Calderoni 2011 - Where is the Mafia in Italy

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Where is the mafia in Italy? Measuring the presence of the mafia acrossItalian provincesFrancesco Calderonia

a Università Cattolica del Sacro Cuore and Transcrime, Milan, Italy

Online publication date: 13 February 2011

To cite this Article Calderoni, Francesco(2011) 'Where is the mafia in Italy? Measuring the presence of the mafia acrossItalian provinces', Global Crime, 12: 1, 41 — 69To link to this Article: DOI: 10.1080/17440572.2011.548962URL: http://dx.doi.org/10.1080/17440572.2011.548962

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Global CrimeVol. 12, No. 1, February 2011, 41–69

Where is the mafia in Italy? Measuring the presence of the mafiaacross Italian provinces

Francesco Calderoni*

Università Cattolica del Sacro Cuore and Transcrime, Largo Gemelli 1, 20123 Milan, Italy

This article presents the Mafia Index (MI), an index measuring the presence of mafiasat the provincial level. In the abundant literature on Italian mafias, relatively few stud-ies have attempted to measure the presence of mafias across the country. A review ofprevious attempts points out the limitations and methodological shortcomings of exist-ing measurements. The study provides an operational definition of ‘mafia’ and selectsthe most appropriate indicators and variables according to multiple criteria. The MIcombines data on mafia-type associations, mafia murders, city councils dissolved forinfiltration by organised crime, and assets confiscated from organised crime and coversthe period between 1983 and 2009. The MI highlights not only the strong concentrationof the mafias in their original territories but also their significant presence in the centraland northern provinces. This confirms that mafias should not be regarded as typicallySouthern Italian phenomena, but rather as a national problem.

Keywords: mafia; mafias; organised crime; data; index; Italy; Italian provinces; mea-surement

1. Introduction

Among the Italian words best known in the world, mafia is surely the most infamous.Among Italy’s many achievements, being the country of origin of the mafias is certainly themost inglorious.1Italy also has primacy in literary and scientific production on mafias. The

*Email: [email protected]. For the purpose of this study, ‘mafias’ refer not only to the Sicilian Mafia but also to othercriminal groups that share some significant features with the latter (although they are not thesame phenomenon). Traditionally, there are four main mafias in Italy: besides the Sicilian Mafia,there are the Camorra, the ′Ndrangheta, and the Sacra Corona Unita. Some authors talk of a‘fifth’ mafia, referring to criminal phenomena exhibiting some of the significant features ofthe four main groups. This denomination has been applied to the criminal groups in Sicily,Sardinia, Basilicata, and Veneto. See, for example, Giuseppe Bascietto, Stidda. La quinta mafia,i boss, gli affari, i rapporti con la politica (Palermo: Pitti, 2005); Pantaleone Sergi, Gli annidei basilischi: mafia, istituzioni e società in Basilicata (Milano: FrancoAngeli, 2003). In gen-eral, the category ‘mafias’ is widely accepted in the Italian literature (where mafie is the pluralform of the word). See, for example, Umberto Santino, Dalla mafia alle mafie: Scienze socialie crimine organizzato (Soveria Mannelli: Rubbettino, 2006); Giovanni Fiandaca and SalvatoreCostantino, eds., La mafia, Le mafie: Tra vecchi e nuovi paradigmi (Roma: Laterza, 1994).The category ‘mafias’ is also accepted at the international level (usually including other phe-nomena such as the Yakuza, the Triads, and the so-called Russian Mafia). See, for example,Federico Varese, ‘How Mafias Migrate: The Case of the ′Ndrangheta in Northern Italy’, Law &

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42 F. Calderoni

Italian literature on the mafia is so abundant that it could fill an entire library.2 Surprisingly,however, a relatively small number of studies and publications have attempted to measurethe presence of mafias on the Italian territory. This may appear remarkable, for the analysisof a problem is a key step towards solving it. An appropriate analysis requires reliabledata and information. The unavailability of direct and easily accessible data should notpreclude attempts to estimate problems, with a view to improving knowledge about themand consequently to devising solutions. Probably, better data and information sharing, andtherefore better measurements, could contribute to Italy’s efforts to prevent mafias or toenforce the law against them.

The aim of this article is to contribute to the existing measurements of the presenceof mafias across the Italian territory. It presents and discusses the Mafia Index (MI here-inafter), which is a composite index measuring the presence of mafias at the provinciallevel in Italy.

Section 2 discusses the shortcomings of the existing measurements of mafias in Italy,reviewing the most recent attempts to create indexes of the presence of mafias and/ororganised crime. Section 3 presents the methodology used to create the MI. Section 4analyses and discusses the MI. Section 5 concludes.

2. Analysis of existing attempts to measure the presence of mafias and organisedcrime in Italy

This section briefly reviews the reports and scientific studies seeking to measure the pres-ence of mafias and/or organised crime in Italy (Section 2.1). It highlights that most of theexisting measurements have significant shortcomings and that there is a need for a new andbetter index (Section 2.2).

2.1. The most recent measurements of mafias and organised crime in Italy

A review of the current state of the art in measurement of the presence of mafias and/ororganised crime in Italy points up problems and difficulties with the existing measurement

Society Review 40 (2006): 411–44; Robin T. Naylor, ‘Mafias, Myths, and Markets: On the Theoryand Practice of Enterprise Crime’, Transnational Organized Crime 3 (1997): 1–45. Moreover, it iscustomary to apply the term ‘mafia’ (in the singular) to criminal organisations other than the SicilianMafia.

In Italy, the allocation of other similar criminal groups to the category ‘mafia’ also occurs in crim-inal law. The last paragraph of Article 416 bis of the Italian Criminal Code (mafia-type association)explicitly states: ‘the provisions above apply also to the camorra, the ′Ndrangheta and other asso-ciations, however known or called, even foreign, which use the intimidatory power of the group toachieve the goals typical of a mafia-type association’.2. Several publications have exclusively focused on compiling bibliographies on the mafia. See, forexample, Gioconda Chindemi and Mimma Corso, eds., Bibliografia Sulla Mafia Sicilia/biblioteche3 (Palermo: Biblioteca centrale della Regione siciliana, 1987); Aurora Dioguardi, Bibliografia SullaMafia, 1987–2000, Sicilia/biblioteche 53 (Palermo: Regione siciliana, Assessorato dei beni culturalie ambientali e della Pubblica istruzione, 2000); Gian Roberto Lanfranchini and Bea Marin, eds., PerConoscere La Mafia: Una Bibliografia (Milano: Strumenti editoriali, 1993); Angela Bedotto, ed.,Mafie: Panorama bibliografico [1945–1993] (Milano: FrancoAngeli, 1994). As far as possible, thisarticle will cite and refer to English publications (either original works or translations from Italian).However, most references will inevitably be to Italian works.


Global Crime 43

exercises.3 This review focuses on the overall aim of the studies and on the measurementmethodology adopted.

2.1.1. The organised crime index by ISTAT

The Italian National Institute of Statistics (ISTAT) has created an organised crime index(OCI)4 whose purpose is to support the evaluation of public policies to reduce socio-economic disparities in Southern Italy. The ISTAT index includes data on a wide variety ofcrimes at regional5 level from 1995 to 2006 (although data for 2004 and 2005 are missing).6

ISTAT calculates the OCI by summing the absolute values for each crime weighted for theaverage statutory penalty. The OCI is parameterised to 1995 (1995 = 100). Table 1 reportsthe OCI for all Italian regions.

2.1.2. The Eurispes mafia penetration index

The Institute of Political, Economic and Social Studies (Eurispes) created the Indice dipenetrazione mafiosa (IPM)7 in 2004.8 The aim of the IPM is to measure the level ofpermeability of a given territory to organised crime.9

The 2004 and 2005 editions of the IPM focused only on the provinces of Calabria.10

The IPM includes several socio-economic variables.11 For each indicator, the province withthe highest value (i.e. the worst situation) receives a score of 10. The other provinces

3. For the sake of brevity this article focuses only on contributions published in the past 5 years.4. ISTAT. ‘B. Indicatori di contesto chiave e variabili di rottura’ (September 2010),http://www.istat.it/ambiente/contesto/infoterr/azioneB.html#tema (accessed January 17, 2011).5. In Italy, regions are the highest level of local administration. As a consequence of progressivelegislative reforms, regions have acquired significant powers and autonomy, including legislativecompetence on a wide variety of matters. There are 20 Italian regions, and their number did notchange during the time period covered by this study.6. ISTAT has adopted the operational definition of organised crime used by the Italian Ministry of theInterior. The definition comprises mafia murders, bomb or fire attacks, arsons, serious robberies (e.g.bank or post offices). The source of the data is the operational database of the Italian law enforcementagencies. Until 2003 this database was known as ‘modello 165’, while since 2004 a new system(‘SDI’, acronym for Sistema di Indagine) has replaced the previous one.7. Mafia penetration index.8. Eurispes, 16 Rapporto Italia (Roma: Eurilink, 2004).9. Ibid., 425.10. In Italy, provinces are mid-level administrative units. They have particular importance froma criminological point of view because law enforcement agencies are frequently organised on aprovincial basis. Until 1992 there were 95 provinces. In 1992, seven provinces were created (Verbano-Cusio-Ossola, Biella, Lecco, Lodi, Rimini, Prato, Crotone, Vibo Valentia), bringing the total to 103provinces. In 2001, four new provinces were created in Sardinia (Olbia-Tempio, Ogliastra, MedioCampidano, and Carbonia-Iglesias) and implemented in 2004. In 2004, three new provinces werecreated (Monza e della Brianza, Fermo, and Barletta-Andria-Trani) and were implemented in 2009.This study is based on crime statistics. These refer, for the 1983–1995 period, to the pre-1992 set of95 provinces for which data are available since 1996 (due to a lag in adaptation of the data collectionprocedures). For the 1996-onward period, the study refers to the set of 103 provinces, once againowing to lags in adaptation of the data collection procedures.11. Unemployment rate, trust in the institutions, crime rates for offences committed by mafia-typeassociations (extortion, smuggling of goods, drug production, drug possession, and drug dealing,criminal association, mafia-type association, exploitation of prostitution, handling stolen goods), citycouncils dissolved for mafia infiltration, intimidatory acts against local administrators and, only forIPM 2005, mafia murders.


http://www.istat.it/ambiente/contesto/infoterr/azioneB.html#tema

44 F. Calderoni

Tabl

e1.

The

ISTA

Tor

gani

sed

crim

ein

dex.

Reg

ion

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Pie

dmon

t10

0.0

112.

112

0.4

146.

016

3.7

156.

414

6.9

148.

514

8.1

NA

NA

160.

0A

osta

Val

ley

100.

079

.459

.916

6.2

81.0

40.1

149.

010

1.6

142.

4N

AN

A69

.6L

omba

rdy

100.

095

.810

3.1

113.

799

.299

.610

0.6

108.

310

7.2

NA

NA

141.

9T

rent

ino-

Alt

oA

dige

100.

012

4.1

98.3

93.0

84.4

82.2

46.0

83.8

95.5

NA

NA

68.1

Ven

eto

100.

011

2.7

118.

313

9.8

174.

615

3.8

118.

612

8.2

140.

6N

AN

A12

4.1

Friu

li-V

enez

iaG

iuli

a10

0.0

128.

013

1.3

130.

614

7.4

135.

594

.710

1.4

95.4

NA

NA

77.6

Lig

uria

100.

010

2.4

108.

212

6.2

145.

411

4.8

116.

296

.213

3.8

NA

NA

231.

4E

mil

ia-R

omag

na10

0.0

102.

311

4.6

128.

312

0.8

146.

513

6.3

127.

512

2.0

NA

NA

140.

6Tu

scan

y10

0.0

95.5

104.

012

3.4

122.

810

5.8

111.

111

6.8

115.

0N

AN

A12

6.1

Um

bria

100.

015

6.5

126.

416

4.1

166.

025

9.3

264.

716

2.4

223.

4N

AN

A30

4.3

Mar

che

100.

015

2.5

121.

522

0.0

193.

221

7.3

191.

616

1.1

184.

8N

AN

A23

3.2

Laz

io10

0.0

91.0

89.6

120.

097

.014

5.5

124.

911

9.8

112.

4N

AN

A15

3.3

Abr

uzzo

100.

011

9.6

114.

214

8.0

156.

017

1.7

127.

512

3.4

170.

1N

AN

A19

3.6

Mol

ise

100.

068

.710

6.4

157.

056

.811

0.7

118.

873

.310

8.7

NA

NA

250.

8C

ampa

nia

100.

095

.597

.910

6.8

80.9

94.9

98.9

96.0

105.

0N

AN

A13

2.3

Apu

lia

100.

098

.610

6.3

111.

412

1.7

132.

112

9.1

104.

811

7.3

NA

NA

119.

3B

asil

icat

a10

0.0

90.6

102.

757

.484

.417

0.2

138.

488

.474

.5N

AN

A99

.8C

alab

ria

100.

091

.591

.382

.888

.074

.490

.584

.498

.2N

AN

A11

1.2

Sic

ily10

0.0

97.7

89.6

91.1

87.4

74.2

88.0

78.4

96.8

NA

NA

48.3

Sar

dini

a10

0.0

83.4

105.

014

6.3

136.

510

9.7

99.5

97.5

112.

7N

AN

A42

.2It

aly

100.

098

.510

0.7

111.

910

8.5

108.

010

7.4

101.

711

1.4

NA

NA

111.

7

Not

e:N

A,n

otap

plic

able

.So

urce

:IS

TAT

(201

0).


Global Crime 45

Table 2. The Eurispes IPM for 2010.

Province IPM 2010 Province IPM 2010

Napoli 65.4 Salerno 32.7Catania 52.4 Messina 31.9Caserta 51.0 Trapani 29.4Brindisi 51.0 Avellino 29.3Reggio Calabria 50.5 Enna 29.2Foggia 47.3 Agrigento 28.9Catanzaro 41.2 Benevento 28.9Bari 41.0 Crotone 28.6Siracusa 38.6 Ragusa 28.4Vibo Valentia 37.5 Cosenza 27.1Palermo 35.5 Taranto 24.8Caltanissetta 33.1 Lecce 18.3

Source: Eurispes (2010).

receive decreasing scores according to their rank among all the provinces analysed. Thesum of the scores provides the value of the IPM. Eurispes did not calculate the IPM in2006.

In 2007 and 2008, Eurispes extended the measurement of the IPM to the four Italianregions with a traditional presence of mafia-type groups (Apulia, Calabria, Campania, andSicily, see footnote 1) and modified the indicators.12 In 2009, Eurispes did not calculate theIPM.

The IPM changed once again in 2010. It now includes only the rates for a numberof offences attributable to mafia-type associations.13 The IPM covers the 24 provinces ofthe regions with a traditional presence of mafias, and the year 2008. For each offence, theprovince with the highest rate receives a score of 7.7. The other provinces receive a scoreproportional to their rate. The sum of the scores on each offence yields the IPM. Table 2reports the IPM for 2010.

2.1.3. The analyses by Censis

The Centro Studi Investimenti Sociali (Censis) has performed two analyses to measure thepresence of organised crime in a territory.14

The first analysis (see Table 3) measured the presence of mafia-type organisations usingthree proxy indicators, and it covered the four Italian regions with a traditional presence of

12. Unemployment rate, crimes attributable to mafia associations per 10,000 inhabitants (sum ofextortion, drug production, possession, and dealing, mafia-type association, exploitation and facil-itation of prostitution, handling stolen goods, bomb, and fire attacks), mafia murders per 10,000inhabitants, city councils dissolved for mafia infiltration (absolute values), number of episodes ofterrorism or political violence (absolute values), and number of phone interceptions in the provinces(absolute values). Eurispes, 20 Rapporto Italia (Roma: Eurilink, 2008), 470.13. Bomb or fire attacks, mass murders, handling stolen goods, robberies, extortions, usury, kidnapfor ransom, mafia-type association, money-laundering, smuggling of goods, drug production andtrafficking, exploitation and facilitation of prostitution, mafia murders. Eurispes, 22 Rapporto Italia(Roma: Eurilink, 2010), 597.14. Censis. Il condizionamento delle mafie sull’economia, sulla società e sulle istituzioni delMezzogiorno (Roma: Censis, 2009), http://www.censis.it/censis/attachment/protected_download/624?view_id=35


http://www.censis.it/censis/attachment/protected_download/624?view_id=35

http://www.censis.it/censis/attachment/protected_download/624?view_id=35

46 F. Calderoni

Table 3. Censis measurements of mafias’ presence.

Affected municipalities Population in affectedmunicipalities (% of the

total)

Surface area of theaffected municipalities

(% of the total)

Province andregion Absolute values %

Avellino 19 16 38.2 13.4Benevento 28 35.9 56.2 31.1Caserta 49 47.1 77.9 50.2Napoli 73 79.3 95 86.4Salerno 34 21.5 69.5 24.9Total Campania 203 36.8 81.3 33.7Bari 27 56.3 79.8 66.9Brindisi 12 60 80.2 79.9Foggia 15 23.4 70 50.9Lecce 26 26.8 52.2 46.6Taranto 17 58.6 78.5 71.5Total Apulia 97 37.6 72.5 59.9Catanzaro 20 25 65.3 32.2Cosenza 18 11.6 41.7 16.2Crotone 11 40.7 72.6 55.8Reggio Calabria 51 52.6 85.3 58.7Vibo Valentia 15 30 59.7 32.3Total Calabria 115 28.1 62.5 33.4Agrigento 37 86 95.9 93.8Caltanissetta 17 77.3 95.2 91.4Catania 32 55.2 79.7 56.7Enna 12 60 73.8 59.4Messina 16 14.8 57.1 21.8Palermo 46 56.1 90.9 55.9Ragusa 6 50 57.5 47.5Siracusa 13 61.9 88.7 77.1Trapani 16 66.7 91 81.8Total Sicily 195 50 82 63.2Total of four

regions610 37.9 77.2 50.8

Source: Censis (2009).

mafia-type groups and a time period of 3 years, probably from 2004 to 2006.15 It calculatedthe number of municipalities exhibiting at least one of the above-mentioned ‘contiguitysigns’ per province, as well as their percentage on the total number of municipalities, theirpopulation on the total provincial population, and their land area on the total provincialarea.16

The second analysis (see Table 4) measured the presence of offences ‘directlyattributable to organised crime’17 in the Italian regions and in the provinces of the four

15. Mafia ‘clans’ identified in reports by the Ministry of Interior and by the Anti-camorraObservatory of the Campania region, the number of city councils dissolved for mafia infiltration,and the number of assets confiscated from organised crime. The study does not clearly state the timespan of the data.16. Censis, Il condizionamento delle mafie sull’economia, 10.17. Bomb or fire attacks, mafia murders, extortion, usury, mafia-type associations, money-laundering,arson, smuggling of goods, association for drug production and trafficking, association for drugdealing.


Global Crime 47

Table 4. Organised crime offences by Censis.

Trend 2004–2007

RegionsAbsolute

values 2007

Rates per100,000

inhabitants %

Variationof therates

Campania 4.663 80.2 61.5 30.4Apulia 2.848 69.9 26.5 14.5Calabria 3.228 160.8 26.3 33.6Sicily 2.411 47.9 14.4 5.9Total of four regions with

traditional presence ofmafias

13.150 77.7 34.2 19.6

Piedmont 1.384 31.4 11 2.6Aosta Valley 20 15.9 −20 −4.5Lombardy 2.796 29 20.2 4.2Trentino-Alto Adige 185 18.4 −8.9 −2.5Veneto 919 19 11.5 1.5Friuli-Venezia-Giulia 253 20.7 24 3.8Liguria 953 59.2 25.4 11.5Emilia-Romagna 1.157 27.1 19.9 3.8Tuscany 1.202 32.7 10.3 2.4Umbria 361 40.8 47.3 12.3Marche 489 31.5 33.2 7.3Lazio 2.535 45.6 61.5 15.8Abruzzo 615 46.5 48.6 14.6Molise 325 101.3 82.6 46Basilicata 171 28.9 0 0.3Sardinia 451 27.1 −12.3 −4.1South 14.712 70.6 32.8 17.2Centre-North 12.254 31.6 24.7 5.5Italy 26.969 45.2 29 9.5

Source: Censis (2009).

Italian regions with a traditional presence of mafia-type groups.18 The analysis covered thetime period 2004–2007 and calculated the sum of the offences and the rates per 100,000inhabitants. Moreover, for each offence, the analysis calculated the provincial rates per100,000 inhabitants, comparing the 2007 data with the 2004 or 1998 data.19

2.1.4. Other contributions in the literature

Some contributions in the literature have sought to measure the presence of mafias inItaly. There follows a rapid review of the most recent studies, focusing exclusively on theirattempts to create indexes measuring the presence of mafias and/or organised crime.

Daniele and Marani analysed the relation between organised crime and foreign directinvestments (FDI).20 In the study, criminal associations and mafia-type associations, fire orbomb attacks, and arsons and extortions at the provincial and regional level for the period

18. Censis, Il condizionamento delle mafie sull’economia, 13.19. The study does not explain the reasons for the difference in the comparison years.20. Vittorio Daniele and Ugo Marani, ‘Organized Crime, the Quality of Local Institutionsand FDI in Italy: A Panel Data Analysis’, European Journal of Political Economy


48 F. Calderoni

Table 5. Extortion, criminal association, attacks, and fires. Rates per 10,000 inhabitant,2002–2005 (Italy = 100).

Region Extortion Criminal association Attacks Arson

Abruzzo 108 119 47 67Basilicata 87 222 29 94Calabria 185 196 717 346Campania 162 155 99 107Emilia-Romagna 77 56 24 66Friuli-Venezia-Giulia 64 96 29 52Lazio 86 109 35 78Liguria 70 83 39 121Lombardy 70 71 35 65Marche 77 68 26 53Molise 124 104 34 110Piedmont 102 51 58 83Apulia 150 119 200 146Sardinia 74 36 429 149Sicily 143 177 186 166Tuscany 88 81 41 92Trentino-Alto Adige 49 90 28 59Umbria 75 89 35 66Aosta Valley 44 86 26 38Veneto 52 62 18 50Centre-North 76 74 34 71South 144 147 220 153

Source: Daniele and Marani (2008).

2002 – 2005 were used as indicators.21 However, the bulk of the analysis of the relationbetween FDI and organised crime used 2002–2004 data for all four crimes (see Table 5) andcreated an OCI representing the ‘the sum of extortion and mafia-type association crimesper 10,000 inhabitants’ (see Map 1).22

Mennella analysed the impact of organised crime on the labour market by creating anOCI including 2004 data for a number of offences.23 The index summed the crimes andthen calculated the provincial rates per 1000 inhabitants.

Lavezzi analysed the structure of the Sicilian economy compared with those of otherItalian regions to highlight the importance of factors favourable to organised crime (largesize of the construction sector, large number of small firms, low level of technology, anda large public sector).24 Lavezzi acknowledged the limits of his study in estimating thepresence of the mafia, which was measured with a dummy for Sicily.25

(in press, corrected proof), http://www.sciencedirect.com/science/article/B6V97–4YXP15D-1/2/a7dfd9bede7fcf282752a05acdb7d5ce21. Ibid., 19.22. Ibid., 16.23. Antonella Mennella, ‘Reti sociali, criminalità organizzata e mercati locali del lavoro’ (Universityof Sassari, 2009), www.aiel.it/bacheca/SASSARI/papers/mennella.pdf. Criminal association,mafia-type association, slaughter, bomb and fire attacks, mafia murders, extortions, arsons, handlingstolen goods, usury, drug crimes, exploitation and facilitation of prostitution.24. Andrea Mario Lavezzi, ‘Economic Structure and Vulnerability to Organised Crime: Evidencefrom Sicily’, Global Crime 9 (2008): 198–220.25. The dummy should help explain the greater relevance of specific economic sectors in areas witha presence of organised crime.


http://www.sciencedirect.com/science/article/B6V97%E2%80%934YXP15D-1/2/a7dfd9bede7fcf282752a05acdb7d5ce

http://www.sciencedirect.com/science/article/B6V97%E2%80%934YXP15D-1/2/a7dfd9bede7fcf282752a05acdb7d5ce

http://www.aiel.it/bacheca/SASSARI/papers/mennella.pdf

Global Crime 49

50−7575−100100−125125−150>150

Map 1. Extortion and criminal associations in the Italian regions for the period 2002–2005∗.

Note: ∗Index calculated as the sum of crimes for the period 2002–2005 per 10,000 inhabitants.Source: Daniele and Marani (2008).

Caruso focused on the relation between organised crime and economic life in the Italianregions.26 His study adopted the OCI compiled by ISTAT.

Centorrino and Ofria analysed the relation between organised crime and labour pro-ductivity in the Italian regions.27 For each region, the proxy measure for organised crimewas mafia murders from 1983 to 2005 on the total population.

Calderoni and Caneppele sought to measure the extent of infiltration by mafia in publicprocurement in the provinces of Southern Italy (Table 6).28 The study created a criminalcontext index (indice di contesto criminale or ICC), consisting of five indicators intended to

26. Raul Caruso, Spesa pubblica e criminalità organizzata in Italia: evidenza empirica su datipanel nel periodo 1997–2003 (University Library of Munich, Germany, January 2008), http://ideas.repec.org/p/pra/mprapa/6861.html27. Mario Centorrino and Ferdinando Ofria, ‘Criminalità organizzata e produttività del lavoro nelMezzogiorno: un’applicazione del modello “Kaldor-Verdoorn”’, Rivista economica del Mezzogiorno1 (2008): 163–87.28. Francesco Calderoni and Stefano Caneppele, eds., La geografia criminale degli appalti: le infil-trazioni della criminalità organizzata negli appalti pubblici nel Sud Italia (Milano: Franco Angeli,2009).


http://ideas

50 F. Calderoni

Table 6. Calderoni and Caneppele’s ICC.

ProvinceMafia

murdersMafia-typeassociation

Dissolutionof city

councilsConfisca-ted assets

Offences relatingto public

procurement ICC Rank

Reggio Calabria 74.3 100.0 49.6 67.1 100.0 78.2 1.0Crotone 100.0 42.2 23.2 25.5 99.5 58.1 2.0Napoli 70.8 25.0 100.0 9.9 38.0 48.7 3.0Palermo 10.0 33.5 58.6 100.0 27.6 45.9 4.0Caltanissetta 14.9 82.2 47.5 21.8 54.7 44.2 5.0Catanzaro 50.2 55.5 18.3 9.4 57.3 38.2 6.0Catania 35.3 53.5 32.4 29.2 18.2 33.7 7.0Caserta 50.0 25.7 44.2 22.4 22.9 33.0 8.0Trapani 2.2 27.3 43.6 44.2 40.5 31.6 9.0Vibo Valentia 12.9 27.9 20.9 21.9 72.1 31.1 10.0Enna 21.3 61.7 0.0 8.2 60.8 30.4 11.0Ragusa 11.7 65.1 17.4 13.2 20.6 25.6 12.0Agrigento 13.2 18.6 19.5 24.1 40.9 23.2 13.0Messina 11.1 28.5 3.9 10.8 51.4 21.1 14.0Cosenza 13.3 17.7 0.0 3.0 70.0 20.8 15.0Benevento 2.2 10.8 2.7 0.0 80.9 19.3 16.0Brindisi 7.8 23.1 0.0 20.1 35.2 17.3 17.0Bari 17.6 10.4 21.8 16.2 17.7 16.7 18.0Siracusa 11.1 34.2 0.0 6.9 30.6 16.6 19.0Salerno 5.6 13.5 6.6 5.8 36.4 13.6 20.0Lecce 10.7 15.3 4.3 6.8 24.5 12.3 21.0Potenza 3.2 4.3 0.0 0.0 49.0 11.3 22.0Matera 0.0 23.5 0.0 5.6 26.7 11.2 23.0Avellino 6.6 13.8 5.3 1.2 28.4 11.0 24.0Taranto 0.5 18.5 0.0 13.3 22.0 10.9 25.0Cagliari 0.0 0.7 0.0 2.6 50.8 10.8 26.0Oristano 0.0 3.6 0.0 0.2 43.9 9.5 27.0Foggia 14.2 11.9 0.0 3.8 14.0 8.8 28.0Sassari 0.7 0.0 0.0 5.1 27.6 6.7 29.0Nuoro 1.2 3.2 0.0 0.8 15.1 4.1 30.0

Source: Calderoni and Caneppele (2009).

yield multiple information on mafia infiltration of public procurement.29 For each indicator,the province with the highest rate received a score of 100. The other provinces were scoredproportionally.30 The ICC was the mean of the scores of the five indicators.

2.2. Analysis of existing attempts to measure the presence of mafias in Italy

Based on the foregoing brief review of existing attempts to measure the presence ofmafias in Italy, this subsection analyses the current state of the art and identifies the

29. Rates of mafia murder 1996–2004; rates of mafia-type associations 1995–2004; rates of citycouncils dissolved for mafia infiltration 1991–2007; rates of assets confiscated from organised crime1998–2007; rates of offences connected with public procurement (corresponding to the offencesenvisaged by Article 640 bis, 316 bis, 316 ter, 353 of the Italian Criminal Code and Article 53 bisof Legislative Decree 22/1997). All rates are per 100,000 inhabitants, except city councils (rate per100 councils in the province).30. In some cases, to reduce the impact of outlier values for some indicators, provinces with outliervalues were assigned the same rate as the province with the second higher rate.


Global Crime 51

problems with such research. Some of the measurements considered do not allow compari-son among different areas. This is the case of the OCI compiled by ISTAT, which measuresregional trends compared with their level in 1995. The OCI cannot be used to assesswhether there is more organised crime in Sicily than, for example, in Calabria or Veneto.31

Surprisingly, Caruso seemed unaware of the nature of the OCI and compared the trendsof the OCI among the Italian regions, analysing its relation with certain socio-economicvariables.32

The measurements reviewed are frequently made at the regional level or do not includeall the Italian provinces. In the former case, the analysis is limited to regions, which are rel-atively large areas and may comprise very different socio-economic and criminal contexts.Most of the measurements reviewed above were at regional level. Some studies conductedanalysis at the provincial level (IPM, Censis, Mennella, Daniele and Marani, Calderoniand Caneppele) but only the studies by Mennella and Daniele and Marani analysed all theItalian provinces.

Most of the studies reviewed used data covering a limited time span. This may sig-nificantly affect the perception and measurement of the mafia. The latter, in fact, is anenduring and complex system that can hardly be measured with data relative to 1 or 2years. Constructing an index with data limited to only a few years may prove problematic,given that the presence of the mafia lasts and changes over time periods longer than a cal-endar year. This problem affects most of the studies and indexes cited above. The ISTATOCI, the Eurispes IPM, Centorrino and Ofria, Mennella, and Lavezzi used only data from1 year to construct their indexes. Daniele and Marani and Censis used data covering 3 or 4years.

In some cases, the geographical scope and the variables used have changed amongdifferent editions of the measurements. This applies especially to the IPM and affects thepossibility of comparing the IPM 2004 with the other editions (2005, 2007, 2008, and2010) to analyse the trends of the provinces.33

The variable selection is frequently problematic, and there is a significant varietyamong the measurements reviewed. First, the variables selected do not always directlyconcern organised crime. For example, data on bomb or fire attacks, usury, or moneylaundering include crimes not committed by the mafias. Although it may be assumed thatthe mafia commits a proportion of these offences, the current data comprise crimes com-mitted by single individuals as well as by mafia organisations. There is no informationabout the actual share of mafia-related offences for each selected crime. Furthermore, theshare of mafia-related arsons may be different from the share of mafia-related usury orrobbery. For this reason, the use of such data to measure the presence of the mafia mayprovide unreliable information. In some cases, moreover, the measurements include indi-rect crimes and exclude offences more directly related with the mafia. For example, theISTAT OCI includes arsons and serious robberies, but it excludes mafia-type associations.Second, some specific types of crimes suffer from a very high ‘dark figure’ (i.e. unre-ported crimes), so that the official statistics are not likely to reflect the actual distribution

31. For example, the OCI for Sicily in 2006 is 48.3 whereas for Umbria (a small central region) it is304.3.32. Because the OCI measures the regional trend compared to 1995 (1995 = 100), its use for a sta-tistical analysis across Italian regions appears unclear and potentially misleading. Indeed, the OCImeasures whether the presence of organised crime has increased compared to 1995 in a given regionand does not allow to compare to regional trends.33. For example, in the ranking of the IPM 2004 Crotone was the last province of Calabria. In 2005,the IPM 2005 Eurispes included mafia murders in the index. Crotone ranked first in IPM 2005.


52 F. Calderoni

of crimes, but rather the population’s propensity to report them. For this reason, these dataare extremely unreliable and should be analysed with great caution. Extortion is a typicalexample. The threat of retaliation for reporting extortion to the police is very serious whenit involves a mafia group, because it is relatively certain, is immediate, and may imply seri-ous damage, including death. It is consequently likely that in areas where organised crimeexerts strong control over the territory, data on extortion are severely underestimated.34

For these reasons, the official data on extortion are probably distorted, underestimating thedistribution of the offence in provinces under the close control of mafia-type associations.Other provinces may have higher rates although this may be due to a higher propensity toreport among victims, perhaps encouraged by less-pervasive control of the area by crimi-nal organisations. Despite the importance of extortion in the dynamics of the mafia, dataon extortion should be analysed with extreme care and not be considered as furnishingdirect measures of mafia presence. The above-reviewed attempts to measure the presenceof mafia frequently overlooked the difficulties involved in the use of official crime statis-tics, and they did not verify whether the variables selected were directly and reliably relatedto the mafia. There is no discussion on the selection of the variables and no analysis of thepossible problems relative to the use of these data.35

The procedures for calculating the measurements exhibit various problems. The IPMby Eurispes included data in absolute values for the number of phone interceptions in theprovinces. This severely affected the comparability among the provinces, because largerand more densely populated provinces are likely to have more interceptions than smallerones. It is not surprising that the biggest cities, such as Napoli, Palermo, and ReggioCalabria, appear at the top of the IPM. It is widely acknowledged that comparison amongvariables whose distribution is affected by the size of the population studied is achievedby calculating rates. In this case, the rate per 100,000 inhabitants, or better per phonelines, would have yielded comparable information. Moreover, the ISTAT calculates theOCI by summing the absolute values for the crimes selected and weighting them for theaverage statutory penalty. This is an attempt to consider the severity of different crimes.However, the average statutory penalty does not seem to be the most appropriate criterionin this case. In the absence of reliable statistics enabling calculation of the averages ofthe actual penalties inflicted for each offence, probably the maximum statutory penaltywould have been a better (and simpler) alternative. For example, if crime A is punishedwith up to 5 years of imprisonment and crime B is punished with between 2 and 4 years,the ISTAT procedure would give weighting factors of 2.5 and 3, respectively, implyingthat crime A should be considered less serious than crime B. Probably, most people wouldjudge the matter in reverse, arguing that crime A is more serious than crime B.36

34. Vittorio Daniele, ‘Organized Crime and Regional Development. A Review of the Italian Case’,Trends in Organized Crime 12 (October 2009): 211–34; Adam Asmundo and Maurizio Lisciandra,‘Un tentativo di stima del costo delle estorsioni sulle imprese a livello regionale: il caso Sicilia’, inI Costi Dell’illegalità: Mafia Ed Estorsioni In Sicilia, ed. Antonio La Spina (Bologna: Il mulino,2008): 113–36; and Stefano Caneppele and Francesco Calderoni, ‘Extortion Rackets in Europe:An Exploratory Comparative Study’, in Cross-Border Crime: Inroads on Integrity, ed. PetrusC. van Duyne et al., Colloquium on Cross-Border Crime 11 (Nijmegen: Wolf Legal Publishers,2010): 309–36.35. In another study, Daniele examines the problems of measuring extortion and mafia in Italy.Daniele, ‘Organized crime and regional development. A review of the Italian case’, 227.36. In any case, the letter of the law as an estimate of the seriousness of a crime is an extremesimplification which does not consider that criminal law has elements allowing parameterisationof the penalty to the actual seriousness of a crime (e.g. aggravating or attenuating circumstances,discretionary powers of the court). Therefore, the statutory penalty is merely a starting point and thefinal applicable penalty may significantly differ from it.


Global Crime 53

Moreover, this procedure does not seem preferable to alternative weightingsystems.37

Other measurements sum the different variables and subsequently calculate the rates.This procedure is inevitably affected by the overall values of the summed crimes. Forexample, in 2008 the police reported to the judicial authorities 104 mafia murders, 6646extortions, 10,728 cases of damage followed by arson, and 34,082 drug offences.38 It isclear that the sum of the provincial values will be most influenced by drug and damagefollowed by arson offences. This implies that very frequent and generic (not directly mafia-connected) offences are mixed with crimes that are direct signals of mafia presence, suchas mafia-type murder. The indexes by Mennella, Daniele and Marani, and ISTAT (withthe above-criticised weighting system) adopted this mechanism. In practice, these indexesreflect the distribution of the most numerous crimes, which are frequently the ones moreindirectly (if ever) related to the mafia.

Analysis of the existing attempts to measure the presence of the mafia in Italy highlightsseveral problems and issues. These relate to the selection of the variables most directlyrelated to the mafia, to the geographical and chronological scope of the data analysed, andto the procedures used to calculate the index. All the measurements reviewed exhibit one ormore problems relating to these points. Surprisingly, the current literature does not providea measurement of the presence of the mafia in Italy, notwithstanding the wealth of analysesof the mafia from multiple perspectives. One of the studies reviewed confirms this lackof knowledge: Lavezzi acknowledges dissatisfaction with the current measurements andargue that ‘the measurement of organised crime would therefore require a specific study’.39

This study aims to fill this gap by creating the MI, which is designed in particular to:

• accurately select the most directly mafia-related variables;• cover a prolonged time span;• provide scores at the provincial level; and• use a clear calculation procedure accounting for the different values and distributions

of the selected variables.

3. The creation of the Mafia Index

A methodology based on three steps was used to create the MI. The first step defined theconcept of mafia and devised an operational definition comprising multiple dimensions(Section 3.1). The second step identified possible indicators for each dimension and oper-ationalised them (Section 3.2). The third step created the MI by combining the variablesselected (Section 3.3).

3.1. The different dimensions of the mafia

The concept of mafia is an extremely complex one, and the literature has offered a num-ber of definitions from different epistemological perspectives. However, defining the mafiawould fall outside the scope of this study, which relies on two main definitions of ‘mafia’

37. For example, a 1–3 seriousness scale where the researchers would assess the seriousness of eachoffences according to various factors (possibly including the penalties provided by the law).38. Data are available on the ISTAT website ‘Justice in Figures’, giustiziaincifre.istat.it (accessedJanuary 17, 2011).39. Lavezzi, ‘Economic Structure and Vulnerability to Organised Crime’, 206.


54 F. Calderoni

for its purposes. The first is the well-known legal definition of ‘mafia-type association’ pro-vided by Article 416 bis of the Italian Criminal Code. Paragraph 3 of the provision definesthe ‘metodo mafioso’ (mafia method) and the goals of the mafia as follows:

An association is of mafia-type when its members exploit the potential for intimidation whichtheir membership gives them, and the consequent subjection and omertà to commit offences,or to assume, directly or indirectly, the management or control of financial activities, con-cessions, permissions, enterprises and public services, or for the purpose of deriving profitor wrongful advantages for themselves or others, or to hamper or to prevent during publicelections the free exercise of the right to vote or to obtain votes for themselves or for others.(Author’s translation)

The second definition is the ‘paradigm of complexity’. This is a sociological definition thatdescribes the mafia as ‘a system of violence and illegality that aims to accumulate wealthand to obtain positions of power; which also uses a cultural code and which enjoys a certainpopular support’.40 Some other scholars in Italy and abroad have adopted or aligned withthe paradigm of complexity,41 which among its various implications postulates that ‘mafia’is a complex, multifaceted concept.42

Based on these two definitions, both of which highlight the complexity of the mafiaand the variety of its activities and functions, this study adopts the following operationaldefinition of mafia: a criminal system characterised by the presence of criminal groupsproviding illicit goods and services; using violence, threat, or intimidation; and infiltrat-ing the political and the economic system. This definition includes the elements presentin both Article 416 bis and the paradigm of complexity: the provision of illicit goodsand services recalls the aim of ‘deriving profit or wrongful advantages’ from the crimi-nal offence, and the ‘accumulation of wealth’ of the paradigm of complexity. The use ofviolence, threat, or intimidation is mentioned both in the Criminal Code (‘potential forintimidation’) and by the paradigm of complexity (‘a system of violence and illegality’).The relation with the political system is implicit in one of the mafia-type association’sgoals, that of interfering with elections, and in the aim ‘of obtaining positions of power’highlighted by the paradigm of complexity. Infiltration of the economic system is listed byArticle 416 bis among the objectives of a mafia-type association (‘the management of eco-nomic activities’), and it can be considered a consequence of the definition of the paradigmof complexity, which points up the objectives of power and profit and the enjoyment of ‘acertain popular support’.

According to this operational definition, the mafia has four main dimensions:

(1) presence of criminal groups providing illicit goods and services;(2) use of violence, threat, or intimidation;

40. Umberto Santino, ‘Mafia and Mafia-Type Organizations in Italy’, in Organized Crime: WorldPerspectives, ed. Jay S. Albanese, Dilip K. Das, and Arvind Verma (Upper Saddle River, NJ: PrenticeHall, 2003), 87; Santino, Dalla Mafia Alle Mafie.41. Fabio Armao, Il Sistema Mafia: Dall’economia-Mondo Al Dominio Locale (Torino: BollatiBoringhieri, 2000); Letizia Paoli and Cyrille Fijnaut, ‘Introduction to Part I: The History ofthe Concept’, in Organised Crime In Europe: Concepts, Patterns and Control Policies in theEuropean Union and Beyond, ed. Cyrille Fijnaut and Letizia Paoli (Dordrecht: Springer, 2004),21–46; and Vincenzo Scalia, ‘From the Octopus to the Spider’s Web: The Transformationsof the Sicilian Mafia Under Postfordism’, Trends in Organized Crime (September 2010),http://www.springerlink.com/content/29g434039045q121/

42. Santino, ‘Mafia and Mafia-Type Organizations in Italy’, 87.


http://www.springerlink.com/content/29g434039045q121

Global Crime 55

(3) infiltration of the political system; and(4) infiltration of the economic system.

3.2. The selection of the variables

On the basis of a systematic review of the literature of existing attempts to measure thepresence of mafia in Italy, and of the available data sources, selection was made of anumber of possible indicators and related variables with which to measure the above fourdimensions.43

Table 7 lists the four dimensions, the indicators identified within each dimension,the variables measuring each indicator, and the available years. Two variables (‘Numberof mafia-type associations identified by the investigative authorities’ and ‘Offence ofmafia-politics vote-trading reported by the police to the prosecution service’) were notavailable.

Subsequently, the selected and available variables were analysed according to threecriteria. Table 8 presents the variables selected according to these criteria.

The first selection criterion was the availability of data for a sufficiently long period oftime. This criterion may appear trivial, but it has important implications. As argued above,a mafia is an established and long-lasting criminal system. To measure its presence in theItalian territory, it is necessary to take account of this persistent and continuous nature.Therefore, the selection of data for a limited time period may affect the analysis of thephenomenon and distort perception of it. The data collected for the study had differenttime spans. Long time series were available for 12 variables (at least 19 years available).For eight variables, data were available for shorter periods (between 3 and 10 years).

The second selection criterion was content validity.44 Each identified variable waschecked for its content validity, that is how it reflected one or more dimensions of theoperational definition of mafia. This criterion paid particular attention to how the variabledirectly reflected mafia activities. Some variables were directly and univocally related tothe mafia. This was the case, for example, of mafia-type associations or mafia murders.Clearly, these variables measured the phenomena that were directly related to the conceptof mafia. By contrast, some of the variables identified were not directly and univocally

43. In Italy, there are no victimisation surveys or other periodic surveys measuring the presenceor the perception of mafias (such as, the Transparency International’s Corruption Perceptions Indexor Global Corruption Barometer, World Bank’s Worldwide Governance Indicators). Such surveyswould provide important information with which to complement existing data (see Caneppele andCalderoni, ‘Extortion Rackets in Europe: An Exploratory Comparative Study’). Although thesesources have problems (sampling, memories of the respondents, costs), they have been used forthe analysis of organised crime (see Jan van Dijk, ‘Mafia Markers: Assessing Organized Crime andIts Impact Upon Societies’, Trends in Organized Crime 10, 2007: 39–56). The only existing surveyis the Italian Business Crime Survey conducted by the Italian Ministry of Interior and Transcrimein 2008 (see Giulia Mugellini, ‘Measuring Crime Against Business in the EU: The Problem ofComparability’ (PhD thesis, Milan, Università Cattolica del Sacro Cuore, 2010), 89). This surveyhas also covered offences related to organised crime (e.g. corruption, extortion), but results are avail-able only at regional level and have not yet been officially published (see Giulia Mugellini, ‘TheVictimization of Businesses in Italy: Key Results’ (paper presented at the annual meeting of the ASCAnnual Meeting, St. Louis Adam’s Mark, St. Louis, Missouri, November 12, 2008).44. Content validity refers to how ‘the measure covers the full range of the concept’s meaning’. RonetBachman and Russell K. Schutt, The Practice of Research in Criminology and Criminal Justice(Thousands Oaks, CA: Sage, 2010), 95.


56 F. Calderoni

Tabl

e7.

Dim

ensi

ons,

indi

cato

rs,a

ndva

riab

les

for

the

Mafi

aIn

dex.

Dim

ensi

onIn

dica

tor

Var

iabl

ePe

riod

Pre

senc

eof

crim

inal

grou

pspr

ovid

ing

illi

citg

oods

and

serv

ices

Pre

senc

eof

mafi

a-ty

peas

soci

atio

nsM

afia-

type

asso

ciat

ions

a19

83–

2008

Pre

senc

eof

mafi

a-ty

peas

soci

atio

nsO

ffen

ces

ofm

afia-

type

asso

ciat

ions

indi

cted

byth

epr

osec

utio

nse

rvic

eb19

94–

2003

Pre

senc

eof

mafi

a-ty

peas

soci

atio

nsN

umbe

rof

mafi

a-ty

peas

soci

atio

nsid

enti

fied

byth

ein

vest

igat

ive

auth

orit

iesc

NA

Pre

senc

eof

crim

inal

asso

ciat

ions

Cri

min

alas

soci

atio

nsa

1983

–20

08D

rug

traf

fick

ing

Dru

gof

fenc

esa

1983

–20

08P

rost

itut

ion

Exp

loit

atio

nof

pros

titu

tion

a19

83–

2008

Usu

ryU

sury

a20

04–

2007

Cou

nter

feit

ing

Cou

nter

feit

inga

2004

–20

07S

mug

glin

gof

good

sS

mug

glin

gof

good

sa20

04–

2007

Tra

ffick

ing

ofw

aste

Org

anis

edac

tivit

yfo

rth

eil

lici

ttra

ffick

ing

ofw

aste

a20

02–

2009

Use

ofvi

olen

ce,t

hrea

t,or

inti

mid

atio

nH

omic

idal

viol

ence

Mafi

am

urde

rsa

1983

–20

08H

omic

idal

viol

ence

Att

empt

edm

afia

mur

ders

a20

04–

2007

Inst

rum

enta

lvio

lenc

eE

xtor

tion

sa19

83–

2008

Inst

rum

enta

lvio

lenc

eK

idna

ppin

gsfo

rra

nsom

a19

83–

2007

Inst

rum

enta

lvio

lenc

eA

rson

sa19

83–

2008

Inst

rum

enta

lvio

lenc

eD

amag

efo

llow

edby

arso

na19

83–

2008

Inst

rum

enta

lvio

lenc

eB

omb

orfi

reat

tack

sa19

83–

2008

Infi

ltra

tion

ofth

epo

liti

cals

yste

mIn

filt

rati

onof

loca

lgov

ernm

ents

Cit

yco

unci

lsdi

ssol

ved

for

infi

ltra

tion

byor

gani

sed

crim

ed19

91–

2009

Infi

ltra

tion

ofel

ecti

ons

mafi

a-po

liti

csvo

te-t

radi

nga

NA

Infi

ltra

tion

ofth

eec

onom

icsy

stem

Infi

ltra

tion

ofpu

blic

proc

urem

ent

Off

ence

sre

late

dto

publ

icpr

ocur

emen

tb20

03–

2005

Mon

eyla

unde

ring

Mon

eyla

unde

ring

a20

04–

2007

Inve

stm

ents

byth

em

afia

Ass

ets

confi

scat

edfr

omor

gani

sed

crim

ee19

83–

2009

Not

es:a O

ffen

ces

repo

rted

byth

epo

lice

toth

epr

osec

utio

nse

rvic

e.O

pera

tion

alda

taba

sefo

rIt

alia

nla

wen

forc

emen

tage

ncie

s.U

ntil

2003

,thi

sda

taba

sew

askn

own

as‘m

odel

lo16

5’,w

here

assi

nce

2004

ane

wsy

stem

(‘S

DI’

acro

nym

for

Sist

ema

diIn

dagi

ne)

has

repl

aced

the

prev

ious

one.

bTe

rrit

oria

lIn

form

atio

nS

yste

mon

Just

ice

data

base

com

pile

dby

ISTA

Tan

dM

inis

try

ofJu

stic

e.c,

dM

inis

try

ofIn

teri

orda

ta.e A

genz

iade

lDem

anio

data

.f Dat

afr

omL

egam

bien

te,R

appo

rto

Eco

mafi

a.So

urce

:Aut

hor’s

com

pila

tion

.


Global Crime 57

Table 8. Selection of the variables for the MI.

Dimension VariableTime period

(years)Contentvalidity

Criterionvalidity

Presence ofcriminal groupsproviding illicitgoods andservices

Mafia-type associations 26 Yes YesMafia-type associations 10 Yes YesCriminal associations 26 No YesDrug offences 26 No NoExploitation of prostitution 26 No NoUsury 4 No NoCounterfeiting 4 No NoSmuggling of goods 4 No NoOrganised activity for the illicit

trafficking of waste8 No No

Use of violence,threat, orintimidation

Mafia murders 26 Yes YesAttempted mafia murders 4 Yes YesExtortions 26 No YesKidnapping for ransom 25 No YesArsons 26 No YesDamage followed by arson 26 No YesBomb or fire attacks 26 No Yes

Infiltration of thepolitical system

City councils dissolved forinfiltration by organised crime

19 Yes Yes

Infiltration of theeconomic system

Offences related to publicprocurement

3 No Yes

Money laundering 4 No NoAssets confiscated from

organised crime27 Yes Yes

Source: Author’s compilation.

related to the mafia: for example, statistics on drug offences, money laundering, and extor-tion. It was impossible to know from the data available whether the suspects/perpetratorsof these offences were related to the mafia (e.g. as members or other partners) or isolatedsingle criminals. It is legitimate to hypothesise that these offences are frequently commit-ted by criminal organisations, and even by the mafias. Indeed, most of the studies reviewedearlier did so. However, it is impossible to establish the share of the total offences actuallycommitted within the mafia groups and not by single individuals. Furthermore, the ‘mafiashare’ may vary from offence to offence. For this reason, variables not directly and uni-vocally related to the mafia did not pass the test for content validity. The reason for theirexclusion was to avoid the use of data whose connection with the mafia was only partialand unclear. Among the available variables, only six were directly related to the mafia.45

The other variables (n = 14) were not directly and univocally related to the mafia.The third selection criterion consisted in criterion validity.46 Each identified variable

was analysed, verifying its statistical correlation with the other variables (see Appendix 1:

45. Mafia-type associations (reported by the police), assets confiscated from organised crime, mafiamurders, city councils dissolved for infiltration by organised crime, mafia-type associations (indictedby the prosecution), and attempted mafia murders.46. Criterion validity refers to how ‘the scores obtained on one measure can be compared tothose obtained with a more direct or already validated measure of the phenomenon (the criterion)’.Bachman and Schutt, The Practice of Research in Criminology and Criminal Justice, 95.


58 F. Calderoni

Correlation matrix). Among the identified variables, 13 variables had a positive (Pearson’scorrelation r > 0.3) and statistically significant correlation with at least half of the othervariables.47 Seven variables were not correlated to any other variables (exploitation of pros-titution, drug crimes, and waste trafficking) or were correlated to between one and fourvariables (counterfeiting, money laundering, smuggling of goods, and usury).

3.3. The creation of the Mafia Index

Based on the above-described selection procedure, only four variables that successfullypassed the three selection criteria were included in the MI. They are as follows:

• mafia-type associations;• mafia murders;• city councils dissolved for mafia infiltration; and• assets confiscated from organised crime.

Each of the variables selected covered a different dimension of the operational concept ofmafia identified in Section 3.1. Consequently, the MI measures all four dimensions of themafia.

The literature has frequently adopted one or more of the variables selected as a reli-able proxy for the presence of mafias.48 Indeed, the presence of a mafia-type association(reported by the police to the prosecution service) reflects the actual presence of a crim-inal group operating in a given province. The commission of a mafia murder shows thatthe mafias have some form of control, or at least are able to reach their targets with rel-ative ease. The dissolution of a city council and the presence of assets confiscated fromorganised crime are reliable proxies for infiltration of the political and economic systems.Although the four variables satisfied the three selection criteria and are frequently used instudies on the Italian mafias, they cannot be considered immune to problems. Indeed, itis widely acknowledged that official/administrative crime statistics should be used withgreat caution, especially for non-conventional crimes such as mafia-related ones.49 In fact,these sources may reflect the efforts and performance of the criminal justice system ratherthan the actual trends of the crimes. The variables included in the MI are no exception.However, some elements suggest that these variables are sufficiently reliable. For example,mafia murders should have a limited dark figure. In some cases, mafias may conceal themurders that they commit, for example by resorting to the so-called ‘lupara bianca’, which

47. Criminal association correlated to 16 other variables; extortion and city councils dissolved fororganised crime infiltration to 13; mafia-type association (police reported), mafia murders, assetsconfiscated to organised crime, mafia-type association (indicted), attempted mafia murders, damagefollowed by arson, bomb or fire attacks, arsons and offences related to public procurement to 12; andkidnapping for ransom to 11.48. Most of the indexes and studies reviewed in Section 2.1 used one or more of the selected variablesto measure the presence of mafias in Italy. Other publications have focused on one specific indica-tor among those selected. See Giorgio Chinnici and Umberto Santino, La Violenza Programmata:Omicidi e Guerre Di Mafia a Palermo Dagli Anni ’60 ad Oggi (Milano: FrancoAngeli, 1989); GiorgioChinnici, ‘L’omicidio a Palermo’, in Rapporto Sulla Criminalità in Italia, ed. Marzio Barbagli(Bologna: Il mulino, Ricerche e studi dell’Istituto Cattaneo, 2003), 235–58; and Nello Trocchia,‘Appendice: Alcuni dati sui patrimoni mafiosi’, Federalismo Criminale: Viaggio Nei Comuni ScioltiPer Mafia (Roma: Nutrimenti, 2009).49. Caneppele and Calderoni, ‘Extortion Rackets in Europe: An Exploratory Comparative Study’.


Global Crime 59

consists in concealment of the victim’s corpse, thus impeding the detection and investiga-tion of the murder. More frequently, however, mafias do not conceal their murders. Indeed,the exercise of homicidal violence emits a very strong signal of the power and controlexerted by the mafias. Once the decision to murder has been taken, mafias may want tomaximise their effects, making it generally known that they are capable of killing theirenemies. Therefore, this variable does not appear to be excessively influenced by the per-formance of the criminal justice system; rather, it is likely to reflect the actual distributionof mafia murders across the national territory. The other variables show an extremely strongcorrelation with mafia murders and among them (see below and Appendix 1). This veryprobably confirms that provinces with high values on one variable also have high values onthe other three variables. Furthermore, the variables of the MI cover a time span of nearly30 years (except for city councils dissolved for mafia infiltration, a variable which coversthe 1991–2009 period). In such a (relatively) long time period, it appears difficult to arguethat the highest values of a province are due to a systematic outperformance (or underper-formance) of the criminal justice system in that province. Obviously, these elements do notcompletely dispel the risk that the variables depict the performance of the criminal justicesystem, at least in part. However, it appears justifiable to assume that the values of thevariables selected primarily reflect the distribution of mafia-related phenomena and onlymarginally the performance of the Italian criminal justice system. The scores of the MIsubstantially confirm this assumption (see Section 4).

Two different procedures were adopted to calculate the MI. The first of them (MI(rate))calculated the average of the annual rates for each variable and for each province.50 It thennormalised the rates, attributing the score of 100 to the province with the highest averagerate. The average of the scores for each indicator provided the final score for each province(third column in Table 9, ‘MI(rate)’).

The MI(rate) measures the presence of mafia in the Italian provinces, but it is greatlyaffected by the unequal distribution of the variables analysed. Indeed, all four indicatorswere extremely concentrated in a limited number of provinces, with the highest rates verydistant from the average and median rates (see Figures A1–A4).

The concentrated distribution of the selected variables may jeopardise a satisfactoryestimation of the actual presence of the mafia on the Italian territory. In particular, it mayoverestimate the presence of the mafia in a few provinces of Southern Italy. These arethe original areas of mafia-type organisations, and it is therefore not surprising that theyshow high rates on the indicators selected. For this reason, crimes and data may over-estimate the presence of the mafia, whereas for other provinces it may be more difficultto attribute a crime to a mafia-type group. Moreover, given the traditional presence ofmafia-type groups, these areas are also the target of extremely intensive law enforcementoperations. Consequently, the figures reflecting police reports and other data may be higherowing to more intensive enforcement. In general, the variables selected have very low val-ues, because the crimes are relatively rare and complex (compared, e.g., with robbery ortheft). Hence, even a very low rate (compared with other crimes) may still be an importantsignal of mafia presence in a given province. To measure the presence of mafia in Italy bet-ter, it may be more useful to focus on each province’s rank among all the Italian provinces.

50. Rates for mafia-type associations, for murders, and for confiscated assets are per 100,000 inhab-itants in the province; rates for city councils dissolved for mafia infiltration are per 100 city councilsin the province.


60 F. Calderoni

Table 9. The Mafia Index.

MIrank Province

MI(rate)a

MI(rank)b

MIrank Province

MI(rate)a

MI(rank)b

1 Reggio Calabria 80.58 98.32 53 Genova 1.13 12.672 Napoli 47.28 87.03 54 L’aquila 0.88 12.523 Caserta 35.33 84.73 55 Bologna 0.91 12.434 Caltanissetta 42.20 84.50 56 Lucca 0.96 12.435 Palermo 50.37 83.22 57 Trento 0.78 11.666 Catania 32.12 82.50 58 Pavia 0.75 11.547 Crotone 34.11 81.22 59 Macerata 0.90 10.818 Trapani 29.42 77.86 60 Asti 0.88 10.709 Catanzaro 32.83 76.97 61 Belluno 0.80 10.48

10 Vibo Valentia 26.08 74.13 62 Ferrara 0.62 10.1411 Agrigento 23.52 71.75 63 Arezzo 0.86 9.6312 Ragusa 17.83 61.82 64 Bergamo 0.70 9.1813 Messina 15.44 60.82 65 Trieste 0.66 9.1814 Enna 17.21 57.74 66 Pesaro Urbino 0.52 9.1615 Salerno 12.02 57.65 67 Pistoia 0.58 8.5316 Bari 12.83 55.72 68 Lodi 0.58 8.2117 Siracusa 12.74 50.71 69 Nuoro 0.55 7.3518 Lecce 7.50 48.76 70 Padova 0.70 7.2519 Brindisi 11.85 47.11 71 Modena 0.92 7.1620 Avellino 8.06 46.29 72 Udine 0.57 7.0321 Cosenza 7.22 44.10 73 Livorno 0.86 6.9522 Matera 6.99 39.75 74 Ravenna 0.59 6.9223 Foggia 4.56 36.64 75 Cremona 0.61 6.7124 Taranto 5.91 35.25 76 Pescara 0.62 6.3925 Benevento 5.16 34.80 77 Parma 0.53 6.1726 Latina 4.30 34.16 78 Viterbo 0.54 6.1727 Roma 2.92 27.89 79 Reggio Emilia 0.67 6.0728 Novara 4.53 25.24 80 Alessandria 0.43 5.9429 Milano 2.53 24.93 81 Mantova 0.45 5.9430 Como 2.16 24.10 82 Grosseto 0.41 5.6231 Torino 1.71 23.68 83 Isernia 0.49 5.6232 Sassari 1.54 21.68 84 Sondrio 0.38 5.4033 Verbano-Cusio-

Ossola2.05 21.53 85 Ascoli Piceno 0.50 4.87

34 Teramo 1.89 21.08 86 Rovigo 0.43 4.8735 Lecco 4.05 20.69 87 Ancona 0.54 4.6736 Brescia 1.92 20.50 88 Massa Carrara 0.56 4.3537 Potenza 1.98 20.35 89 Vercelli 0.22 4.3138 Rimini 1.67 19.79 90 Cuneo 0.28 4.1039 Frosinone 1.74 19.58 91 Siena 0.26 4.1040 Imperia 1.64 19.04 92 Pisa 0.38 3.9041 Varese 1.55 18.07 93 Perugia 0.47 3.6942 Venezia 1.47 17.84 94 Oristano 0.34 3.6743 Savona 1.44 16.66 95 Vicenza 0.39 3.1544 Piacenza 1.26 14.60 96 Treviso 0.35 3.0445 Gorizia 2.37 14.54 97 Rieti 0.39 2.7246 La Spezia 1.30 14.39 98 Chieti 0.32 2.6047 Firenze 1.58 14.21 99 Prato 0.15 2.5948 Cagliari 0.98 13.72 100 Bolzano 0.25 1.6349 Verona 1.02 13.72 101 Terni 0.25 1.6350 Aosta 1.26 13.64 102 Pordenone 0.10 0.5451 Forlì 0.83 13.59 103 Biella 0.00 0.0052 Campobasso 1.44 12.80

Notes: aAverage of the scores on the four indicators (for each indicator, the max average annual rate = 100).bAverage of the scores on the four indicators (for each indicator, the highest rank = 100).Source: Author’s calculations.


Global Crime 61

This approach makes it possible to offset the problems relating to the distribution of theselected variables.

For these reasons, a second calculation procedure was developed. This was theMI(rank), which was based on the rank of each province among all the Italian provinces foreach indicator, instead of the average of the annual rates. For each indicator, the MI(rank)calculated the average of the annual rates for each province. It then ranked all the Italianprovinces in decreasing order. It attributed the score of 100 to the province with the high-est rank and proportionally lower scores to the other provinces, according to their rank.The average score for each indicator provided the MI for each province (fourth column inTable 9, ‘MI(rank)’).

The two procedures yielded very closely correlated provincial scores.51 The provinceswith the highest rate (first procedure) ranked high also in the second procedure. However,the impact of the outliers was reduced and the overall distribution of the provinces was lessconcentrated.

To verify the overall quality and reliability of the MI, another measurement, includingother variables in the index, was performed. This index (MI Enlarged, or MIen) includedall the variables that had satisfied at least two of the three selection criteria (availabilityfor a long period, content validity, and criterion validity). The selection of the variables forthe MIen was more flexible and included variables that presented some issues relating totheir availability, their direct relation with the mafia, and their statistical correlation withthe other variables (see Table 8). The variables composing the MIen were as follows:

• mafia-type associations;• mafia murders;• city councils dissolved for mafia infiltration;• assets confiscated from organised crime;• criminal associations;• attempted mafia murders;• extortions;• kidnapping for ransom;• arsons;• damage followed by arson; and• bomb or fire attacks.

The same calculation procedures were used to create the MIen(rate) and the MIen(rank)(see Table 10). Once again the two procedures yielded provincial scores with highcorrelations.52

4. Analysis of the Mafia Index

The MI and the MIen yielded very similar results (Maps 2–5), with closely correlatedscores.53 This may be because the MIen included variables that were present in the MI.However, the correlation was confirmed, albeit at a slightly lower level, also if the MIwas compared with the set of variables included only in the MIen.54 This shows that both

51. Pearson’s r was 0.895 and statistically significant at 0.01 level.52. Pearson’s r was 0.918 and statistically significant at 0.01 level.53. Pearson’s r was 0.903 for the MI(rate) and MIen(rate) and 0.941 for the MI(rank) and MIen(rank).Both correlations were statistically significant at 0.01 level.54. Pearson’s r was 0.788 for the rate procedure and 0.856 rank procedure. Both correlations werestatistically significant at 0.01 level.


62 F. Calderoni

Table 10. Mafia Index Enlarged (MIen).

MIenrank Province

MIen(rate)a

MIen(rank)b

MIenrank Province

MIen(rate)a

MIen(rank)b

1 Reggio Calabria 75.29 96.96 53 Lecco 8.25 26.712 Vibo Valentia 55.06 87.92 54 Savona 9.48 25.753 Catanzaro 50.00 87.46 55 Trieste 10.33 25.294 Caltanissetta 56.33 85.78 56 Pistoia 9.54 25.205 Crotone 46.91 85.51 57 Como 7.10 24.656 Catania 36.72 81.11 58 Gorizia 10.79 24.347 Napoli 38.70 75.46 59 Prato 16.41 24.038 Caserta 31.19 73.94 60 Massa Carrara 9.09 24.019 Trapani 29.69 70.11 61 Aosta 9.92 23.33

10 Agrigento 25.24 69.49 62 Trento 7.69 23.1811 Palermo 34.99 68.4 63 Livorno 10.10 22.2612 Messina 28.37 67.89 64 Brescia 8.21 22.0013 Siracusa 30.25 67.61 65 Alessandria 8.82 21.8014 Brindisi 27.41 65.69 66 Padova 8.03 21.2115 Ragusa 25.46 63.46 67 L’aquila 8.19 20.916 Bari 22.73 63.39 68 Venezia 8.33 20.8017 Enna 25.41 62.44 69 Bolzano 6.40 20.7618 Salerno 18.77 60.13 70 Sondrio 8.30 20.7619 Foggia 27.03 59.78 71 Viterbo 8.84 20.5320 Lecce 18.58 58.71 72 Vercelli 7.72 19.9221 Cosenza 19.25 55.69 73 Arezzo 8.27 19.9022 Taranto 17.72 52.18 74 Rovigo 8.77 19.5423 Benevento 17.89 50.42 75 Ascoli Piceno 8.80 19.3824 Matera 16.13 50.38 76 Ravenna 8.15 19.1325 Avellino 15.13 50.13 77 Bergamo 7.85 18.926 Latina 17.36 48.22 78 Macerata 9.04 18.7527 Rimini 21.80 42.39 79 Pesaro Urbino 8.46 17.8728 Sassari 14.76 41.60 80 Terni 8.82 17.6629 Nuoro 26.34 40.68 81 Siena 8.31 16.1630 Potenza 14.09 40.20 82 Rieti 8.00 15.6931 Imperia 13.54 40.00 83 Verona 6.89 15.6032 Roma 12.52 36.18 84 Udine 6.87 15.2033 Biella 14.58 35.01 85 Pisa 7.75 14.7634 Isernia 12.90 34.87 86 Chieti 7.41 14.5135 Asti 12.91 34.13 87 Piacenza 6.84 14.1436 Cagliari 12.93 34.11 88 Pavia 6.19 13.8437 Teramo 13.77 33.47 89 Ancona 7.70 13.3038 Novara 9.58 33.15 90 Perugia 7.36 13.1639 Pescara 12.88 32.85 91 Lodi 5.19 13.0140 Frosinone 12.30 32.23 92 Pordenone 6.41 12.7641 Bologna 12.41 31.98 93 Ferrara 7.22 12.5242 Campobasso 11.72 31.57 94 Reggio Emilia 6.85 12.3843 Torino 10.76 31.32 95 Grosseto 5.55 12.1544 Lucca 9.97 30.47 96 Belluno 5.53 11.9345 Varese 9.41 29.82 97 Modena 6.72 11.5846 La Spezia 11.46 29.35 98 Cuneo 7.10 11.4047 Verbano-Cusio-

Ossola9.43 29.04 99 Mantova 5.96 10.77

48 Milano 9.65 28.99 100 Parma 5.53 9.4349 Forlì 11.93 28.69 101 Cremona 5.92 8.0550 Oristano 12.95 27.69 102 Vicenza 6.12 7.8051 Genova 10.32 27.12 103 Treviso 5.32 6.9852 Firenze 10.27 27.06

Notes : aAverage of the scores on the 11 indicators (for each indicator, the max average annual rate = 100).bAverage of the scores on the 11 indicators (for each indicator, the highest rank = 100).Source: Author’s calculations.


Global Crime 63

LegendMafia Index (rate)

<2,9

2

2,93

−8,0

6

8,07

−26,

08

26,0

9−50

,37

>50,

38

N

Map 2. Mafia Index (rate)∗.

Note: ∗Classes created through Jenks Natural Breaks Classification.Source: Author’s calculations.

LegendMafia Index (rate)

<8,5

3

8,54

−18,

07

18,0

8−36

,64

36,6

5−61

,82

>61,

83

N

Map 3. Mafia Index (rank)∗.


indexes converged in measuring the same phenomenon. The MI and the MIen did not differsignificantly. The more rigorous selection of the variables of the MI did not exclude fromthe MI important patterns of mafia presence that may have been included through othervariables.

The MI thus appears to be a reliable index measuring the presence of mafia at theprovincial level. The inclusion of other variables, as tested in the MIen, does not affectthe provincial scores and their distribution in a significant way. Furthermore, the MI isa relatively simple index, in that it is composed of only four variables. This makes it


64 F. Calderoni

LegendMafia Index Enlarged (rate)

<9,9

7

9,98

−15,

13

15,1

4−22

,73

22,7

4−38

,70

>38,

71

N

Map 4. Mafia Index Enlarged (rate)∗


LegendMafia Index Enlarged (rank)

<16,

16

16,1

7−27

,69

27,7

0−42

,39

42,4

0−70

,11

>70,

12

N

Map 5. Mafia Index Enlarged (rank)∗.


easier to calculate and update. However, the variables selected satisfy all three selectioncriteria, namely availability for a prolonged period, direct relation with the mafia, and cri-terion validity. The four variables of the MI are strongly correlated with each other (seeAppendix 1 for the correlations; for the MI, Cronbach’s alpha = 0.908).55

For the above reasons, and for the purpose of this study, it seems appropriate to adoptthe MI and discard the MIen. Indeed, the MIen consists of more variables, available forshorter periods and less directly related to the mafia.

55. van Dijk, ‘Mafia Markers: Assessing Organized Crime and its Impact upon Societies’, 42.


Global Crime 65

Maps 2 and 3 show the distribution of the MI across the Italian provinces. The pres-ence of mafia appears to be concentrated in some Southern Italian provinces: in particular,the provinces of Naples and Caserta (Campania), Southern Calabria (Reggio Calabria,Vibo Valentia, Crotone, and Catanzaro), Western Sicily (Palermo, Trapani, Agrigento,Caltanissetta), and Catania have high values in the index. These results confirm the exten-sive literature (both scientific studies and public reports), identifying those areas as mostaffected by the presence of mafia-type organisations.56 Indeed, the Camorra concentratesparticularly in the provinces of Naples and Caserta (in which is situated the town of Casaldi Principe, hometown of the Casalesi clan described in Roberto Saviano’s Gomorra57).58

The ′Ndrangheta is historically based in Southern Calabria.59 Similarly, the Sicilian Mafiaoriginated in the western Sicilian provinces.60

Notwithstanding the concentration in their original areas of mafia-type associations,many other southern provinces record high values in the MI(rate). This is the case of someprovinces of Apulia (Bari, Brindisi, and Lecce) where the ‘fourth mafia’, the Sacra CoronaUnita, arose in the 1980s.61

The analysis of the MI(rate) in Map 2 highlights only three provinces with low-mediumvalues outside the southern regions. These are the provinces of Novara (Piedmont), Lecco(Lombardy), and Latina (Lazio). However, when focusing on the MI(rank) in Map 3, newand more interesting patterns emerge. In particular, some large provinces in Central andNorthern Italy, such as Rome, Milan, Turin, and Brescia, present medium-level scores.Alongside these, some minor provinces in the Centre-North emerge, such as Verbano-Cusio-Ossola and Novara (Piedmont), Imperia (Liguria), Lecco and Como (Lombardy),Rimini (Emilia-Romagna), Latina and Frosinone (Lazio), Teramo (Abruzzo), and Sassari(Sardinia). Several other central and northern provinces record values higher than that ofthe lowest class in Map 3. Although these provinces do not reach the scores of those men-tioned above, they further demonstrate that the mafia is present outside the regions whereit originally developed.

In general, several provinces of the Central and Northern Italy present non-negligiblescores in the MI(rank), which highlights that the mafia cannot be considered a merelysouthern problem affecting only economically and socially underdeveloped provinces;rather, it is a national problem which is significantly present in all the major Italian citiesand several other provinces outside the South. These remarks should not be taken asunderestimating the critical situation of many southern regions and provinces. However,although the South has received much attention in the existing literature on the mafia, the

56. Rocco Sciarrone, Mafie vecchie, mafie nuove: radicamento ed espansione (Roma: DonzelliEditore, 1998), 155.57. Roberto Saviano, Gomorra: viaggio nell’impero economico e nel sogno di dominio della camorra(Mondadori, 2006).58. Tom Behan, The Camorra (London and New York: Routledge, 1996); Felia Allum, Camorristi,Politicians, and Businessmen: The Transformation of Organized Crime in Post-War Naples, Italianperspectives 11 (Leeds, UK: Northern Universities Press, 2006).59. Varese, ‘How Mafias Migrate,’ 422; Enzo Ciconte, ′Ndrangheta (Soveria Mannelli, Catanzaro:Rubbettino, 2008), 22–23.60. Diego Gambetta, Diego, The Sicilian Mafia: the business of private protection (Cambridge, MAand London: Harvard University Press, 1993), 81–85; Letizia Paoli, ‘Italian Organised Crime: MafiaAssociations and Criminal Enterprises’, Global Crime 6 (2004): 19.61. Monica Massari, La sacra corona unità: potere e segreto (Roma and Bari: Laterza, 1998).


66 F. Calderoni

existence of the mafia in the Centre and North is more disputed, particularly at theinstitutional level.62

5. Conclusions

The MI is intended to be a reliable tool to measure the presence of organised crime amongthe Italian provinces. It seeks to solve the main problems outlined in the review of attemptsto measure the presence of the mafia in Italy.

First, the selection of the variables composing the MI followed a detailed procedure thatoperationalised the concept of mafia and provided multiple dimensions. Each dimensionwas associated with more than one possible indicator and variable. The variables finallyselected were data available for a prolonged period and the satisfaction of both contentand criterion validity. Moreover, they covered all the four dimensions of the operationaldefinition of mafia.

Second, the MI covers the 1983–2008 time period (except for the variable ‘city councilsdissolved for mafia infiltration’, which refers to the 1991–2008 period). Consequently, theindex provides a long-period analysis of the mafia, avoiding the risks of relying only ondata relative to a few years.

Third, the MI is disaggregated at the provincial level. This level is more detailed thanthe regional one and enables identification of different patterns within the Italian regions,even within those with a traditional mafia presence.

Fourth, the MI was calculated using two different procedures. The first (MI(rate))reflected the actual distribution of the selected variables among the Italian provinces. Thesecond (MI(rank)) focused on each province’s rank among all the provinces, thus highlight-ing the relative positions instead of the actual rates. This second procedure sheds light onthe presence of the mafia outside the regions with a traditional mafia presence. The MI wastested against the MIen, an alternative index comprising a further seven variables amongthose most frequently used by existing measurements in the literature. The two sets of indi-cators yielded very similar results. This further confirmed the quality of the MI, which isbased only on four indicators and variables.

The scores of the MI confirm the critical situation of some provinces in Southern Italywhere the mafia-type associations have been traditionally present. However, the MI(rank)produces significant scores outside the South of Italy as well, highlighting that the mafia isa national problem that should not be reduced to a problem specific to the South (implicitlyrelated to underdevelopment and poverty).

Notes on contributorFrancesco Calderoni is a research fellow at the Università Cattolica of Milan and a researcher atTranscrime - Joint Research Centre on Transnational Crime, Università Cattolica del Sacro Cuore ofMilan and Università degli Studi di Trento (www.transcrime.it).

62. Both the mayor and the prefect of Milan have minimised the threat of the presence of themafia in the North: ‘I soldi son desideri’ (Annozero. Rai Due, Maggio 25, 2010. http://www.rai.tv/dl/RaiTV/programmi/media/ContentItem-6ee35904-a87f-4a5c-bd9b-2416251964c6. html?p=0);‘La Moratti ad Annozero: la mafia a Milano non esiste’ (la Repubblica, Luglio 19,2010. http://tv.repubblica.it/edizione/milano/la-moratti-ad-annozero-la-mafia-a-milano-non-esiste/50700?video); and Andrea Galli, ‘Il prefetto: a Milano la mafia non esiste - Milano’ (Annozero. RaiDue, Maggio 25, 2010. http://www.rai.tv/dl/RaiTV/programmi/media/ContentItem-6ee35904-a87f-4a5c-bd9b-2416251964c6.html?p=0).


http://www.transcrime.it

http://www.rai.tv/dl/RaiTV/programmi/media/ContentItem-6ee35904-a87f-4a5c-bd9b-2416251964c6.html?p=0

http://tv.repubblica.it/edizione/milano/la-moratti-ad-annozero-la-mafia-a-milano-non-esiste






Global Crime 67

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for

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bles

Not

es:4

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afia

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ns

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68 F. Calderoni

80

60

40

20

00,00 1,00 2,00

416 bis

3,00 4,00

Fre

quen

cy

Figure A1. Frequency distribution of mafia-type associations at an average provincial annual rate(for the period 1983–2008) per 100,000 inhabitants.Source: Author’s calculations (N = 103).

100

80

60

Freq

uenc

y

40

20

00,00 1,00 2,00 3,00

Omma4,00 5,00 6,00

Figure A2. Frequency distribution of mafia murders at an average provincial annual rate (for theperiod 1983–2008) per 100,000 inhabitants.Source: Author’s calculations (N = 103).


Global Crime 69

100

80

60

40

20

00,00 0,50 1,00 1,50

Com2,00 2,50 3,00

Fre

quen

cy

Figure A3. Frequency distribution of city councils dissolved for organised crime infiltration at anaverage provincial annual rate (for the period 1991–2009) per 100 municipalities.Source: Author’s calculations (N = 103).

100

80

60

40

20

00,00 2,00 4,00 6,00

Beni8,00 10,00 12,00

Fre

quen

cy

Figure A4. Frequency distribution of assets confiscated from organised crime at an averageprovincial annual rate (for the period 1983–2009) per 100,000 inhabitants.Source: Author’s calculations (N = 103).


Documents

Calderoni 2011 - Where is the Mafia in Italy