage
of non-nationals with not more than junior secondary school diplomas
could be a better indicator, since the lower their level of education the
higher their malintegration and deviance (Rumbaut et al. 2006), also
according to the theory of control.
It seems sound to take into consideration the variation over time of
non-national populations in the host countries. A rapid increase might
usher in bigger problems of adjustment and, therefore, greater tendency
to crime. Also irregularities in the demographic structure of the nonnational population – such as a scarce incidence of the youngest age group,
which implies few families and, generally speaking, social difficulties
(Casacchia and Strozza 1995) – could be significant. This low child-family
incidence could also be regarded as a negative measure of external controls
according to control theory.
Illegal immigration could be a further determinant of immigrant crime:
(i) illegal immigration bypasses the filters applied to legal immigration to
prevent the admission of undesired subjects; and, more importantly, (ii)
illegality is part and parcel of the clandestine immigrant’s condition. This
illegality is not circumscribed by the immigrant’s arrival and presence but
includes his/her working activities, which can only be offered by the
‘hidden economy’ – euphemistically the economy outside the rules. This
leads the immigrants to believe that illegality is normal, reducing their
resistance to crime. In addition, clandestine immigration comes with a
corollary of illegality, exploitation, abuse and socio-economic marginality
(Calavita 1998; Valenzuela 2006), favouring crime in conformity with both
anomic strain and control theories. Unsurprisingly, illegal immigrants might
be the group most prone to deviance (Barbagli 1998; Albrecht 2002).
Therefore, we used an indicator of illegal immigration level based on the
above-mentioned international estimates.2
Non-national Origin
Western Europe’s immigrants come from many countries; and, as
noticed, the various national group shares vary from host country to host
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country. Each national group of immigrants presents its own characteristics. This can imply, for some of them, a wider gap, in terms of development, culture and religion, vis-à-vis the host country. Such a gap, in
turn, could affect non-national adjustment, integration and, ultimately,
tendency to crime, also according to the culture conflict theory. Besides,
immigrants’ cultural characteristics could act as internal controls, affecting
their crime rates. However, it must be remembered that: (i) the national
groups of immigrants with the highest crime rate are not the same everywhere; and (ii) the same groups’ involvement in crime varies considerably from country to country (Martinez and Lee 2000). This fact
suggests that the origin factor influence ought to be considered, not apart
from, but in concert with, the characteristics of the host country and
those regarding integration. As origin indicators, we used the shares of
non-nationals belonging to the EU (or EFTA), other European countries,
and non-European countries. Higher percentages of EU’s immigrants
correspond to lower crime rates and vice-versa (Albrecht 2002). Then,
since in Europe non-nationals coming from less developed countries
(LDCs) would show higher crime rates (Danmarks Statistik 2002), we
used this feature too. For immigrants from both the EU and LDCs it is,
of course, just a matter of average propensity to crime. For example, it is
well known that immigrants from some LDCs such as Sri Lanka and the
Philippines present low crime rates – something that could be relevant
for control theory. For this reason too, we considered, also, more restrictive criteria of origin: Central-Eastern Europe, Rest of Europe, Africa,
North America, Latin America, Asia and Oceania; as well as the origin
from specific countries.
All these indicators are based on stock data. However, it could be helpful
to test indicators based on stock and flow data: for example, incidence of
non-Europeans in a given year, combined with variation of this incidence
over the following ten years.
Associations between the Imprisonment Index and the
Socio-economic Parameters
The regression analysis (see Appendix) shows that the non-national
imprisonment index is higher in countries with lower and unstable inflationary economic development, particularly low incomes for the poor,
unfair income distribution, little social protection, high corruption level,3
scarce rule of law, demanding business regulations, widespread hidden
economy, limited education, and limited diffusion of knowledge and communication tools (newspapers, computers). Our imprisonment index is
also higher where: (i) non-nationals have rapidly grown over the last few
years; (ii) their children are fewer (that is, families less numerous and
roots limited); (iii) illegal immigration is common; and (iv) non-nationals
from non-European and LDCs are numerous and growing.
The imprisonment index is also correlated with a lower educational
level among non-nationals. The coefficient (0.31), however, is much lower
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ISSN 0265-5527, pp. 133–159
than that relating to the host country education. The context indicator,
therefore, prevails over that measuring immigrant characteristics.
Indicators of legality (corruption, rule of law, hidden economy and
illegal immigration) show coefficients higher than those shown by indicators of economic well-being, equity and relative deprivation (that is, the
first five variables in the Appendix). Legality indicators’ coefficients are
also higher than origin indicators’ coefficients.
Indicators of economic well-being and income distribution are all intercorrelated (for the intercorrelations’ signs, see factor scores in the Appendix). Newspaper and computer diffusion, in turn, are strongly correlated
(>0.77) with corruption control and rule of law. Eloquently, corruption,
rule of law, business freedom, illegal immigration and the hidden economy
are all strongly intercorrelated (0.72 to 0.94). Among other things, this
suggests that: (i) illegal immigration is fed by corruption and hidden
economy; and (ii) the hidden economy, in turn, thrives where corruption
is rife and business regulations are demanding. Last, the three indicators
of origin are all intercorrelated.
To sum up these relations we had recourse to factor analysis.4 This
helped identify three main factors summarising the forces underlying
non-national crime involvement. The first factor (see Appendix and
Figure 1) is characterised by high scores for the variables population with
diploma, newspapers and computer diffusion, corruption control, rule of
law, business freedom (Figure 1, first chart, on the right): countries so
characterised show low levels of illegal immigration and hidden economy
(same chart, on the left). We might call this factor ‘Culture, respect for
rights and social capital’. The second factor is characterised by high
scores for income level and income redistribution, economic stability, and
social protection: in particular, positive scores for social protection (first
chart, at the top), negative scores for GDP for food, etc. (same chart, at
the bottom). We might call it ‘Wealth and social equity’. This factor
includes, also, the share of non-nationals aged 0–4 years. Unsurprisingly,
the higher the social protection, the higher the percentage of children in
the immigrant population. The third factor revolves round variables of
origin. We might call it ‘New, disadvantaged immigration from LDCs’.
The third factor variables are graphically opposed to the variables measuring equity, social protection and culture (Figure 1, second chart).
Therefore countries with high ‘Culture, respect for rights and social
capital’ are also characterised by low ‘Immigration from LDCs’. Subsumed in the first factor, corruption control, rule of law and illegal immigration may be considered a latent factor corresponding to the concept
‘prevalent legality/illegality’. This factor shows that the higher the level of
‘prevalent legality’, the lower the non-national imprisonment index
(Figure 2).
Discussion
After World War Two and reconstruction, Western Europe has progressively assumed a more dissimilar profile vis-à-vis Third and former
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0.5
Social protection
Immigrants aged 0-4
GNP poorest decile
Computers
Newspapers
Sec. school completed
Rule of law
Corruption control
Bus. reg. freedom
Immigr. var.
0.0
Non-Europ. LDC
Non-Europ.
Illegal immigr.
Non-Europ. LDC & var.
-0.5
Hidden economy
GDP for food
Inflation
Ratio H/L quintile
-1.0
Wealth & Social Equity
1.0
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-0.8
-0.4
0.0
0.4
0.8
0.5
Social protection
Immigrants aged 0-4
GNP poorest decile
Computers
Sec. school completed
Newspapers
Rule of law
Corruption control
Bus. reg. freedom
0.0
Immigr. var.
Non-Europ. LDC
Non-Europ.
Illegal immigr.
Non-Europ. LDC & var.
Hidden economy
-0.5
Wealth & Social Equity
1.0
Culture, Rights & Social Capital
GDP for food
Inflation
-1.0
Ratio H/L quintile
-0.5
0.0
0.5
1.0
New Disadvantaged Immigration from LDC
FIGURE 1
Distribution of the Main Variables Regarding the Socio-economic Situation, Integration and
Migratory Flow in West European Countries: First and Second Factor; Second and Third Factor
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R = .901
R-squared = .811
8
95% Confidence interval
E
6
EL
4
I
NL
P
F
S
B
NOR
D
DK
2
A
IRL
CH
FIN
L UK
ISL
0
Relative index of imprisonment for non-nationals
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-7
-6
-5
-4
-3
-2
-1
Rule of Law, Control of Corruption & Illegal Immigration
FIGURE 2
Countries of Western Europe in Relation to Some Variables of the First Factor and to the Nonnationals’ Imprisonment Index: Linear Regression Fit Line, Confidence Interval and Coefficients
Second World countries, both for its population stagnation and economic and social well-being achieved. The emerging of this different
development coincided with an escalating migration towards Western
Europe: a flow now originating mainly from countries distant in terms
of demographic, economic and political features. Illegal immigration
soared and was channelled towards the hidden economy. Immigrants’
chances of rapidly obtaining satisfactory vocational integration decreased. The immigrant-native relationships often turned sour. At the
same time, a marked increase in the non-national contribution to crime
figures emerged in Europe. This increase has included non-nationals,
both charged with criminal offences and in prison, without taking into
consideration those charged with illegal entry and kept in immigration
removal centres.
The present study – the first to conduct a cross-national analysis on a
sizeable set of countries – confirms the averagely high non-national share
in criminal justice figures, but also the fact that such a share varies greatly
from country to country; it also suggests that the usual explanations of the
migration-crime link do not fit the current picture.
The popular opinion according to which immigrants, and some
national groups in particular, are crime-prone, is challenged by some
facts: in some countries the non-national contribution to crime figures is
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ISSN 0265-5527, pp. 133–159
limited and the same national groups show – in the various countries – a
dissimilar contribution to crime. If we add that the immigrant crime rate
was much lower in the 1950s to 1960s in Europe and is still low in the
US, Canada and Australia, we can conclude – against stereotypical beliefs
– that the migration-crime relationship is (i) historically contingent, and
(ii) significantly dependent on the context in which immigration occurs.
Immigration, therefore, is not tantamount to high crime rates, and some
countries show us that it is possible to keep immigrant crime under
control.
In turn, the theory of culture conflict does not fit the case of present
immigrants in Western Europe who commit mainly common, not ‘cultural’, offences. It does not fit, even if our data do not include all foreignborns but only non-nationals, that is, more recent immigrants, for whom
one would expect greater culture conflict. There is, somehow, room for
this theory only with regard to the incidence of a few particular offences,
such as rape. However, the bulk of crime ascribed to non-nationals seems
to have little to do with cultural differences. Neither the dissimilarities in
non-national contributions to crime in the various countries, nor the
dissimilar contribution to crime by the same national groups in the
various countries seem to be the effect of different levels of culture conflict. Our data show that non-European immigration from LDCs is correlated with higher crime involvement level. However, this is not
necessarily due to culture conflict. Immigration from LDCs implies not
only culture dissonance but also greater distance in terms of socioeconomic conditions, leading to problems of integration. And even
culture dissonance can lead to malintegration rather than to culture conflict. Crime involvement is associated more with characteristics of the host
country and of the migration flow than it is with immigrant origins (see
Appendix). This suggests that facts of integration prevail over cultural
facts.
Also control theory does not fit our results – which relate to nonnational populations made up of first-generation immigrants – very well.
These immigrants, especially the most marginalised, can easily find
themselves in a situation of scant external controls – those controls usually
exerted by the host society – precisely because their integration in it is
limited. However, these immigrants are expected to be restrained by
internal controls, since they are under the influence of the culture and
upbringing of the society from which they recently came. And they are
more ‘committed’, since they invested their energies in the migration
project, which is a demanding undertaking. Also the low correlation
we found between the non-national education level and their crime
involvement is at odds with control theory. The significance of other
indicators, such as child presence and illegal immigration, instead, bolsters this theory, but not conclusively. These variables are measures of
external controls but also of the difficulties met by immigrants and, in
the case of illegal immigration, they are measures of the legality level
in the host country as well. Last, the low crime rates usually associated
with immigrants from some particular country belonging indifferently
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The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
to developed or to LDCs could be regarded as a point in favour of
the relevance of internal controls – even though we could not find
any statistically significant evidence for this in our analysis of
dissimilar non-national shares of prison populations in the various
countries.
With regard to labelling theory, to the earlier remarks we can add that
it is ill-equipped to explain the marked increase in the non-national
contribution to crime figures in Europe over the last decades. It would be
arguable to claim that earlier immigrants were less negatively labelled. On
the contrary, one can hardly deny that more attention is paid today – also
by the legislator – to racial biases against immigrants.
The anomic strain theory looks better equipped vis-à-vis the results
shown here. Indicators of inequality and relative deprivation – such as
income for the poorest layer, income ratio of the richest to the poorest
layers – emerge as associated with the crime index. This tallies with the
hypothesis that crime refers to the gulf between the ‘universal’ goal of
socio-economic success and the limited means usually available to nonnationals to achieve it. All in all, anomic strain theory seems supported by
the present situation. The new immigrants are allured by great expectations aroused by a richer and richer Europe, but they often find themselves poor amidst wealth.
However, one would presume that the non-nationals with whom we
are dealing – usually first-generation immigrants from countries with
much lower wages and life conditions – have expectations more limited
and, therefore, more easily met than expectations of full socio-economic
success. So, they would be less disconcerted by relative deprivation,
which, instead, would seriously distress second-generation immigrants,
caught between two worlds. Second-generation immigrants are usually host
country citizens and therefore they are not recorded as non-nationals.
Moreover, the fact that the highest contributions by immigrants to crime
figures are recorded in South European countries, that is, far away
from the Protestant-Calvinist culture and its idealisation of economic
achievement, seems at variance with anomic strain theory. Also the comparison between the high share of immigrant crime in Western Europe
and the low share in the US does not support anomic strain theory, since
the US is a society both idealising achievement, through a winner/loser
culture, and showing greater income inequality than any country in
Western Europe (immigrants’ lower contribution to crime in the US on
the other hand, cannot be attributed to their more rigorous selection,
because this country has to cope with a mass of illegal immigrants on
whom no selection is conducted). Last, the fact that non-national
involvement in crime is associated more with legality variables – such as
corruption, rule of law, hidden economy and illegal immigration – than
it is with economic inequality and relative deprivation, should not be
disregarded.
The present spiralling upwards of non-national crime involvement in
some European countries, therefore, seems to be related not only, and not
so much, to a picture of economic inequality and relative deprivation. It
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The Howard Journal Vol 51 No 2. May 2012
ISSN 0265-5527, pp. 133–159
seems related, rather, to a low level of legality and scarce rule of law, with
the concomitant corruption and exploitation of the weaknesses of immigrants, especially the most marginalised ones, like the illegal immigrants
(for example, via the hidden economy). The variables clustering around
the concept of legality, as well as the freedom from business regulations
variable, can affect integration, but they are, above all, measures of
fairness and universalism towards immigrants. This picture is supported
by the relevance of other variables, such as the resident population’s
education and knowledge, which have little to do with the concept of
deprivation but much with that of openness. It seems reasonable, moreover, to presume that a society caring about legality and showing fairness
towards immigrants would be able to contain the negative effects of a
sizeable level of socio-economic inequality and relative deprivation.
Whereas a society without these qualities would find it difficult to limit –
under the same circumstances – the diffusion of crime. Crime would
assume the form of economic motivated offences (theft, robbery, drug
trafficking) as well as that of reactive violent offences (homicide, rape), as
shown by Table 4.
In looking for determinants of crime, and of immigrant crime in particular, scholars’ attention has been mainly focused on economic inequality,
vocational malintegration, cultural differences, and lack of social controls.
Transparency, fair justice and other aspects of institutionalised social
capital have been disregarded. However, lack of justice, particularism,
corruption and all that follows, can easily gut social controls, exacerbate
the cultural gap between ethnic groups, make vocational integration
unsatisfying and economic inequality intolerable.
Ultimately, the extraneousness, vis-à-vis host societies, of a substantial
part of actual immigrants and the low quality of their vocational integration seem capable of contributing to higher crime involvement. However,
generosity towards the most vulnerable, and especially respect for rights,
universalism, legality and openness by the host society, seem to be significant in holding down non-national crime involvement. For the host
society, these qualities can be a form of enlightened self-interest. Societies,
after all, are made up of men and, as Alexander Pope said (Essay on Man,
1733–34, III, 6):
Man, like the gen’rous vine, supported lives;
The strength he gains is from th’ embrace he gives.
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The Howard Journal Vol 51 No 2. May 2012
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Appendix
5
Selected Independent Variables and the Relative Index of Imprisonment (1991–1995 and
1996–2000, Controlling for Gender, Age Groups and Illegal Immigration): Longitudinal Linear
Regression (Between Effects), t-scores and Probability; Factor Scores for the Independent Variables
Variables
t-scores
(prob.)
1st
factor
2nd
factor
3rd
factor
• GDP spent on food (%)
• Inflation rate yearly
• GNP per capita of the poorest 10% of
the population
• Ratio income of the richest 20% to
the poorest 20%
• Public expenditure on social
protection per capita (total)
• Pop. (25–64 yrs) that completed
senior sec. school (%)
• Daily newspapers per 1,000 pop.
• Corruption control index
• Rule of law index
• Hidden economy, share of GDP
• Business freedom index
• Personal computers per 1,000 pop.
• Non-national population, variation
• Non-nationals aged 0–4 yrs (%)
• Illegal immigration, estimated level
• Non-nationals from non-European
countries (%)
• Non-nationals from less developed
non-European countries (%)
• Non-nationals from less developed
non-European countries and their
variation over time
3.37 (.004)
3.30 (.005)
-3.40 (.004)
-.364
-.214
.319
-.571
-.656
.728
.247
.101
-.506
2.82 (.012)
-.389
-.748
.260
-2.54 (.022)
.359
.746
-.351
-3.06 (.007)
.539
.472
-.565
(.006)
(.000)
(.000)
(.000)
(.001)
(.000)
(.002)
(.008)
(.000)
(.036)
.745
.746
.563
-.394
.775
.622
-.104
.329
-.747
-.064
.490
.277
.410
-.254
.226
.513
.033
.725
-.163
-.104
-.346
-.270
-.395
.289
-.184
-.365
.187
-.157
.322
.972
2.67 (.017)
-.155
.005
.971
3.94 (.001)
-.061
-.134
.751
-3.15
-6.05
-5.97
5.29
-4.26
-4.65
3.62
-3.04
7.35
2.29
Notes
1 Abbreviations and their order for the West European countries in Tables 3 to 5 and
Figure 2 are those used by Eurostat: B = Belgium, DK = Denmark, D = Germany, EL
= Greece, E = Spain, F = France, IRL = Ireland, I = Italy, L = Luxembourg, NL = the
Netherlands, A = Austria, P = Portugal, FIN = Finland, S = Sweden, UK = United
Kingdom, ISL = Iceland, NOR = Norway, CH = Switzerland.
2 A dummy with values of 1, 2 or 3, for a low, medium or high level of illegal
immigration.
3 In the Transparency International index, lower corruption corresponds to higher
values; to avoid confusion, we changed the values sign.
4 The extraction method used was principal components analysis (PCA). The matrix was
then rotated, for an easier interpretation, by means of the equamax method with Horst
normalisation. The first three factors explain 84% of the total variance.
5 The independent variables data refer – if not specified – to the period of time covered
by the relative imprisonment index, usually to its early years, for obvious reasons of
causality.
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