Born in the Purple: Political Dynasties and Electoral Success

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Born in the Purple: Political Dynasties and Electoral Success*

Gianmarco Daniele

Vrije Universiteit Brussel (VUB), Department of Applied Economics, Pleinlaan 2, B-1050 Brussel, Belgium

Email: [email protected]

This version:

February 2015

Abstract:

Political dynasties are a common phenomenon in many modern democracies. In this article,

we study to what extent the electoral advantage enjoyed by dynastic politicians is driven by

heuristic signals unrelated to their real ability. This is important since it allows evaluating

voter responsiveness to such politicians’ perceived rather than real ability. Empirical

identification relies on a comparison of dynastic politicians in their first election (when voters

have little information about candidates’ ability and might rely on heuristics) and later

elections (when information about actual ability is available). Consistent with the hypothesis

that voters treat membership of a political dynasty as a valuable heuristic when little

information about the candidate is available, we find – using a large dataset of Italian mayoral

candidates in the period 2000-2013 – that dynastic candidates’ electoral advantage is

substantially larger during their first election compared with successive elections.

Keywords: Political dynasty, Local government, Elections, Heuristics.

Word count: 8232 words

* The author is grateful to Tommaso Aquilante, Andrea Colombo, Ernesto Dal Bó, Benny Geys, Joshua Holm,

Marc Jegers, Luca Livio and Denni Tommasi for helpful comments and discussions. He also gratefully

acknowledges the hospitality of the Department of Political Science at Stanford University during part of the

research underlying this article and thanks FWO Vlaanderen (grant number G.0022.12) for financial support.

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“The political talent and policy depth so evident in the first

generation isn’t always present in the second generation, in

part because it’s not as necessary to fuel the rise [to power].”

Jeffrey Smith (former Missouri State Senator)

1. Introduction

Even though positions in democratic societies are generally awarded via elections, families

often continue to play a significant role in politics. In fact, political dynasties are common in

as diverse settings as, for instance, Argentina (Rossi, 2011), Japan (Fukui and Fukui, 1992;

Asako et al., 2013), the Philippines (Querubin, 2013) and the United States (Dal Bó et al.,

2009; Feinstein, 2010). The emerging academic literature on such political dynasties has thus

far predominantly focused on whether and when political dynasties arise and/or persist, rather

than why and how they arise. Specifically, a first group of studies focuses on the causal

impact of politicians’ tenure length on the probability of having a family-member in politics

in the future (Dal Bó et al., 2009; Rossi, 2011; Querubin, 2013). Rossi (2011), for instance,

exploits an exogenous change in the Argentinian electoral law to show that longer tenure in

the Argentinian congress increases the probability of having a relative in future congresses.

Dal Bó et al. (2009) and Querubin (2013) find very similar results for the United States and

the Philippines using a regression discontinuity design. A second group of studies more

directly investigates the electoral performance of dynastic versus non-dynastic politicians

(Feinstein, 2010; Asako et al., 2013). Controlling for other individual characteristics, these

articles show that dynastic politicians have higher probabilities of success in national

elections in the United States and Japan.

In contrast to previous studies, this article’s main contribution lies in its focus on the

mechanisms that induce dynastic politicians’ apparent electoral advantage. Overall, two main

channels have been suggested to explain the ability of political dynasties to perpetuate their

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power through electoral advantages. On the one hand, dynastic politicians might have a

higher political ability than non-dynastic politicians deriving from the fact that they inherit

political knowledge, connections and networks from their predecessors (Fukui and Fukui,

1992; Dal Bó et al., 2009; Feinstein, 2010; Asako et al., 2013; Querubin, 2013). On the other

hand, their electoral appeal might be elevated due to factors independent of their personal

political abilities. This could include, for instance, a name recognition advantage (Feinstein,

2010), since “name recognition, as a heuristic for decision making, can play an important role

in electoral politics” (Kam and Zechmeister, 2013: 971). It arises whenever dynastic

politicians inherit their ancestors’ political fame (Clubok et al., 1969). Importantly, and in

contrast to the first channel, this effect arises regardless of politicians’ real ability.

In this article, we particularly concentrate on the empirical relevance of the latter channel.

The reason is that a dynastic electoral advantage arising from higher levels of political

abilities is arguably unproblematic in terms of political selection. However, when voters

consider membership of a political dynasty as a heuristic signal that influences their voting

decision (alongside other similar signals such as candidates’ beauty; Berggren et al., 2010),

this would imply that dynastic politicians receive an electoral advantage based on their

perceived rather than their real ability. This is particularly problematic for the outcome of the

political selection process when these heuristics are independent of candidates’ personal

political abilities – and thus do not provide relevant evidence for the presence of real political

ability.1

1 This line of argument directly relates to a growing literature on the consequences of the selection – and self-

selection – of politicians for the ‘quality’ of the political class (e.g., Besley 2004; Caselli and Morelli 2004;

Messner and Polborn 2004; Poutvaara and Takalo 2007; Mattozzi and Merlo 2008; Keane and Merlo 2010;

Gagliarducci and Nannicini, 2013; for an early review, see Besley 2005).

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To empirically identify the role of heuristics unrelated to politicians’ ability in dynastic

politicians’ electoral advantage, we exploit the idea that heuristics are likely to play a more

important role in voters’ decision-making when less information about candidates is available.

The intuition is that when voters have little information about a politician, they might turn to

simple heuristics – such as politicians’ looks or membership of a political family – as low-

cost signals of politicians’ likely ability (McDermott, 2005; Little et al., 2007; King and

Leigh, 2009; Berggren et al., 2010; Mechtel, 2014). Conversely, when specific information

about politicians’ actual political ability is available from their past performance in office –

for instance, in elections where politicians seek re-election – voters will rely less on heuristics

as potential quality signals (King and Leigh, 2009; Berggren et al., 2010), and more on the

available performance information (Lewis-Beck and Paldam, 2000). Following this line of

argument, if membership of a political dynasty represents a signal at least in part unrelated to

ability, dynastic politicians’ electoral advantage should be strongest when voters have little

other information about a candidate – and decline once more direct information about

politicians’ ability becomes available. In such case, it will indeed hold that past performance

invalidates voters’ ex ante expectations about dynastic politicians’ ability, which undermines

their electoral advantage. Conversely, if dynastic politicians’ electoral advantage mainly

derives from their higher political ability, information from politicians’ past performance will

validate voters’ ex ante expectations. As a result, dynastic politicians’ electoral advantage

should persist – and possibly even strengthen – during later elections when voters can obtain

information from politicians’ past performance in office. Consequently, our identification

relies on a direct comparison of elections where voters have little information about

candidates (i.e. candidates’ first election) to elections where voters are well informed about

them (i.e. candidates’ subsequent elections).

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The empirical analysis is based on a novel dataset including nearly 38,000 mayoral

candidates in roughly 7,000 Italian municipalities across the period 2000-2013. Following

recent work on academic dynasties in Italy (Allesina, 2011; Durante et al., 2011) and inter-

generational social mobility (Clark, 2014; Clark and Cummins, 2014), we operationalize

political dynasties based on elected politicians sharing the same surname. To deal with the

imperfections in this measure and increase the validity of our inferences, we implement

robustness checks taking into account the local distribution of family names.

Comparing dynastic and non-dynastic politicians, our results first of all show that –

controlling for other personal characteristics – dynastic politicians receive a higher share of

votes and are more likely to win mayoral elections in Italian municipalities. This confirms

previous studies’ findings of a significant electoral advantage in dynastic versus non-dynastic

politicians (Feinstein, 2010;Asako et al., 2013). More importantly, however, we show that

this dynastic electoral advantage strongly depends upon voters’ information about politicians.

Comparing dynastic candidates at their first election and dynastic candidates during

subsequent elections, we uncover that the dynastic electoral advantage substantially decreases

(or even disappears) during politicians’ later elections. This suggests that, all else equal, the

real difference in the political abilities of dynastic and non-dynastic politicians is limited. We

interpret this as supportive of the idea that a substantial part of the dynastic electoral

advantage arises independently of politicians’ real ability.

The next section presents the data and operationalization of our key variables, while section 3

summarizes the empirical approach and the key results. We discuss alternative interpretations

of the results in section 4. Section 5 concludes.

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2. Data and operationalization

2.1. Institutional setting: Italian municipalities

The Italian political system is characterized by four levels of governance, where

municipalities (about 8,000 across the country) represent the lowest level, followed by

provinces (110), regions (20) and the national level. Nonetheless, as in most other European

countries, municipal governments have important responsibilities with respect to education,

social welfare, culture and recreation, city planning, transport, economic development, waste

management and local police. They also have important fiscal powers, whereby setting the

local property tax rate is the central annual financial decision (Bordignon et al., 2003).

Local elections are held every five years to elect council members and the (directly elected)

mayor. The exact electoral system depends on the size of the municipality. In cities of fewer

than 15,000 inhabitants, voters effectively have only one vote, which they cast for a candidate

mayor and her supportive list of candidates for the municipal council (though additional

‘preference votes’ for candidates within this list of council candidates are possible). Elections

take place in a single round, in which the mayoral candidate obtaining most votes is elected

and her supportive list is allocated at least 66% of the council seats. The remaining seats are

allocated proportionally to the vote share of all remaining mayoral candidates’ supportive

lists. In municipalities with more than 15,000 inhabitants, voters are faced with parties (or

coalitions thereof) which present a list of candidates for the municipal council and support a

candidate mayor. They cast one vote for a candidate mayor and one vote for a list of

candidates for the municipal council (which can, but need not be, the list supporting a voter’s

preferred mayoral candidate). Elections for mayor here follow a run-off system, whereby the

two top candidates run in a second round whenever no candidate obtained an outright

majority in round one. The list(s) supporting the winning mayor are allocated at least 60% of

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the council seats, and now there is a 3% threshold for the proportional allocation of the

remaining seats (see Bordignon et al., 2013, for more details).2

2.2. Dataset

To empirically assess whether political dynasties provide an electoral advantage, we rely on a

dataset containing information about candidates running for mayor in the period 2000-2013

(N=38,053), in almost 7,000 municipalities (out of about 8,000 municipalities in Italy).

Crucially, this dataset includes not only actually elected mayors, but also candidates who

failed to win the mayoral election. This is important since it allows us to explicitly rule out

potential self-selection effects arising from the fact that dynastic politicians might be more

likely to run for mayor compared to non-dynastic politicians. Specifically, we collect data on

mayoral candidates who win elections and on defeated candidates who are elected as council

members. Therefore, all candidates whose ‘supportive list(s)’ obtained at least one seat in the

municipal council (see note 2) are included in our dataset.

Unfortunately, data on non-elected candidates is not available. Yet, this arguably reinforces

our analysis since it induces a selection of only the “best” losing candidates in our dataset. If,

as suggested, dynastic politicians receive an electoral advantage and are more likely to be

elected, this selection predominantly affects non-dynastic politicians – such that only the

“best” non-dynastic politicians will be part of our sample. This makes for a more exacting

environment in which to test our key hypotheses, since it creates a bias towards

underestimating the real electoral advantage of dynastic candidates.

2Note that, independent of the size of the municipality, politicians defeated in their campaign for mayor are

generally elected as council members as long as their ‘supportive list(s)’obtains at least one seat in the

municipal council.

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The original dataset is available from the Italian Ministry of Interior

(http://amministratori.interno.it; in Italian). It contains information on politicians’ socio-

demographic background (e.g. age, gender, education) and the date(s) of their mayoral

candidacies. Since the main data includes mayoral elections beginning only in 2000, we

collect equivalent additional information – also from the Italian Ministry of Interior – about

all elected local politicians (i.e. including council members) starting from the year 1985 (N ≈

550,000 politicians). This allows us to precisely identify whether – and when – any given

politician was elected to some political office in the same municipality beginning in 1985.

Evidently, controlling for such previous experience is important since information about such

individuals’ political ability would be available to voters during these individuals’ first

mayoral election.

2.3. Measuring political dynasties

The central explanatory variables throughout our analysis reflect the position of each

politician within a political dynasty. Similar to recent studies on academic and political

dynasties (Allesina, 2011; Durante et al., 2011; Querubin, 2013) as well as inter-generational

social mobility (Clark, 2014; Clark and Cummins, 2014), we look at individuals with the

same surname to identify (presumed) family ties. Our identification of dynastic politicians

thereby builds on the dataset including all elected local politicians since 1985. This allows us

to identify, for each candidate running as mayor in the period 2000-2013, whether or not she

has (presumed) family ties with politicians elected within her municipality at any point

starting from the year 1985.

The operationalization of dynastic ties proceeds in two steps. In the first step, we locate all

politicians with the same surname in the same municipal council throughout the period of

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observation. This indicates that in 71 percent of all cases, the surname is unique among all

politicians from the same municipality, across all years of our sample period. Approximately

17 percent of all politicians have surnames which appear twice and 11 percent have surnames

which appear three times or more in their municipalities. Then, as a second step, we

determine the position of each politician within a dynasty and define a dummy (i.e. Dynasty)

equal to 1 whenever a mayoral candidate is a second or third (or later) generation politician.3

The reference category in the estimations below thus is made up of mayoral candidates who

are either not a member of a political dynasty, or the first politicians with a given surname in

a given municipality (henceforth, the first ‘generation’). ‘First-generation’ dynastic

politicians are included in the control group because they were not (yet) part of a political

dynasty at the time we first observe them. As such, we have no theoretical reason to view

them as different from non-dynastic politicians at that point in time.

Using surnames to operationalize political dynasties is a valid approximation in our Italian

setting since children receive the surname of their father. Even so, one can wonder about the

precision of a dynastic variable based on surnames, since people can have the same surname

without having any kinship ties (i.e. surname homonymy).4 Moreover, this operationalization

only catches ties between family members when they have the same surname. While these

reflect the closest family ties that are likely to generate the strongest effects (e.g. children,

grandchildren), it may overlook more distant kinship ties (e.g. cousins, nephews, son-in-law)

as well as ties among spouses and daughters that have changed their name upon marriage.

Although data availability prevents us from directly addressing both issues, it is important to

3All results reported below remain valid when defining separate dummies depending on the position of the

candidate within the dynasty (i.e. second generation, and third-or-later generation politicians). The results are

available upon request. 4Note that such surname homonymy is not overly problematic in our case since, in line with our hypothesis, a

‘name recognition’ effect might arise also among politicians sharing the same surname without being from the

same family. Still, as this would not constitute an actual 'dynastic' effect, we will return to such potential non-

dynastic name recognition effects more explicitly in the analysis below.

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observe that they bias our estimates towards zero. Both issues indeed imply that we fail to

define a certain number of dynastic politicians as part of a dynasty, such that these remain in

the control group. Since dynastic politicians are expected to have an electoral advantage

compared to non-dynastic politicians, this mis-allocation pushes the average vote share and

winning probability in the ‘control’ group (i.e. non-dynastic politicians) closer to the average

vote share and winning probability in the ‘treatment’ group (i.e. dynastic politicians) –

inducing a bias in our estimates towards zero. This not only stacks the deck against us, but

also implies that our findings below reflect a lower bound of the true effect of political

dynasties.

An additional potential concern about using surnames to operationalize political dynasties is

that there may exist a correlation between surnames and individuals’ characteristics. For

instance, it might be that more common last names are associated with a lower socio-

economic background. As such social background might be correlated to unobserved

characteristics determining electoral success, this could bias our estimates. Still, we consider

this unlikely in our setting for two reasons. First, hereditary surnames started to be generally

used in Italy in the 12th

century (Marcato, 2010). Even if their distribution originally might

have been partially tied to social class, several centuries of up and downward social mobility

are unlikely to have sustained this to any significant extent. Second, the main drivers of

Italian surname distributions are geography and history, and not social categories. This is due

to substantial “linguistic fragmentation and (…) the late process of standardization following

the late and slow diffusion of a national language”

(http://www.treccani.it/enciclopedia/cognomi_(Enciclopedia-dell'Italiano)/, accessed on 29

January 2014, own translation). Nonetheless, as a more formal test, we evaluate the

correlation between surname frequency in our dataset (ranging between 1 and 189

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occurrences) and the average education level of individuals with a surname of a given

frequency. This correlation is almost zero (r=0.004; p=0.394), which strongly suggests that in

our setting more common last names are not associated with lower socio-economic status (as

indicated by a lower average education level). Even so, we will directly control for the

distribution of last names at the provincial level in our estimations (see below), whereas our

robustness checks further address this concern by excluding the most common regional

surnames from the estimation sample.

3. Empirical analysis

Our empirical analysis proceeds in two steps. First, in section 3.1, we analyze whether, and to

what extent, dynastic politicians demonstrate an electoral advantage over non-dynastic

politicians. This part of the analysis predominantly aims to replicate previous findings in the

United States (Feinstein, 2010) and Japan (Asako et al., 2013) in our Italian setting, and

thereby provides additional evidence about the generalizability of dynastic politicians’

electoral advantage across different institutional and cultural settings. Then, in section 3.2, we

proceed with the central part of our empirical analysis, and evaluate whether the dynastic

electoral advantage is particularly strong when information about the electoral candidates is

limited. As argued above, this allows us to evaluate whether this electoral advantage derives

at least in part from heuristic signals unrelated to politicians’ real ability (King and Leigh,

2009; Berggren et al., 2010) – and thus assesses voter responsiveness to perceived rather than

real ability.

3.1. Political dynasties and electoral advantage

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To evaluate whether dynastic politicians benefit electorally from their political ancestry, we

estimate the following model (subscript i refers to politicians, c to municipalities, and t to

time):

𝐸𝑙𝑒𝑐𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑖𝑐𝑡 = 𝛼 + 𝛽1𝐷𝑦𝑛𝑎𝑠𝑡𝑦𝑖𝑐𝑡 + 𝛽2𝐼𝑁𝐷𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖𝑐𝑡+ 𝐶𝐼𝑇𝑌𝑐 + 𝜀𝑖𝑐𝑡

(1)

Our main dependent variable (𝐸𝑙𝑒𝑐𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑖𝑐𝑡 ) is an indicator variable equal to 1 if a

politician i in municipality c at time t runs for mayor and wins the election (0 if (s)he runs for

mayor, but loses the election). As an alternative specification of the main dependent variable,

we also estimate the model using candidates’ vote share obtained in the mayoral election. In

the latter case, 𝐸𝑙𝑒𝑐𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑖𝑐𝑡 represents the share of votes of politician i in municipality c

at time t. Clearly, this is a continuous variable taking values between 0 to 1.

Beside our key dynastic variable (i.e. a dummy equal to one whenever a candidate is a

dynastic politician), the set of control variables at the individual level (𝐼𝑁𝐷_𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖𝑐𝑡)

includes politicians’ gender (1 if female), age, education (a dummy equal to 1 if a politician

has a university degree), the year in which she runs for election and the years of political

experience. This last variable is operationalised by introducing a set of dummy variables that

take value one only for politicians who have been in office a given number of electoral terms

in the municipality – measured as the number of electoral terms since a politician’s first

election in that municipality. This creates a very flexible functional form that allows for

period-by-period variation in the estimated effect of political experience. We thereby look at

five-year periods as this coincides with the length of the standard legislative term in Italian

municipalities (note that the reference category is represented by politicians never elected

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before or who have been elected for less than five years, i.e. less than one electoral term).5

We also include a variable (Freqi) capturing the relative prevalence of a candidate’s surname

in each Italian province. This is calculated as the ratio between the number of all inhabitants

with a certain surname in a province and the total population of that province.6 Finally, we

introduce municipality-level fixed effects to control for any unobserved sources of local

heterogeneity (𝐶𝐼𝑇𝑌𝑐).

In table 1, we focus on the effect of political dynasties on candidates’ probability of winning

mayoral elections (our first main dependent variable). We report estimates using both logit

models in columns 1, 2 and 3 (to accommodate the binary nature of our dependent variable)

and OLS (for ease of interpretation) in columns 4, 5 and 6. The baseline model is reported in

columns 1 and 4, whereas in columns 2, 3, 5 and 6 we take an additional step to mitigate the

potential influence of surname homonymy on our findings (i.e. besides the inclusion of

provincial surname frequency as a control variable; see above). Specifically, we report results

using samples retaining only surnames with fewer than 20 (column 2 and 5) and 10

occurrences (column 3 and 6) in our dataset (the maximum number of occurrences is 189).

Since these cut-offs naturally are arbitrary, it is important to observe that our results remain

robust also when imposing different thresholds.

__________________

Table 1

__________________

5This exhausts all available individual-level information about politicians. We do not include politicians’

partisan attachment because local-level politics in Italy is often characterised by purely local lists based on

specific policy issues or political personalities. 6 This information has been kindly provided by Giovanna Labartino.The original source is Dinastie d'Italia: gli

ordini tutelano davvero i consumatori?(Università Bocconi, 2012).

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We start the discussion of our findings with a brief look at our control variables.

Unsurprisingly, a higher level of political experience (reflected in the number of terms since

politicians’ first election) is associated with a higher probability of winning mayoral elections.

This finding is in line with the vast literature documenting an electoral incumbency

advantage (Gelman and King, 1990; Besley and Case, 1995). Interestingly, however, the

effect of political experience is found to be non-linear, and levels off after two terms in office.

Additional terms in office do not provide a further electoral advantage, which may suggest

that politicians have completed their ‘learning’ of the political trade (or that voters simply

don’t perceive any further developments in politicians with two or more terms of experience).

We also find that female as well as younger politicians are less likely to be elected as mayor.

Finally, politicians who have a university degree are more likely to win elections.

Interestingly, the variable 𝐹𝑟𝑒𝑞𝑖 is not significant, suggesting that more common surnames in

a certain province are not associated with higher probabilities of winning elections. Note that

this result indicates that mere recognition of (common) names does not automatically bring

an electoral advantage. This is important for the interpretation of the dynastic findings

discussed below, because it provides indirect evidence that these dynastic findings are not

simply ascribable to unobservable characteristics of individuals with more common surnames.

Turning now to the dynastic variable, we can observe that all specifications confirm a

positive effect of political dynasties on the probability of winning mayoral elections. The

estimated size of the effect lies at about six percentage points. This is substantial compared to

the effects of some of our main control variables. For instance, the effect of being a dynastic

politician is very similar to the effect of having a university degree, and about half the effect

of holding office for a first term (inference taken from the OLS coefficients). Moreover, the

size of the coefficients, as well their statistical significance, remains unchanged when

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dropping the most common surnames from the sample (columns 2, 3, 5 and 6). This

corroborates that our results are not driven by unobserved characteristics of very common

surnames.

Table 2 replicates the above analyses using the share of votes received by each candidate as

the dependent variable. Column 1 reports the OLS7 baseline specification, whereas columns 2

and 3 report the results dropping the most common surnames.

__________________

Table 2

__________________

The results from table 1 are generally confirmed in table 2. Specifically, being a new,

younger or female politician is associated with a lower vote share, whereas politicians with

more years of political experience and holding a university degree obtain a larger share of

votes. Similar to our earlier results, being a dynastic politician provides a substantial and

statistically significant electoral advantage. That is, dynastic politicians receive

approximately three percentage points more votes than non-dynastic candidates. Overall, the

results of table 1 and table 2 indicate that being a dynastic politician is a strong predictor of

political success in Italian mayoral elections. These results are in line with previous findings

for national elections in the United States and Japan (Feinstein, 2010; Asako et al., 2013), and

thus indicate that these findings can be generalized to our different institutional and cultural

setting, electoral system and level of election.

7 While OLS does not accommodate the truncated nature of the dependent variable, this is less problematic in

our setting since only very few politicians obtain vote shares near the extremes of the possible distribution.

Moreover, the results are unchanged when using tobit models that do account for the constrained nature of our

dependent variable.

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3.2. Political dynasties and voters’ information

Having thus established the electoral benefit provided by political dynasties, we can now turn

to testing whether this depends upon voters’ information about the candidates. As explained

above, our test relies on the argument that if a political dynasty represents a heuristic signal

unrelated to ability, the dynastic advantage should matter more when voters have little

information on politicians than when they can observe politicians’ past performance. This

key condition is valid when voters face politicians who have never been elected before,

whereas it breaks down when voters face politicians who have already been in office (in

which case they can base their vote choice on politicians’ observed performance; Lewis-Beck

and Paldam, 2000). In terms of our empirical model, this implies the introduction of

interaction terms between the variables measuring the length of political experience and the

dynastic dummy. The empirical results are reported in table 3 (which focuses on whether a

candidate wins the elections) and table 4 (which contains results on the share of votes

received by each candidate).

__________________

Table 3 and Table 4

__________________

All specifications in table 3 display a very comparable set of results. The interaction terms

show that the dynastic advantage crucially depends upon the amount of information voters

are likely to have about politicians. Dynastic politicians receive a much larger advantage

during their first election. Conversely, such advantage substantially and gradually decreases

for dynastic politicians who have already held public office in the same municipality.

Specifically, the interaction terms in the OLS models of table 3 indicate that compared with

politicians at their first election, the dynastic advantage is reduced for dynastic politicians

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who have already been in office for one or two terms. Moreover, the dynastic electoral

advantage completely disappears for politicians who have been elected three or more times,

i.e., the difference between the dynasty variable and the interaction terms are not different

from zero (e.g. in column 4, 0.103-0.123, p=0.29; 0.103-0.100, p=0.91).

The results in table 4 are qualitatively similar to those in table 3. Also in this case, the

dynastic coefficients are similar in size and statistical significance across all models. Dynastic

politicians receive a four percentage points higher share of votes at their first election, while

they receive only a one percentage point higher share of votes when they have been in office

two electoral terms. Such advantage completely disappears for politicians having three to

four terms of political experience (e.g. in column 1, 0.045-0.04, p=0.82). Overall, these

results strongly support the idea that the dynastic advantage – similar to other heuristic

signals (e.g. beauty; King and Leigh, 2009; Berggren et al., 2010) – is relevant especially for

politicians in their first election. As voters have relatively limited information available about

candidates in such first elections, we can interpret our findings as being in line with the fact

that (at least) part of the dynastic advantage is driven by aspects not related to ability (such as

name recognition).

4. Discussion

Our main inferences rely on interpreting the different effects of being a dynastic politician

between first and subsequent elections as reflecting differences in voters’ information on the

candidates. In this section, we discuss two alternative conceivable explanations.

A first alternative explanation could be that dynastic politicians might inherit political skills

that other candidates only learn with experience in office. This could explain, at least in part,

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why their electoral advantage does not persist over time, since non-dynastic politicians would

then 'catch up' in terms of political skills. There are, however, two observations that make this

interpretation unconvincing. First, a closer look at tables 3 and 4 highlights the exact nature

of the temporal development of dynastic politicians’ electoral advantage. It indeed indicates

that the reduction in dynastic politicians’ electoral advantage following a first term in office

is often statistically insignificant (see table 4) or, at best, significant at 90% confidence (see

columns 4 to 6 in table 3). Moreover, it stabilizes – or even increases again – when looking at

politicians with more than four terms of political experience. In terms of a ‘catching-up’ story,

this would require us to assume a very particular learning scheme whereby dynastic and non-

dynastic politicians learn at the same rate (or, at least, that voters perceive the same

development) during their first term in office. Dynastic politicians should then learn at a

slower pace during the next two terms, but pick up pace again afterwards. This appears

intuitively unlikely. Second, our discussion of table 1 indicated that the effect of political

experience levels off after two terms in office, suggesting that politicians on average have

completed their ‘learning’ of the political trade by then (or, at least, that voters perceive that

to be the case). This would imply, however, that much of the decline in dynastic politicians’

electoral advantage observed in tables 3 and 4 takes place after this learning process is

completed. While this does not exclude that part of our findings might in fact reflect

‘catching-up’ among non-dynastic politicians, it cannot provide a complete explanation for

our findings.

A second alternative explanation of our findings might be that the sample of dynastic

politicians in our dataset completing more terms in office represents a selection of less

‘competitive’ dynastic politicians. Such selection may arise when the electorally more

successful dynastic politicians run for more prestigious (higher-level) offices at subsequent

19

elections (e.g. the regional parliament) – and thus drop out of our municipal-level dataset.

The dynastic politicians that remain active at the municipal level for a longer period of time

may therefore simply be the less competitive ones that will be less likely to win elections (or

obtain a large electoral margin). To assess this concern, we first of all replicated our analysis

using only the subsample of candidates running in small municipalities (defined at different

population thresholds: i.e. 5,000, 10,000 and 15,000 inhabitants). The idea is that mayors

from small towns are substantially less likely to run for higher-level (including regional)

elections. They not only generally lack the broad popular appeal, notoriety and media

attention to successfully compete in such higher-level campaigns, but, compared to mayors in

bigger municipalities, their mayoral experience is less likely to credibly signal their ability to

successfully govern a larger political jurisdiction. When restricting the sample to candidates

in small towns, all the previous results remain unaffected (the results are available upon

request). An additional counter-argument comes from the fact that the first level of

governance above the municipalities (i.e. the regional parliaments) has about 1,200 council

members.. This far exceeds the number of Italian mayors (about 8000), such that only few

mayors can in fact move up the political ladder. Moreover, looking in more detail at the1,237

regional council members currently holding office (i.e. January 2015), we find that only 108

have been previously elected as mayors, and only 31 are defined as dynastic politicians (i.e.

with at least one other politician with the same surname elected before them in the same

municipality). Therefore, upward mobility of politicians in our sample is a minor concern,

and therefore cannot explain the results presented in tables 3 and 4.

5. Conclusion

Recent studies in both political science and public economics have begun to investigate

whether and when political dynasties arise in a variety of institutional and cultural contexts.

20

In this article, we focus on membership of political dynasties as a heuristic electoral signal

provided to voters. We argue that membership of a political dynasty represents a signal about

electoral candidates that is likely to be largely unrelated to politicians’ real ability. As a test

of this proposition, we show– among a sample of approximately 38.000 Italian mayoral

candidates in the period 2000-2013 – that dynastic candidates receive an electoral advantage

predominantly in contexts of more limited information (i.e. during candidates’ first election).

We interpret this as supporting the hypothesis that a substantial part of the dynastic political

advantage depends upon aspects largely unrelated to ability (such as name recognition or

political forebears’ fame). The results are robust to different specifications and are unaffected

by the introduction of another signal of politicians’ ability, as namely, their level of education.

To conclude, as differences in politicians’ ability might lead to differences in policy making,

future studies should more directly focus on the consequences of political dynasties.

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23

Table 1 - Electoral benefits of dynasties

(1)

Logit All

(2)

Logit Cut-off 20

(3)

Logit Cut-off 10

(1)

OLS All

(2)

OLS Cut-off 20

(3)

OLS Cut-off 10

Dynasty 0.236 0.247 0.243 0.061 0.063 0.062

(8.93)**** (8.54)**** (7.54)**** (7.98)**** (7.60)**** (6.74)****

Woman -0.292 -0.279 -0.259 -0.072 -0.068 -0.062

(7.63)**** (6.75)**** (5.69)**** (7.09)**** (6.21)**** (5.22)****

Age -0.005 -0.004 -0.004 -0.001 -0.001 -0.001

(4.41)**** (3.49)**** (3.35)**** (4.02)**** (3.23)*** (3.18)***

Degree 0.306 0.312 0.290 0.078 0.078 0.073

(12.13)**** (11.33)**** (9.61)**** (10.78)**** (10.12)**** (8.63)****

Freq. -0.032 -0.006 0.187 -0.009 -0.000 0.043

(0.46) (0.04) (0.87) (0.44) (0.01) (0.71)

Political exp. 1 term 0.664 0.649 0.624 0.169 0.162 0.154

(21.67)**** (19.58)**** (17.24)**** (23.56)**** (21.11)**** (18.61)****

Political exp. 2 terms 0.837 0.841 0.827 0.214 0.213 0.207

(24.89)**** (23.08)**** (20.59)**** (24.48)**** (22.65)**** (20.25)****


(21.74)**** (20.33)**** (17.56)**** (21.10)**** (19.78)**** (17.08)****

Political exp. > 4 terms 0.856 0.856 0.820 0.216 0.213 0.203

(18.31)**** (16.85)**** (14.72)**** (16.92)**** (15.62)**** (13.73)****

Year Fixed Effects YES YES YES YES YES YES

Municipality Fixed Effects YES YES YES YES YES YES

N 37,237 31,969 26,817 38,052 33,789 29,470

R2 0.04 0.03 0.03 0.04 0.04 0.03

Note: Dependent variable is an indicator variable equal to 1 if a politician was elected as mayor (0 otherwise). Coefficient estimates (t-statistics in parenthesis) are

represented as coefficients from Logit model in columns 1, 2 and 3. Estimates in columns 4, 5 and 6 are based on OLS models (robust standard errors). Columns 2, 3, 5

and 6 are estimated without politicians having most common surnames in our sample. *p<0.05; **p<0.01;***p<0.001.

24

Table 2: Share of votes and dynasties

(1)

All

(2)

Cut-off 20

(3)

Cut-off 10

Dynasty 0.030 0.031 0.029

(11.07)**** (10.46)**** (9.12)****

Woman -0.024 -0.023 -0.021

(6.78)**** (6.05)**** (5.00)****

Age -0.000 -0.000 -0.000

(1.92)* (1.96)* (1.53)

Degree 0.041 0.041 0.039

(16.18)**** (15.32)**** (13.40)****

Freq 0.003 0.001 0.034

(0.46) (0.06) (1.60)

Political exp. 1 term 0.064 0.062 0.061

(24.14)**** (21.75)**** (19.78)****

Political exp. 2 terms 0.086 0.085 0.084

(26.77)**** (24.58)**** (22.29)****


(23.84)**** (22.54)**** (19.98)****

Political exp. > 4 terms 0.088 0.087 0.085

(19.87)**** (18.28)**** (16.66)****

Year Fixed Effects YES YES YES

Municipality Fixed Effects YES YES YES

R2 0.06 0.06 0.05

N 37,329 33,158 28,914

Note: Dependent variable is the share of votes of candidates running for mayoral elections. Coefficient estimates

(t-statistics in parenthesis) are represented as coefficients from OLS model (robust standard errors).

Columns 2 and 3 are estimated without politicians having most common surnames in our sample. *p<0.05;

**p<0.01;***p<0.001.

25

Table 3: Electoral benefits of dynasties – interaction terms –

(1)

Logit All

(2)

Logit Cut-off 20

(3)

Logit Cut-off 10

(1)

OLS All

(2)

OLS Cut-off 20

(3)

OLS Cut-off 10

Dynasty 0.454 0.471 0.476 0.103 0.106 0.107

(10.36)**** (9.88)**** (9.10)**** (9.66)**** (9.21)**** (8.47)****

Woman -0.296 -0.285 -0.264 -0.073 -0.069 -0.063

(7.74)**** (6.86)**** (5.78)**** (7.16)**** (6.29)**** (5.28)****

Age -0.005 -0.004 -0.004 -0.001 -0.001 -0.001

(4.42)**** (3.51)**** (3.38)**** (4.02)**** (3.25)*** (3.21)***

Degree 0.306 0.311 0.291 0.078 0.078 0.073

(12.12)**** (11.31)**** (9.63)**** (10.77)**** (10.10)**** (8.64)****

Freq. -0.033 0.007 0.218 -0.009 0.002 0.051

(0.47) (0.05) (1.01) (0.44) (0.04) (0.83)

Political exp. 1 term 0.745 0.732 0.706 0.179 0.173 0.165

(18.40)**** (16.89)**** (15.01)**** (19.70)**** (17.97)**** (15.92)****


(23.07)**** (21.67)**** (19.79)**** (22.97)**** (21.49)**** (19.57)****


(21.89)**** (20.44)**** (18.13)**** (21.21)**** (19.88)**** (17.60)****

Political exp. > 4 terms 0.994 0.989 0.943 0.243 0.239 0.226

(18.60)**** (17.16)**** (14.97)**** (17.22)**** (15.91)**** (13.90)****

Dynasty* Pol. exp. 1 term -0.175 -0.189 -0.189 -0.025 -0.028 -0.028

(2.88)*** (2.86)*** (2.61)*** (1.73)* (1.80)* (1.65)*

Dynasty* Pol. exp. 2 terms -0.357 -0.380 -0.424 -0.074 -0.078 -0.089

(5.33)**** (5.21)**** (5.27)**** (4.22)**** (4.16)**** (4.30)****

Dynasty* Pol. exp. 3 terms -0.529 -0.522 -0.577 -0.123 -0.119 -0.133

(6.61)**** (5.96)**** (6.00)**** (5.78)**** (5.21)**** (5.28)****

Dynasty* Pol. exp. > 4 terms -0.451 -0.446 -0.391 -0.100 -0.099 -0.084

(4.40)**** (3.95)**** (3.11)*** (3.52)**** (3.19)*** (2.48)**

Year Fixed Effects YES YES YES YES YES YES

Municipality Fixed Effects YES YES YES YES YES YES

N 37,237 31,969 26,817 38,052 33,789 29,470

R2 0.04 0.03 0.03 0.04 0.04 0.03

Note: Dependent variable is an indicator variable equal to 1 if a politician was elected as mayor (0 otherwise). Coefficient estimates (t-statistics in parenthesis) are

represented as coefficients from Logit model in columns 1, 2 and 3. Estimates in columns 4, 5 and 6 are based on OLS models (robust standard errors). Columns 2, 3, 5

and 6 are estimated without politicians having most common surnames in our sample. *p<0.05; **p<0.01;***p<0.001.

26

Table 4: Share of votes and dynasties – interaction terms –

(1)

All

(2)

Cut-off 20

(3)

Cut-off 10

Dynasty 0.045 0.045 0.045

(11.60)**** (10.90)**** (9.92)****

Woman -0.024 -0.023 -0.021

(6.87)**** (6.14)**** (5.07)****

Age -0.000 -0.000 -0.000

(1.93)* (1.97)** (1.56)

Degree 0.041 0.041 0.039

(16.18)**** (15.31)**** (13.42)****

Freq. 0.003 0.002 0.037

(0.46) (0.12) (1.74)*

Political exp. 1 term 0.066 0.064 0.063

(19.37)**** (17.61)**** (16.27)****


(24.67)**** (22.62)**** (20.94)****


(23.33)**** (21.97)**** (20.06)****

Political exp. > 4 terms 0.099 0.098 0.096

(19.97)**** (18.49)**** (16.95)****

Dynasty* Pol. exp. 1 term -0.005 -0.005 -0.006

(0.97) (0.84) (0.92)

Dynasty* Pol. exp. 2 terms -0.029 -0.027 -0.031

(4.60)**** (4.07)**** (4.22)****

Dynasty* Pol. exp. 3 terms -0.043 -0.042 -0.050

(5.78)**** (5.18)**** (5.70)****

Dynasty* Pol. exp. > 4 terms -0.044 -0.045 -0.044

(4.66)**** (4.47)**** (3.98)****

Year Fixed Effects YES YES YES

Municipality Fixed Effects YES YES YES

R2 0.04 0.04 0.04

N 37,329 33,158 28,914

Note: Dependent variable is the share of votes of candidates running for mayoral elections. Coefficient estimates

(t-statistics in parenthesis) are represented as coefficients from OLS model (robust standard errors).

Columns 2 and 3 are estimated without politicians having most common surnames in our sample. *p<0.05;

**p<0.01;***p<0.001.

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Appendix A

Table A1: Descriptive Statistics

Variable Obs. Mean Std. Dev. Min Max Source

Dependent Variables

Winner 38053 0.399469 0.489795 0 1 Our

Calculations

based on data

from the

Italian

Ministry of

Interior

Share of Votes 37329 0.349850 0.192860 0 1

Explanatory Variables

Dynasty 38053 0.291334 0.454381 0 1

Political exp. ≤ 1 term 38053 0.131737 0.338209 0 1

Political exp. 1 term 38053 0.096654 0.295490 0 1

Political exp. 2 terms 38053 0.067195 0.250364 0 1

Political exp. 3 terms 38053 0.035240 0.184389 0 1

Political exp. ≥ 4 terms 38053 0.017580 0.131423 0 1

Woman 38053 0.115891 0.320098 0 1

Age 38052 45.85076 11.19151 18 91

Year 38053 2006.938 3.510444 2000 2013

Surname Relative Occurrences 38053 0.000928 0.001873 3.53E-07 0.021807

Degree 36209 0.452316 0.497727 0 1

Born in the Purple: Political Dynasties and Electoral Success

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