Self-Selecting Candidates or Compelling Voters: How Organized Crime A ects Political Selection - AISRe
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Self-Selecting Candidates or Compelling
Voters: How Organized Crime A↵ects
Political Selection ⇤
†
Anna Laura Baraldi Giovanni Immordino‡
Marco Stimolo§
March 4, 2021
Abstract
Using data on mayoral candidates and elected mayors in Italian municipalities, this
paper seeks to unveil the mechanism underlying the evidence that the infiltration of
organized crime lowers politicians’ quality. We exploit the dissolution of city gov-
ernments for presumed mafia infiltration as an exogenous shock to the presence of
organized crime in local politics. Our results indicate that the active presence of or-
ganized crime does not discourage highly qualified candidates from running but does
induce citizens to vote for less qualified ones.
JEL Classification: H70; K42; D72.
Keywords: Organized Crime, Politician’s Quality, Political Self-selection,
Voters’ Behavior.
⇤
Acknowledgements: We would like to thank Luca Anderlini, Salvatore Capasso, Gianmarco Daniele,
Tullio Jappelli, Roberto Nisticò, Tommaso Oliviero, Lorenzo Pandolfi, Marco Pagano, Erasmo Papagni,
Paolo Pinotti, Annalisa Scognamiglio, Silvia Vannutelli, Alberto Zazzaro and seminar participants at the
University of Naples Federico II and CLEAN seminar series at Bocconi. This work is part of the research
project “VALERE: VAnviteLli pEr la RicErca”.
†
Dipartimento di Economia, Università degli Studi della Campania ”Luigi Vanvitelli”, 81043, Capua
(CE), Italy. E-mail: annalaura.baraldi@unicampania.it.
‡
Dipartimento di Scienze Economiche e Statistiche, Università di Napoli ”Federico II” and CSEF, 80122,
Napoli (NA), Italy. E-mail: giovanni.immordino@unina.it.
§
Dipartimento di Economia, Università degli Studi della Campania ”Luigi Vanvitelli”, 81043, Capua
(CE), Italy. E-mail: marco.stimolo@unicampania.it.
11 Introduction
In recent years scholars have begun to investigate the link between criminal organizations
and politics, finding evidence that mafia infiltration of the body politic lowers the quality
of politicians (see, for instance, Acemoglu et al., 2013, Baraldi et al., 2020, Daniele, 2019,
Daniele and Geys 2015). Thus, where criminal organizations are influential, politicians
are more likely to enact poor policies that prevent the flourishing of the economy (e.g.,
Pinotti, 2015, Fenizia and Saggio, 2021). This paper is intended to explicate the mechanism
underlying the adverse impact of organized crime on politicians’ quality.
The political economic literature refers to two main channels through which organized
crime can a↵ect politics. First, the recorded negative e↵ect of mafia activity may reflect a
change in self-selection that discourages qualified men and women from entering politics. Or
else, organized crime can influence voters’ behavior, redirecting their votes to less qualified
candidates.
Our contribution is to determine which of these two channels is the more likely driver of
the e↵ect of organized crime on politicians’ quality. Our empirical analysis is based on the
following logic. If organized crime induces self-selection of politicians, then we should find
a deterioration in the quality of the people running for public office. If such a decrease in
candidates’ quality is not found, then we can most likely exclude self-selection and turn our
attention to assessing the influence of mafia activity on voters’ behavior.
The strand of literature supporting the self-selection hypothesis refers to the model in
Dal Bó et al. (2006), where pressure groups aiming to reap public resources corrupt public
officials through bribes and credible threats of punishment. Hence, when the pressure group
is active, the costs of corruption (e.g. the risk for the politician of being detected) and
punishment (e.g. legal harassment and physical violence by the pressure group) decrease the
official’s payo↵. This makes a political career unattractive to highly qualified individuals,
whose opportunity wage in di↵erent job sectors is higher.1 In this setting, better law en-
forcement makes punishment more costly to the pressure group, which reacts by increasing
bribes, so as to induce self-selection of the well qualified.
Few empirical analyses o↵er evidence for this hypothesis. Gagliarducci and Nannicini
(2013) clearly shows that higher remuneration for politicians induces significantly higher
education levels for both mayoral candidates and elected mayors in Italy. Further studies
provide evidence of this remuneration e↵ect in di↵erent countries (Ferraz and Finan, 2008,
Kotakorpi and Poutvaara, 2011, Dal Bó et al., 2017). To the extent that organized crime can
a↵ect politicians’ expected payo↵s, this evidence means that pressure groups may influence
the quality of candidates. Accordingly, Alesina et al. (2019) interprets the evidence of a spike
in violence against politicians in the pre-electoral period (i.e., a year before the elections)
1
For the theoretical analysis on the relationship between politicians’ quality and the incentives of the
political career see also Caselli and Morelli (2004) and Dal Bó and Finan (2018).
2as a signal from organized crime scaring o↵ the candidates of the opposing party. Finally,
Daniele (2019) finds that murders of local politicians by mafia-type organization in southern
Italy lowers the quality (as measured by years of education) of elected politicians, possibly
because violence deters qualified individuals from running.
This empirical evidence, however, does not rule out the possibility of pressure groups
using the ”bribe and threat” strategy to influence the electorate. As the seminal contribution
of Gambetta (1996) points out, mafias tightly control the territory through credible threats
of violence inducing widespread fear of reprisal. This means that a mafia can control citizens’
votes and sell them to the preferred party in exchange for public funds and lenient legislation.
Building on this intuition, Acemoglu et al. (2013) provides and verifies a probabilistic model
where paramilitary forces — a kind of pressure group — control votes in specific regions
of Colombia and sell them to parties in line with their political preferences. In the same
spirit, Buonanno et al. (2016) finds that the vote share of the Italian center-right Forza Italia
is significantly correlated with gauges of the presence of mafia in southern municipalities.
De Feo and De Luca (2017) extends the model of Acemoglu et al. (2013) to find that the
old Christian Democratic party (Democrazia Cristiana) captured more votes in the Sicilian
municipalities where the mafia was strongly active.2 Since several studies show that citizens
respond positively to candidates’ competence (e.g., Banerjee et al., 2011, Butler and Powell,
2014, Kendall et al., 2015), mafias’ control of the territory is likely to induce them to cast
their preferences on less qualified ones.
We build a dataset on the quality of candidates and mayors in municipalities of Calabria,
Campania, Puglia and Sicily, as proxied by years of completed education. We exploit the
enforcement of Law 164/1991 (empowering the national government to decree the dissolution
of the city council where there is evidence of linkage of local government members with
criminal organizations) as an exogenous indicator of mafia infiltration of local government.
That is, since local government dissolution stems from a national-level investigation (kept
secret until its implementation) it can be deemed as good as exogenous to local politics. This
allows us to identify a treatment group of municipalities that experienced the dissolution of
the local government due to mafia infiltration and a control group of never dissolved (i.e.,
never treated ) ones. We can, therefore, use a di↵erence-in-di↵erence analysis to compare the
quality of the pool of candidates and elected mayors between dissolved (treatment group)
and undissolved (control group) municipalities, before and after the dissolution.
First, we falsify the hypothesis of self-selection as the mechanism behind the negative
e↵ect of organized crime infiltration on politicians’ quality. We document that the mean
education level of the candidates pool as well as the level of education of the best and
worst candidate do not di↵er significantly between treated and control municipalities, before
and after the dissolution of the city council. Furthermore, using an alternative measure
2
For further evidence and theoretical models on pressure groups’ influence over citizens’ votes see also
Baland and Robinson (2008) and Esteban and Ray (2006).
3of quality, we show that organized crime infiltration does not a↵ect the share of graduate
candidates within the pool compared to control municipalities. That is, organized crime
does not discourage qualified individuals for running for election.
We, then, replicate our DiD analysis in order to investigate the influence of organized
crime on voters’ behavior. Initially, the dependent variable is the distance between the
education level of the elected mayor and the average education level of the pool of candidates.
We find that when organized crime is not active, voters choose a candidate with education
above the average of the pool, whereas, when organized crime infiltrates the local government,
they vote for one whose education is closer to the mean of the pool of candidates. Specifically,
the active presence of organized crime decreases the di↵erence in education level between
mayors and candidates by about 1.4 year. Also, we show that the influence of criminal
organizations on voters increases (decreases) the educational di↵erence between the most
highly (the least) educated candidate and the elected mayor by 1.3-1.5 year; and, it lowers
the probability of electing a mayor with a university degree by 42-44 percentage points.
Finally, to shed some light on the mechanisms behind the reported measures of the
influence of organized crime on voters’ behavior, we o↵er suggestive evidence consistent with
the hypothesis of Gambetta (1996) that mafia a↵ects electoral outcomes through coercive
acts or vote buying.
Overall, with due caution as to the validity elsewhere of our results on southern Italian
municipalities, our evidence indicates that the active presence of organized crime does not
discourage qualified individuals from running for the election, but it does induce citizens to
vote for less qualified candidates.
The rest of the paper is organized as follows. Section 2 describes the institutional frame-
work and the data. In Section 3 illustrates the empirical strategy. Section 4 shows robust
evidence falsifying the hypothesis of self-selection, and Section 5 analyzes the e↵ect of mafia
infiltration on voter behavior. We conclude with Section 6.
2 Data and Institutional Framework
To shed light on the two channels through which organized crime could a↵ect politicians’
quality, we collected data both on candidates and on elected mayors. The analysis of may-
oral candidates and elected mayors, who head municipal governments, is crucial because
pressure groups naturally aim at the public officials with the greatest discretionary power
over resources allocation (Dal Bó et al., 2006). Indeed, empirically the negative e↵ect of
mafia infiltration is stronger for mayors than for aldermen and city councilors (Daniele and
Geys, 2015).
We focus on the 1608 Italian municipalities of Calabria, Campania, Puglia, and Sicilia,
the regions where organized crime (respectively, ’ndrangheta, camorra, sacra corona unita,
and mafia) is traditionally concentrated. Given our Di↵erence-in-Di↵erences approach, this
4restricted sample allows comparison of municipalities where mafia involvement in the political
process is detected with municipalities where it is not, controlling for other factors.
Municipal elections are held every five years, but if a municipal government is dissolved
new elections are called before the scheduled end of the previous council; Thus, municipal
elections are staggered in time, and the number of electoral rounds for which we have relevant
information ranges from 1 to 8, depending on municipality.
As noted, we measure politicians’ quality by their educational degree, which is recognized
in the literature as a good proxy for human capital (Besley and Reynal-Querol, 2011, Dal Bó
et al., 2006, Fortunato and Panizza, 2015, Galasso and Nannicini, 2011, Glaeser et al., 2004,
Kotakorpi and Poutvaara, 2011). While for mayors this information is publicly available, for
mayoral candidates it is difficult to obtain — the data are gathered only by local electoral
offices and are not published by the Interior Ministry. We have, therefore, relied on two
sources. The richest is the local elected politicians database (the Interior Ministry has data on
identity, gender, age, regional function, previous job and education of local administrators).
And under the electoral law for municipalities, loosing mayoral candidates get a position
as city councilors if they get a large enough proportion of the vote. The information on
other non-elected candidates, instead, has been gathered by direct request to the municipal
electoral offices.This produced a database with information on education and previous jobs
of almost the 90% of mayoral candidates between 1993 and 2016.3
We translate the qualitative information on the degrees attained by candidates and may-
ors into years of education. We upgrade the criterion followed by De Paola et al. (2010),
Baltrunaite et al. (2014) whose years of education range from from 0 to 18.4 Specifically,
we match the qualification and the previous occupation of each politician to attribute a
more appropriate number of years of education. For example, if a politician reported being
a specialized surgeon, we attribute 5 years of education above the 18 needed to hold the
university degree.5 In this way, years of education range from 0 to 23. Figure 1 plots the
trends in the education levels of candidates and mayors in the electoral rounds from 1993 to
2016, documenting that on average mayors are better educated than the pool of candidates.
3
The data on candidates are available only since 1993, as of the Law 81/1993 made mayors directly
elected by their own constituents, whereas previously they had been appointed by municipal councilors.
4
They measure education as the minimum number of years necessary to obtain a certain degree: no
education = 0 years; primary education = 5 years; lower secondary = 8 years; upper secondary = 13 years;
university or more = 18 years.
5
Table 5 in Appendix shows the conversion approach.
5Figure 1: Years of education of candidates and mayors in the whole sample of municipalities
Note. The red line represents the number of the years of
education of elected mayors. The blue line represents the
average number of years of education of candidates. On the
vertical axis there are the years of education. 1993-2016.
Following the recent literature on the influence of organized crime on politics (Daniele and
Geys, 2015, Di Cataldo and Mastrorocco, 2020, Fenizia and Saggio, 2021, Galletta, 2017),
we exploit Law 164/1991, which prescribes the dissolution of local governments where there
is evidence of mafia infiltration. The municipal government suspected of ties with organized
crime is dissolved by the national government and replaced for the next 12-24 months by
three commissioners. A new local government is then elected. We collected data on ad-
ministrations dissolved for suspected mafia infiltration between 1993 and 2016. Our sample
counts 137 dissolved administrations; 31 municipalities experienced more than one dissolu-
tion. Most occurred in the provinces of Reggio Calabria (31), Naples (25), Palermo (20) and
Caserta (18). None were registered in the provinces of Bari, Barletta-Andria-Trani, Enna,
Foggia, Lecce and Taranto.6
We also track a number of observable municipal characteristics, to support our causal
interpretation. In Italy the mayor’s salary increases with the resident population. Gagliar-
ducci and Nannicini (2013) show that higher salaries for politicians induce a significant rise
in the average education level of both candidates and mayors. Thus, the introduction of the
resident population taken by the census (Population) controls for this remuneration e↵ect.
The 1991, 2001 and 2011 censuses also enable one to derive data on average educational
attainment (defined as the ratio of secondary degree holders to population) (Education) and
the unemployment rate (Unemployment).7
6
Another 40 municipalities, dissolved for mafia infiltration, were not added to the sample because the
city council was elected before 1993 and data on candidates are therefore not available.
7
See Table 6 in Appendix for the descriptive statistics.
63 Empirical strategy
If organized crime induces self-selection, then the pool of candidates of treated municipalities
should show statistically di↵erent characteristics before and after the dissolution compared
to the change observed in control municipalities where mafia infiltration is absent or not
detected. Conversely, if the characteristics of the pool of candidates do not significantly
di↵er before and after the dissolution, then the e↵ect of organized crime can be imputed to a
change in voters’ behavior. Accordingly, we test whether the quality of mayoral candidates
is a↵ected by criminal organizations.
The panel structure of our database, combined with the shock to local politics due to
Law 164/1991, allows us to isolate the e↵ect of criminal infiltration on candidates’ quality
from any time-specific e↵ect – general trends in education, say – which might drive the
results. Thus, we take a di↵erence-in-di↵erences approach, observing our outcome variable
in each election year in each city before and after dissolution. Moreover, the assignment
to the treatment group (i.e., the timing of dissolution) does not depend on the candidates’
quality.
Since the analysis focuses on the pool of candidates at each electoral round, the time-
series dimension of the panel is, for each municipality, the election years.8 Since dissolutions
were not all at the same time, the panel structure of our database allows us to consider these
di↵erences and separate the e↵ect of the dissolution from possibly unobserved time-specific
events, thus strengthening the identification assumption (Angrist and Pischke, 2008). The
baseline specification is as follows (subscript i is for municipalities, t for years).
Yit = ↵i + 1 M af iaBackT o1993it + 2Y eart + 3 T rendit + 4 P rovY earpt + 0 Xit + ✏it (1)
Yit measures the quality of candidates at election t in municipality i. Specifically, we take
three relevant moments of the distribution of years of education: the mean, the most and
least educated candidate. We also use the share of graduate candidates as a further measure
of politicians’ quality.
↵i is the set of municipality fixed e↵ects that control for heterogeneity across municipal-
ities. Y eart is the set of year fixed e↵ects that control for time-varying changes common to
all municipalities.9 Since, in the absence of treatment, our identification strategy assumes
the same temporal development of each municipality, the variable T rendit — represent-
ing the complete set of (linear) municipality-specific time trends — controls for potential
di↵erential temporal developments not linked to the treatment. P rovY earpt is the set of
8
The previous literature on this issue takes as time-dimension every year within the time-span.
9
Year fixed e↵ects capture also the impact of gender quotas (Law 81/1993, enforced between 25 March,
1993 and 12 September 1995) on our outcome variable because in the four regions of interest about 90%
of municipalities voted while it was in e↵ect. Indeed, as documented by Baltrunaite et al. (2014), the
introduction of the gender quotas raised average education levels reducing the number of low-educated men
elected due to an increase in the number of more educated women.
7province-year fixed e↵ects controlling for time-varying changes at provincial level. Xit is the
vector of variables controlling for time-varying municipal characteristics to take account of
important socio-economic changes during our sample period — resident population (in nat-
ural log, Population), average educational attainment (Education) and the unemployment
rate (Unemployment). ✏it is the idiosyncratic error term. Standard errors are robust to
heteroskedasticity and clustered at municipality level.
The variable of interest in the baseline analysis is M af iaBackT o1993it . This is set
equal to 1 for municipalities in the entire period preceding the dissolution (that is, for all
the electoral rounds between 1993 and the round that elected the administration then put
under commission) and 0 in the period after the dissolution and for the never dissolved
municipalities.10 If mafia a↵ects the quality of politicians adversely by inducing less able
individuals to run for office, we should expect 1 significantly below 0 in the regression of
the average, the most and the least educated candidate, as well as of the share of graduate
candidates in the pool. Instead, if 1 were insignificant, this would falsify the self-selection
mechanism.
We experiment with a number of di↵erent operationalizations of the treatment variable
to assess the robustness of our results. In particular, we relax the hypothesis of mafia
infiltration since 1993 and define Rounds 6, Rounds 5, Rounds 4 Rounds 3 and Rounds 2 as
treatment variables that date mafia infiltration back respectively five, four, three, two and
one electoral round before that of the dissolved administration. Since our main hypothesis
(M af iaBackT o1993it ) may introduce asymmetry across municipalities as the years of the
electoral rounds advance, these alternatives eliminate this asymmetry and assume a shorter
duration of mafia infiltration.
4 Politicians’ quality and self-selection
In this section, we implement a DiD analysis to test and reject the hypothesis of candidates’
self-selection. Preliminary, we replicate Daniele and Geys (2015)’s finding by showing that
the invocation of Law 164/1991 induces an increase of 14 months in mayors’ education by
reducing the presence of organized crime.11
Our relevant results are reported in Table 1. In column 1, the dependent variable is
the average years of education of the mayoral candidates in municipality i at election year
t (Mean Cand ). However, this dependent variable could be a↵ected by the number of
candidates, which in turn is a↵ected by the treatment (see Table 4). Therefore, we also use
order statistics by analyzing the treatment e↵ect on the years of education of the most and
10
This definition of the treatment excludes from the sample 15 municipalities dissolved for mafia infiltration
during the last government available within the sample period in order to get at least one observation after
the commissioners have turned the power back to new elected government.
11
See Table 7 in Appendix.
8the least educated candidate in municipality i at election year t respectively in columns 2
and 3 (Best Cand, Worst Cand ). Overall, all the estimated coefficients are not statistically
significant.
Next, in column 4 of Table 1 we test the self-selection hypothesis using as an alternative
measure the share of graduate candidates (Share of Grad Cand ). The relevant coefficient
is not statistically di↵erent from zero, meaning that the active presence of organized crime
does not a↵ect the share of university graduates running for mayor.
Table 1: Self-Selection
(1) (2) (3) (4)
Dep. Var. Mean Cand Best Cand Worst Cand Share of Grad Cand
Panel A
MafiaBackTo1993 -0.392 -0.325 -0.427 -0.0727
(0.410) (0.393) (0.680) (0.0592)
Constant 15.99*** 17.32*** 14.27*** 0.836***
(0.265) (0.263) (0.476) (0.050)
Observations 7,360 7,360 7,360 7,387
R-squared 0.400 0.394 0.380 0.405
Number of id 1,591 1,591 1,591 1,591
Municipality FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Municipality-trend Yes Yes Yes Yes
Province-Year FE Yes Yes Yes Yes
Controls No No No No
Panel B
MafiaBackTo1993 0.441 -0.368 –0.481 -0.0792
(0.411) (0.391) (0.681) (0.0593)
Constant 15.69 20.76* 2.738 0.351
(11.35) (12.58) (16.43) (1.590)
Observations 7,359 7,359 7,359 7,386
R-squared 0.402 0.395 0.381 0.406
Number of id 1,591 1,591 1,591 1,591
Municipality FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Municipality-trend Yes Yes Yes Yes
Province-Year FE Yes Yes Yes Yes
Controls Yes Yes Yes Yes
Notes. The dependent variables are: in columns 1 and 4, the average years of education of the may-
oral candidates (Mean Cand ); the years of education of the highest and lowest educated candidate
(Best Cand and Worst Cand, respectively in columns 2 and 5, 3 and 6). MafiaBackTo1993 is a
dummy taking value of 1 for dissolved municipalities in the entire period preceding the dissolution
of the government and 0 otherwise. The mean of Mean Cand is 16.121; the mean of Best Cand is
17.907; the mean of Worst Cand is 14.155. Controls include: Population, Education and Unem-
ployment. Coefficients of municipality FE, year FE, province-year FE, linear municipality-trend
and controls are not reported. Robust standard errors adjusted for clustering at municipality level
are in brackets. Significant coefficients are indicated by * (10% level), ** (5% level) and *** (1%
level).
9All estimations contain municipality, year and province-year FE, as well as (linear)
municipality-trend in Panel A. The inclusion of municipality controls in Panel B (popu-
lation, the municipal level of education and the unemployment rate) does not alter the sign,
significance or magnitude of the coefficient of interest with respect to the previous speci-
fications.12 The stability of the main coefficients supports our causal interpretation of the
results by reducing the possibility of omitted variable bias.13
Since our classification extends the years of education to 23, while the literature caps
them to 18, one possible concern is that our results may have been driven by outliers. We
address this concern by showing that the distribution of the years of education for candidates
in treated and control municipalies do not document the presence of outliers.14 Furthermore,
the variable Share of Grad Cand entails that candidates with an education level higher than
or equal to 18 years are considered identical; this confirms that results in Table 1 are not
driven by outliers.
As is clarified in Section 3, we also use alternative hypotheses on mafia infiltration. As
documented in Figure 2, the distribution of the dissolutions over time is roughly homoge-
neous; thus, roughly the same number of dissolutions can be used to estimate the coefficients
of interest for Rounds 6, Rounds 5, Rounds 4, Rounds 3, Rounds 2. We confirm that mafia
infiltration does not a↵ect the quality of the pool of candidates.15
12
Our results hold when we use the variance and the range of variation of the pool of candidates as
dependent variables.
13
The coefficients of interest do not change when a more flexible functional form of squared municipality
time trends is introduced (see Tables 8 in Appendix). Since there are 216 observations with only one
candidate (196 in the control and 20 in the treatment group), we replicate the analysis excluding elections
with only one candidate, confirming our main results (see Table 9 in Appendix).
14
See Figure 5 in Appendix.
15
See Table 10 in Appendix.
10Figure 2: Number of mafia dissolved administrations by year of election
Note. The graph shows the number of administrations dissolved
because of mafia infiltration by year of election. For instance,
11 administrations elected in 1993 were dissolved at some point
after 1993. Note that, since our dataset spans from 1993 to 2016,
it is not surprising that there are no administrations elected in
2015 and 2016 dissolved after that dates.
Since in our sample 31 municipalities experienced more than one dissolution it could be
that Law 164/1991 is not always e↵ective against mafia infiltration. To take this possibility
into account, we exclude the municipalities that underwent repeated dissolution from the
sample. This restriction does not alter the main findings on the e↵ects of organized crime
on the education level of candidates.16
We also control for province FE (rather than municipality FE). Analogously to the fore-
going results, the coefficient of the dependent variable is not statistically significant, no
matter what definition of the treatment variable is adopted.17
Overall, our evidence can be taken as a refutation of the hypothesis of self-selection,
as detected mafia infiltration does not a↵ect any relevant measure of the education of the
pool of candidates. Hence, political self-selection due to law enforcement cannot be the
mechanism behind the increase in politicians’ quality - as measured by years of education
- following city council dissolution. Referring to this result, in what follows we conduct a
series of robustness checks.
4.1 Sample selection bias
The central assumption in the di↵-in-di↵ analysis is that, was it not for infiltration of orga-
nized crime, treated and control municipalities would have shown a similar trend in outcomes.
Thus, the issue of sample selection is extremely relevant in this case. We deal with that by
16
See Table 11 in Appendix.
17
See Table 12 in Appendix.
11proposing three refinements of our analysis: 1) the propensity score matching technique; 2)
the exclusion from the sample of administrations dissolved for reasons unrelated to mafia
infiltration 3) the restriction of the sample to homogeneous units (similar provinces and
neighboring municipalities).
Propensity score matching In order to select control municipalities that are more likely
to share a common trend with the treated ones, following Daniele and Geys (2015) and
Fenizia and Saggio (2021) we match each of the treated municipalities to counterfactual
municipalities using nearest-neighbor propensity score matching.18 The potential untreated
municipalities are selected by matching them with treated ones according to a propensity
score obtained by running a probit model. Specifically, we fit a probit model of treatment
status using as covariates (at municipal level) the (log) male and female population size,
the average educational attainment, the unemployment rate, the share of female in munic-
ipal council and the number of mafia-related homicides (in the province).19 Although this
substantially restricts the sample, it induces the highest possible similarity in terms of the
predicted probabilities of government dissolution. The resulting sample displays very similar
pattern between the matched set of treated and untreated municipalities according to the
covariates included. Figure 3 shows the distributions of the estimated propensity scores for
the treated group (i.e. all municipalities put under commissioners; right-hand side) and the
control group (i.e. their nearest neighbors as derived from the matching procedure; left-hand
side). They substantially overlap ensuring that the key requirement for the di↵-in di↵- ap-
proach is fulfilled and the inference is valid. The results in Panel A Table 2 replicate the
main analysis for this matched sub-sample of municipalities, confirming our results.
18
We use psmatch2 (Leuven and Sianesi, 2003).
19
The probit estimation coefficients suggest that larger male municipal population size, more mafia-related
homicides and higher unemployment rate all significantly increase the probability of government dissolution.
The female municipal population size has the reverse e↵ect. The share of female politicians and the average
educational attainment have no statistically significant e↵ect.
12Figure 3: Overlap in Propensity Scores in Treated and Matched Samples
Note. Distributions of the estimated propensity scores of dissolved municipalities for
the treated group (i.e. all municipalities put under commissioners; right-hand side)
and the control group (i.e. their ‘nearest neighbour’ as derived from the matching
procedure; left-hand side). 1993-2016.
Dissolution for non-mafia reasons. Sample selection bias may arise if we count early
dissolution of city councils not for mafia. Law 142/1992 establishes that local governments
may be dissolved for a number of other reasons as well: resignation of the mayor or more
than 50% of councilors; failure to organize elections; some special cases of ineligibility of the
mayor; failure to pass the budget; and political crisis in ruling coalitions. Such dissolutions
are fairly common. In the sample, 1227 administrations were dissolved for reasons unrelated
to mafia infiltration. The early termination of an administration might reduce the probability
of mafia infiltration, albeit present, being detected. Therefore, those administrations would
be erroneously included in the control rather than in the treatment group. To attenuate
this potential selection bias, our estimates limit the control group to administrations that
never experienced early termination for non-mafia reasons. The findings are robust to this
restriction, so a preexisting relationship between early termination and the probability of
dissolution for mafia is not likely to drive selection into the two groups (Panel B Table 2).
Restriction of sample to homogeneous units. Our DiD identification strategy relies
on comparison of the infiltrated municipalities with a counterfactual control group that is
similar in terms of unobservable social, political, cultural characteristics. Therefore, first
we exclude municipalities in the provinces of Bari, Barletta-Andria-Trani, Enna, Foggia,
Lecce and Taranto, where no city council were dissolved for mafia infiltration in our sample
13period. We further restrict the control group by considering only municipalities neighboring
of those in the treatment group (neighboring municipalities drawn from National Institute
of Statistics, ISTAT).20 Neither of these robustness checks changes the main findings (See
Panels C and D Tables 2.).
Table 2: Sample selection bias
(1) (2) (3) (4)
Dep. Var. Mean Cand Best Cand Worst Cand Share of Grad Cand
Panel A: propensity score matching
MafiaBackTo1993 -0.146 -0.520 0.253 -0.0221
(0.431) (0.442) (0.718) (0.0664)
Observations 1,942 1,942 1,942 1,948
R-squared 0.507 0.503 0.500 0.519
Number of id 421 421 421 421
Panel B: Dissolution for non-mafia reason
MafiaBackTo1993 -0.704 -0.579 -0.902 -0.0990
(0.486) (0.469) (0.825) (0.0703)
Observations 6,165 6,165 6,165 6,191
R-squared 0.474 0.478 0.461 0.481
Number of id 1,591 1,591 1,591 1,591
Panel C: Sample restriction to homogeneous provinces
MafiaBackTo1993 -0.440 -0.360 -0.490 -0.0786
(0.411) (0.392) (0.681) (0.0592)
Observations 6,004 6,004 6,004 6,025
R-squared 0.407 0.401 0.385 0.410
Number of id 1,334 1,334 1,334 1,334
Panel D: Sample restriction to neighboring municipalities
MafiaBackTo1993 -0.346 -0.370 -0.358 -0.0608
(0.459) (0.442) (0.737) (0.0664)
Observations 2,490 2,490 2,490 2,497
R-squared 0.509 0.483 0.488 0.524
Number of id 565 565 565 565
Municipality FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Municipality-trend Yes Yes Yes Yes
Province-Year FE Yes Yes Yes Yes
Notes. The dependent variables are: in columns 1 and 4, the average years of education of the may-
oral candidates (Mean Cand ); the years of education of the highest and lowest educated candidate
(Best Cand and Worst Cand, respectively in columns 2 and 5, 3 and 6). MafiaBackTo1993 is a
dummy taking value of 1 for dissolved municipalities in the entire period preceding the dissolution
of the government and 0 otherwise. The mean of Mean Cand is 16.121; the mean of Best Cand is
17.907; the mean of Worst Cand is 14.155. Coefficients of municipality FE, year FE, province-year
FE, linear municipality-trend and controls are not reported. Robust standard errors adjusted for
clustering at municipality level are in brackets. Significant coefficients are indicated by * (10%
level), ** (5% level) and *** (1% level).
20
ISTAT, “Matrici di contiguità”, https://www.istat.it/it/archivio/137333.
145 Mafia and voters’ behavior
Having rejected the self-selection hypothesis, the next step is to measure the e↵ect of mafia
infiltration on voters’ behavior. To this end, we replicate the DiD approach of equation 1
taking as dependent variables four measures of the change in voters’ behavior due to mafia
activity. Since we seek to measure the e↵ect of mafia infiltration on voters’ choices, we
exclude all the observations where there is only one candidate for mayor: in that case, we
could not gauge any behavioral change due to mafia activity.
The empirical analysis proposed in this section is grounded in three premises. First,
as documented above, mayors are more educated than candidates on average. Second, the
infiltration of organized crime in local politics reduces the average quality of elected mayors.
Third, our test of the self-selection hypothesis robustly shows that organized crime activity
does not a↵ect the quality of the pool of candidates (i.e., Mean Cand, Best Cand, Worst
Cand ). Thus, the di↵erence between average mayors’ quality and the average of the three
above mentioned measures indicates that the negative e↵ect of organized crime infiltration
on the elected is due to a change in voters’ behavior. To ease interpretation we construct
our three dependent variables as positive di↵erences. For this reason, we put the average
quality of mayor as the second term in our second measure (Best Cand-Mayor ).21
The first measure is the di↵erence between mayors’ years of education and candidates’
average years of education for each municipality at each electoral round (Mayor-Mean Cand );
hence, the parameter 1 associated with MafiaBackTo1993 measures the e↵ect of mafia
infiltration on the di↵erence between the quality of the elected mayor and the average quality
of candidates. Here, we expect 1 to be significantly negative, meaning that mafia infiltration
induces voters to elect a mayor closer to the average education of the candidates’ pool,
compared to the counterfactual where mafia is not (or at least less) active.
The second measure is the di↵erence in the years of education between the most highly
educated candidate and the mayor elected for each municipality at each electoral round
(Best Cand-Mayor ); hence 1 measures the e↵ect of mafia infiltration on the di↵erence
between the quality of the mayor and of the best-educated candidate. We expect 1 to
be significantly positive, as mafia infiltration should make people vote so that the distance
between quality of the elected mayor and the best-educated candidate is greater than in the
control municipalities.
The third measure is the di↵erence in the years of education between the elected mayor
and the least educated candidate for each municipality at each electoral round (Mayor-
Worst Cand ); hence, 1 measures the e↵ect of mafia infiltration on the di↵erence between
the quality of the mayor and of the least educated candidate. We expect 1 to be significantly
negative, for the opposite reason to what we have just been said above.
21
Table 6 in Appendix reports the averages of these three di↵erences. Specifically, Mayor-Mean Cand =
0.354, Best Cand-Mayor = 1.501 and Mayor-Worst Cand = 2.397.
15To further corroborate the analysis, we use as an alternative measure of voters’ behavior
a dummy variable taking value 1 if the elected mayor has a degree (and there are at least
two candidates, one with and one without a degree), and 0 otherwise (Prob Grad Mayor ).
Thus, 1 measures the e↵ect of mafia infiltration on the probability of electing a graduate.
In this case, we expect 1 to be significantly negative, as mafia infiltration should lower
the probability of electing a graduate. Since mafia infiltration does not a↵ect the share of
graduate candidates (see column 4 in Table 1), this e↵ect can be imputed to a change in
voters’ behavior.
Table 3 reports the results for our panel FE model in columns 1-3 and the linear prob-
ability model (LPM) in column 4. We estimate the model specifications controlling for
municipality, year and province-year FE as well as for (linear) municipality-specific time
trends in Panel A. We also introduce our usual control variables for robustness in Panel B.
In the first column of Table 3, the coefficient of the dependent variable is negative and
statistically significant. The di↵erence in education level between mayors and the average
candidate decreases by 16 to 17 months (respectively in Panels A and B) in the pre-dissolution
period compared to what it would have been, ceteris paribus, in undissolved municipalities.
In other words, the active presence of organized crime induces voters to elect mayors whose
education is closer to the mean of the pool of candidates. The magnitude of the coefficient
indicates that the di↵erence narrows by four times its actual mean (0.354) when a new city
government is elected after dissolution.
Column 2 of Table 3 shows that the di↵erence between the most educated candidate and
the elected mayor is by 1.33 to 1.4 years wider in the dissolved municipalities than in the
counterfactual scenario. The recorded variation of the di↵erence is equal to its actual mean
(1.501). Similarly, in column 3 we show that the di↵erence between the elected mayor and
the least educated candidate is by 1.48 to 1.55 years lower in the dissolved municipalities
than otherwise. This change is slightly more than half of its actual mean (2.397).
Finally, the linear probability estimate in column 4 of Table 3 shows a significant and
negative coefficient, indicating that the probability of electing a graduate mayor when there
are at least two candidates, one with and one without a degree, decreases by roughly 42/44
p.p. before council dissolution vis-à-vis municipalities never experiencing dissolution. The
variation in the probability of electing a graduate mayor is slightly less than its actual mean
(0.657).
16Table 3: Voters’ behaviour
(1) (2) (3) (4)
Dep. Var. Mayor-Mean Cand Best Cand-Mayor Mayor-Worst Cand Prob Grad Mayor
Panel A
MafiaBackTo1993 -1.342** 1.329** -1.480* -0.419***
(0.562) (0.665) (0.839) (0.159)
Constant 1.633*** 0.334 3.825*** 0.914***
(0.390) (0.414) (0.564) (0.111)
Observations 6,906 6,906 6,906 4,308
R-squared 0.391 0.380 0.399 0.568
Number of id 1,586 1,586 1,586 1,473
Municipality FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Municipality-trend Yes Yes Yes Yes
Province-Year FE Yes Yes Yes Yes
Controls No No No No
Panel B
MafiaBackTo1993 -1.407** 1.401** -1.551* -0.444***
(0.562) (0.667) (0.838) (0.159)
Constant 21.09 -14.12 37.44** 5.476
(14.36) (15.35) (18.89) (5.188)
Observations 6,905 6,905 6,905 4,307
R-squared 0.392 0.382 0.400 0.570
Number of id 1,586 1,586 1,586 1,473
Municipality FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Municipality-trend Yes Yes Yes Yes
Province-Year FE Yes Yes Yes Yes
Controls Yes Yes Yes Yes
Notes. The dependent variables are: in columns 1 and 4, the di↵erence between mayors’ years of education and
candidates’ average years of education for each municipality at each electoral round (Mayor-Mean Cand ); in columns
2 and 5, the di↵erence in years of education between the highly educated candidate and the elected mayor for each
municipality at each electoral round (Best Cand-Mayor ); in columns 3 and 6, a dummy variable taking value 1 if the
elected mayor has a university degree when there is at least one graduate candidate within the pool and 0 otherwise
(Prob Grad Mayor ). MafiaBackTo1993 is a dummy taking value 1 for dissolved municipalities in the entire period
preceding the dissolution of the government and 0 otherwise. The mean of Mayor-Mean Cand is 0.354; the mean of
Best Cand-Mayor is 1.501; the mean of Mayor-Worst Cand is 2.397; the mean of Prob Grad Mayor is 0.657. Controls
include: Population, Education and Unemployment. Coefficients of municipality FE, year FE, province-year FE, linear
municipality-trend and controls are not reported. Robust standard errors adjusted for clustering at municipality level
are in brackets. Significant coefficients are indicated by * (10% level), ** (5% level) and *** (1% level).
O↵ering graphical support for our results, Figure 4 plots the trends in the average education
level of candidates and mayors, in accordance with the coding MafiaBackTo1993 ; that is,
in Panel 4a we compare the mayor/candidates education outcome where MafiaBackTo1993
takes the value of 1 (plagued) while in Panel 4b we do the same when it takes the value of 0
(not plagued). In the entire period, the lines for candidates and mayors tend to overlap in
Panel 4a, whereas mayor’s education is stably above candidates in Panel 4b.
Overall, our evidence documents that when mafia is not active voters choose candidates
with above average education, but when mafia does infiltrate they tend to vote for less
educated politicians.
17To address the important concern that our results might be driven by local government’s
dissolution as such, rather than by mafia infiltration, we perform a placebo test using mu-
nicipal dissolution unrelated to mafia infiltration. Specifically, we investigate whether voters
react similarly to all types of dissolution, including non-mafia. That is, we run our DiD anal-
ysis as in equation 1 on the four relevant measures of voters behavior, taking as treatment
variable administrative dissolution unrelated to mafia infiltration (NoMafiaDissolution). To
avoid falsifying the test, we exclude from the control group municipalities dissolved for mafia
infiltration. The results, reported in the first four columns of Table 4, indicate that voters in
fact do not react significantly to all types of government dissolution but only to those pur-
suant to Law 164/1991. This suggests that our findings on voters’ behavior are e↵ectively
linked to criminal infiltration.
Figure 4: Di↵erence in the education level between mayors and candidates
(a) (b)
Notes. Panels 4a and 4b compare the number of years of education of mayors (red line) and average number
of years of education of candidates (blue line) in, respectively, the local administrations plagued/not plagued
by mafia infiltration according to the coding of MafiaBackTo1993. The vertical axis gives number of years
of education. Years from 1993 to 2016.
Lacking direct measures of the influence of organized crime on voters’ behavior, to shed light
on the mechanism behind this result, we provide suggestive evidence consistent with the
hypothesis that criminal organizations compel citizens to vote for the candidates they back.
First, we inquire into the ways in which organized crime directs votes to the chosen
candidate by analyzing turnout (the number of voters over the number of eligible voters).
As several empirical works observe, where criminal organizations influence elections through
coercion or vote-buying, this might increase turnout (e.g., Escaleras et al., 2012, Stockemer
and Calca, 2013). And in fact we find that the presence of mafia increases voter turnout by
more than 3 p.p. compared to undissolved municipalities (see column 5, Table 4).
Moreover, organized crime could intimidate potential competing candidates and so ensure
the victory of its own choice. Accordingly, we expect that mafia infiltration should reduce the
number of candidates, and column 6 of Table 4 shows that the active presence of organized
18crime before a dissolution does significantly reduce the number of candidates, compared
to control municipalities. Overall, consistent with Gambetta (1996), our analysis allows a
reading of the change in voters’ behavior as the product of coercion by criminal organizations.
Table 4: Voters’ behavior: Suggestive evidence
(1) (2) (3) (4) (5) (6)
Dep. Var. Mayor-Mean Cand Best Cand-Mayor Mayor-Worst Cand Prob Grad Mayor Turnout N. Cand
NoMafiaDissolution 0.0995 -0.311 -0.298 0.0414
(0.183) (0.206) (0.232) (0.0333)
MafiaBackTo1993 0.0328*** -0.532**
(0.0119) (0.229)
Constant 20.33 -13.87 35.16* 3.261 0.357 4.042
(14.91) (16.00) (19.55) (2.949) (0.262) (4.581)
Observations 6,401 6,401 6,401 5,519 7,386 7,386
R-squared 0.402 0.394 0.414 0.469 0.731 0.381
Number of id 1,464 1,464 1,464 1,415 1,591 1,591
Municipality FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Municipality-trend Yes Yes Yes Yes Yes Yes
Province-Year FE Yes Yes Yes Yes Yes Yes
Controls Yes Yes Yes Yes Yes Yes
Notes. The dependent variables are: in column 1, the di↵erence between mayors’ and average candidates’ years of education for each municipality at
each electoral round (Mayor-Mean Cand ); in column 2, the di↵erence in the years of education between the most educated candidate and the elected
mayor for each municipality at each electoral round (Best Cand-Mayor ); in column 3, a dummy taking value 1 if the elected mayor has a university
degree when there is at least one other graduate candidate and 0 otherwise (Prob Grad Mayor ); in column 4, the election turnout rate (Turnout);
in column 5, the number of candidates at each electoral round (N. Candidates). NoMafiaDissolution is a dummy equal to 1 in the electoral round
that appointed the dissolved administration and in the entire period preceding the dissolution (back to 1993) and 0 otherwise. MafiaBackTo1993 is
a dummy taking value 1 for dissolved municipalities in the entire period preceding the dissolution of the government and 0 otherwise. The mean of
Mayor-Mean Cand is 0.354; the mean of Best Cand-Mayor is 1.501; the mean of Mayor-Worst Cand is 2.397; the mean of Prob Grad Mayor is 0.657;
the mean of Turnout is 0.733; the mean of N. Cand is 2.864; Controls include: Population, Education and Unemployment. Coefficients of municipality
FE, year FE, province-year FE, linear municipality-trend and controls are not reported. Robust standard errors adjusted for clustering at municipality
level are in brackets. Significant coefficients are indicated by * (10% level), ** (5% level) and *** (1% level).
6 Conclusion
We have investigated two possible mechanisms behind the widely recognized negative ef-
fect of organized crime on the quality of elected politicians. Organized crime can induce
self-selection that discourages qualified individuals from entering politics; alternatively, it
influence citizens’ votes, redirecting them towards less well-educated candidates. Using a
large sample of southern Italian local administrations in Calabria, Campania, Puglia and
Sicilia, we exploited Law 164/1991 against mafia infiltration to carry out a DiD analysis of
the average educational level of pools of candidates and elected mayors.
We found robust evidence against the hypothesis of self-selection: the mean, the most
and the least educated candidate, as well as the share of graduate candidates, do not di↵er
significantly between dissolved and undissolved city councils, before and after the dissolu-
tion. Instead, the hypothesis that organized crime a↵ects voters’ behavior finds support.
Specifically, we showed that mafia infiltration in local politics systematically diverts citizens’
votes towards less qualified candidates. Also, to shed light on the mechanism behind this re-
sult, we found suggestive evidence consistent with Gambetta (1996)’s hypothesis that mafia
controls elections through coercion and vote buying.
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