Academic Dynasties: Decentralization, Civic Capital
and Familism in Italian Universities∗
Ruben Durante †
Giovanna Labartino ‡
Roberto Perotti §
November 2014
A BSTRACT
Whether decentralization improves or worsen administrative performance depends on the level of civic capital of the targeted communities: in high-civiccapital areas, where local administrators are effectively monitored by active and
informed citizens, decentralization will increase accountability; in low-civiccapital areas, instead, poorly monitored officials will use their increased power
to favor their particular interests over the general one. We test this hypothesis
by examining the effect of the decentralization of academic recruiting in Italy’s
public university system on the incidence of familism, i.e. favoritism towards
relatives in academic hiring. We find that decentralization lead to a significant
increase in familism in low-civic-capital areas but not in others.
∗ An
earlier version of this paper circulated under the title: “Academic Dynasties: Decentralization and
Familism in the Italian Academia”. Financial support from the European Research Council (Grant No. 230088)
is gratefully acknowledged. We thank Alberto Alesina, Alberto Bisin, Francesco Caselli, Pedro Dal Bó, Andrea Ichino, Tommaso Nannicini, Fausto Panunzi, Michele Pellizzari, and Nicola Persico, seminar participants
at Bocconi, Brown, Sciences Po, IMT Lucca, Trento, Bologna, Modena, ULB, NYU and Rotterdam and participants at the NBER 2011 Political Economy Summer Institute and at the 2011 Petralia Sottana Applied
Economics Workshop for very helpful comments. Elia Boe, Chiara Fratto and Iván Torre provided outstanding
research assistance. The opinions expressed herein are those of the authors and do not necessarily reflect the
views of Confindustria.
† Sciences Po. E-mail: [email protected]
‡ Centro Studi Confindustria. E-mail: [email protected]
§ IGIER - Bocconi University, CEPR, NBER. E-mail: [email protected] (corresponding author)
1
1. I NTRODUCTION
Does administrative decentralization improve or deteriorate public good provision?
Despite a large literature on the functioning of federal systems, this issue remains
controversial. On the one hand, by bringing policy-making “closer to the people”
decentralization can alleviate problems of asymmetric information and make it easier to tailor policies to local needs (Wallis and Oates, 1988; Fisman and Gatti, 2002;
Faguet, 2004). On the other hand, by delegating power to local officials, decentralization may increase their capacity to predate public resources and to favor local
special interests (Prud’homme, 1995; Bardhan and Mookherjee, 2000; Treisman,
2000).
An aspect that the literature has largely overlooked is how the ultimate effect of decentralization may depend on the socio-cultural characteristics of the communities
to which power is devolved, and on how these, in turn, shape the incentives and
motivations of local officials. Intuitively, decentralization is more likely to improve
government performance where local administrators are effectively monitored by
an active and informed citizenry, but may undermine it where public officials face
little scrutiny and can use their increased power to benefit themselves, their family,
or their clientèle.
This paper explores this question by investigating the relation between decentralization and civic capital defined as “those persistent and shared beliefs and values
that help a group overcome the free rider problem in the pursuit of socially valuable
activities” (Guiso et al., 2010).1 In particular, we test the hypothesis that in areas
with strong civic traditions - where citizens are more engaged and informed and
misconduct by local officials is more likely to be detected and stigmatized (Putnam et al., 1993) - decentralization improves administrative performance, while the
opposite occurs in areas with low civic capital.
From an empirical perspective, studying the relation between civic norms and decentralization is a complex task. On the one hand, time-series analyses are limited
by the fact that both civicness and the degree of decentralization tend to be very per1
See (Guiso et al., 2010) for a comprehensive discussion of the concept of civic capital, its relation
with the related notion of social capital, and a survey of the variables used in the empirical
literature to proxy for both.
2
sistent over time. On the other, cross-country studies are generally problematic due
to the endogeneity of cultural traits and the difficulty of comparing administrative
performance across countries. To address these concerns, our empirical analysis exploits within-country variation and looks at the impact of a discontinuous change in
the level of decentralization of one branch of administration on a particular type of
outcome that exemplifies the tension between particularistic and general interests.
Specifically, we investigate the effect of the 1998 reform of the public university
system in Italy - which decentralized academic recruiting from the national to the
local level - on the incidence of familism in academia, i.e. the practice of favoring
relatives for academic jobs regardless of their qualifications. In particular, we test
the conjecture that after the reform, university officials in low-civic-capital areas more so than in high-civic-capital areas - used their increased power to favor their
relatives over other possibly more competent candidates.
Our analysis employs a novel dataset including comprehensive information on all
academic personnel employed in Italian universities between 1988 and 2008, i.e.
roughly ten years before and after the implementation of the reform. Since professors’ family connections are not directly observed, to measure the incidence of
familism across academic departments we exploit the informative content of professors’ last names. In particular, we construct two indices of “homonymy” - i.e.
the presence of multiple professors with the same last name in the same department - that we use as proxy for the presence of relatives.2 Crucially, our measures
of homonymy are based on the comparison between the relative frequency of last
names in a department and in the general population; this is to ensure that they
genuinely capture “abnormal” levels of homonymy, and not just the presence of
relatively common last names.
To measure differences in civic capital across universities, following Putnam et al.
(1993), we use the readership of non-sport newspapers in the province where a university is located. This variable seems especially well-suited to capture the “public
awareness” dimension of civic engagement emphasized above; indeed, it represents
a good proxy for citizens’ willingness to bear the private cost of acquiring informa2
The free Merriam-Webster Dictionary defines homonymy as “the quality or state of being
homonymous”, and homonymous as “having the same designation”.
3
tion to improve the quality of collective decision-making, and make public scrutiny
of local officials more effective.
Our findings provide support for the view that the effect of decentralization depends on the level of civic capital. Indeed, our results document that after the
reform familism increased significantly in areas with low civic capital, but not in
those with high civic capital. The differential effect of decentralization on familism
is sizeable, very robust to the use of different measures of homonymy and different
specifications, and to controlling for both year and department fixed effects, as well
as for time-varying department size. Furthermore, and quite crucially, the effect
does not appear to be driven by any pre-existing trend, or by differences in other
relevant characteristic - such as education, income, employment, North/South differential - that could potentially be correlated with both civic capital and familism.
This result is further reassuring that newspaper readership is picking up the effect
of civic capital and not its spurious correlation with socio-economic development.
Finally, we find little evidence that the effect of decentralization varied across disciplines, a result which suggests that even disciplines in which performance can
arguably be measured with more precision (e.g. hard sciences) are not immune
from familism.
Our work relates to various streams of literature. First and foremost, our paper
contributes to the above-mentioned literature on decentralization in federal systems
by providing novel evidence that the effect of decentralization on administrative
performance can vary across jurisdictions depending on the ability of local citizens
to monitor officials and keep them accountable. In this respect, our research is
especially related to recent work by Boffa et al. (2013) who, in the context of a
political agency model with rent-seeking politicians and heterogeneously informed
voters, show that centralization reduces rent extraction and benefits each region in
inverse proportion to its residents’ level of information. As an indirect test of their
theory, the authors document that, following the enactment of the Clean Air Act
- which centralized environmental policy in the U.S. from the state to the federal
level - pollution decreased faster in states with less informed citizens. Though close
in spirit, our contribution differs from Boffa et al. (2013) in that we examine the
effect of decentralization on the behavior of non-elected officials, and look at very
4
different policy domain and outcome.
Our paper also relates to the large literature on civic (or social) capital, namely to
the work by Banfield and Banfield (1958), Putnam et al. (1993), and Alesina and
Giuliano (2011) that highlight the negative relationship between civic capital and
the strength of family ties. In the context of this literature, our work also relates to
two recent studies on the relation between civic capital and political accountability
(Nannicini et al., 2013; Padro-i Miquel et al., 2012). Focusing on Italy, Nannicini
et al. (2013) show that electors in districts with high civic capital are more willing to punish congressmen for their misbehavior, i.e. criminal prosecution and
absenteeism in Parliament votes.3 Similarly, looking at the introduction of village
elections in China, Padro-i Miquel et al. (2012) document that elections have a
significant positive effect on public goods expenditure and provision but only in
villages with high levels of civic capital. What differentiate our contribution from
this work is the focus on the interaction between civic capital and decentralization,
and the extension of the accountability argument to non-elected public officials.
Our study also relates to previous work on the importance of family connections for
job search. In particular, our findings dovetail nicely with evidence by Sylos Labini
(2004) who, focusing like us on Italy, finds that in areas with low civic capital individuals rely more on family connections to find employment. Other contributions
have instead examined the role of family connections within specific professions.
A relevant example is Dal Bo et al. (2009) who investigate the presence of political
dynasties in the U.S. Congress since 1789. Our research is also related to a large
literature in corporate governance on the effect of family ownership and family
management on firm performance which we discuss in detail in the next section.
Finally, from a methodological point of view, our paper relates to previous contributions that use last names to identify family connections. Example include Angelucci
et al. (2010) who use last names to identify family ties among recipients of Mex3
Relatedly, in a recent study on the presence of “political budget cycles” in local taxation decisions
in Italy, Alesina and Paradisi (2014) document that municipalities choose lower tax rates close
to elections and that this effect is especially pronounced in southern regions. To the extent that
localities in the South are characterized by lower levels of civic capital than in the North, the
authors argue that their findings can be explained by the fact that local politicians in the South
face less public scrutiny.
5
ico’s Progresa program, Acemoglu et al. (2008) who use first and last names of
municipalities’ mayors to measure political concentration in their historical study
of Colombia’s region of Cundinamarca, and Guell et al. (2007) who exploit the
informative content of last names to study intergenerational mobility in Catalonia.
The remainder of the paper is organized as follows. In section 2 we review some
arguments and evidence of why strong family ties may improve or deter efficiency
in organizations, and explain why it is likely that familism has a negative rather than
a positive impact on the allocation of human capital in the Italian academia. Section
3 provides some background information on the Italian academic system and on
the reform that decentralized academic recruitment from the national to the local
level, and presents some anecdotal evidence of the presence of familism in Italian
universities. Section 4 describes the data used in our empirical analysis and the
procedure employed to construct the measures of homonymy. Section 5 describes
our empirical strategy, and section 6 our main findings. Section 7 concludes.
2. I S FAMILISM GOOD OR BAD ?
Whether strong family ties improve or deter efficiency in organizations is unclear
and likely to be context-specific. For example, a large literature in corporate finance
has investigated the impact of family ownership and family management on firm
efficiency, highlighting possibly contrasting effects. In theory, family connections
may, on the one hand, alleviate the classic agency problems between managers
and owners; on the other hand, however, family ownership may reduce the pool of
viable candidates for management positions, and the incentives of designated heirs
to invest in human capital - the so-called “Carnegie effect”.4
Empirical evidence on the effect of family ownership on firm performance is rather
mixed. Van Reenen and Bloom (2007) attribute a large portion of the gap in management practices between U.S. and France to the different incidence of family
management in the two countries. Relatedly, Caselli and Gennaioli (2013) show
that inheritance of firm control across generations can result in large inefficiencies,
4
“The parent who leaves his son enormous wealth generally deadens the talents and energies of
the son, and tempts him to lead a less useful and less worthy life than he otherwise would. . .”
(Carnegie, 1962, p. 56).
6
and Anderson and Reeb (2003) find that family-owned companies are characterized by lower Tobin’s q ratio than comparable ones. In contrast, Holderness and
Sheehan (1988) find that in the U.S. family-owned companies have a higher q ratio.
Along these lines, Adams et al. (2009); Fahlenbrach (2009) and Morck et al. (2000)
document that U.S. and Canadian companies with founding-CEOs have higher evaluations than comparable ones.
There is more consensus, instead, on the effect of descendant management in family
firms. Looking respectively at the U.S. and Canada Pérez-González (2006) and
Smith and Amoako-Adu (1999) find that share prices of family firms fall when
descendants are appointed as managers. Similarly, looking respectively at U.S.
and Thai firms, Villalonga and Amit (2006) and Bertrand et al. (2008) find that
descendant management destroys value in family firms. Two exceptions are the
studies by Sraer and Thesmar (2007) and Barontini and Caprio (2006) which do
not find evidence of worse performance in descendant-managed firms.
Whatever the rationale for a positive influence of family control on company efficiency, it is hard to believe this could apply to the case of familism in academia.
The primary reason is that the sort of information asymmetry family ownership
helps overcome is unlikely to arise in academic recruitment since professional ability - i.e. research performance - can be measured quite accurately via publication
record and bibliometric scores. In contrast, academic familism is likely to produce
a Carnegie effect since family members will expect to be subject to less stringent
selection standards and will be less prone to accumulate human capital. In addition,
the belief that family connections is all that matters to be hired in academia will
presumably deter human capital accumulation also among unconnected outsiders
who will perceive a lower expected return of their investment.
Although the relation between familism and academic performance is not the focus
of this study, in the last part of our empirical section we will discuss some evidence
that corroborates the view that, in the context of Italy, departments with higher
incidence of familism fair worse than others in terms of research achievement.
7
3. BACKGROUND :
THE I TALIAN UNIVERSITY SYSTEM
3.1. T HE STRUCTURE OF THE SYSTEM
As of 2008 the Italian university system consisted of 74 public universities and 15
private ones. Recruitment and promotion of academic personnel in both categories
of institutions is regulated by the same legal framework. Academic positions are
organized in three level of seniority: assistant professor (AP), associate professor
(AsP), and full professor (FP). Depending on their field of specialization, professors
are assigned to one of fourteen areas,5 and, within these, to one or more sub-areas.6
Public competitions for recruiting and promotion of academic personnel are organized at the sub-area level. However, focusing on sub-areas is problematic because,
even in the largest universities, a sub-area may include only a handful of professors; in addition, the same professor may be affiliated with multiple sub-areas. For
this reason, in our empirical analysis we will use the area as unit of analysis. In
particular, with a slight abuse of terminology, we will refer to a department as the
set of professors in a given university affiliated to the same area. Overall, our data,
described in detail in section 4, include information on a balanced panel of 564
departments in 57 universities over the period 1988 to 2008.
3.2. R ECRUITMENT, PROMOTIONS AND
THE
1998
REFORM
An important feature of the Italian university system is that professors’ salaries are
largely determined by seniority. For a given seniority, all academic personnel in
the same rank level - APs, AsPs, and FPs - receive the same salary, with automatic
and uniform pay rises every two years. Only the promotion from one rank level to
5
6
The fourteen areas are: “civil engineering and architecture”, “industrial engineering and information technology”, “agriculture and veterinary”, “biology”, “chemistry”, “’ancient studies,
philology, literature and art history”, “natural sciences”, “economics and statistics”, “physics”,
“law”, “mathematics and computer science”, “medicine”, “political and social sciences”, “history, philosophy, pedagogy and psychology”.
For example, the area “economics and statistics” is divided into the following fifteen sub-areas:
“political economy”, “economic policy”, “public finance”, “applied economics”, “econometrics”, “business economics”, “business management”, “business organization”, “economics of
financial intermediaries and corporate finance”, “commodities studies”, “economic history”,
“statistics”, “economic statistics”, “demography and social statistics”, “mathematics for economics and finance”.
8
the next is not automatic but is determined, like the initial recruitment, via public
competition (concorso).
Our paper exploits a nationwide change in the procedures regulating these competitions. Until 1998, academic recruitment and promotions were highly centralized:
a nationwide competition was held for each sub-area at irregular intervals (usually every three or four years), and all individuals aspiring to be hired or promoted
would apply. For each competition, a nationwide selection committee, elected by
all professors in that sub-area, would evaluate applicants and determine which ones
were “eligible” to be hired or promoted (idonei). Typically, the number of candidates deemed eligible was somewhat higher than the combined number of openings
in that sub-area in all universities, so that each university was able to pick one. This
system offered ample opportunities for corruption and manipulation (especially
to the most senior and influential members of the profession), as attested by ample anecdotal evidence of the horse-trading that traditionally accompanied national
competitions. Usually, each member of the national selection committee would offer to support candidates sponsored by other committee members in exchange for
their support of her own protegé(s). Such collusive conduct was facilitated by the
fact that the number of winners was usually larger than the number of committee
members, so that each member was virtually guaranteed that the candidate(s) she
backed would be selected.7,8
In 1998 the Italian parliament approved a reform of the university system which decentralized academic recruitment and promotions from the national to the university
level. One of the purposes of the reform was precisely to limit the distortions of the
centralized system described above. According to the new law, a department willing to fill a vacancy was required to organize its own local public competition. The
local selection committee would be composed by five members: an “internal” one,
appointed by the local department, and four “external” ones, elected among all pro7
8
Indeed, the prospect of influencing the outcome of the competition was often the biggest incentive to take up the unpaid and onerous task of committee member.
It is worth noting that, in some cases, candidates not sponsored by committee members could also
be declared eligible; indeed, anecdotal evidence suggests that, once the success of the “insiders”
had been ensured, the remaining slots were assigned to “outsiders” based primarily on scientific
merit.
9
fessors in the sub-area in the country. The committee would evaluate candidates and
select three “eligible” ones who, for the following two years, could be appointed by
any university in the country to fill a vacancy at the same rank level in the same
sub-area. Decentralization did not deter horse-trading and collusion; if anything,
it extended them to professors from smaller universities who had traditionally had
little influence on national competitions. With the new system a department would
initiate a public competition only if it had a reasonable expectation that its preferred
candidate - almost invariably a local insider - would be among the winners. Prior
to the competition, the hosting department would informally communicate to the
outside which candidate it would support (and expect to win), and would endorse
friendly colleagues to join the selection committee to ensure that would occur. The
main difference with respect to the national competition was that, with the new
rules, the quid pro quo was inter-temporal rather than simultaneous: A’s support for
B’s candidate in B’s competition today in exchange for B’s support for A’s candidate in A’s competition tomorrow.9
Crucially, while the 1998 reform revolutionized recruitment and promotion procedures, it left other aspects of the organization of universities virtually unchanged.
In particular, universities funding continued to rely on transfers from the central
government largely based on historical parameters and unrelated to universities’
current performance. Similarly, professors’ salaries remained undifferentiated and
solely based on seniority. This implied that any manipulation of the hiring process
by local university officials aimed at favoring some candidates over others would
have no impact on the overall resources available to the university or on the salaries
of current and new professors. In sum, the reform gave more discretion to local
university officials, potentially expanding opportunities for favoritism in hiring and
promotion, without raising the private costs of such behavior.
A look at the outcome of some of the post-reform competitions illustrates to what
extent these were heavily swayed in favor of insiders, i.e. candidates from the university that initiated the competition. Table 1, from Perotti (2008), reports statistics
9
Another relevant feature of local competitions is that, unlike the national one, the number of
committee members exceeded the number of winners. As a consequence, it was more likely that
only candidates sponsored by committee members would prevail, while little room remained for
the sort of high-quality outsiders that sometimes succeeded in national competitions.
10
on forty public competitions for the rank of full professor in the area of economics
held with the new rules. These competitions delivered a total of 117 eligible candidates, 91 of whom were ultimately appointed to open positions. Overall, 57% of
insiders were declared eligible against only 27% of outsiders, and, eventually, 44%
of insiders were appointed by the university that initiated the competition against
only 5% of outsiders. Furthermore, whenever a candidate from another university
was declared eligible in a competition, almost invariably one or more of the external
members in the selection committee would be from that same university.
Obviously, this table does not prove that the selection process became less efficient,
or that the selected candidates were less qualified with the decentralized recruitment
system. In theory, decentralization could make the recruitment process more efficient by alleviating problems of asymmetric information about candidates’ quality,
and by allowing universities to better tailor hiring decisions to their specific teaching
and research needs. On the other hand, decentralization could lead to more inefficient outcomes by allowing local officials to use their increased discretion to benefit
themselves, their family, or their clientèle. This is especially likely to happen when
economic incentives to hire the most productive candidates are absent, and in areas
with low civic capital where public scrutiny of local officials is deficient.
Our study investigates how decentralization influenced the conduct of local university officials by focusing on one specific and extreme form of uncivic behavior:
familism, defined as the practice of favoring family members in academic hiring
and promotion independently of their actual qualifications. In particular, we test
the hypothesis that, after the reform, familism in academic recruitment increased
disproportionately more in departments in areas with low civic capital. In what
follows we start by discussing anecdotal evidence of the importance of family connections in the Italian academia.
3.3. FAMILY CONNECTIONS IN THE I TALIAN ACADEMIA
Familism is a widespread phenomenon in the Italian academia. Cases of familism
involving university officials at the highest level are regularly covered by Italian media and, over the years, the issue has been the object of a lively public debate. Table
2 summarizes a series of cases in which close relatives of top university officials -
11
i.e. university provosts and department heads - were hired or promoted during the
mandate of the these officials.10 For each instance, the table reports: i) the name
of the university, ii) the university’s rank in terms of number of students, iii) the
position of the university official, iv) the period during which (s)he was in office,
v) the relation between the official and the individual that was hired/promoted, vi)
the rank to which this person was hired/promoted, vii) whether the two were part of
the same department. For example, the first case is that of the University of Rome
La Sapienza where the son of the current provost - who was recruited as AP and
promoted to AsP in the department of medicine while his father was head of the
department - was promoted to FP while his father was provost.11 Similarly, a son
and two daughters of his two predecessors were recruited and promoted while their
respective fathers were in office.
Overall, the table reports fifty-five cases of familism recorded in 18 of the 57 universities in our sample - including six of the ten largest ones - located in the North,
the Center, and the South. These episodes concerned mainly children of top officials (37) and, less often, children-in law (8), niblings (7), or spouses (3). There are
several reasons why these numbers are likely to underestimate the true extent of the
phenomenon. First, they do not include similar cases occurred prior to 2000. Second, being based on news reports, this list is likely to miss similar cases occurred
in smaller universities which are generally subject to less media scrutiny. Third,
we restrict our attention to cases occurred while provosts and chairs were in office
thus omitting those instances in which their relatives may have been favored either
before or after this time. Finally and most notably, since ordinary professors who
never become provost or chair are also in the position to influence hiring decisions,
the incidence of familism is likely to be larger than these prominent cases suggest.
An example of how endemic familism can be is represented by the department of
economics of the University of Bari, whose network of family connections as of
2007 is depicted in Figure 1. Overall, out of the 173 members of the department in
2007, 53 had at least one close relative in the department, with the two most repre10
11
We define close relatives as: spouse, children, niblings, children-in-law.
The provosts’ wife is also FP in the same department, while his daughter was promoted to AsP
and then to FP in the university’s second department of Medicine while her father was chair of
the first one
12
sented families accounting for seven members each. Although this is admittedly an
extreme case, it is by no means an isolated one. For example, in 2008 an article in a
main national newspaper on familism in the University of Palermo reported that in
the department of medicine out of 384 members of 58 had at least one close relative
in the department (La Repubblica, 2008); numbers were similarly striking for the
departments of law (21 out of 174), agriculture (23 out of 126), and engineering (18
out of 180). An analogous picture emerged for the University of Messina from the
investigation of another national newspaper: 23 close relatives out of 63 members
in the department of veterinary science, 100 out of 531 in medicine, and 27 out of
75 in law (Il Fatto Quotidiano, 2010).
4. DATA
AND MEASURES OF FAMILISM
The numerous anecdotes discussed above suggest that episodes of familism are
widespread in the Italian academia. Yet, providing rigorous quantitative evidence
of the scope of this phenomenon - and of its relation with local civic capital - is a
challenging task for at least two reasons. First, objective information on professors’
family connections is simply not available. Second, even self-reported measures of
family ties, if available, would not be reliable since, precisely where family connections are more pervasive, concerned individuals would presumably be less inclined
to reveal this information.
To overcome these difficulties in this paper we infer the incidence of family connections in a department from the number of faculty members with the same last
name (“homonymy”). The immediate advantage of this approach is that it allows to
look at the entire Italian university system since comprehensive information on the
last names of all academic personnel is publicly available. An obvious caveat is that
the degree of homonymy is likely to measure the incidence of familism in a given
department only imperfectly. On the one hand, a measure of within-department
homonymy can overestimate familism by counting cases in which individuals with
the same last name are not actually related. On the other hand, however, it may
underestimate the phenomenon by failing to account for relatives with different
13
last names.12 Crucially, whether the presence of multiple professors with the same
last name is informative about the incidence of family connections in a department
depends on how common that last name generally is. Intuitively, the rarer a last
name is the more its repeated occurrence is indicative of an abnormal situation. To
account for this aspect our measures of homonymy will control for the relative frequency of each last name in the general population of the area where the department
is located. In what follows we describe the data used in our empirical analysis and
how the measures of homonymy that we use are constructed.
4.1. DATA
To implement the approach described above we combine data from various sources.
With regard to professors, we use data from the Italian Ministry of Education, Universities, and Research which contains information on the names, age, position, and
department of affiliation of all professors employed in any Italian university, both
public and private. These data are available for each year between 1988 and 2008,
hence about ten years before to ten years after the decentralization reform discussed
above. Each year new hires enter the dataset while faculty that retire exit. The overall (unbalanced) panel includes over one million professor-year observations, with
an average of about 50,000 active faculty per year, accounting, on average, for more
than 20,000 unique last names. As mentioned above, we assign each professor in
our sample to a unique department defined as the set of all professors in a given
university affiliated to the same academic area.
To compute the frequency of last names in the general population, we use data from
Italy’s Internal Revenue Service on the distribution of last names of all taxpayers in
each of Italy’s 110 provinces. The data include information on about forty million
people who filed a tax return in 2005, i.e. the vast majority of the adult population.
Since this information is only available for 2005, we will only consider the distribution of last names in that year assuming it remains fairly stable over time. There are
several reasons why these data can be expected to provide an accurate picture of the
actual distribution of last names in the Italian population (both at the national and
12
This is especially the case for spouses since in Italy, unlike other countries, women use their
maiden name even after marrying.
14
at the local level). First, regarding possible concerns for the balance between male
and female tax payers, since in Italy married couples are not allowed to file jointly,
the dataset includes information on every man and woman with any taxable income
or property. Second, with respect to tax evasion, the dataset includes information
on all individuals that filed taxes (even if they under-reported their revenues), so
only total tax evaders do not appear; this, however, represents a relatively limited
segment of the population even in a country like Italy were tax evasion is pervasive.
Furthermore, there is no reason to believe that the distribution of last names among
total tax evaders should be different than in the rest of the population. An analogous argument holds for very low income people, who are not required to file taxes
and are hence not included in the data.13 For purpose of illustration, in Table 3 we
report the summary statistics for the distribution of last names in the provinces of
Italy’s twenty regional capitals.
Our primary measure of civic capital is newspaper readership. In particular, we use
data on the number of non-sport daily newspapers per 100 inhabitants by province
available from Cartocci (2007) for the years 2001 and 2002. As discussed above,
this measure is especially appropriate for our purpose because it captures citizens’
willingness and ability to acquire information so as to improve public scrutiny of local officials. Figure 2 shows the distribution of newspaper readership across the 41
Italian provinces where at least one university is located, with readership increasing
from dark (below-median) to light colours (above median). Table 4 reports the list
of 57 universities in our sample divided between those located in high-readership
provinces (29), and those located in low-readership ones (28).
Finally, in our empirical analysis we will also control for a range of socio-demographic
characteristics which could potentially correlate with civic capital and have an independent effect on the evolution of familism between the pre- and the post-reform
period. In particular, we will use data on GDP, unemployment and education at the
13
An alternative source of data on last names could have been the Italian phone book directory
(www.paginebianche.it). However, this source has two serious limitations: it does not account
for those individuals who do not have a land line, and, more importantly, it omits all members
of the household (particularly women) other than the person under whom the line is registered.
Ideally, the most comprehensive data would be those collected by the National Office of Vital
Statistics which, however, are not available to the public.
15
province level, available from the Italian Statistical Office.
4.2. M EASURES OF FAMILISM
Combining the data on professors’ last names and on the size of each department
(time-varying) with those on the distribution of last names in the general population (time-invariant), we create two measures of homonymy. These measures are
defined at the university-department-year level, which represents our unit of analysis.
The first measure is the share of faculty members who have at least one namesake in
the department. We denote this measure with SDEP and refer to it as the department
share index. Let d denote the department and t the year: we define Ndt as the total
number of faculty, Nadt as the number of faculty with last name a, and Rdt as the set
DEP is given by:
of repeated last names in the department. Hence, Sdt
Nadt
DEP
(1)
Sdt
= 100 ∗ ∑
a∈Rdt Ndt
The share index ranges from 0, when no last name is repeated, to 100, when all
members share their last names with at least another member. In principle, a high
value of SDEP may simply reflect the fact that some members of the department
have very common last names, rather than the presence of familism. To account
for this aspect we construct an analogous index for the population from which the
members of the department are drawn. This index, denoted as SPOP , captures the
share of repeated last names in a random sample of the reference population of the
same size of the department. Since the vast majority of Italian professors are born
in the same region where their university is located,14 we consider the population
of that region as the benchmark.15 In practice, for a department of size N in region
14
15
Perotti (2008) reports very eloquent statistics on the place of origins of professors of various
universities; for example, as of 2007 the share of professors born in the region where the university is located was 78% for the University of Bari, 76,8% for the University of Cagliari, 67.4%
for the University of Genoa, 69% for the State University of Milan, 82.7% for the University of
Naples Federico II, 89,1% for the University of Palermo, 59.9% for the University of Rome La
Sapienza, and 74.9% for the University of Turin.
We obtain very similar results (available upon request) if we consider as benchmark the population of the province where the university is located, or the entire national population.
16
r we draw 10,000 random samples of size N from the distribution of last names in
region r, compute the share index for each sample using (1), and define SPOP as
the median of these values. Finally, for each department d at time t, we derive the
population-adjusted share index Sdt as the difference between the department index
DEP and the population index SPOP .16
Sdt
dt
The main advantage of the share index is that it is simple and very intuitive. A
potential caveat is that it does not capture the degree of “concentration” of last
names in a given department. A simple example can illustrate this argument. Take
two departments D1 and D2 with the following distribution of last names:
D1
D2
aabbcc aaaaaa
Since in both cases all professors share their last names with at least another professor, the share index takes the same value for both departments (100). However,
most would agree that D2 represents a more extreme case of familism since the
entire department is “colonized” by a single family. This simple example suggests
that the relative concentration of last names is another relevant dimension that a
measure of homonymy ought to capture.
To address this issue, we construct another index, defined as the probability that any
two individuals drawn at random among the members of the department have the
DEP and refer to it as the department
same last name. We denote this measure with Cdt
concentration index. Formally, the probability that two individuals drawn randomly
(without replacement) from the members of department d at time t have the same
last name a can be written as:
padt =
Nadt (Nadt − 1)
Ndt (Ndt − 1)
(2)
DEP is:
Consequently, Cdt
DEP
Cdt
= 100 ∗
∑
padt
(3)
a∈Rdt
16
Alternatively, we could divide SDEP by SPOP ; the disadvantage of such approach, however, is
that it would generate large outliers for very low values of SPOP .
17
The concentration index ranges from 0, when each last names appears only once
in the department, to 100, when all members of the department have the same last
name.17 As an illustration of the properties of the index, returning to the example
DEP is higher for
of departments D1 and D2 introduced above, it is apparent that Cdt
D2 than for D1 (100 and 20 respectively).
Like for the share index, to control for the concentration of last names in the genPOP ) comeral population, we derive a population-specific index of concentration (Cdt
puted as the median concentration of 10,000 random samples of the reference population of size Ndt . As before, we derive the population-adjusted concentration index
DEP and CPOP .
(Cdt ) from the difference between Cdt
dt
Using the procedure described above, we compute the share and concentration indices for a balanced panel of 564 departments in 57 universities over a period of 21
years which represents the baseline sample used in our empirical analysis.18
Tables 5 and 6 report the summary statistics respectively for the three share indices
and the three concentration indices. Furthermore, Appendix Figure A.1 shows the
kernel density of the two population-adjusted indices, C and S. With regard to SDEP ,
the data indicate that, on average, about 3.7 percent of individuals have at least a
namesake in the same department; the median is 1.4 percent and the 98th percentile
18.4 percent. With regard to CDEP , the average is 0.045, the median 0.01, and the
98th percentile 0.28. To put these numbers in perspective, note that, in a department
with 100 members where all members have exactly one namesake (an enormous
degree of homonymy), the probability that two people drawn at random have the
same last name would be about 1 percent and the index CDEP would take value 1.
Also, CDEP is highly concentrated at zero with 49 percent of all department/years
17
18
The concentration index is very similar to the Herfindahl index - largely used in the literature on
ethno-linguistic fractionalization - with the difference that for the Herfindahl index the probability pa is calculated with replacement, i.e. pa = (Na /N)2 . Following this approach, even if a last
name appeared only once in the population, pa would be positive and equal to 1/N 2 ; without
replacement, instead, pa would be zero.
We exclude from the sample all distance-learning universities for which we cannot uniquely
identify the location. Since these universities were created after the reform, their inclusion would
in any case not affect our estimates of the effect of the reform on familism which is based on the
comparison between the pre- and the post-reform period.
18
displaying no repeated last names.19 A quick comparison of the data with the anecdotal evidence discussed above is reassuring of the fact that both indices are a good
proxy for the presence of familism. Indeed, departments in which blatant cases of
familism were documented - like in the universities of Bari, Palermo and Messina score very high in both indices.
5. E MPIRICAL STRATEGY
In this section we use the two indices of homonymy presented above to investigate the relation between civic capital and the effect of decentralization on familism. In particular, we test the hypothesis that, following the 1998 reform, familism
increased disproportionately more in universities in areas with low level of civic
capital.
We start by looking at the evolution of the two (population-adjusted) indices, C and
S, before and after the reform. Figure 3 reports the average values of C for each year
between 1988 and 2008 separately for departments in areas with low civic capital
(blue line) and high civic capital (red line).20
The figure shows that there is no clear trend for either group in the 6-7 years prior
to the reform. After the reform, however, the distance between the two groups
increases steadily: a rise of about 20 percent in the low civic-capital group is accompanied by a decline of about 10 percent in the high-civic-capital one (which is
partly reabsorbed in later years).
Appendix Figure A.3 shows that degree of homonymy in the general population,
CPOP , is very similar in low- and high-civic-capital provinces (note the scale on
the vertical axis) and nearly constant over time. As a result, the graph of CDEP
(Appendix Figure A.2) is virtually identical to the graph of C.
A similar picture holds for the share index, S, whose evolution is depicted in Figure
19
20
That CDEP is zero in roughly half of the observations explains why both the mean and the median
of C are slightly negative, since in most of these cases a small but positive value of CPOP is
subtracted from 0.
We consider as pre-reform the years between 1988 and 1999 and as post-reform the years after
2000. This choice is motivated by the fact, that, although the reform was voted by parliament in
1998, it was not fully implemented until 2000 when the first public competitions with the new
rules were held. All the results discussed below remain similar, and are generally stronger, if,
instead, we consider 2000 as a pre-reform year.
19
4. One difference is that displays a slight upward trend in low-civic capital universities prior to the reform, which reflects a similar trend in both SDEP and SPOP
(Appendix Figures A.4 and A.5).
To investigate these patterns more rigorously we estimate the following diff-in-diff
specification:
Hdt = α LOWd ∗ REFORMt +β Nd,t +γ Xd ∗ REFORMt +δ Zdt + µd +λt +εdt (4)
where d denotes the department and t the year, Hdt is one of the two homonymy
indices, LOWd is a dummy variable for departments in areas with newspaper readership below the median, REFORMt a dummy variable for post-reform years, Nd,t
the size of the department, Xd and Zdt are respectively vectors of time-invariant and
time-varying socio-economic characteristics of the province where the department
is located, and µd and λt are respectively department and year fixed effects.
Our coefficient of interest is α, which can be interpreted as the average differential
effect of the reform on homonymy in departments in low- vs. high-civic capital
areas. Controlling for Nd,t and including department fixed effects allow us to account for any spurious correlation between department size, or any time-invariant
department characteristic, and familism. Similarly, the year fixed effects capture
the impact of any year-specific common shock to the capacity of hiring relatives
(including the aggregate effect of the reform). Finally, controlling for the interaction between the reform dummy and other key socio-economic variables allows
to control for the possibility that the differential impact of the reform on familism
may be driven by differences in other relevant characteristics. This is especially
important since differences in civic capital tend to be correlated with differences
in economic development, education, or, in the specific context of Italy, between
North and South (Putnam et al., 1993). To address this concern, we will control for
the interaction between the reform dummy and indicators of GDP, unemployment,
and education in a baseline year, and a dummy for South (included in Xd ). In addition, we also control for yearly GDP and unemployment (included in Zdt ), the only
variables for which province-level yearly data are available.
20
6. R ESULTS
6.1. BASIC RESULTS
Table 7 reports the results for the concentration index, C. In all regressions we
control for department size, department and year fixed effects; standard errors are
clustered by province (41 clusters) - i.e. at the level at which our regressor of interest, civic capital, is defined - to allow for generic correlation across departments
in universities in the same province.21 In column 1 we regress C on the interaction
between the reform dummy and the dummy for low civic capital (below median).
The coefficient on the interaction term is positive and significant (at the 5% level)
confirming that, after the reform, the concentration of last names increased in areas
with low civic capital relative to those with high civic capital.22 The estimated coefficient, 0.009, corresponds to about 18% of the demeaned standard deviation. The
size of the department has a positive coefficient, though not statically significant. In
the following four columns we gradually introduce interactions between the reform
dummy and indicator variables for departments in provinces: i) with below-median
level of GDP (column 2), ii) with above-median level of unemployment (column
3), iii) with below-median level of education (column 4), iv) in the South of the
country.23 The inclusion of these controls barely affects the size and significance
of the coefficient of interest; furthermore, none of the other interaction terms displays a significant effect and in most cases the point estimates are negative. This
is reassuring since the differential effect of the reform on the concentration of last
names seems indeed to be related to differences in civic capital and not in other
socio-economic conditions.
In the last two columns we also control for yearly unemployment (column 6) and
GDP (column 7). Controlling for time-varying basic economic conditions allow
us to account for any local economic shocks that may affect professors’ incentive
to favor family members in academic hiring, e.g. scant opportunities to find nonacademic jobs when unemployment is high and economic growth is sluggish (see
21
22
23
Standard errors are similar if clustered at the university level (57 clusters), and generally smaller
if clustered by year (20 clusters).
The results are virtually the same if instead of C we use CDEP as dependent variable.
The values of GDP and unemployment are available for 1998, the year the reform was approved;
for education data are only available from the Census, so we take the closest Census year, 2001.
21
Alesina et al., 2001; Kramarz and Skans, 2014). In this case too, the coefficient of
interest remains largely unchanged (if anything larger in column 7). Note, however,
that the sample size is reduced because yearly data on GDP and unemployment at
the province level are not available for a few early years in the sample.24
We find very similar results when using the share index S as dependent variable
(Table 8). Like in the case of C, the coefficient on the interaction between the
reform dummy and the low-civic-capital dummy is positive, significant (at the 1%
level), and rather sizeable (0.714, about 41% of the demeaned standard deviation).
This finding too is extremely robust to controlling for the interaction between the
reform dummy and key socio-economic characteristics, and for time-varying local
economic shocks.
6.2. P RE - EXISTING TRENDS
One possible concern, especially for the share index, is that the documented effect
might be capturing the continuation of the differential pre-existing trend depicted
in Figure 4. A solution that is often advocated for this problem is de-trending (see
Angrist and Pischke, 2008 and Besley and Burgess, 2004 for applications of this
argument). However, while de-trending is correct in several situations, in others
cases it may actually lead to underestimating a genuine differential effect (even if
this is not driven by any actual pre-trend). Appendix Figure A.6, which depicts
a stylized situation similar to that observed in Figure 4, provides a graphical illustration of this argument. In this scenario, prior to the reform, S is constant for
both the low-civic capital group (triangles) and the high-civic-capital one (squares)
and equal respectively to 5 and 4. After the reform, S remains constant for the
high-civic-capital group but increases steadily for the low-civic-capital group, with
annual increments of 2. A standard diff-in-diff estimator would correctly gauge
the average differential effect of the reform. What if instead one had to remove
the group-specific trends and plot the respective residuals? As shown in the lower
part of the figure, this would have no impact on the high-civic-capital group, since
the residuals would be the same as the raw data. However, for the low-civic-capital
group the average de-trended residual would be zero before the reform and negative
24
Data on yearly unemployment and GDP are available respectively from 1991 and from 1995.
22
after that. Consequently, the estimate of α in equation (4) would be negative even
if the true (differential) effect of the reform is positive; a spurious and unintended
effect of de-trending.
Thus, to rule out the possibility that our result is driven by a pre-existing trend,
we adopt a different approach. We estimate the following variant of our baseline
specification (4) in which, instead of interacting LOW with the reform dummy, we
interact LOW with a dummy for each year (1988 being the excluded year):
Hdt = α LOWd ∗ λt + β Nd,t + γ Xd ∗ λt + µd + λt + εdt
(5)
The interaction between LOWd and each year’s fixed effect captures the effect of
being in a low-civic capital area on homonymy in that specific year. Thus, looking
at the value of α for different years, we can get a sense of how the relation between
civic capital and homonymy evolves over time, both before and after the reform.
To this end, in Figure 5 we plot the estimated coefficient for each year with two
standard errors above and below. It is apparent from the graph that, until 1999, the
coefficients are not significantly different from zero and exhibit no particular trend,
but that they increase considerably after 2000.
To investigate this issue more formally, in Table 10 we estimate (5) using index C
as dependent variable and report the differences between the average values of α
in 2001-05 and 1995-99 (row 1), in 2001-08 and 1995-99 (row 2), and in 2001-08
and 1990-99 (row 3), with the corresponding p-values. As before, in all regressions
we control for department and year fixed effects and for the size of the department.
As shown in column 1, the difference is always positive and nearly identical to the
value of the coefficient α in Table 7, and significant at the 5 percent level, except
in row 3 where it is significant at the 10 percent level. Interestingly, the magnitude of the effect is very similar when comparing the five years before and after
the reform (row 1) and when extending the reference period before and after the
reform to up to ten years (row 3); this suggests that familism in low-civic-capital
areas increased quite rapidly once the reform came into effect.25 In columns 2 to 7,
we gradually control for the interaction between the set of year dummies with key
25
This view is further corroborated by the fact that we find similar results (not show) when we
restrict the comparison just to the three years before and three years after the reform (1997-99
vs. 2001-03).
23
socio-economic indicators (low GDP, low education, high-unemployment, South),
and for yearly local economic conditions (GDP, unemployment). The average differences, if anything, increases slightly.
In rows 4 and 5 we test for the presence of pre-existing differential trends, by comparing the difference between the average values of α in 1995-99 and 1990-94 (row
4) and 1997-99 and 1994-96 (row 5). These differences are always small and statistically insignificant, with p-values ranging from .25 to .97. In particular, the result
of row 5 tends to rule out the possibility of a pre-trend starting within the 1995-99
period, which also seems inconsistent with the individual year effects reported in
Figure 5. In Table 9 we replicate the analysis for the index S and find very similar
results, which, if anything are more statistically significant in rows 1 to 3.
6.3. ROBUSTNESS
As depicted in Appendix Figure A.1, a relatively small number of departments display very high level of both the concentration index and the share index. For example, the highest values of C and S corresponds to more than twenty times and
eight times their (demeaned) standard deviations, respectively. To verify that our
results are not driven by such few extreme cases, in Appendix Table A.1 we replicate our analysis excluding observations in the top 1% of the distribution of each
index. In the first four columns we focus on the concentration index C and estimate our baseline specification - with the interaction between reform dummy and
dummy for low social capital - including gradually the following controls: i) department size (column 1), ii) interaction between reform dummy and dummies for
low GDP, high unemployment, low education and South (column 2), iii) yearly unemployment (column 3), and yearly GDP (column 4). The coefficients are very
similar to those presented in Table 7 and generally more significant. In columns 4
through 8 we perform the same check for the share index, S: once again, the effect
of civic capital on homonymy is robust to the exclusion of outliers, both in terms of
magnitude and statistical significance.26
26
The results are virtually the same when we exclude both the bottom and top 1% of the distribution
of either homonymy index. Also, the results are very similar when we exclude the top 2% or
5% of the distribution, rather than just the top 1%. These additional results are available upon
request.
24
Another potential concern may relate to the use of a dichotomous variable to capture differences in civic capital (below-median vs. above-median), and the subsequent risk of overlooking possibly relevant differences in civicness within these
two groups. To address this aspect, in Appendix Table A.2 we replicate the results
of Tables 7 and 8 using as main independent variable the interaction between the
reform dummy and the continuous measure of newspaper readership (i.e. number
of non-political newspapers per 1,000 people). The findings for both C and S are
entirely consistent with those discussed above: lower readership is associated with
significantly higher familism in the years after the reform, a result that is robust to
the inclusion of all usual controls. In terms of magnitude, a one standard deviation
increase in newspaper readership is associated with an increase in homonymy after
the reform of about 13 % of the demeaned standard deviation for C, and 30% for S.
Finally, we explore whether the relation between civic capital and the impact of
decentralization on familism varies across disciplines. In particular, we examine
whether increased discretion in hiring on the part of local officials is more likely to
lead to familism in disciplines in which individual research achievement is harder
to asses, as this would facilitate the manipulation of public competitions to favor
relatives over more qualified candidates. To shed light on this aspect, in Tables 11
and 12 we re-estimate our baseline specification separately for departments in the
hard sciences - in which the use of bibliometric indicators is more widespread and in all other disciplines.27 Overall our results provide rather mixed evidence in
this respect. For the C index (Table 11) the coefficient on the interaction between
reform dummy and low-civic-capital dummy is always smaller and less significant
for the hard sciences. In the regressions for the S index (Table 12), in contrast, the
coefficient of interest is similar in the two groups of disciplines.
6.4. FAMILISM
AND RESEARCH PERFORMANCE
Although identifying the causal impact of familism on academic performance is
beyond the scope of this study, in what follows we briefly explore the empirical
27
The category “hard sciences” include the following disciplines: biology, chemistry, physics,
geo-sciences, mathematics and computer science (the last two are considered as part of the same
discipline in the Italian system). The other disciplines include: medicine, veterinary science,
humanities, law, social sciences, and engineering.
25
relation between our indices of homonymy and a measure of the quality of research
output at the department level. Information on the research performance of Italian
academic departments is available from the evaluation conducted by the Italian National Committee for the Evaluation of Research (CIVR), sponsored by the Italian
Ministry of Education, Universities, and Research. The CIVR evaluation - the first
and so far only nationwide experiment of this kind ever conducted in Italy - was carried out between February and December of 2005 and was aimed at assessing the
volume and quality of the research conducted in the three-year period between 2001
and 2003. To this end, each department or research institute affiliated with a university was required to submit a set of research outputs produced over this period.28
All eligible products were then evaluated by area-specific committees according to
well defined criteria, and an overall score comprised between 0 (poor performance)
and 1 (excellent performance) was finally assigned to each department. In Table
13 we regress the CIVR score on our homonymy indices, C and S. Since only one
score is available for each department for the entire three-year period, we can only
exploit cross-sectional variation. In columns 1 to 3 we regress the CIVR score on
the concentration index computed respectively for 2001, 2002, and 2003, while in
column 4 on the average value of C over the three years. All regressions include
disciplinary area fixed effects (14), and control for the size of the department in
the relevant year or for the average over the three years. To minimize the impact
of outliers, in all regressions we exclude observations in the top and bottom 1% of
the distribution of the relevant C index. In all regressions, the concentration index
displays a negative coefficient which, with the exception of column 1, is statistically significant at the 5% level. The point estimates are quite sizeable: looking at
column 4, for example, a one standard deviation increase in C is associated with a
decrease of 10% of a standard deviation in the CIVR score. Results are quantitatively similar, and generally more significant, for the share index (columns 5 to 8).
Although these findings can hardly be given a causal interpretation, the evidence is
28
Depending on the discipline, eligible categories of outputs included: books, book chapters, conference proceedings, journal articles, patents, projects, compositions, drawings, design products,
performances, shows and exhibitions, art manufactures. Other type of outputs, such as textbooks
or software produced mainly for teaching purposes, conference abstracts, and internal technical
reports were not eligible for purpose of evaluation.
26
consistent with the view that departments characterized by higher levels of familism
tend to fair worse than others, at least with regard to research achievement.
7. C ONCLUSIONS
An extensive literature on the functioning of federal systems has studied the consequences of decentralizing policy-making from the center to the periphery for government performance and public good provision. This body of work has delivered
mixed evidence on the potential impact of decentralization, and has highlighted the
possibility that, even within the same institutional context, decentralization may
improve administrative performance in some jurisdictions while worsening it in
others.
This research attempts to shed light on this puzzle by examining to what extent the
effect of decentralization may depend on the socio-cultural characteristics of the
communities to which power is devolved, namely on the level of information and
civic engagement of the local population. In particular, we advance the hypothesis
that in areas with high civic capital - where local administrators are effectively monitored by an active and informed citizenry - decentralization will increase accountability, whereas in areas with low civic capital poorly monitored officials will use
their increased discretion to favor their particular interests over the general one. To
test this hypothesis empirically we look at one major episode of decentralization in
a specific domain of administration, i.e. public education, occurred in Italy in 1998.
In particular, we study whether the reform of Italy’s university system - which decentralized academic recruiting from the national to the university level - resulted
in an increase in familism - i.e. the practice of favoring relatives in academic hiring
- and whether this effect was more pronounced in areas with low civic capital. Our
analysis employs a novel dataset with information on all professors employed in
any Italian university from ten years before to ten years after the reform. To assess
the prevalence of family connections we exploit the informative content of professors’ last name; in particular, we construct two measures of “homonymy” - i.e. the
presence of multiple professors with the same last name in the same department which capture the abnormal concentration of last names in a given academic department relative to the general population. The richness and longitudinal dimension
27
of our data allow us to estimate the effect of the reform controlling for both year
and department fixed effects, and accounting for a range of possibly confounding
factors.
In line with the hypothesis spelled above, we find that after the reform familism
- as proxied by homonymy - increased significantly in universities in areas with
low civic capital relative to those in areas with high civic capital. The (differential) effect of decentralization on familism in areas with low civic capital is rather
large: between 20 and 40% of a demeaned standard deviation, depending on the
measure used. This results is extremely robust to the use of two different measures of homonymy and different econometric specifications, and does not appear
to be driven by pre-existing trends in the evolution of familism or by differences in
other socio-economic factors including GDP, unemployment, education, and NorthSouth divide.
Our results provide support for the view that decentralizing power from the center to
the periphery can lead to very different outcomes depending on the capacity of citizens to monitor local administrators and keep them accountable for their actions.
These findings underscore a novel link between the literature on decentralization
and that on civic capital, and exemplify how deeply rooted socio-cultural characteristics can have a profound impact on the quality of government today. Although
the evidence presented here is specific to the case of Italy’s higher education, we
believe that some of the insights from our analysis are more general, and can be
useful to understand the potentially nuanced consequences of decentralization in
other countries and policy domains. In particular, assessing the capacity of local
communities to properly scrutinize the actions of local administrators can be crucial for central authorities to determine whether decentralization is truly desirable,
and, when it is, whether appropriate mechanisms should be designed to limit administrative misconduct in less virtuous jurisdictions. Given their complexity and
considerable policy implications, further empirical work is needed to shed light on
these questions, ideally drawing from experiences in different countries and policy
domains.
28
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31
TABLE 1: S TATISTICS ON P OST-R EFORM P UBLIC C OMPETITIONS
FOR THE R ANK OF F ULL P ROFESSOR IN E CONOMICS
Total number of competitions
Share of competitions with insiders
Share of insiders declared as eligible
Share of outsiders declared as eligible
Share of insiders appointed by department X
Share of outsiders appointed by department X
Share of department X’s positions assigned to insiders
Share of eligible candidates appointed by X or own university
40
80%
57%
27%
44%
5%
67%
95%
The table reports summary statistics for forty public competitions for the rank of full professor
in the area of economics, held after the 1998 reform (source: Perotti, 2008). We indicate with
“X” the department that initiated the competition, and define as “insider” any candidate affiliated
to department X at the time the competition started and as “outsider” any other candidate. The
three top-ranked candidates in each competition would become eligible to be appointed to the
position advertised by department X, or to any open position at the same rank level in the same
area in any other university for a period of three years.
32
TABLE 2: E XAMPLES OF FAMILISM IN
THE I TALIAN ACADEMIA ( CONTINUES IN NEXT PAGE )
Rank
University
Role
Mandate
Relation
Level
Same Dept.
or Discipline
Hired or
Promoted
1
Roma La Sapienza
Provost
Provost
1997-2004
2004-2008
Chair
1990 -
Provost
2008 -
S
D1
D2
S
S
S
FP
AP
AP
AP
AsP
FP
No
No
No
Yes
Yes
Yes
H
H
H
H
P
P
2
Bologna
Provost
Provost
1986-2000
2000-2009
D
S
S
DIL
AP
AsP
FP
AsP
No
No
No
No
H
P
P
P
3
Napoli Federico II
Provost
2001-2010
D
AsP
Yes
P
7
Palermo
Chair
2002-2008
W
AsP
Yes
P
9
Bari
Chair
1995 - 2004
Provost
2000-2006
S
D
S1
S2
D
W
W
SIL
AP
AP
AP
FP
AP
AP
AsP
AsP
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
H
H
H
P
H
H
P
P
Chair 2001 - 2007
Provost 2000 - 2009
D
D
AP
AP
Yes
Yes
H
H
10
Firenze
12
Milano Politecnico
Provost
2002 - 2010
S
FP
Yes
P
16
Roma Tre
Provost
1998 -
N
FP
Yes
H
17
Cagliari
Provost
1991-2009
S
AP
Yes
H
The table reports some examples of favoritism towards family members by prominent academics in various
Italian universities over the past decades. “Rank” indicates the university’s rank in terms of total number of
registered students. In the column “Relation", “S” indicates a son, “D” a daughter, “SIL” a son-in-law, “N” a
nephew and “W” the wife. If more than one son is hired or promoted we indicated them using “S1” for the first
son, “S2” for the second, etc.; we do the same for other relatives. In the column “Level", “AP” indicates an
assistant professor, “AsP” an associate professor, and “FP” a full professor.
33
TABLE 2: S OME EXAMPLES OF FAMILISM
IN THE I TALIAN
ACADEMIA ( CONTINUED )
Rank
University
Role
Mandate
Relation
Level
Same Dept.
or Discipline
Hired or
Promoted?
18
Calabria
Chair
1998 - 2007
D
D
AsP
FP
Yes
Yes
P
P
19
Roma Tor Vergata
N1
S
S
N1
N1
N2
S
S
S
DIL
AP
AP
AsP
AsP
FP
FP
AP
AsP
FP
AsP
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
H
H
P
P
P
H
H
P
P
P
D
S
AP
AsP
Yes
No
H
P
S
AP
Yes
H
D
S
SIL
AP
AP
AP
Yes
Yes
Yes
H
H
H
Chair 1991 - 1996
Provost 1996 - 2008
Chair
1996 - 2008
Provost
2008 -
21
Messina
Chair 1999 - 2004
Provost 2004 - 2011
22
Perugia
26
Napoli II
28
Siena
Provost
1994-2006
S
AP
Yes
H
32
Modena
Provost
1999-2008
S
S
S
AP
AsP
FP
No
No
No
H
P
P
45
Napoli Parthenope
Provost
1986 - 2010
D
D
SIL
SIL
SIL
N
N
AsP
FP
AP
AsP
FP
AP
AsP
Yes
Yes
Yes
Yes
Yes
Yes
Yes
H
P
H
P
P
H
P
55
Foggia
Provost
1999-2008
S
SIL
AP
AP
Yes
No
H
H
Chair
2001 - 2010
Chair 1998 - 2006
Provost
2006 -
34
TABLE 3: DATA ON THE DISTRIBUTION OF LAST NAMES IN
Province
Ancona
Aosta
L’Aquila
Bari
Bologna
Cagliari
Campobasso
Catanzaro
Florence
Genoa
Milan
Naples
Palermo
Perugia
Potenza
Rome
Trento
Turin
Trieste
Venice
THE I TALIAN POPULATION
Total population
Number of last names
Mean occurrences
Max occurrences
350.866
97.955
212.580
942.530
754.428
464.976
156.809
218.928
722.605
667.803
2.834.116
1.509.143
668.632
463.916
251.678
2.636.181
389.812
1.638.080
191.029
606.115
32.669
18.794
19.637
32.400
69.902
19.603
12.556
13.188
67.137
72.834
181.971
57.905
27.446
42.811
13.782
160.021
41.925
112.818
35.332
50.862
11
5
11
29
11
24
12
17
11
9
16
26
24
11
18
16
9
15
5
12
1.490
472
895
5.771
3.051
7.543
832
1.705
3.465
5.210
22.689
31.591
3.224
3.509
1.333
11.315
1.691
5.280
500
6.505
The table reports summary statistics for the distribution of last names in the population of the provinces of the twenty
Italian regional capitals (source: Italian Fiscal Census, 2006). The columns indicate: (1) total population; (2) number
of last names in the population; (3) average number of occurrences of each last name; (4) maximum number of
occurrences of a last name.
35
TABLE 4: U NIVERSITIES IN THE SAMPLE
Low Newspaper Readership
High Newspaper Readership
BERGAMO
AQUILA
BARI
BOLOGNA
BASILICATA
CAGLIARI
BRESCIA
FERRARA
CALABRIA
FIRENZE
CAMERINO
GENOVA
CASSINO
MILANO POLITECNICO
CATANIA
MILANO STATALE
CHIETI PESCARA
PADOVA
MACERATA
PARMA
MARCHE POLITECNICA
PAVIA
MESSINA
ROMA SAPIENZA
MODENA REGGIO EMILIA
ROMA TOR VERGATA
MOLISE
SASSARI
NAPOLI FEDERICO II
SIENA
NAPOLI L’ORIENTALE
TORINO
NAPOLI PARTHENOPE
TORINO POLITECNICO
PALERMO
TRENTO
PERUGIA
TRIESTE
PERUGIA STRANIERI
TRIESTE SISSA
PISA
UDINE
PISA NORMALE
VENEZIA CA’ FOSCARI
PISA S.ANNA
VENEZIA IUAV
REGGIO CALABRIA MEDITERRANEA
VERONA
SALENTO
MILANO BOCCONI (private)
SALERNO
MILANO CATTOLICA (private)
TUSCIA
MILANO IULM (private)
URBINO
ROMA LUISS (private)
ROMA LUMSA (private)
The table reports the list of universities in our sample divided into two groups: universities
located in provinces characterized by low newspaper readership (i.e. readership of non-sport
daily newspapers per 100 inhabitants below the median of the overall sample), and universities located in provinces with high newspaper readership (above median). The first sample
includes 28 universities (for a total of 5,670 department-year observations), while the second
one includes 29 universities (6,174 department-year observations).
36
TABLE 5: S UMMARY STATISTICS : S HARE INDICES (S)
SPOP
SDEP
All
Obs.
Mean
Median
First perc.> 0
98th perc.
99th perc.
Max
75th perc.
St. Dev.
St. Dev (demeaned)
HR
11844 6174
3.67
3.53
1.36
2.25
50th
45th
18.36 15.87
22.13 19.60
30.91 27.63
6.06
5.75
5.04
4.46
1.84
1.69
LR
5670
3.82
0.00
55th
20.21
23.85
30.91
6.37
5.61
2.00
All
HR
11844 6174
3.02
3.19
1.91
1.90
1st
1st
14.13 15.79
17.18 18.49
29.68 29.68
3.94
4.01
3.52
3.86
.53
.54
S
LR
All
5670
2.83
1.92
1st
12.99
14.71
28.32
3.84
3.10
.53
HR
11844 6174
0.65
0.34
-0.23 -0.16
64th 62nd
10.44 7.14
13.38 8.32
23.76 16.38
1.93
1.78
3.43
2.82
1.72
1.61
LR
5670
0.98
-0.31
66th
13.37
15.23
23.76
2.22
3.97
1.84
The table reports the summary statistics for the three version of the homonymy share index SDEP, SPOP, and
S for the overall sample and, separately, for the high- and low-newspaper readership groups (labeled as “HR”
and “LR” respectively). “First perc.> 0” indicates the first percentile at which the index takes a non-zero value,
while “St. Dev (demeaned) ” indicates the standard deviation of the residuals of a regression on department
fixed effects.
TABLE 6: S UMMARY STATISTICS : C ONCENTRATION
CDEP
All
Obs.
Mean
Median
75th perc.
98th perc.
99th perc.
First perc. > 0
Max
St. Dev.
St. Dev (demeaned)
11844
0.045
0.009
0.058
0.280
0.395
50th
1.754
0.087
0.052
HR
INDICES
CPOP
LR
6174 5670
0.041 0.050
0.017 0.000
0.048 0.074
0.266 0.294
0.395 0.396
45th 55th
1.515 1.754
0.085 0.089
0.048 0.056
All
11844
0.046
0.036
0.048
0.236
0.237
1st
.293
0.043
0.004
HR
(C)
C
LR
6174 5670
0.045 0.048
0.028 0.043
0.036 0.056
0.237 0.077
0.239 0.080
1st
1st
.293
.094
0.058 0.015
0.004 0.004
All
HR
LR
11844
-0.001
-0.020
0.016
0.210
0.314
65th
1.715
0.084
0.052
6174
-0.004
-0.016
0.012
0.154
0.279
64th
1.489
0.079
0.048
5670
0.002
-0.029
0.024
0.243
0.345
67th
1.715
0.089
0.056
The table reports the summary statistics for the three version of the homonymy concentration index CDEP,
CPOP, and C for the overall sample and, separately, for the high- and low-newspaper readership groups (labeled
as “HR” and “LR” respectively). “First perc.> 0” indicates the first percentile at which the index takes a nonzero value, while “St. Dev (demeaned) ” indicates the standard deviation of the residuals of a regression on
department fixed effects.
37
TABLE 7: D ECENTRALIZATION AND THE EVOLUTION OF FAMILISM , 1988-2008
(R EFORM D UMMY AND D UMMY FOR L OW N EWSPAPER R EADERSHIP )
Dependent variable: Concentration Index (C)
Low Readership * Reform
(1)
(2)
(3)
(4)
(5)
(6)
(7)
0.0094**
(0.004)
0.0094*
(0.005)
0.0093*
(0.005)
0.0106**
(0.005)
0.0100*
(0.005)
0.0105*
(0.005)
0.0132**
(0.005)
-0.0000
(0.005)
0.0014
(0.005)
0.0032
(0.005)
0.0026
(0.005)
0.0022
(0.005)
0.0008
(0.005)
0.0024
(0.005)
0.0029
(0.005)
0.0045
(0.005)
0.0036
(0.004)
0.0014
(0.005)
-0.0039
(0.004)
-0.0052
(0.004)
-0.0037
(0.004)
-0.0010
(0.004)
0.0042
(0.008)
0.0006
(0.007)
-0.0040
(0.007)
0.0003
(0.000)
0.0002
(0.001)
Low GDP * Reform
High Unemployment * Reform
Low Education * Reform
South * Reform
Unemployment
0.0004
(0.001)
GDP
Num. Professors
-0.0009
(0.031)
-0.0009
(0.031)
-0.0006
(0.031)
-0.0004
(0.031)
-0.0005
(0.032)
0.0003
(0.037)
-0.0004
(0.057)
Observations
Num. of departments
R2
11280
564
0.005
11280
564
0.005
11280
564
0.005
11280
564
0.005
11280
564
0.005
9588
564
0.004
6768
564
0.005
Reform is a dummy variable that equals zero for the pre-reform years and one for post-reform years; here Reform takes
missing values for 2000, the year of the implementation, which we treat as a year of transition from one regime to the
next. Low Readership, Low GDP, Low Education, High Unemployment are dummy variables that indicate whether
the department’s university is located in a province characterized respectively by below-median newspaper readership
in 2001-2002, below-median GDP per capita in 1998, below-median level of education in 2001, above-median rate of
unemployment in 1998, while South indicates whether the department’s university is located in the South of the country.
Num. Professors indicates the number of faculty in the department in a given year (in hundreds). Unemployment, and
GDP indicate respectively the rate of unemployment and the level of GDP per capita (in thousands) in the province
where the university is located in each given year. All regressions include department/university fixed effects and year
fixed effects. Standard errors clustered at the province level in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
38
TABLE 8: D ECENTRALIZATION AND THE EVOLUTION OF FAMILISM , 1988-2008
(R EFORM D UMMY AND D UMMY FOR L OW N EWSPAPER R EADERSHIP )
Dependent variable: Share Index (S)
Low Readership * Reform
(1)
(2)
(3)
(4)
(5)
(6)
(7)
0.7144***
(0.199)
0.6825**
(0.263)
0.6849**
(0.266)
0.6961**
(0.268)
0.6831**
(0.290)
0.6516**
(0.283)
0.6435**
(0.289)
0.0629
(0.265)
-0.0059
(0.313)
0.0106
(0.328)
-0.0043
(0.327)
0.0549
(0.306)
-0.0553
(0.282)
-0.1124
(0.261)
-0.1076
(0.264)
-0.0738
(0.284)
-0.0343
(0.260)
0.0269
(0.244)
-0.0359
(0.229)
-0.0642
(0.233)
-0.0614
(0.233)
-0.0355
(0.220)
0.0910
(0.350)
0.0010
(0.330)
-0.0075
(0.280)
0.0189
(0.018)
0.0083
(0.020)
Low GDP * Reform
High Unemployment * Reform
Low Education * Reform
South * Reform
Unemployment
0.0213
(0.063)
GDP
Num. Professors
Observations
Num. of departments
R2
4.6267*
(2.668)
4.6746*
(2.680)
4.5622*
(2.691)
4.5806*
(2.690)
4.5633*
(2.681)
5.7989**
(2.544)
5.4819**
(2.711)
11280
564
0.021
11280
564
0.021
11280
564
0.022
11280
564
0.022
11280
564
0.022
9588
564
0.017
6768
564
0.014
Reform is a dummy variable that equals zero for the pre-reform years and one for post-reform years; here Reform takes
missing values for 2000, the year of the implementation, which we treat as a year of transition from one regime to the
next. Low Readership, Low GDP, Low Education, High Unemployment are dummy variables that indicate whether
the department’s university is located in a province characterized respectively by below-median newspaper readership
in 2001-2002, below-median GDP per capita in 1998, below-median level of education in 2001, above-median rate of
unemployment in 1998, while South indicates whether the department’s university is located in the South of the country.
Num. Professors indicates the number of faculty in the department in a given year (in hundreds). Unemployment, and
GDP indicate respectively the rate of unemployment and the level of GDP per capita (in thousands) in the province
where the university is located in each given year. All regressions include department/university fixed effects and year
fixed effects. Standard errors clustered at the province level in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
39
40
0.144
(0.320)
Yes
Yes
Yes
No
No
No
No
11,844
564
0.026
0.626**
(0.016)
0.636**
(0.019)
0.021
(0.889)
0.146
(0.326)
Yes
Yes
No
No
No
No
No
11,844
564
0.024
0.553***
(0.003)
0.660***
(0.001)
0.215
(0.151)
0.191*
(0.099)
Yes
No
No
No
No
No
No
11,844
564
0.022
[avg(01-08)-avg(95-99)] * Low Readership
[avg(01-08)-avg(90-99)] * Low Readership
[avg(95-99)-avg(90-94)] * Low Readership
[avg(97-99)-avg(94-96)] * Low Readership
Number of Professors
Year Dummies * Low GDP
Year Dummies * High Unemployment
Year Dummies * Low Education
Year Dummies * South
Unemployment
GDP
Observations
Number of Departments
R-squared
(5)
(6)
(7)
Yes
Yes
Yes
Yes
No
No
No
11,844
564
0.026
0.143
(0.377)
0.014
(0.933)
Yes
Yes
Yes
Yes
Yes
No
No
11,844
564
0.027
0.148
(0.404)
-0.006
(0.974)
Yes
Yes
Yes
Yes
Yes
Yes
No
10,152
564
0.021
0.132
(0.431)
-0.018
(0.922)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
7,332
564
0.017
0.164
(0.314)
0.062
(0.410)
0.655** 0.655** 0.647** 0.591**
(0.020) (0.030) (0.030) (0.040)
0.648** 0.658** 0.656** 0.560**
(0.017) (0.021) (0.021) (0.029)
0.611** 0.622** 0.618** 0.643**
(0.026) (0.033) (0.034) (0.038)
(4)
[avg()-avg()] * Low Readership represents the difference in the average coefficient of the interaction term between year dummies (for the
years in parentheses) and a dummy for departments located in provinces with below-median level of newspaper readership. We consider
2000, the year the reform was implemented, as missing. In the first row we compare the five years immediately before the reform (1995-99)
to the previous five years (1990-94); in the second row we compare the five years after the reform (2001-05) to the five years prior to the
reform (1995-99); in the third row we compare all years after the reform (2001-08) to the five years before the reform (1995-99); in the
fourth row we compare all years after the reform (2001-08) to the ten years prior to it (1990-99). Moving from column 1 to column 7
we gradually include the following controls: number of professors in the department in each year; interaction between year dummies and
dummies for the department being located in a province with below-median GDP per capita in 1998, with below-median level of education
in 2001, with above-median rate of unemployment in 1998, in the South of the country, the yearly rate of unemployment and the level of
GDP per capita in the province where the department is located. In all regressions we include department/university fixed effects and year
fixed effects and cluster standard errors by province. P-values in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
0.026
(0.863)
0.637**
(0.020)
0.624**
(0.015)
0.587**
(0.024)
0.588**
(0.025)
0.525***
(0.004)
[avg(01-05)-avg(95-99)] * Low Readership
(3)
(2)
(1)
Dependent variable: Share Index (S)
TABLE 9: D ECENTRALIZATION AND THE EVOLUTION OF FAMILISM , 1988-2008
(Y EAR D UMMIES AND D UMMY FOR L OW N EWSPAPER R EADERSHIP )
41
0.009*
(0.064)
-0.005
(0.246)
0.001
(0.895)
0.009*
(0.051)
-0.002
(0.697)
0.000
(0.924)
[avg(01-08)-avg(90-99)] * Low Readership
[avg(95-99)-avg(90-94)] * Low Readership
[avg(97-99)-avg(94-96)] * Low Readership
11,844
564
0.005
11,844
564
0.006
11,844
564
0.007
Yes
Yes
Yes
No
No
No
No
0.006
(0.904)
-0.005
(0.248)
0.009*
(0.060)
0.012**
(0.017)
0.011**
(0.022)
(3)
(5)
11,844
564
0.008
Yes
Yes
Yes
Yes
No
No
No
0.001
(0.794)
-0.005
(0.275)
0.010**
(0.049)
11,844
564
0.010
Yes
Yes
Yes
Yes
Yes
No
No
0.000
(0.966)
-0.007
(0.166)
0.010*
(0.065)
0.013** 0.014**
(0.018) (0.012)
0.012** 0.012**
(0.029) (0.024)
(4)
10,152
564
0.008
Yes
Yes
Yes
Yes
Yes
Yes
No
0.000
(0.966)
-0.007
(0.177)
0.010*
(0.068)
0.014**
(0.012)
0.012**
(0.023)
(6)
7,332
564
0.008
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.003
(0.489)
0.002
(0.427)
0.012**
(0.026)
0.011**
(0.018)
0.012**
(0.029)
(7)
[avg()-avg()] * Low Readership represents the difference in the average coefficient of the interaction term between year dummies (for the
years in parentheses) and a dummy for departments located in provinces with below-median level of newspaper readership. We consider
2000, the year the reform was implemented, as missing. In the first row we compare the five years immediately before the reform (1995-99)
to the previous five years (1990-94); in the second row we compare the five years after the reform (2001-05) to the five years prior to the
reform (1995-99); in the third row we compare all years after the reform (2001-08) to the five years before the reform (1995-99); in the
fourth row we compare all years after the reform (2001-08) to the ten years prior to it (1990-99). Moving from column 1 to column 7
we gradually include the following controls: number of professors in the department in each year; interaction between year dummies and
dummies for the department being located in a province with below-median GDP per capita in 1998, with below-median level of education
in 2001, with above-median rate of unemployment in 1998, in the South of the country, the yearly rate of unemployment and the level of
GDP per capita in the province where the department is located. In all regressions we include department/university fixed effects and year
fixed effects and cluster standard errors by province. P-values in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
Observations
Number of Departments
R-squared
Yes
Yes
No
No
No
No
No
0.012**
(0.020)
0.010**
(0.021)
[avg(01-08)-avg(95-99)] * Low Readership
Yes
No
No
No
No
No
No
0.011**
(0.024)
0.010**
(0.022)
[avg(01-05)-avg(95-99)] * Low Readership
Number of Professors
Year Dummies * Low GDP
Year Dummies * High Unemployment
Year Dummies * Low Education
Year Dummies * South
Unemployment
GDP
(2)
(1)
Dependent variable: Concentration Index (C)
TABLE 10: D ECENTRALIZATION AND THE EVOLUTION OF FAMILISM , 1988-2008
(Y EAR D UMMIES AND D UMMY FOR L OW N EWSPAPER R EADERSHIP )
42
0.007
R-squared
0.008
4,000
200
0.007
3,400
200
Others
0.010
2,400
200
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.006
7,280
364
Yes
No
No
No
No
No
No
0.013 0.011**
(0.008) (0.005)
HS
Others
Others
0.007
7,280
364
Yes
Yes
Yes
Yes
Yes
No
No
0.006
6,188
364
Yes
Yes
Yes
Yes
Yes
Yes
No
0.008
4,368
364
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.011* 0.011* 0.014**
(0.006) (0.006) (0.006)
Others
Reform is a dummy variable that equals zero for the pre-reform years and one for post-reform years; it takes missing values for 2000, the year
of the implementation, which we treat as a year of transition from one regime to the next. Low Readership is a dummy variable that indicates
whether the department’s university is located in a province characterized respectively by below-median newspaper readership in 2001-2002.
All other controls are described in previous tables. HS denotes the sub-sample of departments in the hard sciences which include: biology,
chemistry, physics’, geo-sciences, mathematics and computer science (the latter two are considered as part of the same discipline in the
Italian system). Others indicates the sub-sample of departments in all the other disciplines including medicine and veterinary, humanities,
law, social sciences, and engineering. All regressions include department/university fixed effects and year fixed effects. Standard errors
clustered at the province level in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
4,000
200
Observations
Number of Departments
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
No
No
No
No
No
No
0.010
(0.009)
0.008
(0.009)
0.006
(0.007)
Number of Professors
Low GDP * Reform
High Unemployment GDP * Reform
Low Education * Reform
South * Reform
Unemployment
GDP
Readership * Reform
HS
HS
HS
Dependent variable: Concentration Index (C)
TABLE 11: D ECENTRALIZATION AND THE EVOLUTION OF FAMILISM , 1988-2008
(H ARD S CIENCES VS . OTHER D ISCIPLINES )
43
HS
4,000
200
0.015
Observations
Number of Departments
R-squared
0.020
4,000
200
Yes
Yes
Yes
Yes
Yes
No
No
0.021
3,400
200
Yes
Yes
Yes
Yes
Yes
Yes
No
0.746
(0.448)
HS
0.023
2,400
200
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.760*
(0.393)
HS
Others
Others
0.026
7,280
364
Yes
No
No
No
No
No
No
0.029
7,280
364
Yes
Yes
Yes
Yes
Yes
No
No
0.023
6,188
364
Yes
Yes
Yes
Yes
Yes
Yes
No
0.777*** 0.677** 0.611*
(0.246)
(0.331) (0.335)
Others
0.018
4,368
364
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.591
(0.352)
Others
Reform is a dummy variable that equals zero for the pre-reform years and one for post-reform years; it takes missing values for 2000, the year
of the implementation, which we treat as a year of transition from one regime to the next. Low Readership is a dummy variable that indicates
whether the department’s university is located in a province characterized respectively by below-median newspaper readership in 2001-2002.
All other controls are described in previous tables. HS denotes the sub-sample of departments in the hard sciences which include: biology,
chemistry, physics’, geo-sciences, mathematics and computer science (the latter two are considered as part of the same discipline in the
Italian system). Others indicates the sub-sample of departments in all the other disciplines including medicine and veterinary, humanities,
law, social sciences, and engineering. All regressions include department/university fixed effects and year fixed effects. Standard errors
clustered at the province level in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
Yes
No
No
No
No
No
No
0.602** 0.729
(0.296) (0.436)
Number of Professors
Low GDP * Reform
High Unemployment GDP * Reform
Low Education * Reform
South * Reform
Unemployment
GDP
Readership * Reform
HS
Dependent variable: Share Index (S)
TABLE 12: D ECENTRALIZATION AND THE EVOLUTION OF FAMILISM , 1988-2008
(H ARD S CIENCES VS . OTHER D ISCIPLINES )
TABLE 13: FAMILISM
AND
R ESEARCH P ERFORMANCE (2001-2003)
Dependent variable: CIVR Research Performance Score
(1)
C (2001)
(2)
(3)
(4)
(5)
(6)
(7)
-0.1368
(0.0859)
-0.1619**
(0.0804)
C (2002)
-0.1608**
(0.0771)
C (2003)
-0.1733**
(0.0759)
C (2001-2003)
-0.0046***
(0.00126)
S (2001)
-0.0047***
(0.00127)
S (2002)
-0.0044***
(0.00134)
S (2003)
-0.0046***
(0.00132)
S (2001-2003)
Observations
R-squared
(8)
492
491
491
491
493
493
493
493
0.311
0.314
0.314
0.315
0.319
0.321
0.318
0.319
The table reports the results of a set of cross-sectional regressions of the CIVR research performance score for the years 20012003 on the concentration index (columns 1 to 4) and the share index (columns 5 to 8) separately for each year (columns 1 to
3 and 5 to 7) and averaged over the entire three-year period (columns 4 and 8). The unit of observation is a department within
a university. All regressions include area fixed effects and the number of faculty in the department in the corresponding year,
or the average over the three years. Robust standard errors clustered by university are reported in parentheses. p<0.1.***
p<0.01, ** p<0.05, * p<0.1.
44
45
D(AsP)
D(AsP)
M(FP)
M(AP)
L(FP)
L(AsP)
N(AP)
N(FP)
H(FP)
H(FP)
O(AsP)
O(FP)
H(AsP)
D(FP)
Y(AP)
Z(FP)
Q(AP)
P(AsP)
R(FP)
R(FP)
Q(FP)
P(FP)
J(FP)
J(AsP)
I(FP)
I(AsP)
I(FP)
E(FP)
E(AsP)
B(FP)
E(AsP)
B(FP)
B(FP)
B(AsP)
B(FP)
K(AsP)
K(FP)
F(FP)
F(FP)
C(FP)
C(FP)
D EPARTMENT OF E CONOMICS OF THE U NIVERSITY OF BARI (2007)
The figure summarizes the network of family connections between the members of the Department of Economics of the
University of Bari as of 2007 (source: Perotti (2008)). Each cell indicates a faculty member represented by the last name (e.g.
A, B, etc.) and, in parentheses, the corresponding academic position (FP for full professor, AsP for associate professor and
AP for assistant professor). Each dashed rectangle includes all faculty members belonging to the same family with vertical
and diagonal thin lines connecting parents to children, horizontal thin lines connecting siblings to each other, and horizontal
thick lines connecting spouses to each other. The shaded cells indicate those professors that, despite being related to other
faculty members, do not share with them a common last name. These cases of relatedness are not captured by our homonymy
indices.
G(FP)
G(AsP)
G(FP)
D(FP)
D(FP)
D(FP)
D(FP)
A(AsP)
A(FP)
X (FP)
CONNECTIONS IN THE
A(AsP)
A (FP)
F IGURE 1: N ETWORK OF OF FAMILY
F IGURE 2: R EADERSHIP OF NON - SPORT DAILY NEWSPAPERS IN I TALIAN PROVINCES
(2001-2002)
The map summarizes the distribution of newspaper readership in Italian provinces
for the years 2001-2002. The light grey areas represent provinces characterized
by low (below-median) newspaper readership, while the dark grey ones represent
provinces with high (above-median) newspaper readership. White areas represent
provinces where no university is located, and which are hence not relevant for our
analysis.
46
F IGURE 3: E VOLUTION OF FAMILISM BEFORE AND AFTER
DECENTRALIZATION
IN AREAS WITH HIGH AND LOW CIVIC CAPITAL :
C INDEX
The figure illustrates the evolution of the concentration index C between the years 1988 and 2008 separately for departments in universities located in high-readership (red) and low-readership (blue) provinces.
The red line indicates the implementation of the reform that decentralized recruitment of professors from
the national to the university level.
47
F IGURE 4: E VOLUTION OF FAMILISM BEFORE AND AFTER
DECENTRALIZATION
IN AREAS WITH HIGH AND LOW CIVIC CAPITAL :
S INDEX
The figure illustrates the evolution of the concentration index S between the years 1988 and 2008 separately for departments in universities located in high-readership (red) and low-readership (blue) provinces.
The red line indicates the implementation of the reform that decentralized recruitment of professors from
the national to the university level.
48
F IGURE 5: E VOLUTION OF FAMILISM BEFORE AND AFTER THE REFORM :
COEFFICIENTS OF THE INTERACTION BETWEEN YEAR DUMMIES AND LOW
READERSHIP DUMMY
(C INDEX )
The figure represents the estimated coefficients ( + and - two standard errors) of the interaction terms
between each year’s dummy and a dummy variable for below-median newspaper readership. The estimates are based on a regression in which the concentration index C is the dependent variable. The
decentralization of professors’ recruitment was implemented in the year 2000.
49
M ATERIAL FOR O NLINE A PPENDIX
F IGURE A.1: D ISTRIBUTION
OF CONCENTRATION INDEX AND SHARE INDEX
S
.15
0
0
.05
2
.1
4
Density
6
.2
.25
8
C
-0.3
0
.3
.6
.9
1.2
1.5
1.8
-15 -10
-5
0
5
10
15
20
25
The graphs depict the kernel density of the concentration index C (left panel) and of the share index S
(right panel) for the entire balanced panel of 564 departments between 1988 and 2008.
50
F IGURE A.2: E VOLUTION OF FAMILISM BEFORE AND AFTER
IN AREAS WITH HIGH AND LOW CIVIC CAPITAL
DECENTRALIZATION
(CDEP INDEX )
The figure illustrates the evolution of the concentration index CDEP between the years 1988 and 2008
separately for departments in universities located in high-readership (red) and low-readership (blue)
provinces. The red line depicts the implementation of the reform that decentralized recruitment of professors from the national to the university level.
51
F IGURE A.3: E VOLUTION OF FAMILISM BEFORE AND AFTER
IN AREAS WITH HIGH AND LOW CIVIC CAPITAL
DECENTRALIZATION
(CPOP INDEX )
The figure illustrates the evolution of the concentration index CPOP between the years 1988 and 2008
separately for departments in universities located in high-readership (red) and low-readership (blue)
provinces. The red line indicates the implementation of the reform that decentralized recruitment of
professors from the national to the university level.
52
F IGURE A.4: E VOLUTION OF FAMILISM BEFORE AND AFTER
IN AREAS WITH HIGH AND LOW CIVIC CAPITAL
DECENTRALIZATION
(SDEP INDEX )
The figure illustrates the evolution of the concentration index SDEP between the years 1988 and 2008
separately for departments in universities located in high-readership (red) and low-readership (blue)
provinces. The red line indicates the implementation of the reform that decentralized recruitment of
professors from the national to the university level.
53
F IGURE A.5: E VOLUTION OF FAMILISM BEFORE AND AFTER
IN AREAS WITH HIGH AND LOW CIVIC CAPITAL
DECENTRALIZATION
(SPOP INDEX )
The figure illustrates the evolution of the concentration index SPOP between the years 1988 and 2008
separately for departments in universities located in high-readership (red) and low-readership (blue)
provinces. The red line indicates the implementation of the reform that decentralized recruitment of
professors from the national to the university level.
54
F IGURE A.6: A N ILLUSTRATION
OF THE POSSIBLE UNINTENDED EFFECTS OF
DETRENDING
55
56
0.016
R-squared
0.016
11,167
564
0.014
9,491
564
Yes
Yes
Yes
Yes
Yes
Yes
No
0.013
6,702
564
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.011**
(0.005)
C
0.017
11,165
564
Yes
No
No
No
No
No
No
0.604***
(0.195)
S
0.017
11,165
564
Yes
Yes
Yes
Yes
Yes
No
No
0.605**
(0.260)
S
0.014
9,482
564
Yes
Yes
Yes
Yes
Yes
Yes
No
0.572**
(0.255)
S
0.013
6,685
564
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.577**
(0.268)
S
Reform is a dummy variable that equals zero for the pre-reform years and one for post-reform years; it takes missing values for 2000, the year of
the implementation, which we treat as a year of transition from one regime to the next. Low Readership is a dummy variable that indicates whether
the department’s university is located in a province characterized by below-median newspaper readership in 2001-2002. All other controls are
described in previous tables. We exclude from the sample the observations in the top 1% of the distribution of C (columns 1-4) and S (column 5-8).
All regressions include department/university fixed effects and year fixed effects. Standard errors clustered at the province level in parentheses;
*** p<0.01, ** p<0.05, * p<0.1.
11,167
564
Yes
Yes
Yes
Yes
Yes
No
No
0.009**
(0.004)
0.009**
(0.005)
0.010***
(0.003)
Yes
No
No
No
No
No
No
C
C
C
Observations
Number of Departments
Number of Professors
Low GDP * Reform
High Unemployment GDP * Reform
Low Education * Reform
South * Reform
Unemployment
GDP
Low Readership * Reform
Dependent variable:
TABLE A.1: D ECENTRALIZATION AND THE EVOLUTION OF FAMILISM , 1988-2008
(R EFORM D UMMY, L OW R EADERSHIP D UMMY, E XCLUDING TOP 1% OF C/S)
57
0.005
R-squared
0.006
11,280
564
Yes
Yes
Yes
Yes
Yes
No
No
0.005
9,588
564
Yes
Yes
Yes
Yes
Yes
Yes
No
-0.017**
(0.007)
C
0.006
6,768
564
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-0.021***
(0.008)
C
S
S
S
0.023
11,280
564
Yes
No
No
No
No
No
No
0.024
11,280
564
Yes
Yes
Yes
Yes
Yes
No
No
0.019
9,588
564
Yes
Yes
Yes
Yes
Yes
Yes
No
0.018
6,768
564
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-1.005*** -1.112*** -1.077** -1.167***
(0.232)
(0.395)
(0.402)
(0.428)
S
Reform is a dummy variable that equals zero for the pre-reform years and one for post-reform years; it takes missing values for 2000, the year of the
implementation, which we treat as a year of transition from one regime to the next. Readership is a continuous variable indicating the readership
of non-sport newspapers (100s per 1,000 inhabitants) in 2001-2002 in the province where the university is located. All other controls are described
in previous tables. All regressions include department/university fixed effects and year fixed effects. Standard errors clustered at the province level
in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
11,280
564
Observations
Number of Departments
Yes
No
No
No
No
No
No
-0.018**
(0.007)
-0.014**
(0.005)
Readership * Reform
Number of Professors
Low GDP * Reform
High Unemployment GDP * Reform
Low Education * Reform
South * Reform
Unemployment
GDP
C
C
Dependent variable:
TABLE A.2: D ECENTRALIZATION AND THE EVOLUTION OF FAMILISM , 1988-2008
(R EFORM D UMMY AND N EWSPAPER R EADERSHIP AS C ONTINUOUS VARIABLE )