Origins of the Sicilian Mafia:

The Market for Lemons

Arcangelo Dimico∗ Alessia Isopi Ola Olsson

December 31, 2012

Abstract

In this paper, we study the emergence of an extractive institution that hamperedeconomic development in Italy for more than a century: the Sicilian mafia. Since itsfirst appearance in the late 1800s, the origins of the Sicilian mafia have largely remaineda mystery. Both institutional and historical explanations have been proposed in theliterature through the years. In this paper, we develop an argument for a market struc-turehypothesis, contending that mafia arose in towns where firms made unusually highprofits due to imperfect competition. We identify the production of citrus fruits as asector with very high international demand as well as substantial fixed costs that actedas a barrier to entry in many places and secured high profits in others. We argue thatthe mafia arose out of the need to protect citrus production from predation by thieves.Using the original data from a parliamentary inquiry in 1881-86 on Sicilian towns, weshow that mafia presence is strongly related to the production of oranges and lemons.This result contrasts recent work that emphasizes the importance of land reforms and abroadening of property rights as the main reason for the emergence of mafia protection.We also show that the local distribution of the mafia in the 1880s is a strong predictorof mafia activity in recent years.

Keywords: mafia, Sicily, protection, barriers to entry, dominant positionJEL Codes:

1 Introduction

The institutional hypothesis of economic development, emphasizing the importance of private

property rights and constraints against the ruling elite, has recently received strong support

in empirical studies. Several cross-country studies have shown that countries with a firm

rule of law typically also have better economic performance. But how do socially inefficient

institutions arise and how can they sometimes survive for centuries?

The Sicilian mafia is arguably one of the most infamous and economically harmful in-

stitutions in the world. After its first appearance in Sicily in the 1870s,1 it soon infiltrated

the economic and political spheres of Italy and the United States and has at times been

∗Corresponding author. Email: a.dimico@qub.ac.uk. Special thanks to Giuliano Isopi for excellent researchassistance. We gratefully acknowledge comments from Gani Aldashev, Jean-Marie Baland, Michael Bleaney,Paolo Casini, Chris Colvin, Giacomo de Luca, Stefano Fenoaltea, Halvor Mehlum, Kalle Moene, KyriacosNeanidis, Jean-Philippe Platteau, Oleg Shchetinin, Yves Zenou, and seminar participants in Gothenburg,Leuven, Birkbeck College, the EEA-ESEM meetings in Malaga, Manchester, Namur, Oslo, and Stockholm.

1The first evidence of the presence of a secret sect (referred to as cosca) we have found is an account from1872 by Dr Galati, a landlord who used to own a lemon grove just outside Palermo, who wrote about a “manof honor” who made increasing use of violence and extortion to force him to sell his lemon grove (Dickie,

1

considered a serious threat to the rule of law in both countries. Although outcomes of the

mafia’s actions such as murder, bombings, and embezzlement of public money have been

readily observed during the last 140 years, its origins have largely remained a mystery.

In this paper, we provide a study of the origins of the Sicilian mafia using a unique dataset

from the latter part of the 19th century. The main hypothesis that we investigate is that the

origins of the Sicilian mafia are strongly associated with the unusually high profits made by

citrus fruit producers following a boom in international demand. We argue that the primary

source of these abnormal profits, especially for lemon producers, is found in a particular

market imperfection: the high and geographically varying fixed costs of production. These

high barriers to entry implied that citrus trees could only be cultivated in certain areas and

that a few local producers earned substantial profits from the boom.2 The combination of

high profits, a weak rule of law, a low level of interpersonal trust, and a large number of poor

people implied that lemon producers were a natural object of predation by thieves. Given

the poor ability of the government in protecting private property rights at the time, lemon

producers resorted to hiring mafia affiliates for private protection.

Using historical statistics and a formal model featuring households, producers, and the

mafia, we develop the argument that the market for citrus faced an exceptionally high de-

mand during the mid- to late 1800s and that the high and regionally varying fixed costs

of production implied supernormal profits in certain locations. In the empirical section, we

then test the main implications of the model using a unique dataset from Sicilian towns and

districts gathered from a parliamentary inquiry conducted in 1881-86 (Damiani, 1886). Our

results indicate that mafia presence in the 1880s is strongly associated with the prevalence

of citrus cultivation, controlling for a number of other potential covariates. No other crop or

industry has a robust impact on mafia presence. We interpret these findings as being con-

sistent with a market structure-explanation to the origins of mafia. We conclude by showing

that the historical distribution of mafia is a strong predictor for more recent mafia activity.

Our paper relates to several different strands of literature.3 First, it is related to the

literature on the historical emergence of “extractive” institutions that hamper economic

development and that can appear at critical junctures in a country’s history (Acemoglu et al.,

2006; Acemoglu and Robinson, 2012). The mafia is undoubtedly an example of an extractive

institution that emerged during a critical period in history (Italian unification). Our analysis

is, however, somewhat different from this tradition since we emphasize the economic or

market structure related factors behind mafia organizations rather than political origins

(such as a weak and oppressive Bourbon state in Sicily with substantial social inequalities,

as discussed further below).

2004). When the Minister of Home Affairs learned about Galati’s note, he soon asked for a written reportfrom the chief of police in Palermo and then ordered two parliamentary inquiries (the Bonfadini Inquiry in1876 and later the Damiani Inquiry in 1881-5) to better understand the economic conditions and the situationof crime in Sicily.

2We argue that citrus production was associated with unusually heavy barriers to entry due to the highfixed cost of planting trees and waiting several years for them to grow, the need to build protective walls tokeep thieves out, etc. Due to a large regional variation in the climate and soil suitability for growing lemons,the fixed costs of starting up a cultivation was very different across towns.

3Please see the working paper version for a more extensive literature review.

2

Our results are also strongly associated with research on the “curse of natural resources”

(see Van der Ploeg, 2011, for a recent overview). We find that the boom in citrus exports

in the late 19th century is a key factor behind the rise of mafia. This is consistent with

the more recent finding that windfall gains from natural resources are often associated with

intense rent seeking and patronage politics. For instance, Sala-i-Martin and Subramanian

(2003) argue that political corruption related to oil revenues hampered Nigeria’s growth for

decades. Acemoglu et al. (2004) show how mineral wealth in Zaire allowed President Mobutu

to buy off political challengers. A main theme in this tradition is that resource windfalls

might actually destabilize and deteriorate institutions if key groups in society believe that

predation is more profitable than production (Mehlum et al., 2006; Congdon Fors and Olsson,

2007).

Another literature that our analysis connects to is that on the economic analysis of or-

ganized crime, which focuses on weak institutions, predation, and enforcement of property

rights (Fiorentini, 1999; Grossman, 1995; Anderson, 1995; Skaperdas and Syropoulus, 1995;

Skaperdas, 2001). Grossman (1995) and Skaperdas (2001) both consider mafia as an alterna-

tive enforcer of property rights. Using a model with two actors (a self-governing community

and mafia) and potential robbers, Skaperdas (2001) shows that in the absence of an enforcer

of property rights, mafia can represent a sort of second best solution.4 Regarding the eco-

nomic costs of organized crime, Reuter (1987) and Gambetta and Reuter (1995) analyze the

effect of organized crime on the enforcement of cartel agreements in legal markets.

Regarding the weak institutions-hypothesis, the loss of social capital and public trust may

also play a key role for the development of a private provider of protection. Putnam (1993),

for example, analyzes the loss of social capital in Southern Italy due to the repeated foreign

dominations experienced in the region. Gambetta (1996) also considers private trust rather

than public trust as very important for the development of mafia in southern Italy. Social

capital together with a loss of public trust may further affect the development of organized

crime because of kinship relations, corruption, etc. (see Fukuyama 2000; Gambetta, 2009;

Levi, 1996; Hardin, 1999; Newton, 2001).

Yet, our paper is most closely related to Bandiera (2003). Bandiera’s main hypothesis

is that it was the increase in land fragmentation following the Bourbon-era land reforms

(1816-1860) that provided the breeding ground for mafia protection. A higher number of

land owners increased the need for private protection. In Bandiera’s model, a key feature is

that protection of one producer generates a negative externality on other producers, since it

makes them more likely objects of predation. In an empirical section where she uses infor-

mation from the Summary Report presented to the Italian parliament by Damiani (1886),

Bandiera (2003) finds that a variable capturing differences in land fragmentation is a signif-

icant determinant of mafia presence.5

4The idea of a weak state and private protection is well illustrated by Don Calo Vizzini, one of the historicalbosses of the Villalba mafia. In an interview with Indro Montanelli, he said that ”...the fact is that in everysociety there has to be a category of people who straighten things out when situations get complicated. Usuallythey are functionaries of the state. Where the state is not present, or where it does not have sufficient force,this is done by private individuals” (Montanelli, 1949).

5The information available in the Damiani Inquiry (see Footnote 1) has previously been used also by

3

While our analysis also identifies landowners’ demand for private protection as the main

process through which the mafia was mobilized, we explicitly focus on the role of the market

structure rather than on land fragmentation. With respect to Bandiera (2003), we improve

the sample size by using the original survey where pretori (lower court judges) provided

answers related to crimes within the survey that Damiani sent out in 1883 (see Figures A1

and A2 in the Appendix). The original copy of the Daminai Inquiry permits us to increase

the sample size from the 70 towns located in the western part of the island in Bandiera (2003)

to almost all available Sicilian towns (143 in total) for which pretori provided answers. With

this more complete sample, we find that the land fragmentation variables indeed explain some

of the variation in mafia presence. However, we also find support for a link between mafia

and the prevalence of large scale plantations. The latter finding confounds the interpretation

that the mafia appeared as a result of land reform. Our main result is that the most robust

determinant of mafia activity is production of citrus fruits.

The working paper by Buonanno et al. (2012) also studies the importance of export

markets for mafia appearance using data from Cutrera (1900), a police officer in Palermo.

Cutrera uses as sources Colajanni (1900), Alongi (1887) and other data from local police

to elaborate a map of Sicily where the intensity of mafia activity is outlined for every city.6 Even though the data provide figures on the level of mafia in almost all Sicilian cities

at the beginning of the 20th century, they capture the situation prevailing almost 20 years

later than the Inquiry. Over these twenty years, the mafia extended its activity to cities that

initially were not affected and hence the data from Cutrera are more useful for understanding

the temporal evolution of the mafia.7 Buonanno et al. (2012) find that sulphur production

had a strong association with mafia presence in 1900. However, they do not develop an

explicit argument for why export revenues were associated with mafia revenues in certain

sectors.

Besides the economic literature, our analysis is also related to a long tradition of works in

anthropology, sociology and history on the Sicilian mafia. The classical contributions include

early investigations such as Villari (1875), Sonnino and Franchetti (1877), and Colajanni

(1885, 1895). In recent years, the origin of the mafia has also been discussed in Gambetta

(1996), Dickie (2004), and Lupo (2009).8 While Lupo (2009) and Dickie (2004) consider

profits related to the lemon industry in the Western part of the island as a pre-condition

for the development of the mafia, Gambetta (1996) focuses on the division of land resulting

from the abolition of feudalism and other policies introduced by the Italian government after

1860 (i.e., sale of land owned by the church and the crown before the unification). These

other scholars studying the origins of the Sicilian mafia; see for instance Colajanni (1885, 1895), Hess (1973),Arlacchi (1986), Catanzaro (1992), Gambetta (1996), Dickie (2004) and Lupo (2007).

6Alongi (1887) and Colajanni (1900) in their turn use the information available from the original inquiry.Therefore, their books represent a further elaboration of the results collected by the Damiani Inquiry, whichcould potentially add a bias in the data provided in Cutrera (1900).

7This is confirmed by Gambetta (1996) who argues that in the period between the late 1870s and late1890s, the mafia evolved quite markedly as a result of “economic and political conflicts among local fac-tions, especially in connection with the institutional changes affected by the Italian state between 1869-1890”(Gambetta, 1996, pg.83).

8See also Lupo (2009) for a general history and Monroe (1909) for a description of the agricultural practicesin Sicily at the time.

4

policies opened a market for private protection where the mafia acted as an incumbent.

The extensive literature discussed above provides plausible explanations for the origins of

the mafia. Yet, with the exception of Bandiera (2003) and Buonanno et al. (2012), it is still

difficult to understand on the basis of these works why we observe a substantial variation

across regions experiencing very similar social conditions. If a weak state, a high regulatory

burden, and a lack of public trust are the factors that matter for the development of mafia,

then we should not observe any local variation within the territory at hand. However, this

is not the case as across counties and villages exposed to these same conditions there is a

notable variation in mafia presence. Actually, organized forms of crime initially appeared

only in a small number of localities before expanding over the entire region.

The market structure hypothesis advanced in our paper not only complements existing

theories of mafia emergence, for instance those focusing on political factors, but is also

extremely consistent with the timing of the origins of mafia. The scientific discovery in the

late 1700s that citrus fruits could cure scurvy led to a strong surge in demand during the

following century. By 1880 (the period for which we have data on mafia activity), Sicily

had become the largest supplier of lemons and lemon by-products in the world. In fact, the

island covered more than 78 percent of total lemon imports in the US, which at the time was

the largest importer from Sicily. This exogenous shock in the international lemon market

therefore provides us with the basis for considering profits in the sector as the factor able to

explain the cross-regional variation in the appearance of mafia.9

In summary, we believe our paper makes the following contributions to the existing

literature: First, it provides a formal model of how the market structure and the prevalence

of cross-sectional variation in fixed costs affect the demand for mafia protection. Second,

we offer the most comprehensive empirical analysis to date on the origins and persistence of

mafia since the 1880s and identify a novel explanation for the emergence of the mafia during

the period.

The paper is structured as follows: Section 2 gives a background to the Sicilian economy

and the role of citrus fruits. Section 3 outlines the formal model. Section 4 includes the

econometric specification and a discussion of the data, whereas the main empirical results

are found in Section 5. Section 6 concludes the paper.

2 The Sicilian Economy and the Role of Citrus Fruits

Sicily is the largest island in the Mediterranean. It is easily accessible by the sea from all

directions and has a varied and fertile terrain and, above all, an unparalleled location: the

island in fact has been both a gateway and a crossroads, on the one hand dividing the

eastern and western Mediterranean, on the other linking Europe and Africa as a stepping

stone (Finley et al., 1987).

Because of the island’s importance and strategic location, its past has been marked by

9Following this exogenous shock, one might expect local producers to have increased supply. However,this did not happen everywhere, but mostly along the coastline. The reason is related to the cross-regionalvariation in the climate that we claim represents a barrier to the expansion of the sector inland.

5

continuous foreign domination and the list of migrants and colonizers is long: First the

Greeks, then the Romans followed by the Byzantines, then the Arabs, the Normans, the

Spanish, the French, and then the Spanish again. This long period of different foreign rulers

had an important impact on the the historical development of Sicily. The material advantages

of the foreign powers were created at the expense of the Sicilians through rents, taxes, and

plain looting. This parasitical interest inevitably did great harm to the countryside and to

the people.

At the beginning of the 19th century, overseas commerce was mostly carried out by the

French and a native industry in Sicily hardly existed except at household level. Sicily lacked

the main prerequisites for an industrial revolution: no iron, no coal, no navigable canals

and rivers and very poor internal communications. The middle class remained professional

and bureaucratic rather than commercial and industrial, while the leaders of society lived

off rents.

Before 1861, the vast majority of the population was employed in agriculture. The

production system in the agricultural sector reflected the typical feudal system. Villari

(1875) reckons that there were three main social classes in Sicily: 1) landlords, 2) a middle

class (gabelloti), 3) and peasants who were normally exploited by the gabelloti. The gabelloti

leased the land from landlords and then rented small pieces of it to the peasants. Peasants

worked the land and gave back to the gabelloti a share of the harvest depending on the kind

of contract stipulated. The contracts were relatively short and generally designed to exploit

the peasants. When the yield was not enough, peasants usually had to borrow from the

gabelloti at interest rates that made it impossible to pay back the debt.

The situation did not change much after Sicily joined the Reign of Italy. In 1887 the share

of citizens that owned the land was still the lowest in Italy with an average of less than 2.05

owners per 100 citizens, compared to for instance 15 owners per 100 in Piedmont (Colajanni,

1885).10 In addition, almost 56 percent of the population employed in agriculture owned less

than one hectare of land and most of them were hired by the landlord on a daily basis at

less than one lira per day rate.

Despite its underdeveloped economy, Sicily was a leading producer of wheat, olive oil,

wine, and citrus fruits. The international demand for lemons started increasing from the

late 1700s when lemons and lemon juice became a standard preventive treatment against

scurvy.11 In the words of Baron (2009): ”The Sick and Hurt Commission agreed to supply

all naval ships on foreign service with lemon juice, extended in 1799 to all the ships on the

British coast. Between 1795 and 1814 the admiralty issued 1.6 million gallons of lemon juice.

Sweet lemons were imported, especially from the Mediterranean region turning Sicily into a

vast lemon juice factory” (Baron, 2009, pg 324).

In the late nineteenth century, Sicilian production of citrus fruits represented almost 73

percent of the total production in Italy (Pescosolido, 2010).12 Table 1 reports the distribution

10In 1840, 127 princes, 78 dukes, 130 marquises, and an unknown number of earls and barons had completecontrol over most of the land (Travelyan, 2001).

11Scientific support for the theory that consumption of citrus fruits cured scurvy was established by JamesLind, a British naval officer and surgeon, in the latter part of the 18th century.

12Other reasons why Sicily may have had a dominant position in this sector are historical (related to the fact

6

of lemon trees in southern Italy in 1898. Palermo and Messina were the two provinces with

the largest absolute number of lemon trees in Sicily, accounting for almost 59.5 percent of

the total trees, followed by Catania with approximately 12 percent. Outside Sicily, in the

Calabria region, Reggio Calabria was also a large producer of lemon with an absolute number

of trees of 1,232,675 (almost 18 percent).13

Table 1: Distribution of lemon trees in Southern Italy in 1898

In the late 19th century, the dominant position of Sicily was consolidated as a result

of a significant expansion in global demand. The total surface area devoted to the citrus

production went from 7,695 hectares in 1853 to 26,840 hectares in 1880 (Pescosolido, 2010).

The expansion was a result of the large returns associated with citrus productions. Monroe

(1909) estimates that revenues were almost $200 per acre (in 1908 US dollars), providing a

net profit of more than $150 per acre. The importance of these figures is further emphasized

by Dickie (2004) when he argues that “citrus cultivation yielded more than sixty times the

average profit per hectare for the rest of the island” (Dickie, 2004, p.39).

In the period 1881-85, the quantity of citrus exported was almost 949,000 units, compared

to 250,000 units in 1850 (Pescosolido, 2010). A large share of this production was exported

to the US, which was one of the largest markets for Sicilian citrus exports. A combination of

factors contributed to this outcome: a favorable international context, elimination of exports

duties, and a considerable improvement of transports.

Table 2 shows figures on the lemon trade between Italy and the US in 1898-1903. The

left-hand side of the table shows the total Italian lemons exports and the relative percentage

exported to the US. The right-hand side shows the total US lemons imports and the estimated

percentage coming from Italy.14 The average quantity of lemons exported from Italy (and

therefore mainly from Sicily) over this period amounts to 389 million pounds and the average

share of fruits imported from the US is almost 34 percent of the total Italian production.

On the other hand, most lemons imported form the US in this period came from Italy.

Calculating the total Italian exports to the US, using percentages to the right in the table,

we can estimate that almost 78.4 percent of the total US lemons imports between 1898-1903

came from Italy. Besides the US, the UK and Austria were also two large importers of

lemons. Over the decade 1898-1908, the UK imported 17.7-25 percent of the total Italian

lemons exports, and the Austro-Hungary imported 14.4-22.8 percent (Powell, 1909).

Table 2: Total Italian exports of lemon and total United States imports

Table 3 shows the total Italian exports of citrate of lime (which is a soft drink) as well

as total imports of it into the USA 1899-1903 period. The US is again the most important

that the plant developed in the Mediterranean starting in the Roman era) and geographical (the importanceof the Mediterranean in international trade).

13Some trees were also planted in the central parts (almost 798,214 trees) and in northern Italy (almost564,559). In total, there were 8,287,758 lemon trees across the peninsula, but Sicily and Calabria regionsaccounted together for almost 91% of the total Italian production.

14These data should to be evaluated with some caution given that the total Italina lemons exports refer tothe calendar year while the total US imports refer to the fiscal year (and the two do not coincide).

7

market for citrate. Besides the US, France and the UK are also important markets for citrate

from Italy and according to Powell (1909) the total quantity imported by these two countries

combined was at least as large as that for the US. A similar pattern can also be shown for

the export of essential oils from lemons and oranges.

Table 3: Total Italian exports of citrate of lime and total United States imports

Given the extent of the production and the international demand for lemons, the sector

was of strategic importance for the Sicilian economy.15 As shown in Table 4, the total export

revenue from the harbor of Messina in 1850 was approximately 21.6 million lire (at current

price). Revenues from citrus and derived products equaled almost 9.2 million lire, accounting

for almost 42.4 percent of the total export revenues. In 1873 the percentage went up to more

than 50 percent of the total exports (Battaglia, 2003).

Table 4: Exports from the Harbor of Messina in 1850

As mentioned earlier, these substantial revenues are mainly explained by Sicily’s ge-

ographic location and associated climate. In particular, the island’s hot coastal plains,

together with the exceptionally fertile soil made of limestone base with heavy coatings of

lava, were well-suited for growing citrus fruits. Lemon trees have a very poor tolerance for

extreme climates and require very specific climatic conditions. The average temperature re-

quired for them to grow and vegetate is 13-30 ◦C, and flowers and fruits die after only a few

minutes of exposure to temperatures below 1-2 ◦C. This high intolerance to frost explains

the local variation in places where the trees can vegetate: places slightly above the coastline

are more suitable because of the relatively low variation in daily (and annual) temperature,

while locations in the mountains with a larger daily (and annual) variation in temperature

are less suitable for cultivation. In addition, lemon trees require constant provision of water.

The irrigation system used in the plains surrounding Palermo worked thanks to the norie,

a quite advanced technology that enabled farmers to collect water very deeply in the soil.16

Yet, given that local institutions often failed to provide an adequate funding of the irrigation

system, local producers had to make costly investments in order to build and maintain it.

We argue that the high export revenues from citrus, combined with the sizeable initial

investments needed for the development of citrus cultivation, made local producers very

vulnerable to predation. In the absence of state protection, the mafia exploited this systemic

vulnerability to extort part of the profits made by the industry. Consequently, we consider

profits coming from imperfect market structures as a natural condition for the development

of the mafia.

15The leading role of Sicily for the US lemons market continued until the end of the 19th century, whenthe production in Florida became substantial.

16The norie were initially moved by animals, but already in the 1872 they started to be moved by steampumps.

8

3 The Model

The model considers three active agents -households, lemon producers, and the mafia - as

well as a (latent) government that determines the strength of property rights. The aim of

the model is to explain how the structure of fixed costs affected the decision of whether to

produce lemons or not and, if production occurred, under what circumstances the lemon

producers chose to pay the mafia for protection against thieves. The model is meant to

describe the situation in the 1880s and is not necessarily relevant for understanding the

contemporary nature of mafia operations.

3.1 Households

Let us assume that the utility of a representative household is given by

U = α lnC + (1− α) lnX

where U is utility, C is consumption of lemons from Sicily, and X is consumption of other

goods.17 The parameter α indicates the taste for lemons within the representative household.

For simplicity, we assume that the island of Sicily has a monopoly in the production of

lemon.18 The representative household might be thought of as the average Western household

in the last decades of the 1800s. The individual household’s budget constraint is Y =

pC + X, where Y is average household income at the time and p is the relative price of

lemon consumption (price of other goods X is normalized to unity).

From the first-order conditions for profit maximization, we can obtain the inverse demand

function for Sicilian lemons:

p (C) =αY

C

As usual, there is a negative association between price and the total level of demand C,

whereas demand rises with income and with the preference for lemons α.

3.2 Lemon producers

There are in total I > 0 towns or municipalities in Sicily and 1 < n ≤ I towns where lemons

are produced. For simplicity, we assume that each town has one (representative) producer.

Total supply of Sicilian lemons is C =Ii=1 Ci where Ci is the local level of production in town

i. Total supply always equals total demand.

Profit of the local producer in town i is

πi = p (C) · Ci − γ (Ci)− Fi =αY

C· Ci − ψCi − Fi

17We will only refer to lemons in the model below, but as indicated in the section above, what we reallyhave in mind is the market for lemons, oranges, limes, and other citrus products.

18This is a simplification. However, Table 2 shows that in the dominant US market, Sicilian lemon accountedfor 100 percent of all imports during certain years in the 1898-1903 period.

9

where p (C) is the price level (which depends on total demand), γ (Ci) is a marginal cost

function such that γ′ (Ci) = ψ > 0, and Fi is the local fixed cost of entry into lemon

cultivation. Fi depends on local characteristics such as soil quality, water access, altitude,

and slope of the land, as well as non-community specific fixed costs such as costs of building

protective walls, etc. Typically, it takes several years before planted lemon trees have grown

to produce lemons. Once a lemon plantation has been established, the marginal cost ψ is

the same across localities.

The first-order condition for profit maximization can be written as

p (C)

(1 + p′ (C) · Ci

p (C)

)= γ′ (Ci) .

Since marginal cost ψ and inverse demand p (C) are the same everywhere, Ci must in

optimum be identical in every town. Hence, C = nCi. The expression above can therefore

be written asαY

nC∗i

(1− 1

n

)= ψ

The fact that the number of towns n ≤ I is bounded from above implies that there will

be a positive mark-up over marginal cost and that the market is not fully competitive.

Solving for the Cournot equilibrium supply of lemon from town i gives us

C∗i =αY (n− 1)

n2ψ. (1)

Not surprisingly, equilibrium supply will increase with the typical income Y and decrease

with marginal cost ψ. Furthermore, it can easily be shown that C∗i will decrease with n for

all n > 2.

Inserting C∗i back into the profit function, we receive after some algebra the optimal

profit level

π∗i =αY

nC∗i· C∗i − ψC∗i − Fi =

αY

n2− Fi.

In this very simple expression, profits increase with income and decrease with the number

of towns producing n. Obviously, lemons will only be produced in community i if π∗i =αYn2 − Fi ≥ 0. Hence, fixed costs and the number of other producers are potential barriers to

entry into lemon production.

Let us assume that towns i ∈ {1, 2, 3, , , I} are ordered such that F1 < F2 < F3... < FI .

Let us further assume that fixed costs are uniformly distributed across towns and that they

are simply given by

Fi = a+ bi

where a > 0 is a component common to all towns and where b > 0 is a parameter describing

the gradient of fixed costs across towns. One might for instance think of a as capturing the

cost of building protective walls, which is roughly the same everywhere, whereas b might

capture the difference in fixed costs that arises due to differences in soil quality or access to

10

surface water for irrigation which make it more costly in terms of time and effort to establish

a lemon plantation in some places rather than in others. Clearly, a b close to zero would

imply small differences between towns. The mean fixed cost across towns is F = a+ I · b/2.

With these assumptions, the last producer who will choose to produce lemon (i = n) will

be the one for which the following condition holds:19

π∗n =αY

n2− Fn =

αY

n2− a− bn = 0. (2)

All potential producers i ∈ {1, 2, 3, , , n} will thus produce whereas i ∈ {n+ 1, ...I} will

not. By using the implicit function theorem, we can deduce from the equation above that

the equilibrium level of lemon-growing towns is a function n = n (a, b) such that

∂n

∂a= na =

−12αYn3 + b

< 0;∂n

∂b= nb =

−n2αYn3 + b

< 0.

Although the explicit solution to n is mathematically messy, it is easily illustrated in a

graph as in Figure 1. The figure shows the two components of the profit level, αY/i2 and

a+ bi, as a function of i when towns are ordered, starting from the one with the lowest fixed

costs to the left. The equilibrium occurs at the point where the two lines cross. At n, profits

for the nth firm are zero whereas they are given by the distance between αY/n2 and a + b

for the firm with the lowest fixed costs.20 The triangle D in the figure shows the total profits

of the lemon producing sector in Sicily.

Figure 1: Equilibrium number of lemon-producing towns

It is clear from the figure that an increase in a and/or b would shift the Fi curve to the

left, resulting in a lower n. Over time, it is likely that such barriers to entry have varied

in the lemon trade just as in other sectors. As a thought experiment, one might imagine

another agricultural good (perhaps wheat) with the same profit function except that it had

lower barriers to entry al < a and bl < b, as shown in the bottom of Figure 1. Such low

levels of fixed costs would imply that all towns (n = I) would produce the good and that

average profits would be quite small. Total profits in the sector are given by the distance

between the fixed cost curve Fi = al + bli and the profit level αY/I (the area E).

Hence, the individual profit for an actual producer is

π∗i =αY

n (a, b)2− a− bi ≥ 0 for all i ≤ n.

An increase in the fixed cost coefficients a and b thus has two effects on equilibrium

profits: it reduces the equilibrium number of lemon producers, which has a positive effect

on profits in town i, and leads to an increase in the fixed costs for all producers, which

decreases profits. The sign of the comparative statics will depend crucially on the level of

19In the expression below, we assume for simplicity that there is always a level of profits where π∗n = 0.

In reality, the equilibrium number of lemon-producing towns n∗ would probably rather be defined by n∗ =arg min max

{αYn2 − a− bn, 0

}.

20The profit level for the 1st firm is equal to b (n− 1) > 0.

11

i.21 In general, for a given n, profits fall with i. Profits always increase with household

demand αY . We can therefore express π∗i = π (αY, a, b, i).

3.3 Government

As described above, Sicily in the 1880s was characterized by weak property rights institutions

and a substantial number of thieves who predated on agricultural production. An implicit

assumption in this section is that the “predation technology” in the lemon business was

particularly favorable for thieves. Compared to other agricultural goods like grapes or wheat,

lemons are very easy to collect quickly by a prospective thief, and the price per stolen bucket

is further very high. These factors contributed to lemon plantations being in particular need

of protection.

Let us assume that in each community there are d > 0 thieves.22 In the absence of

property rights and other forms of protection, thieves would steal the full profit from lemon

production and each thief would obtain an amount of π∗i /d. Lemon production would then

make zero profits. The government in Rome offers some protection of property rights cap-

tured by the term θ ∈ [0, 1], where θ = 1 implies perfect enforcement of property rights

whereas θ = 0 implies total absence of government protection. For Sicily in 1880, θ was

presumably closer to 0.

The total proportion of profits saved from thieves by the individual lemon producer is

given by the ”predation success function”

ρ (mi) =mi

mi + d (1− θ)

where mi is the level of private protection offered in i.

The functional form implies that if θ = 1, there is no need for private protection since

ρ (mi) = 1 for any level of mi. If θ > 0, then ρ′ (mi) > 0 and ρ′′ (mi) < 0, i.e., the proportion

of protected profits is a positive, concave function of the level of private protection. Lemon

producers then retain ρ (mi) · π∗i of their profits and lose (1− ρ (mi)) · π∗i to the thieves.

Lemon producers cannot provide protection themselves and hence need to employ people

to do this job for them. This is where the mafia comes in.

3.4 Mafia

The nature of the original organization of local mafia groups (cosca) remains largely a mys-

tery. What we know about such groups is that they formed a secret society of sworn-in

men who managed to overcome the collective action problem through various measures (like

brutal punishments in the case of defection). Mafiosi were recruited among men with very

21We can for instance see from Figure 1 that a rise in b with a unchanged will increase profits for the townwith the lowest fixed costs, whereas the previous nth firm will then have negative profits and should cease toproduce.

22In a richer model, the number of thieves might be endogenized so that individuals self-selected into beinga mafioso, a thief, or a normal peasant in a process where marginal returns were the same in equilibrium.The number of thieves in Sicily in the 1880s was reportedly very high due to a general release of prisonersafter the Italian unification and the breakup of feudal estates, which made many workers redundant.

12

diverse occupations in society, including peasants, sheep-herders, doctors, and politicians.

In these early days, mafiosi typically performed their daily jobs as an integrated part of

society while also undertaking mafia activities on the side. The key mafia activity was the

protection of businesses (Gambetta, 1996).

We assume that the local mafia organization in i has no influence over n (there was

no central coordinating mafia authority in the 1880s) and that a representative mafioso

considers the choice of allocating effort either to protecting local lemon producers or to

pursuing normal economic activity. A representative mafioso’s utility function in town i is

UMi = ωρ (mi)π∗i + (1−mi)A

where mi ∈ [0, 1] is available effort that can be spent on protecting the local lemon producers’

profits. The parameter A > 0 reflects productivity in normal production (farming, fishing,

herding sheep, etc). This type of production is one option available to mafiosi and is the

only available option for the majority of ordinary people. ω ∈ (0, 1) is the share of total

protected profits that the local producers offer to the mafia in return for protection. For

now, let us take ω as given.23 Note that ω must be somewhere within the interval (0, 1) for

any interaction to occur between the two.

The mafia maximizes the utility function

maxmi

UM =ωmiπ

∗i

mi + d (1− θ)+ (1−mi)A.

After manipulating the first-order conditions, we can solve for the optimal (interior so-

lution) level of mafia activity in town i:

m∗i = d (1− θ)

(√ωπ∗i

d (1− θ)A− 1

)(3)

Clearly, m∗i > 0 will only be the case if the term under the square root sign exceeds 1.

The expression in (??) implies that we can express the following proposition:

Proposition 1: The mafia will be active in town i (m∗i > 0) only if ωπ∗i = ω(

αYn(a,b)2

− a− bi)>

d (1− θ)A.

This proposition offers some of the key insights of the model. If the opportunity costs of

being a mafioso A are very large, there will be no mafia. If the offer from the producers ω

is very low, the mafioso will not find protection worthwhile. Furthermore, it will obviously

be the case that there will be no mafia if property rights are fully enforced, i.e. if θ = 1.

It can be shown that m∗i is a decreasing, convex function of θ so that the mafia shrinks as

government-enforced property rights are strengthened. Similarly, there will be no mafia if

23Please see Figure A3 in the Appendix for an extension and simulation of the model where ωi is endoge-nously determined in a two-stage game between lemon producers and the mafia. The main comparativestatics results below still hold after this extension.

13

there are no thieves so that d = 0. All these factors are assumed to be identical throughout

Sicily but might explain the varying presence of mafia over time.

What distinguishes towns is the level of profits in lemon production π∗i . The central result

is of course that the likelihood of mafia presence increases with π∗i . As discussed above, we

argue that one of the key distinguishing features of lemon production at the time was the

relatively high demand αY and the high barriers to entry due to high and geographically

differentiated fixed costs, represented by the parameters a and b. If these are high, then only

n < I towns will be able to produce and the average profit among these producers will be

relatively high. For other goods, we argue that a and b should be fairly low, implying low

profits in general and no large geographical variation in profits. The lower part of Figure 1

depicts such a scenario. Profits are then less likely to motivate a mafia to arise from (??).

The most likely place for mafia presence would be town i = 1 where fixed costs of lemon

production are lowest and profits are highest. In our empirical investigation, we do not have

data on profits from various types of production. What we do have data on is the presence

of sectors in each town. According to our model and the data discussed above, the presence

of lemon production in some towns should be an indicator of profitability and of low fixed

costs. Similarly, the presence of other types of production are interpreted as indicating that

profits in that sector were also positive. Holding the presence of other types of production

constant, we hypothesize that the prevalence of lemon production in a town should thus have

a positive association with the probability of mafia activity.

4 Econometric Specification and Data

4.1 Econometric Specification

From an econometric point of view, we can consider equation (??) as the latent equation

that will determine the probability of mafia presence. In this equation, the probability of

mafia presence depends on profits, the enforcement of property rights, and the number of

thieves. The latter are considered equally distributed across the region even though there

may be variation in the efficiency of the state at town level, which can explain the presence

of mafia across the region. For this reason, the efficiency of the enforcement of property

rights will represent part of the control variables.

The model to be estimated can be written as:

M?i,p = ηp + β1Πi,p + β2Zi,p + µi,p (4)

where

Mafiai,p =

{1 if M?

i,p ≥ 0

0 if M?i,p < 0

In the latent equation (??), the dependent variable M?i,p represents the response variable

that drives the probability of mafia in town i in province p. A response variable larger than

zero is associated with towns with a positive level of mafia presence. The probability of

14

mafia is zero if the response variable (M?i,p) is smaller than zero.

The main explanatory variable is local profits from citrus production, which we denote

by Πi,p. Profits depend on fixed costs, which in our model represent a sort of barrier to entry.

As a result, the smaller the number of producers in the industry (n), the larger the profits

made, which in turn increases mafia activity. Even though we do not have data on profits,

we can consider the dominant position in the citrus market (73 percent of the total Italian

production and almost 78 percent of the total US lemon imports) as the result of a fixed

cost which prevented the entry of new competitors in the market. This dominant position

generated large profits for peasants and thus we expect the probability of mafia to increase

with the production of citrus.

Zi,p represents a set of control variables that may also affect the probability of mafia.

This set of variables includes controls for the degree of trust citizens have in the law and for

the peripherality of the town. These measures do not perfectly capture the enforcement of

property rights by the incumbent state, but should provide an idea of the efficiency of the

state in enforcing property rights. Finally, ηp represents provincial fixed effects that may be

correlated with the error term µi,p.

4.2 Data

Data both on town and district level (mandamento)24 for the entire island are collected from

the original Damiani Inquiry which represents one of the earliest and most important primary

sources concerning the economic and social conditions of Sicily in the 1880s. 25 Damiani’s

investigation is part of a larger inquiry, approved in March 1877 and proposed by the Member

of Parliament Stefano Jacini, aimed at assessing the conditions of the agricultural sector

and peasantry in every region of Italy. Abele Damiani was the MP in charge of surveying

Sicily. Our sample considers all seven provinces (Caltanissetta, Catania, Girgenti, Messina,

Palermo, Syracuse, and Trapani) providing a total of 143 observations.26

The section of the Inquiry that matters for our analysis is divided into two parts. The

first one discusses the situation of agriculture with particular reference to tax burden, wages,

the kind of crops produced, and the relations between peasants and landlords (i.e., tenancy

contract, fractionalization of land, etc.). Questionnaires relative to this first part were sent

out to almost 357 mayors, but less than half of them provided complete information. For

unknown reasons, the folder for this section on the province of Caltanissetta is not available

in the Archive of State in Rome. In order to get data for the agricultural conditions for this

province we use the information available from the Summary Report that Damiani presented

to the parliament, which has also been used by Bandiera (2003) and Buonanno et al. (2012).

The second part of the Inquiry provides information on the moral and social conditions

24A mandamento is a judicial district of competence of the pretore.25The original handwritten copy of the Damiani Inquiry is still available from the Archive of State in Rome,

even though the conditions of manuscripts are far from perfect and some pages are very hard to read (seefigures in the Appendix).

26Syracuse became a province in 1865 replacing Noto, which was a province at the time of the unification in1861. Except for mafia activity, all the information regarding Caltanissetta comes from the Summary Reportthat Abele Damiani presented to the parliament.

15

of peasants. In this case questionnaires were sent to 179 pretori (lower court judges).27 The

information provided gives us a unique picture of the moral and social conditions of Sicilian

peasants at the time. Questions related to this part of the survey regard the lewdness and

religiousness of people, corruption of the clergy, the rule of law, and the effect of introducing

a compulsory military service. However, the section mostly relevant to our analysis is the

one regarding the form and level of crime on the island. The question asked to the pretori

in the Inquiry is: “What is the most common form of crime in the district? What are their

causes?” There is a range of possible crimes that the pretori considered. Some of them relate

to rustling, robbery, murders, and of course the mafia. Our dependent variable is labeled

mafia and represents the criminal organizations’ activity. 28

Bandiera (2003) and Buonanno et al. (2012), on the other hand, use a different dependent

variable: an ordinal variable for the intensity of mafia collected from the Summary Report

that Abele Damiani wrote to the Italian parliament on the basis of the original Inquiry.

However, from the original Inquiry it emerges that very few pretori reported the intensity of

mafia (10 out of 143). Since the source of this additional information is unclear, we prefer

to use the information we have available from pretori in the original Inquiry for which we

are certain about the source.29

There are potential concerns with the data on mafia presence. Firstly, could the mafia

still be present in a district even though the pretore did not list it as the most common form

of crime? Because of the structure of the survey in the Inquiry, it is indeed possible that

some districts had mafia activity even though the pretore did not report it as being a major

crime. This problem may slightly affect our results.

Second, were pretori themselves mafiosi and hence likely to understate the presence of

mafia? The answer to this question is most likely no, although there is no conclusive evidence

in either direction. Pretori were directly appointed by the Minister of Justice with a Regio

decreto (royal order). Their appointment, and any other aspect concerning their career, was

subject to an evaluation by a local committee of experts of the local Court of Appeal. For

the first 10 years of their career, pretori changed districts (mandamento) very frequently,

which may have restricted their connection with the local environment. Another question

in the Inquiry reveals that many experienced difficulties administering justice without the

cooperation of local people since in several trials witnesses did not dare to say the truth due

to mafia threats or collusion with the mafiosi.30

Third, did the pretori have a common understanding of what the term mafia implied?

This indeed appears to have been the case. In the 1880s, the term mafia already meant a

criminal organization. The term was used to identify this sort of organized crime in Sicily at

27There are much fewer pretori than mayors since the office of pretura is only present in larger provincesso that one pretore often serves several towns.

28Samples of these surveys are provided in the Appendix. Figure A1 shows the table summarizing infor-mation provided by pretori. Figure A2 shows a sample of the survey completed by the mayor of Cefalu.

29Pretori were lower court magistrates. Because of their role, their information on criminal activity can beconsidered the most indicative.

30According to Pezzino (1990), the pretore in Bagheria said “There is a tendency to deny the truth. Notonly people does not answer truthfully, but they deliberately lie either because of mafia or because of moneyor because they are scared.”

16

least since 1863, when a comedy titled “I Mafiusi di la Vicaria” was shown in Palermo. In

1865, the prefect of Palermo (Filippo Gualterio) used the term mafia in a private document

to identify the criminal organization. In addition, since 1871 mafia membership has been a

public law offence. We therefore regard it as highly unlikely that the term was misinterpreted.

These problems are common to most empirical analyses based on survey data, and cause

a measurement error that is part of the error term. However, as long as the error term is not

correlated with independent variables, there is no reason to believe that these problems will

affect estimates. This is important for our analysis given that the possible misinterpretation

of the question as well as the collusion between pretori and mafia (if any) are likely to be

distributed randomly across towns with mafia. Hence, it seems reasonable to assume that

the independent variable that we base our analysis on (whether the city produces citrus)

should not be correlated with such an error and should thus provide unbiased estimates.31

Table 5 reports descriptive statistics for our variables. The dependent variable, mafia, is

a binary dummy for whether the pretore of the town reckons mafia as the most important

source of crime in the district. 32

Figure 2 shows the local distribution of mafia in our sample. On average, 36 percent

of all towns were strongly affected by mafia, which means that almost 51 out of the 143

towns in our sample had mafia listed as the most common form of crime. Girgenti is the

province with the highest incidence of mafia, with almost a strong mafia presence in 14 out

of 17 towns. In Trapani, the mafia operates in 6 out of 15 towns, and in the Caltanissetta

province in 7 districts out of 16. Almost one third of the districts in Palermo (mainly those

in the Conca d’Oro) and Catania provinces are mafia infested. Messina and Syracuse are

the provinces with the lowest incidence. These summary statistics are consistent with the

description in Colajanni (1885), where Sicily is divided into three macro-regions, considering

Girgenti as the one with the highest rate of murders and convictions and therefore the one

with the highest incidence of mafia.33

Table 5: Table 5: Distribution of mafia and agricultural production across

provinces in 1881-86

Figure 2: Mafia and non-mafia towns in Sicily in the 1880s

To control for some of the most important sources of mafia presence discussed in the

previous section, we include three sets of independent variables. Consistent with our model,

Colajanni (1885), Dickie (2004), and Lupo (2009) all identify the profitable production of

31Assume that the dependent variable is measured with error and that yi = yi + ψi. Then we can writethe composed error term in (??) as εi = µi +ψi. If ψi is random, it should be uncorrelated with independentvariables.

32Data on mafia are available for 162 districts, but when merged with independent variables the largestsample covers 143 districts.

33Using information available in Damiani (1886), Colajanni identifies three macro-regions on the basis ofeconomic and social conditions. The first region includes the province of Catania and Messina, where therewere good economic and social conditions. The situation was slightly worse in the provinces of Syracuse,Trapani, Caltanissetta, and Palermo. The third region includes Girgenti, where both economic and socialconditions were very poor and there was a high level of crime.

17

goods, like citrus and sulphur, as important determinants of mafia presence. For this reason,

the first set of independent variables in Table 5 are related to agricultural production. Citrus,

Wheat, Olives, Grape, and Sulphur are the commodities we consider. In order to assess what

dominant crops were produced in each town, we use dummy variables coded 1 if the town is

a dominant producer of these crops and 0 otherwise. The relevant question we refer to in the

Damiani Inquiry is: “Which is the dominant crop produced in the city?” Mayors normally

listed more than one crop (for some cities they also reported quantities), and hence, the

dummies for crops are not mutually exclusive.

More information on actual quantities produced is provided from the Damiani’s Summary

Report. However, after comparing data in the Summary Report with data in the Inquiry, we

found that the Summary Report does not always match the original information provided

by mayors. For example, for Lercara Friddi, the Inquiry reports that grapes and wheat are

the predominant crops and both crops extend for almost 145 hectares. Yet in the Report

(Volume XIII Tomo II), the predominant crops are grape and wheat (and also Indian figs),

each extending for 2,000 and 220 hectares respectively, which is not consistent with data

provided by mayors. The same problem applies for Castelbuono. The extension of grapevine

and olive trees in the Inquiry is 142 and 193.27 hectares, respectively, while the Summary

Report records 490 and 640 hectares, respectively. Given that the Inquiry does not always

report data on hectares per crop, we prefer using a dummy variable (only recording whether

the crop is predominant or not) in order to minimize the potential measurement error of the

data available from the Summary Report.

Buonanno et al. (2012), on the other hand, use a measure of crop suitability to proxy

citrus, arguing that it is preferred to actual data on crop production because of possible

reverse causality. Even though this is generally true there are at least two reasons why in

this case a measure of actual production is preferable (given that data are available). First,

the risk of reverse causality between mafia presence and citrus production in this occasion

is minimal, given that production of citrus in Sicily responded to an external shock in the

demand related to the use of the fruit as a treatment for scurvy. According to all available

accounts, the boom in the citrus sector preceded the emergence of the mafia.

The second reason why a measure of actual production is preferable relates to the huge

revenues in the sector inducing producers to undertake large investments in water irrigation

and similar technologies (for example, the norie previously mentioned) that increased the

efficiency of production. These investments made producers vulnerable to violence, predation

and therefore mafia retaliation.

Since data on citrus production are of particular relevance to test our hypothesis, we

compare our data with the ones available in Di Vita (1906).34 We can confirm that the

original Damiani Inquiry provides an accurate distribution of local citrus production.35

Data on sulphur mines are also provided by Di Vita (1906).36 As argued by Colajanni

34Di Vita (1906) wrote a statistical bulletin reporting summary statistics for all Sicilian cities.35In a couple of cases the Inquiry reports that the lemons industry is relevant in the city but then does not

list lemons among the relevant crops. These cases are compared with Di Vita (1906) and coded according tothis latter source.

36See Figure A4 in the Appendix for a more recent distribution of crops and fruit trees in Sicily.

18

(1885), sulphur mines are almost exclusively concentrated in the province of Girgenti (12 out

of 17 towns). Outside Girgenti, there are 5 mines in the province of Catania, 3 in Palermo,

and 1 in the province of Messina and Trapani. Wheat is produced in the entire province

of Girgenti and the production is the lowest in the province of Messina. Grapes and olives

are almost equally distributed across the island. Our summary statistics seem to match the

picture provided in Colajanni (1885) quite well.

The second set of explanatory variables is related to the political status of towns and to

other policies aimed at increasing the small-scale ownership of land (Table 6). Since the 13th

century, Sicilian towns have had three different types of political organization. The feudal

system was the first type of political organization, where a small minority (elite) owned the

land and a large peasant population passively accepted the subservient role. The second

form of political organization was labeled ecclesiale (church-ruled cities), where bishops used

to act as typical lords. Finally, there were the crown-ruled towns (demaniali). Demaniali

towns were independent of local lords and bishops and were self-regulated. Using data from

Di Vita (1906), we find that feudal cities represent almost 62 percent of our sample, i.e., 79

out of the 127 had a feudal heritage. Crown-ruled (demaniali) cities account for one fourth

of the sample (32 cities), and 10 cities were instead ecclesiali cities (church ruled).

The last column of the table shows the effectiveness of fractionalization policies imple-

mented 1812-1870. We consider three types of policies: i) the abolition of feudalism and

the auction/allocation of land to small holders; ii) the enfiteusi, a perpetual lease that al-

lowed farmers to use the land as if they were owners; and iii) the seizure of church ruled

terrains after the unification of Italy and the consequent land auction. These policies were

more effective in the provinces of Caltanissetta, Girgenti, and Catania and less effective in

the provinces of Palermo and Messina. However the distribution of land in the latter two

provinces has always been highly fractionalized and hence, there was little room for such

policies. In contrast, in Caltanissetta, Catania and Girgenti the latifund was much more

common and hence policies were more successful, even though in relative terms, land was

still more fractionalized in Palermo and Messina than in Girgenti and Catania. This was

mainly due to the fact that the policy increased fractionalization but not among peasants:

rich landowners were the only ones who could afford to bid for land in auctions and therefore

the policy had little effect in increasing the private ownership among peasants.

Table 6: Descriptive statistics for political organization across provinces before

the Italian unification in 1860

Table 7 provides descriptive statistics for the scale of plantations and the fractionalization

of land.37 The question asked for the scale of plantation and fractionalization is: “What is

the dominant scale of the plantation? And what is the fractionalization of land?” Most of the

time, mayors answered that a large, a medium, and a small scale are dominant, therefore the

sum of the three variables is larger than 1. Regarding land distribution, we note that there is

37The Damiani Inquiry (1886) is the source of these data, except for Caltanissetta whose data are fromDamiani’s Summary Report.

19

high fractionalization in almost 50 towns whereas fractionalization is relatively low in almost

45 towns. The scale of plantation tends to be relatively high in Palermo, whereas in the other

provinces the percentage of high scale plantations tends to be around 33 percent. Small-scale

plantations are relatively frequent in all provinces and particularly so in Trapani, Messina,

and Catania. Girgenti and Caltanissetta are the provinces with the lowest frequency of

small-scale plantations.

Table 7: Land fractionalization and scale of plantations by province in 1881-86

Finally, in Table 8 shows the pairwise correlation among selected variables. Mafia seems

to be positively correlated with Citrus (0.39 correlation) and Large-scale plantation (0.25

correlation) as well as with the effectiveness of the fractionalization policy (0.26 correlation).

The presence of sulphur mines is also positively correlated with Mafia (0.19 correlation).

A feudal origin and the high fractionalization of land, which also have been considered

important for the diffusion of organized crime, are weakly correlated with Mafia (0.05 and

0.04 correlation, respectively). Finally, Population density, capturing the wealth of a locality,

is weakly correlated with Mafia. All these variables are weakly cross-correlated, preventing

problems of multicollinearity.

Table 8: Pairwise Correlations

5 Empirical Analysis

5.1 Mafia presence in the 1880s

Table 9 presents probit estimates with mafia presence in the 1880s as the dependent variable.

We start by estimating a simple model where mafia presence in the 1880s depends only on

variables capturing the economic activity in the town. We then proceed by introducing

additional variables to control for observables. In Column 1, the diffusion of the mafia

significantly depends on production of citrus. At the mean, the production of citrus increases

the probability of mafia by 45 percent. In Column 2, we re-estimate the same model but now

control for province dummies to capture regional fixed effects. The previous results still hold.

In Column 3 we drop the observations for the province of Caltanisetta in order to detect any

potential bias due to a different source of information. The estimated effects in Column 3

are almost unchanged. Coefficients and z-statistics suffer from negligible variation.

In Column 4, we change specification by dropping the variables that are not significant

(to prevent an excessive reduction of the degrees of freedom) and we enter two additional

controls. We use a Fractionalization policy dummy and the variable Population density

to account for policies that may have affected the private ownership of land and wealth.

Population density is not significant, whereas the Fractionalization policy dummy has a

strong significant effect on the probability of mafia presence. In Column 5, we include the

dummy for the high fractionalization of land to see whether the fractionalization policy

dummy truly captures the effect of increasing the fractionalization of land (as argued by

20

Bandiera, 2003) or something else. As argued before, fractionalization policies had little

effect in increasing private ownership among peasants. Hence, the Fractionalization policy

dummy could capture an increase in the use of the gabella because existing landowners

bought most of the available land.38 Therefore, even though mayors consider these policies

effective in increasing land fractionalization, land still remained quite concentrated in the

hands of a few, thus preventing peasants from acquiring rights. In line with our hypothesis,

the dummy variable High land fractionalization turns out to be non-significant.

Because of the fixed costs that farmers had to sustain in order to expand citrus production

(and also other crops that could generate higher profits such as grapes), we expect profits

to be higher in towns with a relatively low land fractionalization. To test this hypothesis,

Column 6 includes the dummy variable Large-scale plantation. The idea is that investments

for expanding the sector were more likely made in towns with a large-scale plantation (be-

cause of the decreasing costs), making producers more vulnerable to a potential loss due to

extortion. The variable turns out to be highly significant, increasing the probability of mafia

presence by 39 percent. The set of covariates specified in Column 6 represents our preferred

specification. In this model, the presence of mafia is significantly determined by the citrus

production, the effect of policies for private ownership, and by the scale of the plantation.

Table 9: Mafia Probit Model

In Table 10, we proceed by testing the robustness of our preferred specification. We start

by including in Column 1 control variables to account for the distance from Palermo and

from Mazzara del Vallo. The first variable should capture the distance from a well-known

mafia-base center and also a key port, and the variable Distance from Mazzara del Vallo is

used to capture the possible diffusion of citrus. In the 10th century, lemon plants were in

fact originally introduced by the Arabs entering Sicily from Mazzara del Vallo. The former

variable turns out to be non-significant, whereas the latter is marginally significant, i.e., the

presence of mafia decreases if the distance from Mazzara del Vallo increases.

In Column 2 we introduce the dummy variable Feudal to test whether this political sys-

tem, often associated with patronage and kinship relations, had any impact on the presence

of mafia. The variable turns out to be non-significant.

In Columns 3 and 4, we control for the level of law enforcement. We use the distance

from the nearest railway station (collected from Di Vita, 1906) as a measure of peripherality

and three dummies for whether citizens trust, mistrust, or do not care about the law (the

excluded group is whether they fear the law). These dummies are coded using information

available in the Damiani Inquiry (1886). The results show that being close to the railway

station has a negative effect on mafia presence (consistent with the peripherality hypothesis).

Towns where citizens do not care about the law have almost 60 percent higher probability

of mafia presence.

38Gabella is synonymous of tax used in the Medieval era.

21

Finally, in Column 5 we control for the length of the tenancy contract which also turns

out to have a significant and negative effect on the probability of mafia presence. The Citrus

dummy still increases the probability by 57 percent and the Fractionalization policies dummy

increases the probability by an average of 23 percent.

Table 10: Mafia Probit Model with additional controls

Given the potential downward bias due to the entry barriers that mafia presence could

have represented for possible entrants, we decide to compare our estimates with a consistent

estimator like the IV model where Citrus is instrumented using data on the altitude above

sea level. This instrument is chosen because of the particular climatic conditions required

by this plant and their influence on fixed costs, as outlined in the theoretical model. The

causality link can be represented as follows:

Altitude → Citrus→ (Profits) → Mafia

where Altitude affects the climate and both its daily and annual variation. The high

variation in climate and the high probability of frost at high altitudes represent an important

fixed cost for the production of lemons since, as we discussed earlier, lemon plants are totally

intolerant to frost. Therefore, lemon production will occur in towns characterized by a mild

climate with low seasonal variation. High profits will be generated by this activity and more

protection against potential losses will be required from local producers, hence affecting the

level of mafia activity. The choice of the variable Altitude as an instrument is also consistent

with the categorization of the fruit industry suggested by Monroe (1909) who argues that

the fruit industry in Sicily may be divided into three zones: “The marine, or lemon belt,

from sea-level to fifteen hundred feet. The middle, or orange zone, from fifteen hundred to

three thousand feet. The forest belt above three thousand feet” (Monroe, 1909, pg. 190-91).

Therefore, we expect to have a larger concentration of lemons below 1,500 feet.

In Table 11, we check whether the variable Altitude can be excluded when entered to-

gether with our proxy for citrus. Even though an omitted variable bias might still be an

issue, the results support the potential exclusion of the variable Altitude, which turns out to

be significant in Column 2. When entered together with Citrus, Altitude loses significance

as a result of multicollinearity. However, the effect of Citrus dominates that of Altitude

(p-values 0.103 against 0.311 for Altitude).

Table 11: Exclusion of Altitude

In Table 12, we start with a Linear Probability Model to evaluate the potential bias with

respect to OLS estimates. In Column 1, all variables in the baseline model are statistically

significant at least at the 5 percent level. Citrus increases the probability of mafia pres-

ence by almost 32 percent, Fractionalization policies by almost 25 percent, and Large-scale

plantations by almost 23 percent.

22

In Column 2, we show results from the IV estimator to control for endogeneity issues.

The results provide us with a picture quite similar to the OLS estimates, except for the

effect of Citrus on mafia activity, which increases by almost 13 percent (compared with the

OLS estimates).39 The scale of plantation is still significant at the 5 percent level or lower.

In Column 3, we estimate our model by IV-Probit and the results still hold. Finally, in

Column 4, we use a Spatial Linear Probability Model to control for spatial autocorrelation

in the error term. The idea is that mafia presence in one town could positively influence the

probability of mafia presence in a neighboring town as a local spillover effect. In this model,

the dummy Citrus significantly increases the probability of mafia presence by 32 percent,

the scale of the plantation by 21 percent, and the fractionalization policies dummy by 23

percent.

Table 12: Robustness Check – Alternative Estimators

5.2 Mafia persistence

Over the 140 years since its origin, the presence of mafia has spread all over Sicily. Repeated

clashes between clans have further affected the territorial distribution. However, the districts

where mafia originally developed still remain hot spots for mafia activity. Table 13 shows

data, at province level, on mafia murders reported to the police in 1999 according to the

416 bis article of the Italian penal law and data on the number of asset confiscations from

mafiosi by the Italian government during 1997-2008.40 Except for the province of Palermo,

where numbers are inflated by the fact that the city of Palermo itself is the most relevant hub

for the Sicilian mafia’s activity, it is important to note that Trapani and Agrigento (former

Girgenti) are the two provinces with the highest number of per capita assets confiscated.

They also used to be the two provinces with the largest presence of mafia according to the

Damiani Inquiry (1886).

Table 13: Mafia persistence, 1880-2008

In order to get a better idea of this persistence, we estimate the impact of our dummy

variable for mafia presence in the 1880s from Damiani (1886) on the per capita number of

assets confiscated from the mafia at town level. As Table 13 shows, the presence of mafia

in the 1880s explains a significant portion of the variation in the dependent variable. Towns

with mafia in the 1880s have a significantly higher per capita number of assets confiscated

in the 1997-2008 period (the marginal effect is almost 0.38 as seen in Column 1). After

controlling for a full set of province dummies (Column 2), the result still holds. In our view,

this suggests a strong persistence of mafia presence throughout centuries.

Table 14: Historical mafia presence as a determinant of current mafia activity

39For completeness, in Table A2 in the Appendix we also provide estimates for the sample of provincesexcluding Caltanissetta. The results still hold.

40The 416 bis article prescribes 3-6 years of prison for members of the mafia criminal organization.

23

6 Conclusions

The issue of how socially inefficient institutions arise and persist is currently a very active

research topic in economics. In this paper, we have developed a market structure hypothesis

for understanding the origins of the Sicilian mafia. Unlike existing works that emphasize

political and historical factors, our analysis studies the importance of a boom in international

demand for citrus fruits, combined with the presence of fixed costs in lemon production, as a

source of market imperfections and very high profits in certain localities. This profitability,

combined with a general lack of rule of law prevailing at the time, provided an ideal breeding

ground for a mafia thriving on providing private protection to lemon producers.

In the empirical analysis, using data from a parliamentary inquiry from the 1880s, we

show that the presence of mafia is strongly related to the production of citrus fruits. The

results continue to hold when we include several control variables and use alternative es-

timators. The historical presence of mafia is further a strong predictor of current mafia

activity.

We believe that our results contribute to shedding light on the emergence of one of the

most harmful economic institutions in Europe during the modern era. Additional studies

on country or regional level along the same lines could potentially uncover the micro level

mechanisms for how and why other inefficient institutions arise and often persist for extended

periods.

24

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27

Table 1: Distribution of lemon trees in Southern Italy in 1898

Region Number of trees

Reggio Calabria 1,232,675

Messina 1,634,231

Palermo 2,488,475

Catania 828,640

Syracuse 460,125

Calatanissetta 8,210

Girgenti (Agrigento) 56,379

Trapani 216,610

Total 6,924,985

Source: Powell (1909)

Table 2: Total Italian exports of lemon and total United States imports

Total Italian exports

Total US imports

Year Quantity (pounds)

Value (US $) Exports to the US (%)*

Quantity (pounds)

Value (US $) Imports from Italy (%)**

1898 325,504,061 3,419,486 41.3 133,374,95 2,521,985 100^

1899 359,473,041 3,234,489 36.7 298,634,448 4,399,160 44.1

1900 331,563,577 3,000,286 29 159,384,389 3,655,926 60.3

1901 368,801,294 3,328,610 29.2 148,334,112 3,516,877 72.5

1902 490,033,260 3,432,677 35.3 162,962,091 3,318,909 100^

1903 459,622,020 3,218,948 31.2 152,775,867 3,087,244 93.8

*Percentages provided by Powell (1909) ** Percentages estimated using percentages on quantity exported from Italy above. For example for the year 1900 the quantity exported to the US is 331,563,577*0.29=96,153,473 which divided by 159,384,389 provides a percentage equal to 60.32 percent. ^ In 1898 and 1902 the percentage exported from Italy to the US exceeds the total import into the US. This is because Italian figures refer to the calendar year, while USA figures refer to the fiscal year. Source: Powell (1909)

Table 3: Total Italian export of citrate of lime and total United States imports

Total Italian exports

Total US imports

Year Quantity (pounds)

Value (US $) Exports to the US (%)*

Quantity (pounds)

Value (US $) Imports from Italy (%)**

1899 3,142,248 151,295 32.8 1,577,804 157,482 65.3

1900 3,743,448 196,826 37.6 1,944,803 204,243 72.4

1901 3,120,202 147,502 35.3 2,416,658 209,583 45.6

1902 7,517,541 329,965 32.5 3,056,904 293,293 79.9

1903 7,229,647 632,905 38 2,657,110 240,486 100^

*Percentages provided by Powell (1909) ** Percentages estimated using percentages on quantity exported from Italy above. For example for the year 1901 the quantity exported to the US is 3,120,202*0.353=1,101,431 which divided by 2,416,658 provides a percentage equal to 45.57 percent. ^ In 1903 the percentage exported from Italy to the US exceeds the total import into the US. This is because Italian figures refer to the calendar year, while USA figures refer to the fiscal year

Source: Powell (1909)

Table 4: Exports from the harbor of Messina in 1850

Product Quantity Units Lire Description

Silk 1,100 Balle 4,469,850 Oz 645 x Balla

Olive Oil 320,000 Cafissi 2,419,200 Approx 3 Litres x Cafisso

Oranges 500,000 Casse 2,726,325 Approx 240 oranges x cassa.

Lemons 600,000 Casse 3,779,622 Approx 360 lemons x cassa

Lemon Juices 1,000 Barili 503,986 Oz 40 per barile

Salted Lemons 200 Barili 151,200 Oz 6 per barile

Citrus Parfumes 400,000 Libre 2,014,740 Sulphur 90,000 Quintali 302,211 Wheat 50,000 Salme 2,477,790 Flax 20,000 Salme 1,007,937 4 salma = 2 hl

Wine 2,000 Salme 37,800 1/2 salma= 801 hl

Nuts 4,000 Salme 655,169 4 salma = 3 hl

Almond 20,000 Cantaia 1,763,370 Oz 7 x cantaio

Pistacchio 200 Cantaia 30,240 Oz 12 x Cantaio

Walnuts 2,000 Salme 50,387 4 salma = 3 hl

Liquorice 16,000 Cantaia 680,400 Oz 9 per cantaio

Sardines 4,000 Barili 151,162 Oz 2 per Barile

Carob 4,000 Sacchi 90,720 24 sacchi = 90Kg

Wool 2,000 Cantaia 453,600 6 cantaio = 80Kg

Linen 7,000 Quintali 264,600 Oz 3 x quintali

Cotton 4,000 Quintali 30,240

Source: Battaglia (2003)

Table 5: Distribution of mafia and agricultural production across provinces in 1881-86

Dominant production of:

Province Towns Mafia Citrus Grape Olive Wheat Sulphur

Caltanissetta 16 0.437 0.471 1 0.471 1 0.526

Catania 22 0.318 0.478 0.826 0.347 0.782 0.226

Girgenti 17 0.823 0.5 0.611 0.388 1 0.625

Messina 25 0.24 0.608 0.739 0.521 0.478 0.07

Palermo 27 0.296 0.37 0.777 0.444 0.74 0.133

Siracusa 21 0.142 0.35 0.85 0.35 0.75 0

Trapani 15 0.4 0.4 0.8 0.6 0.8 0.066

Total 143 0.357 0.454 0.797 0.441 0.776 0.23

Numbers in the table refer to the share of towns within each province with mafia presence and/or with dominant production of each commodity. Each variable in the table is a binary dummy. Mafia=1 if mafia is perceived to be the most common form of crime in the town and 0 otherwise, as explained in the text. Citrus, Grape, Olive, Wheat and Sulphur are also binary dummies taking on the value of 1 if the commodity is listed by the pretore as one of the key agricultural goods produced in the town, as explained in the text. Source: Damiani (1886)

Table 6: Descriptive statistics for political organization across provinces before the Italian unification in 1860

Political organization:

Province Towns Feudal Crown-ruled Church-ruled Frac. policies

Caltanissetta 12 0.833 0.167 0 0.714

Catania 31 0.452 0.29 0 0.625

Girgenti 24 0.583 0.125 0 0.714

Messina 28 0.357 0.28 0.214 0.285

Palermo 30 0.566 0.267 0.133 0.346

Siracusa 22 0.818 0.181 0 0.5

Trapani 15 0.666 0.333 0 0.285

Total 162 0.574 0.216 0.0617 0.413

Numbers in the table refer to the share of towns within each province that were characterized by feudal organization, was crown- or church-ruled, or had a substantial degree of fractionalization polices. Each of the variables of Political organization is a binary dummy. Source: Damiani (1886)

Table 7: Land fractionalization and scale of plantations by province in 1881-86

Fractionalization of ownership

Scale of plantation

Province Towns High Low Large Small

Caltanissetta 12 0.545 0.454 0.333 0.5

Catania 23 0.409 0.227 0.26 0.695

Girgenti 14 0.461 0.461 0.357 0.461

Messina 20 0.277 0.444 0.35 0.789

Palermo 26 0.52 0.16 0.461 0.576

Siracusa 20 0.35 0.5 0.35 0.666

Trapani 14 0.333 0.333 0.285 0.923

Total 129 0.413 0.347 0.348 0.661

Numbers in the table refer to the share of towns within each province that were characterized by high or low fractionalization in land ownership and large or small plantations. Source: Damiani (1886)

Table 8: Pairwise correlations

Mafia Citrus Wheat Olive Grape Sul-phur

Feudal Frac. policy

Pop. density

High frac.

Large scale plant.

Mafia 1

Citrus 0.3863 1

Wheat 0.0946 -0.239 1

Olive 0.0014 0.2289 -0.185 1

Grape 0.068 0.0424 -0.015 0.1391 1

Sulphur 0.1914 -0.0124 0.2903 -0.036 -0.058 1

Feudal 0.0556 -0.1212 0.0598 -0.011 -0.132 0.0519 1

Frac. policies

0.2637 0.0713 0.2214 -0.056 0.0552 0.1771 0.0531 1

Pop. density -0.05 0.1145 -0.341 -0.039 0.0492 -0.182 -0.237 -0.057 1

High land fract.

0.0413 0.0491 -0.104 0.0946 -0.065 -0.048 -0.038 0.073 0.1596 1

Large scale plant.

0.2465 0.1623 0.1054 -0.020 0.1406 0.0299 0.0642 -0.15 -0.2317 -0.1886 1

Table 9: Mafia probit model

Dependent variable: Mafia (in 1880)

(1) (2) (3) (4) (5) (6)

Citrus 1.164** 1.401** 1.488** 1.363** 1.479*** 1.342**

(0.453) (0.567) (0.701) (0.581) (0.540) (0.606)

Grape 0.273 0.620 0.603

(0.371) (0.422) (0.431)

Olive -0.322 -0.451 -0.325

(0.209) (0.291) (0.315)

Wheat 0.455 0.269 0.342

(0.321) (0.402) (0.423)

Sulphur 0.416 -0.195 -0.351

(0.303) (0.387) (0.533)

Population density

-0.0291

(0.0676)

Fractionalization policies

0.831*** 0.970*** 1.100***

(0.246) (0.198) (0.197)

High land fractionalization

-0.128

(0.293)

Large scale plantation

0.962***

(0.173)

Constant -1.468*** -1.311*** -1.461*** -0.891*** -1.144*** -1.311***

(0.411) (0.354) (0.416) (0.203) (0.304) (0.281)

Predicted probability 0.16 0.28 0.31 0.34 0.35 0.39

Area under ROC curve 0.76 0.83 0.85 0.86 0.86 0.88

Provinces All All Without

Caltanissetta All All All

Province dummies No Yes Yes Yes Yes Yes

Observations 138 138 123 124 113 119

Notes: The estimator is binomial probit in all specifications. Clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 10: Probit regressions with additional controls

Dependent variable:

Mafia (in 1880)

(1) (2) (3) (4) (5)

Citrus 1.327** 1.183** 1.167** 1.472*** 1.318** (0.612) (0.598) (0.547) (0.545) (0.571) Fractionalization policies 1.003*** 0.823*** 0.970*** 1.037*** 0.988*** (0.217) (0.229) (0.258) (0.245) (0.234) Large scale plantation 0.987*** 0.902*** 0.903*** 0.832*** 0.397 (0.232) (0.180) (0.225) (0.256) (0.244) Distance from Palermo (in log) -0.311

(0.295) Distance from Mazzara del Vallo (in log) -0.220* (0.116) Feudal

-0.156

(0.512) Distance from railway station (in log) -0.216*

(0.129) Not care of law (dummy)

1.654**

(0.642) Mistrust in law (dummy)

0.149

(0.528) Trust in law (dummy)

0.584

(0.809) Length of contract (in log)

-0.606***

(0.121) Constant 0.729 -1.671** -1.618** -2.424*** -0.631 (1.528) (0.756) (0.778) (0.906) (0.628)

Predicted probability 0.4 0.35 0.36 0.41 0.34 Area under ROC curve 0.89 0.87 0.87 0.89 0.86 Province dummies Yes Yes Yes Yes Yes

Observations 119 117 112 112 100

Notes: The estimator is binomial probit in all specifications. Clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 11: Exclusion of altitude

Dependent variable: Mafia (in 1880)

(1) (2) (3)

Citrus 1.342**

1.031 (0.606)

(0.633)

Fractionalization policies 1.100*** 1.012*** 0.960*** (0.197) (0.172) (0.195) Large scale plantation 0.962*** 1.135*** 0.920*** (0.173) (0.187) (0.222) Altitude (in log) -0.270*** -0.122

(0.0882) (0.121)

Constant -1.311*** -0.0846 -1.173 (0.281) (0.221) (1.041)

Predicted probability 0.34 0.27 0.34 Area under ROC curve 0.86 0.83 0.86 Province dummies Yes Yes Yes

Observations 119 119 119

Clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 12: Robustness checks with alternative estimators

Panel A

Dependent variable:

Mafia (in 1880)

2nd stage estimates

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

Estimator LPM IV IVPROBIT SPATIAL

LPM

Citrus 0.319** 0.431** 1.714*** 0.316*** (0.126) (0.204) (0.333) (0.0749) Fractionalization policies 0.254*** 0.249*** 0.825** 0.210** (0.0470) (0.0812) (0.326) (0.0816) Large scale plantation 0.230** 0.209** 0.679** 0.225*** (0.0650) (0.0893) (0.305) (0.0859) Spatial autocorrelation

-0.143

(0.409) Constant 0.176*** 0.144 -1.894*** 0.0196 (0.0410) (0.155) (0.669) (0.0934)

Predicted probability 0.377 0.376 0.319 0.379 Province dummies Yes Yes Yes Yes

Observations 119 119 119 116 R-Squared 0.44 0.42 0.902 0.44.

Panel B

1st stage estimates for Citrus

Excluded Instrument: Altitude (in log) -0.160*** -0.156***

(0.030) (0.044)

Anderson LR Statistics 21.921 Cragg-Donald F-Statistics 22.027 Stock and Yogo 10% critical value 16.38 Partial F-statistics 28.21 Endogeneity Test (p-values) 0.5424

Notes: The estimator is a Linear Probability Model (LPM) in column 1, IV in column 2, IVPROBIT in column 3,

and Spatial LPM in column 4. In columns 3-4, we run two-stage estimations with Citrus as the endogenous variable

and with Altitude as the excludable instrument. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, *

p<0.1.

Table 13: Mafia persistence, 1880-2008

Mafia indicators

Province Mafia (in 1880)

Mafiosi reported in 1999

Mean mafiosi

per capita in 1999

Assets confiscated from the

mafia 1997-2008

Assets confiscated per capita in 1997-2008

Caltanissetta* 0.437 31 0.071 5.368 0.535

Catania 0.318 56 0.051 4.566 0.132

Grigenti

(Agrigento)

0.823 9 0.019 5.125 0.447

Messina 0.24 27 0.041 2.384 0.079

Palermo 0.296 30 0.024 70.900 0.855

Siracusa* 0.142 43 0.059 4.045 0.128

Trapani 0.4 5 0.018 17.266 0.679

Total 0.346 201 0.040 19.027 0.377

Numbers in the first column refer to the share of towns within each province that had mafia in 1880. The second column shows the number of mafiosi cases reported in 1999 per province whereas the third column shows the number of reported cases per capita in each province. Columns four to five show the number of assets confiscated from the mafia during 1997-2008 in absolute and per capita terms. See table A1 in the Appendix for an explanation of the variables. *Caltanissetta is merged with Enna and Siracusa with Ragusa as in the Damiani Inquiry. # Population is in thousands

Table 14: Historical mafia presence as a determinant of current mafia activity

Dependent variable: Assets confiscated per capita

1997-2008

(1) (2)

Mafia (in 1880) 0.303** 0.247* (0.140) (0.141) Province dummies No Yes Observations 143 143 R-Squared 0.04 0.19

Notes: The estimator is OLS in both specifications. Robust standard errors in parentheses. *** p<0.01, ** p<0.05,

* p<0.1. Trapani is the excluded province in column 2.

Figure 1: Equilibrium number of lemon producing towns

1 n I

Fi=a+bi αY/i

2

αY

a+b

αY/n2=a+bn

D

(Fi=al+bli) (al+bl)

E αY/I

Figure 2: Mafia and non-mafia towns in Sicily in 1880s

Appendix (online publication only)

Table A1: Variable descriptions Variables Description Source Mafia Dummy variable for whether the pretori

reports mafia as one of the most common form of crimes The relevant question in the Inquiry is: What is the most important form of crime?

Damiani (1886). Original surveys completed by pretori

Citrus, Grape, Olive, Wheat Dummy Variable proxying the most important crop produced in the town. The question in the Inquiry is: What is the dominant crop in the town?

Damiani (1886). Original surveys completed by mayors excepted for the Province of Caltanissetta for which we use the summary Report (Damiani). Di Vita (1905) is used to check the information in Damiani.

Sulphur mines Dummy variable for whether there is a sulphur mine.

Di Vita (1905)

Feudal, Church-Ruled, Crown-Ruled.

Dummy variable coding the political organization within the town before the Unification of Italy.

Di Vita (1905)

Scale of the Plantation Dummy variable for the scale of the plantation. Question: Is the large, medium, or the small scale of the plantation prevalent? To be noticed that there is not a universal criterion to classify the scale. For example the mayor of Caltagirone considers large a plantation extending for more than 2000 hectares and small a plantation extending for less than 20 hectares. On the other hand the mayor of Paterno’ considers large a plantation extending over 50 hectares and small one extending over less than 1 hectare. Most of the times this difference reflects the extent of the town. This difference is important because affects the answer on the fractionalization of land below

Damiani (1886). Original surveys completed by mayors excepted for the Province of Caltanissetta for which we use the summary Report (Damiani).

Fractionalization of Land Dummy variable for the level of fractionalization of land. Question: What is the fractionalization of the land and which factors have affected it? As mentioned above this is affected by the classification of the scale of the plantation above

Damiani (1886). Original surveys completed by mayors excepted for the Province of Caltanissetta for which we use the summary Report (Damiani).

Fractionalization of Policies Dummy variable coded one if policies aimed at increasing peasants’ ownership have been effective in fractionalizing the land. Policies considered are: Enfiteusi, Abolishment of Feudalism, and Privatization of the church’s assets. Question: Which factors have affected the fractionalization?

Damiani (1886). Original surveys completed by mayors excepted for the Province of Caltanissetta for which we use the summary Report (Damiani).

Altitude and Distance from the Altitude above the sea level (in metres) Di Vita (1905).

Railway and distance in Km from the closer railway station.

Rule of Law Dummy variable for the trust people have in the application of law. Question: Do people trust, mistrust, care, of fear the law?

Damiani (1886). Original Surveys completed by pretori.

Length of Tenancy Contract Question: What is the average length of the tenancy contract? In some cases the mayor reports that the contract lasts for the entire life. In this case we assume an average life expectancy of 60 years and an average duration of the contract of 40 years. We also try with different numbers (20, 30, 50 years) but results do not change.

Damiani (1886). Original survey completed by mayors excepted for the Province of Caltanissetta for which we use the summary Report (Damiani).

Mafia Crimes Number of mafia crimes reported to the police which are punishable according to the 416bis article of the Italian Penal Law.

National Institute for Statistics - ISTAT (1999)

Assets Confiscated Number of assets (i.e. apartments, buildings, etc) confiscated from the mafia in the town.

Agenzia del Demanio (2008)

Table A2: IV without the province of Caltanisetta

Panel A

Dependent variable:

Mafia (in 1880)

2nd stage estimates

(1) (2)

Estimator IV IVPROBIT

Citrus 0.455** 1.779***

(0.213) (0.331)

Fractionalization Policies 0.225*** 0.731**

(0.0818) (0.328)

Large Scale Plantation 0.232** 0.759**

(0.0922) (0.339)

Constant 0.136 -1.901***

(0.157) (0.707)

Province Dummies Yes Yes

Caltanissetta Excluded Yes Yes

Observations 109 109

R-squared/Predicted Prob 0.4 0.86

Panel B

1st stage estimates for Citrus

Excluded Instrument: Altitude -0.157*** -0.154***

(0.030) (0.0458)

Anderson LR Statistics 21.095 Cragg-Donald F-Statistics 21.353 Stock and Yogo 10% critical value 16.38 Partial F-statistics 27.24 Endogeneity test (p-values) 0.4285

Figure A1: Variables in the original Inquiry Damiani (1886) related to mafia

Note: Figure A1 shows a picture of the second part of the table from which we got data on mafia. The first row reports variables. Starting from the left, the first

variable is Scostumatezza (lewdness). There are three possible causes among which the prefect can choose. The first one is Adulterio (adultery), the second Incesto

(incest), the third is Nascite Illegali (illegally born child), and the last one is Varie (various). The second variable relates to the religiousness of people in the town,

the third one relates to the clergy (corrupt or exemplary), and the fourth variable regards perjury. The last variable is Vagabondaggio accattonaggio (vagrancy and

begging). The fifth variable is the one we use to get information on mafia. The variable is labelled Reati (crimes) and then it is asked what the most common crimes

are and the extent of these crimes. Most of the time, pretori only answered providing information on the sort of crime committed. The most common forms of

crime were rustling, mafia, bloody crimes, and bloody crimes for passion. In addition poverty was described as the most common cause of crime.

Figure A2: Inchiesta Agricola (Agricultural Inquiry) in Damiani (1886)

Note: Figure A2 shows the first page of the Agricultural Inquiry for the city of Cefalu. The column on the left reports the question (Quesiti). The column on the

right reports the answer of the mayor. For example the first question asks “What is the surface area of the city”, “In how many areas the territory can be divided?”

and “What is the extent of each area?” The mayor answers that the surface land of Cefalu is 52,943,492 sq.mt. and the territory is divided in three zones: 1) plain; 2)

hills; 3) mountains. The first zone extends for almost 2,500,000 sq.mt.; the second for almost 20,000,000 sq.mt.; and the third for almost 30,443,492 sq.mt. The

second question relates to the physical and chemical characteristics of the territory. This first part of the inquiry which is titled the Condition of Agriculture also

reports information on the kind of crops produced, the sort of manufactures developed in the city, and so on. The second part of the Inquiry relates to the

relationship between peasants and landlords, while the third part regards the moral conditions of peasants.

Figure A3: Optimal mafia effort m* at varying level of profits π*

Note: The initial example above (giving the curves m* and ω*) assumes the following parameter levels: d=0.4, θ=0.5,

π*=8, A=10. In the second example, we alter only the level of profits to π*=32 which yields the new curves m** and

ω**. The equilibrium level of mafia activity thereby increases from m*=0.09 to m**=0.236.

10.750.50.250

0.625

0.5

0.375

0.25

0.125

0

om ega

m

om ega

m

Profits

increase

m*

m**

ω*=0.526 ω**=0.297