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CESifo, a Munich-based, globe-spanning economic research and policy advice institution
Venice Summer Institute 2014
Venice Summer Institute
July 2014
THE ECONOMICS AND “POLITICAL ECONOMY”
OF ENERGY SUBSIDIES
Organiser: Jon Strand
Workshop to be held on 21 – 22 July 2014 on the island of San Servolo in the Bay of Venice, Italy
CLEAN OR DIRTY ENERGY: EVIDENCE OF
CORRUPTION IN THE RENEWABLE ENERGY
SECTOR IN SOUTHERN ITALY
Caterina Gennaioli and Massimo Tavoni
CESifo GmbH • Poschingerstr. 5 • 81679 Munich, Germany
Tel.: +49 (0) 89 92 24 - 1410 • Fax: +49 (0) 89 92 24 - 1409
E-Mail: [email protected] • www.cesifo.org/venice
Clean or Dirty Energy: Evidence of Corruption in the Renewable Energy
Sector in Southern Italy.
Caterina Gennaioliú
Massimo Tavoni
†
Corresponding author: Caterina Gennaioli. Email Contact: [email protected], Address:
Grantham Research Institute on Climate Change and the Environment, London School of Economics and Political Science. Tower 3, Clements Inn Passage, London WC2A 2AZ, UK. Telephone:
+4402071075433
London School of Economics, Grantham Research Institute.
Fondazione Eni Enrico Mattei (FEEM) and Euro-Mediterranean Center on Climate Change (CMCC). Address:
Corso Magenta 63, Milan, Italy. Email: [email protected]
ú
†
1
Abstract
This paper studies the link between public policy and corruption, for the case
of renewable wind energy. The insights of a model of political influence by interest
groups are tested empirically using a panel data of provinces in the South of Italy
for the period 1990-2007. We find significant evidence that: i) criminal association
activity increased more in windy provinces, especially after the introduction of a
more favorable policy regime and, ii) the expansion of the wind energy sector
has been driven by both the wind level and the quality of political institutions
through their effect on criminal association. Our findings show that in the presence
of poor institutions, even well designed market-based policies can have an adverse
impact. The analysis is relevant for countries, which are characterized by heavy
bureacracies and weak institutions, like the South of Italy, and by large renewable
potential. JEL: D73, O13, H23. Keywords: Corruption, Government Subsidies,
Natural Resources, Renewable Energy.
2
"If you [an energy company] are interested in investing in Calabria, I can reassure you
that it will be like a highway without toll gates" wiretapping of an ongoing judicial inquiry
about wind power. (Source: Corriere della Sera, October 25th 2012)
"Put up the money or no pole [for windmills] will be driven in the ground" wiretapping
of a Mafia affiliate in Sicily in an ongoing judicial inquiry about wind power. (Source: La
Repubblica, June 10th 2013)
1
Introduction
The aim of this paper is to study and quantify the effect of public policy on corruption. As
an example of government intervention, we focus on the case of renewable energy. Renewable
energy provides an interesting and policy relevant case for a variety of reasons. The energy
sector is known to be both a target and a source of corruption, due to the characteristics of
the energy resources, the possibility of generating rents, and the key oversight role played
by the government. For example, power generation and transmission as well as oil and gas
are among the most bribery prone sectors, according to the Bribe Payers Index of Transparency International1 . International organizations such as the World Bank, which have
been involved in the financing of energy infrastructure in the developing world, have recognized the need to reduce corruption, often by trying to strengthen governance. As Mauro
(1998) suggests, the need for secrecy characterizing the corrupt deals implies that “it will
be easier to collect substantial bribes on large infrastructure projects or highly sophisticated
defense equipment than on textbooks or teachers’ salaries”. Complicated regulations and
public spending are among the major factors that can promote corruption (Tanzi (1998))
and both elements characterize renewable energy projects.
Renewable energy is also extremely policy relevant. Renewable sources such as wind and
solar have been growing incredibly fast in recent years, especially in developed economies
and largely spurred by public support schemes aimed at promoting low carbon energy alternatives. Developing regions like Africa host huge potential for renewables, which could be
harvested in the near future to alleviate energy poverty and enable growth, two key com1
http://bpi.transparency.org/bpi2011/
1
ponents of the U.N. Sustainable Energy For All initiative2 . Moreover, policy instruments
that promote market flexibility in the regulation of greenhouse gases have incentivized the
trade of emission reduction credits between developed and developing countries (through the
so-called Clean Development Mechanisms -CDM- of the Kyoto protocol). Renewable energy
now represents the largest share of the CDM projects in the pipeline. Overall, renewable
energy provides an important case for testing whether public incentives fuel rent-seeeking
and corruption in this sector.
Anecdotal evidence in Europe and elsewhere suggests the diffusion of corruption practices
related to public incentives for the renewable energy sector. Several official inquiries made
by the Italian police have been made public, and have led to the arrest of managers and
local politicians who allegedly used corrupt practices and bribes in order to obtain licenses to
build wind farms. For example, according to a famous inquiry called “P3”, the entrepreneur
Flavio Carboni and a former politician Pasquale Lombardi set up a criminal association
able to collect private funds from other entrepreneurs to pay bribes in exchange for wind
farm permits in the region of Sardinia3 . Similar scandals have occurred in Spain, where 19
persons were arrested in 2009 with charges of corruption in the wind sector. In the U.S.,
the bankruptcy of the solar power manufacturing firm Solyndra has led to a controversy
over the potential influence of the Department of Energy on the loan guarantee the firm was
granted. The aforementioned CDM schemes have been shown to not represent real emission
reductions, mostly as a result of perverse incentives and the quality of institutions (Victor
(2011)). Many countries are now evaluating the public support policies which have been
implemented over the past several years with the aim of promoting renewables. In most
cases, ex post policy assessment has focused on issues of efficiency and effectiveness, but
has essentially disregarded the role of the main political economy factors at play. To our
knowledge, this is the first attempt to study whether public incentives to renewable energy
resources could lead to an increase in illegal activities.
The main contribution of the paper is to understand whether the presence of a renewable
natural resource, such as wind energy, creates scope for rent seeking practices and corruption
when public incentives make it profitable to harvest the renewable potential. We do so by
first presenting a model of political influence by interest groups, which yields predictions
2
3
http://www.sustainableenergyforall.org/
http://www.repubblica.it/cronaca/2010/07/08/news/arrestato_flavio_carboni-5471098/
2
on the relation between corruption, renewable resources, and public support policies. These
insights are then tested on a panel dataset of provinces in the South of Italy for the period
1990-2007. In the analysis we can exploit the presence of the renewable energy resource
(e.g. wind potential) as inducing an exogenous variation in the expected rent opportunities.
The empirical analysis provides strong evidence that supports our model, establishing that
the expectations of high public incentives in the wind energy sector have fueled corruption,
measured by the level of criminal association activity. Our main findings are that: i) criminal
association activity increased more in high-wind provinces, especially after the introduction
of a more favorable public policy regime and, ii) the expansion of the wind energy sector has
been driven by both the wind level and the quality of political institutions, through their
effect on criminal association. In particular, comparing provinces with a similar (low) quality
of institutions, we find that the development of the wind sector has been higher in provinces
where economic agents and bureaucrats have engaged more in criminal association activity,
due to a high level of wind and of associated expected profits. The magnitude of the effect
is significant: for an average wind park (of 10 MW) installed after 1999, and which receives
about 1.5 Million euro per year in public support, the number of criminal association offenses
has increased by 6% in the windy provinces with respect to the less windy ones.
Overall, the paper points out that even well designed market-based policies can have an
adverse impact where institutions are poorly functioning. This effect would be greater in
places with high resource potential. This has important normative implications especially for
countries, which are characterized by abundant renewable resources and weak institutions,
and thus are more susceptible to the private exploitation of public incentives.
Related Literature
The paper is related to the literature on corruption, which has shown
how highly regulated environments that render bureaucrats more powerful favor the spread
of corruption practices (Shleifer and Vishny (1993), Tanzi (1998))4 . Several studies have
found that corruption is more detrimental to economic growth than taxation (Shleifer and
Vishny (1993), Fisman and Svensson (2006)), given the uncertainty and secrecy required by
the bribing process and the fact that corrupt deals cannot be enforced in courts. Moreover,
corrupt officials might slow down the administrative process in order to collect more bribes
4
Olken and Pande (2012), Sequeira (2012) and Svensson (2005) provide a comprehensive review of the literature
on corruption.
3
(Myrdal (1968), Kaufman and Wey (1998)). Although we cannot analyze the effect of
corruption on the economic efficiency of the renewable energy projects, we contribute to this
literature by finding evidence of corruption in a heavily regulated sector and showing that
more corrupt provinces have actually attracted a higher number of wind energy projects.
More specifically, a large literature has focused on the effect of public spending on political and economic outcomes. Tanzi and Davoodi (1997) present cross country evidence
showing a positive correlation between public investment and corruption. Hessami (2010)
detects a positive relationship between the perceived level of corruption and the share of
spending on health and environmental protection. Finally, Lapatinas et al. (2011) develop
a theory to study the relationship between environmental policy and corruption. Using an
overlapping generation model with citizens and politicians, they show that corrupt politicians cause increased tax evasion which leads to a reduction of total public funds and thus,
of environmental protection activities. Although suggestive, the evidence cannot establish
the direction of causality. At the micro level, Gennaioli and Onorato (2010) analyze the link
between public spending and organized crime activity in the Italian context, considering
reconstruction funds in the aftermath of the earthquake in the Umbria and Marche regions
as an exogenous increase in public expenditure. Using an instrumental variable approach,
they find that an unexpected increase in capital expenses per capita raises the incidence of
the number of Mafia-related crimes. In the authors’ view, organized crime acts as an entrepreneur ready to move wherever economic opportunities arise, exploiting them to promote
illegal activity.
Our paper takes a different perspective; the presence of the natural resource, when supported by public policy, attracts criminal appetites and favors the formation of criminal
associations. However, the actual investments in the sector are not exogenous but driven by
the presence of a well established criminal association between entrepreneurs and politicians
able to influence the authorization process and thereby receive significant amounts of public
incentives. In this context, the level of wind capacity installed is expected to be determined
by the level of criminal association activity. A similar mechanism is described in a recent
work by Barone and Narciso (2011), showing that criminal organizations may distort the allocation of public investment subsides toward their areas of influence. Focusing on the effect
of a raise in the expected rents from wind energy due to a government intervention, our study
4
is also related to a number of papers that have analyzed the impact of natural resource rents
on corruption, political institutions and state stability (Bhattacharyya and Hodler (2010);
Arezki and Bruckner (2011); Vincente (2010)). Vincente (2010) for example, analyzes the
effects of an oil discovery announcement in the Island of Sao Tome and Principe. The author
finds that the announcement increased the value of being in power for politicians and this
in turn created scope for resource misallocation, as vote-buying. Compared to Vincente,
we are interested in the effects of a government intervention supporting renewables on the
behavior of economic and political agents, namely the entrepreneurs operating in the wind
energy sector and the local bureaucrats who grant authorization permits. In this sense our
paper is somewhat related to the literature on the effect of political connections (Fisman
(2001), Fisman et al. (2012)), which studies how firms take advantage of being connected to
a politician by circumventing regulation and securing permits, for example. To our knowledge, our paper is the first which is able to quantify the impact of renewable energy subsidies
on corruption.
The paper proceeds as follows. In the next section we introduce a simple theoretical
framework which provides testable implications on the relationship between level of wind,
corruption and the development of the wind sector. Section 3 describes the data and the
institutional background. In Section 4 we outline the empirical strategy to test the model,
and we present the empirical results. The final section concludes.
2
A Model of Corruption
The following model is linked to the broad literature on political influence by interest groups
and, in particular, it builds on the theoretical framework developed by Dal Bo et al. (2006).
While in their model groups can influence policies both through bribes (plata) and threat
of violence (plomo), we only consider bribes as a form of influence. Dal Bo et al. derive
predictions on the quality of public officials, while here we make predictions on the equilibrium level of corruption and the number of active entrepreneurs as a function of windiness
and institutional quality. Although the model is simple, it provides us with several testable
hypotheses which we assess using empirical evidence for Italy.
The economy is divided in N administrative districts called provinces. Each province is
5
populated by a politician and an exogenously given number n of individuals, characterized
by a uniformly distributed ability parameter –, with U (0, 1). We consider a two stage game;
in the first stage, individuals decide whether investing in wind power generation by building
a wind farm, or carrying on their previous activity. If they do not invest in wind, they can
earn a wage equal to their ability –. In order to invest, entrepreneurs in the wind energy
sector have to ask the politician for a permit. In the second stage, they decide whether to
pay the politician a bribe, b, so as to increase the probability of obtaining the permit. In
this case the entrepreneur bears a cost of bribing, defined as ⁄Â(b). Â() is an increasing
function of b and the parameter ⁄ captures institutional factors that might affect the cost of
paying a bribe, such as the level of effort needed to keep corruption secret, which negatively
depends on the degree of social acceptance of bribing (it can also capture the quality of
law enforcement, as suggested by Dal Bo et al.). In line with this interpretation, ⁄ can be
interpreted as a measure of the level of social capital.
The probability to obtain a permit in case of bribing is p, while in case of not bribing
is q, with p > q. All other things being equal, if the politician receives a bribe, she will
put more effort in the bureaucratic process, increasing the probability of the permit being
granted. Once the entrepreneur obtains the permit, she can make the investment and build
a wind farm. The return to investment is defined as I(w, F ), and depends on the revenues
associated with the energy produced (increasing in wind w) on one side, and by the public
incentives on the other, F . The public support incentive can either remunerate the actual
electricity generated (for example through feed in tariff or a tradeable certificate system) or
subsidize the building of the wind farm (irrespective of how much it generates). In the first
case, the introduction of a more favorable regime (increase in F ) leads to a higher Iw (), ’w.
A regime switch from the second to the first support scheme would also make revenues more
dependent on the wind level, and thus provide additional incentives to build in the windiest
sites. The return to investment does not depend on the ability parameter since the wind
sector has traditionally been characterized by low levels of competition and revenues were
mainly driven by public incentives.
In case of bribing, the entrepreneur gets away with probability c, while with probability
(1 ≠ c) she is caught by the police and gets a payoff equal to zero. As in Dal Bo et al.,
the utility of the politician linearly depends on money and on a moral cost, m, of being
6
corrupted. For simplicity, we normalize the politician’s wage to zero. Let the number of
available permits in each province be greater than n, such that entrepreneurs do not compete
for permits; since we focus our attention on the first period after the introduction of wind
power generation in Italy, we do not expect the limit in the number of permissions to be
binding. We solve the model for one province, so we can ignore index i in the notation. The
following results can obviously be generalized for all provinces.
2.1
Equilibrium
We solve the model backward, starting from the second stage when an entrepreneur active
in the wind energy sector has to decide whether to bribe the politician or not. Then we go
back to the first stage, analyzing the entrepreneur’s decision to enter the renewable energy
sector. Following Dal Bo et al., we assume that the entrepreneur holds all the bargaining
power and makes a "take-it or leave-it" offer to the politician.
2.1.1
Equilibrium Bribes
The politician accepts the bribe whenever the payoff under bribing is higher than the payoff
without bribing, which is normalized to be equal to zero;
b≠mØ0
(1)
The entrepreneur chooses the level of the bribe, by solving the following problem:
M ax fi(b) © pc I(w, F ) ≠ ⁄Â(b)
b
s.t. b ≠ m Ø 0
(2)
Let bú denote the value of b that maximizes the profit of the entrepreneur under bribing, and
fi(bú ) be the optimal profit, with fi(bú ) = pc I(w, F ) ≠ ⁄Â(bú ). The entrepreneur, holding
all the bargaining power, will offer the minimum bribe which makes the politician accept it.
Let fi() © q I(w, F ), be the entrepreneur’s expected profit in case of not bribing. Then, the
equilibrium bribe can be characterized as follows:
7
Proposition 1 An entrepreneur who decides to corrupt the politician offers a bribe bú =
m. Therefore whenever fi(m) Ø fi(), we will observe bribing in the wind energy sector.
For any fi < fi, i.e. when the expected profit from bribing is lower than the one without
bribing, the entrepreneur will not offer the politician any bribe. Substituting for fi() and
fi(), the bribing condition can be rewritten as:
I(w, F ) Ø
where
⁄Â(m)
P OL
(3)
P OL © (pc≠q) is defined as an inverse measure of the quality of political institutions
in a given province.
P OL represents the difference in the probabilities of getting the
permit and not being caught; equivalently, it represents the net marginal return to bribing
compared to non-bribing option, in terms of probabilities. This measure captures the quality
of politicians or political institutions in general; the more a politician has been corrupted
in the past, intervening in the bureaucratic process, the more she will be able to increase
p compared to q, thanks to her connections and experience. Note that
P OL can also
be interpreted as a measure of the efficiency of the bureaucratic process. Recalling the
interpretation of ⁄, the following definition is introduced.
⁄Â(m)
Definition 1 Let
, be defined as a general index of the quality of institutions
P OL
( QI), where the numerator refers to its social dimension (i.e. social capital), while the
denominator measures its political component.
QI denotes the threshold level for the expected revenues in the wind sector, such that the
entrepreneur is indifferent between bribing and not bribing. Note that QI can be interpreted
as an inverse measure of the extent of corruption or the likelihood to observe bribing, since it
represents the set of possible values for which bribing is not profitable for the entrepreneur.
Intuitively, the lower the general quality of institutions in a certain province is, the more
likely it is to observe corrupted deals; if QI is sufficiently low, the net marginal return to
bribing increases and, as a consequence, the incentive to bribe rises for entrepreneurs active
in the wind energy sector. Summing up, taking into account both the political and the social
dimension of the QI index, active entrepreneurs have more incentive to bribe the politicians
in provinces where political institutions are badly functioning and bribing is socially tolerated
(lower social capital), such that less effort is required to keep illegal deals secret.
8
2.1.2
Entry Decision
Turning to the first stage, we analyze the entry decision. An entrepreneur will decide to enter
if the expected return to wind energy investment is higher than her reservation wage, which
is equal to her ability type. Let us focus on non-trivial solutions, studying the case where
P OL > 0. Taking into account the condition for bribing, the equilibrium is characterized,
distinguishing between two cases:
Lemma 1 (a) If I(w, F ) 1 QI; all entrepreneurs with ability type – 0 pc I(w, F ) ≠
⁄Â(m), enter the wind energy market and choose the bribing option. (b) If I(w, F ) < QI;
all ability types with – 0 q I(w, F ), enter the wind energy market and do not bribe.
According to the model, two contrasting types of markets for wind energy can emerge in
equilibrium; one in which all agents are corrupted, and the other where all agents behave
honestly. First notice that, in both types of markets, the ability type of active entrepreneurs
is increasing in the expected revenues of wind investment; as long as the return to investment
does not increase much compared to the one in the non-wind sector, only the lower ability
type will enter the renewable energy market. These low types would anyway achieve higher
profits in the wind sector, thanks to the amount of public incentives. Whether a province
will experience a corrupted wind market or not depends both on QI and the wind level.
The equilibrium outcome, described in Lemma (1), is intuitive; the level of wind, affecting
the returns to investment, leads economic agents to enter the wind energy market. Whether
this is correlated with an increase in bribing practices depends on institutional quality; if
the politician’s intervention significantly increases the probability to obtain the permission
and corruption is generally tolerated in the society, high wind is correlated with a higher
number of corrupted agents active in the wind energy market; if instead, without the direct
intervention of the politician, the entrepreneur faces almost the same probability to get a
permit and corruption is viewed poorly in the society, then a high wind level is correlated
with a higher number of honest agents active in the market. From now onwards, we focus
on the emergence of the corrupted type of market and study some related comparative
statics. Since in the empirical part only the Southern regions of Italy will be considered,
it is reasonable to assume that all provinces in our sample satisfy condition (a), due to a
similar, and relatively low, quality of institutions. Let corr be the extent of corruption in a
province (belonging to group (a)); since, according to the previous lemma, all entrepreneurs
9
entering the wind sector bribe the politician, we define the level of corruption as the number
of entrepreneurs active in the market.
Definition 2 Given lemma (1), the equilibrium level of corruption in a province belonging
to group (a), can be defined as:
corrú = F [pc I(w, F ) ≠ ⁄Â(m)]
(4)
where F is the pdf of –. Given the assumption on –’s distribution, we can simply rewrite
(4) as:
corrú = pc I(w, F ) ≠ ⁄Â(m)
(5)
Consistent with the intuition provided above, corruption in this equilibrium depends both
on the level of wind and the quality of institutions. In other words, taking the wind level as
constant, one should observe a higher level of corruption in provinces with worse institutional
quality, in both its social and political dimensions.
2.1.3
Comparative Statics
Some interesting comparative statics can be derived, in terms of wind level and the type of
policy in place.
Lemma 2 The equilibrium level of corruption is increasing both in the wind level and in
pc, which is inversely related to the quality of political institutions. Formally:
ˆcorrú
ˆcorrú
= pc Iw (w, F ) ,
= I(w, F ),
ˆw
ˆ (pc)
ˆcorrú
ˆcorrú
=
= Iw 1 0
ˆwˆpc
ˆpcˆw
(6)
(7)
Condition 7 shows the complementarity between windiness and the quality of political institutions, in determining the equilibrium level of corruption; considering two provinces with
the same wind level but with a different probability of obtaining the permit via bribing,
one should expect a higher level of corruption in the province with worse political institutions. On the other hand, comparing two provinces with similar chances of using bribes to
get permits, but a different level of wind, one should expect more corruption in the highwind province. The intuition behind these results is simple; since the marginal return to
10
bribing is increasing in the wind level, we should expect a higher number of entrepreneurs
entering in high-wind provinces and bribing the politician. The negative effect of windiness
will be stronger in provinces characterized by a lower political quality. In the same way, a
lower quality of political institutions increases the number of corrupted entrepreneurs in the
market, and it does so more in high-wind provinces that entail a larger margin of profit.
The next result provides interesting policy implications for the Italian case and elsewhere.
In principle, a policy promoting renewables can be designed in two ways: i) increase the
subsidy to build the wind farm, through a lump sum transfer as development funds (F1 )
or, ii) increase the remuneration of the policy based on the actual production, in our case
proxied by wind level, which can be interpreted as the introduction of a market-based type
of policy such as a feed in tariff or a tradeable certificate system (F2 ).
Lemma 3 Both an increase in the lump sum transfer and in the remuneration of the
ˆcorrú
ˆcorrú
electricity generated increase the extent of corruption, with: a)
= pc and b)
=
ˆF1
ˆF2
w pc.
If one looks at second order effects, the lemma generates an important result, pointing
out a different impact of the two types of policy on the level of corruption. In particular,
while an increase in the lump sum equally raises the level of corruption in all provinces with
similar probabilities of getting the permit through bribing, the introduction of the marketbased system leads to a greater increase in corruption in provinces characterized by a higher
wind level. The logic is the same as before; in general, higher amounts of public incentives
increase the return to wind investments, and lead a larger number of agents to enter the
market. However, if the quality of political institutions is low (as in the region analyzed in
the empirical section), all the entrants opt for the bribing option and, as a consequence, the
number of entrepreneurs involved in bribing becomes larger. Moreover, if the public incentive
becomes more responsive to the actual energy generated (i.e. to the wind level), this effect
becomes stronger in high wind provinces. This result shows that in the presence of poorly
functioning institutions, a market based policy might actually have a larger negative impact,
particularly where the greatest efficiency gains could be obtained, namely in provinces with
the highest energy potential.
After having derived conditions on the entry and bribing decision of the entrepreneurs,
we finally characterize the wind energy sector, analyzing the expected amount of investments
11
in each province. Also in this stage, we focus on provinces belonging to group (a) in Lemma
1.
Definition 3 The expected number of projects, E(P ), in a certain province, is defined as
the total number of active entrepreneurs in the market, weighted by the probability to get the
permit. In particular:
E(P ) = pc
ˆ
0
–
f (–)d–
(8)
Given that – © pc I(w, F ) ≠ ⁄Â(m) and f (–) = 1, and using the definition of corruption,
we obtain that::
E(P ) = pc [corr]
(9)
Expression 9 points out that the expansion of the wind energy sector is a positive linear
function of the level of corruption. In other words, marginal returns to corruption, in terms
of authorized wind plants and installed capacity, are positive and constant. Recalling the
complementarity between the wind level and the political institutions quality in determining
the level of corruption, the following result can be derived.
Proposition 2 The expected number of wind energy projects in a certain province positively depends on its level of corruption. In particular, i) given two provinces with the same
wind level, the expected number of wind energy projects will be higher in the province with
worse political institutions, and, ii) given two provinces with the same (low) quality of political institutions, the expansion of the wind energy sector will be greater in the windiest
province.
As we have seen before, an increase in the returns to bribing (due to a change in the
wind level, the policies or the quality of political institutions), leads a higher number of
entrepreneurs to enter the wind energy sector and bribe the politicians. The level of corruption in equilibrium will be higher and, as a consequence, the actual number of wind projects
put in place will increase as well. Considering provinces where the quality of institutions is
sufficiently low and chances of obtaining permits with bribes are high, this section highlights
that not only the wind level but also the quality of institutions are crucial determinants of
the development of the wind energy sector, through their effect on corruption. This is due
to the fact that in our model, corruption helps to cut the red tape, eventually accelerating
12
the development of the wind sector. Though in principle corruption can also hamper the
development of an industry by imposing additional and unnecessary costs, the evidence accumulated in the official inquiries is consistent with the first hypothesis, especially given the
significant size of the public subsidies in the wind sector (see Section 3.1).
2.2
Discussion
Using a simple model of corruption we have analyzed the main factors which can fuel bribing
in the emerging market of wind energy. Given the data available, we cannot test all the model
predictions and we will mainly focus on the role of the wind level in determining corruption
and wind plant installations. Nonetheless, having data covering a sufficiently long time
period, we can analyze the effects of the transition between the two major policy regimes
implemented to promote wind energy. The main predictions of the theory which we take to
the data are the following:
i) Ceteris paribus, the windiest provinces are more likely to experience corruption.
ii) The increase in the remuneration of wind investments, due to the introduction of a
market-based policy regime, leads to an increase in the extent of corruption, especially in
high-wind provinces.
iii) The number of wind energy projects in a given province increases with the extent of
corruption.
As we have seen, the expansion of the wind energy sector is not only driven by the
level of wind but also by the functioning of social and political institutions. This suggests
that, comparing provinces with the same wind level, the number of wind projects should
be higher in provinces where a larger number of agents have been involved in corruption
practices, due to a poor quality of institutions. Among provinces with a similar (low) quality
of institutions, the expansion of the wind sector should be greater in places where criminal
association activity is more frequent due to a high level of wind and the associated expected
revenues.
13
3
Background and Data
3.1
Wind Power in Italy
The incentives schemes to renewables, including wind power, began in 1992, when a feed-in
tariff known as CIP 6 was introduced to support renewables and "assimilated sources"5 . The
feed-in tariff system managed to jump start investments which were important at a time of
shortage of available power capacity, but also uncovered several flaws.
In order to overcome these pitfalls, and following the liberalization of the Italian electricity
market, in 1999 a market-oriented mechanism based on tradable green certificates (known
as “Certificati Verdi”, CV) was implemented; this required power producers and importers
to have a minimum share of electricity generated by renewable sources. The quota was set
for the initial date of operation in 2001 at 2%, gradually increasing over time (it is now
7% ). Green certificates can be exchanged on either the Italian electricity market or via
bilateral contracts, and last for several years (12 and 15 years if issued before and after
2007 respectively). In the initial phase, an excess of demand pushed the prices of the green
certificates up, above 100Euro/MWh (see Table 1). This induced a sizable increase in the
supply of renewable power, mostly wind, hydro and biomass, so that supply started exceeding
the allocated quotas from 2006. In order to prevent prices from dropping too low, in 2008 the
government intervened and effectively turned the quota system back into a feed-in tariff one.
In addition to the CV incentives, revenues also accrue for electricity generation, thanks to
priority dispatch to the electricity market or alternatively to the option of selling electricity
at a minimum guaranteed price.
5
The terminology has allowed several other sources, including thermal co-generation, to be included among the
beneficiaries of the feed-in tariff, which has undermined the effectiveness of the policy in promoting traditionally
renewable energy. Though the exclusion of ‘assimilated’ sources from the incentives has been mandated in the
European directive 2001/77/CE, a series of waivers have allowed this practice to persist to date.
14
certificates up, above 100Euro/MWh (see Table A). This induced a sizable increase in the
supply of renewable power, mostly wind, hydro and biomass, so that supply started exceeding
the allocated quotas from 2006. In order to prevent prices from dropping too low, in 2008 the
government intervened and effectively turned the quota system back into a feed-in tariff one.
In addition to the CV incentives, revenues also accrue for electricity generation, thanks to
priority dispatchTable
to the
market market
or alternatively
to theand
option
of selling electricity
1. electricity
Green certificate
(source: GME
AEEG)
at a minimum guaranteed price.
average price (Euro/MWh)
overall cost (M Euros)
2003
98.9
243
2004
116.8
263
2005
130.9
332
2006
142.8
488
2007
99.0
306
2008
103.6
400
Table A: Green certificate market (source: GME and AEEG)
The financial incentive regime for investment in renewables in Italy appears to be advanThe financial incentive regime for investment in renewables in Italy appears to be advan-
tageous by
international
standards.
energy
authority
has estimated
the cost of
tageous
by international
standards.The
TheItalian
Italian energy
authority
has estimated
the cost of
The terminology
has allowed
several
including
thermal
to bethe
included
among theof passing
the greenbeneficiaries
certificate
trade
system
atother
400sources,
million
Euros
inco-generation,
2008, with
prospect
of the feed-in tariff, which has undermined the effectiveness of the policy in promoting traditionally
5
renewable energy. Though the exclusion of ‘assimilated’ sources from the incentives has been mandated in the
to 1 billion
in 2013.
In2001/77/CE,
addition,
regional
provincial
support
schemes
have also been put
European
directive
a series
of waiversand
have allowed
this practice
to resist to
date.
into place. The contribution of wind power has
increased significantly after the introduction
14
of the CV incentive system, and now contributes to almost 4% of of the national electricity
consumption. Virtually all the installed wind capacity is concentrated in the “Mezzogiorno”,
which hosts the largest wind potential (see Figure 1 below).
Figure 1. Map of wind resources in Italy (speed at 75m height, source CESI)
Figure 1. Map of wind resources in Italy (speed at 75m height, source CESI)
Figure 2. Distribution of wind installed capacity by province at the end of 2011 (source GSE)
15
The provinces of Foggia (FG), Benevento (BN) and Avellino (AV), which lie on the windy
ridge of the south Appennines, host roughly one third of the whole national capacity. These
sites, together with the ones in Sardinia, were also the first to develop, and are characterized
by higher utilization factors. More recently wind power has considerably expanded to several
provinces of Sicily and Calabria.
Within the provinces, these rents are concentrated in a subset of municipalities, often
of small size and located in areas with low population density. Thus, the royalties that
the wind parks can yield to the local authorities (either legally or illicitly) -in exchange for
the construction authorization- can be considerable, enough to induce corruption practices.
Until 2010, there were no official guidelines on the rules for determining such royalties, and
these were left to the discretion of local authorities, without any national harmonization. In
addition, region-wide energy plans have been introduced quite slowly, allowing for a quite
unregulated environment, which for example has only been partially able to account for
other factors such as the integration with the electricity grids. Though the magnitude of
the incentives is large, it is important to note that the wind sector still represents a small
fraction of the overall economic output. The share of wind profits over the total value added
of the economy does not exceed 2%, even in the most favourable provinces to wind.
For the sake of our analysis, we assume that the regime change that the green certificate
system brought about in 1999 (with effective use from 2001) can be considered the real turning point for policy. In the language of our model, this policy break has increased the return
to wind investment I(w, F ), and thus motivated more illicit activities. Although, as we have
seen in this section, policies supporting renewables were present in the country even before
the turn of the century, there are various reasons to support the idea that the green certificate
system represented a significant policy regime switch. First, the commitment to renewables
of the European Union strengthened markedly around that time, culminating in the RES
(Renewable Energy Support) directive which took effect in 2001 and set national indicative
targets for renewable energy production from individual member states for the years 2010
and 2020. This increased the certainty of the policy support schemes for renewables, also
providing a long term perspective in terms of quotas. Second, the liberalization of the Italian
electricity market and the increased dynamics of the international energy markets, with oil
prices starting to rise in 1999 and especially after 2001, provided an increased opportunity
16
to enter a sector which was traditionally monopolistic. Finally, the wind turbine technology
improved dramatically over time: the investment costs dropped considerably, from roughly
2000 to 1000 US$/kW. The reliability of the technology also increased, as it became better
suited to handle times of strong winds. Thus, the green certificate (CV) system marked a
clear change in terms of public support to the deployment of wind power, as testified by the
marked increase in installed capacity post 2000 (see Figure 5).
The questions of interest for us are, a) whether in general, there is a positive correlation
between the development of the wind energy sector and corruption and, b) if corruption
practices, fueled by the expectation of huge profits (mostly due to public incentives), are
partly responsible for the large expansion of the sector.
3.2
Data Description
We use a panel dataset for the period 1990-2007 with annual observations on 34 provinces
of Southern Italian regions6 . We make use of a data set compiled by the Italian National
Institute of Statistics (ISTAT, “Statistiche Giudiziarie Penali”) to measure criminal activity
in the country. To compute the extent of corruption we use two measures: a) criminal
association activity (CrimAssoc), representing the number of “Criminal Association” offenses
brought to justice by the five sectors of the police force, and b) total criminal association
(TCrimAssoc), the sum of offenses related to “Criminal Association” and “Mafia-related
Criminal Association”. Values are reported in terms of incidence per 100,000 inhabitants.
Criminal association activity represents a very good measure of the level of corruption in the
wind energy sector since, according to the Italian penal code, “it implies a sufficiently stable
organization of two or more individuals who agree on committing illegal activities”. This is
exactly the case of the local bureaucrat which systematically exchanges authorizations for
building wind farms for bribes from entrepreneurs. Actually most of the persons involved in
corruption activity in the wind energy sector have been charged with this type of offence7 .
As a robustness check, we make use of a third and somewhat unrelated measure of criminal
activity, the index of violent crime.
6
For sake of comparison, Italian provinces are similar in size to US counties. During the period analyzed, seven
new provinces were instituted in the South of Italy. We disregard changes to the 1990 province classification, and
attribute data on new provinces to those they originated from.
7
See the example reported in the Introduction.
17
The set of controls includes the log of real GDP per capita and the secondary school
enrollment at the provincial level as provided by ISTAT. These controls are widely used in
the crime literature and represent potential determinants of criminal behavior measuring the
expected earning and cost opportunities. In order to control for the impact of population
density on the level of criminal association, we conform to Bianchi et al. (2012) and include
the log of the resident population in the province among our controls. Finally we also include
the index of violent crime as a control to account for criminal attitudes in a given province.
This is computed on the basis of the offenses brought to justice by the five sectors of the
police force regarding the following crimes: massacre, homicides, infanticide, lesions, sexual
assaults, kidnapping, assassination attempts and theft.
The expected number of projects, E(P ), is proxied by the number of wind plants and the
total capacity installed; for this purpose, since official statistics at the provincial level are only
available from 2008 onwards, we rely on a dataset compiled by ANEV (National Association
for Wind Energy). This covers the totality of wind parks, and provides data on the location,
capacity, size, year of initial operation, and ownership. Wind level, w, is computed using
data coming from the Italian Wind Atlas , which provides the average wind level (at different
distances above sea level) per square kilometer for the whole Italian territory. In order to
assign each province to a certain class of wind, we construct our own measure of windiness.
In particular, we classify provinces according to their wind level8 , taking into account the
size of the province; considering the quartiles of the wind distribution, we divide the wind
measure in four classes, then we compute the fraction of total provincial area lying above
the third quartile, or 5.2 m/s (meters per second). For example the 8 windiest provinces are
the ones with more than 25% of their area above the third quartile of wind distribution, the
following 9 windiest provinces have between 17% and 25% of their area lying above the third
quartile of wind distribution, and so on. The level of windiness we assign to each provice
corresponds to the quota of the provincial area lying above 5.2 m/s multiplied by the average
windiness above the same threshold. The figure below reports the distribution of the Wind
Index across all the provinces in the sample.
8
Wind speed is computed at 25 meters above the sea level
18
Figure 2. Distribution of the Wind Index
3.3
Measurement Issues
TABLE B. Descriptive Statistics
A well known issue inVariables
crime-related literature regards theMean
problem of using counts of reported
CrimAssoc
2.277
crimes. This can lead
the underestimation of the true number
of committed crimes and bias
(1.775)
TCrimAssoc
3.034
the estimates if the explanatory variables of interest are
correlated with the level of under(2.342)
Violent crime Index
115.018
reporting. In our case, the bias in reporting originates (72.496)
from the police investigative activity:
Clear_Up
0.332
(0.115)
criminal association (N.
is Obs.=476)
a difficult crime to detect as most
times it does not involve a direct
Wind_Index
1.227
victim with the interest
to report to the police. Rather,2.236
it is usually the police that report
Wind Plants
(0.95)
(4.848)
this type of offence to
the judiciary
after an investigation
process. In this analysis our major
Capacity
(MW)
18.123
(54.356)
GDP_pc
9.490than the extent of investigation
concern is to measure
criminal association levels rather
activity by the police. Ehrlich (1996) extensively
School
Population
and proposed several remedies9 . We use one of
N. Obs.
(0.184)
0.814
discussed
the reporting bias in crime data
(0.12)
609323
them,
recently adopted by Fougere et al.
(544902.4)
612
(2009) and Bianchi Standard
et al. deviations
(2012),
which consists in exploiting the panel structure of the
in parentheses.
data including time and province fixed effects. This method is very effective since it takes
into account any systematic difference in police reporting activity between provinces (over
time) or between different times (across provinces). We also include province specific time
trends which allows for time varying, province specific bias in crime reporting by the police.
9
See also Levitt (1996), Fougere et al (2009), Gould et al. (2002)
19
40
Finally, we further address this issue using a measure for the efficiency of police activity,
namely the clear-up rate, defined as the ratio of crimes cleared up by the police over the
total number of reported crimes. Since the data are only available until 2003 we just present
some descriptive statistics.
4
Methodology and Results
The main hypothesis we want to test is whether the presence of a renewable natural resource,
namely wind energy, can favor the spread of corruption practices, especially in the presence
of public policies like subsidies. A related question is whether the increase in the expected
returns of investments in wind energy drive economic agents to increasingly engage in rentseeking activities. As we have seen, profits in the wind energy sector started to increase after
the introduction of the green certificate (CV) system, when a significant amount of public
incentives were granted; thus, we would expect an increase in corruption especially after that
period. But what are the effects of this increase in corruption on the actual expansion of the
wind energy sector? To answer this question, in the last part of the empirical exercise we
test the third prediction of our analytical model, namely the positive association between
the degree of corruption and the actual expansion of the wind sector. Summing up, the
causation chain we have in mind and are going to test is the following:
Wind Intensityæ Criminal Association Activity (Corruption)æ Wind Capacity Installed
We start analyzing the first link, and explore the second one in the last part of this
section.
Empirically addressing this set of issues is difficult since there may be confounding factors affecting the level of criminal association activity. As we have seen, the model gives
the wind level and the quality of institutions a central role in determining the extent of
corruption. We exploit the exogenous variation in wind level across provinces to address
our estimation in a robust way. First, considering all the provinces in our sample, we check
whether the wind level is positively associated with the level of criminal association activity,
especially after the policy break of 1999-2001. To further investigate this relationship, we
then focus on the 8 windiest provinces10 which represent the top quartile of our measure (i.e.
10
AV, BN, CA, CZ, FG, PZ, SS, TP
20
provinces with more than 25% of their area lying above the 5.2 m/s threshold). Employing a difference-in-difference strategy, we compare the windiest provinces in the area of the
South of Italy (treatment group) to all other provinces in the sample (control group). This
approach, however, implies a comparison between two highly heterogeneous groups. In order
to consider two similar groups we refine the analysis comparing the 8 windiest provinces to
the 11 neighboring provinces. Through the analysis we divide the time span into several
time windows, focusing on subperiods due to the dynamics of returns to wind energy, our
corruption-inducing variable. Specifically, returns to wind energy experienced a significant
increase after 1999.
As mentioned above, in order to identify the windiest provinces we use a very accurate
database to construct a measure of average windiness that takes into account the fraction
of total provincial area lying above the third quartile of the wind resource distribution. The
choice of the treatment group in the difference-in-difference exercise is not an obvious one,
since the wind threshold can be set at different levels. However, as already noted, there is
a handful of provinces that by far hosts the greatest wind potential, and only very recently
this has begun to expand to other ones. In addition, since our analysis stops in 2007, the
restricted group of 8 provinces is the most meaningful group to look at. We do not have data
to measure the quality of institutions, but since this can be considered a persistent element,
we partly solve the omitted variable problem using province fixed effects.
4.1
Evidence on Criminal Association
We begin our empirical investigation by testing the model predictions i) and ii), namely
whether windier provinces are more likely to experience corruption, and whether the introduction of a new policy regime further increased corruption in windy provinces. In order to
assess the relation between wind and criminal association activity over time for all the 34
provinces in the sample, we first estimate the following equation (a):
CrimAssoci,t =
ÿ
–t≠t+3 W indi,t≠t+3
tœT
+ xi,t ” + “i trendi,t + ui + vt + Ái,t
(a)
Where CrimAssoci,t measures the number of charges made by police forces for criminal
association offenses in province i at time t, W indi,t≠t+3 is equal to the wind level (as measured
by our wind index) in province i between time t and t + 3, and zero otherwise, xit is a vector
21
of control variables at the province level, namely log of the real GDP per capita, secondary
school enrollment, the log of population resident in the province and the level of violent
crime, trendi,t is a province specific trend which accounts for time varying unobservable
factors, ui and vt are province and year fixed effects and ‘it is an observation specific error
term. The specification in (a) allows us to study the association between the wind resource
and criminal association activity throughout four time windows of 4 years each, 1991-1994,
1995-1998, 1999-2002, 2003-2007, and it is particularly useful to rule out the presence of
increasing trends in our dependent variables before 1999. Given our hypothesis, we expect the
coefficients of interest, {–t≠t+3 } with t > 1999 to be statistically higher than the coefficients
{–t≠t+3 } with t < 1999. For this purpose we present some tests of significance for the
difference in the coefficients before and after 1999.
Since we want to focus on the period following the approval of the Green Certificates
system (CV), we also split the post CV period (after 1999) into time intervals, and we
estimate the following equation (b):
CrimAssoci,t =
ÿ
—t≠t+j W indi,t≠t+j
t>1999
(b)
Similarly to (a), W indi,t≠t+j is equal to the wind level in province i between time t and
t + j, with t > 1999, and zero otherwise. We run two specifications of the equation: in the
first case (equation b1) j = 3, such that the post period is divided in two time windows,
1999-2002, 2003-2007 , while in the second case (equation b2) we set j = 1, considering four
time windows (1999-2000, 2001-2002, 2003-2004, 2005-2007). The coefficients of interest are
{—t }t>1999 .
Our empirical strategy is based on the identification assumption that there are no unob-
servable factors, correlated with both criminal association activity and wind level. Formally,
Cov(W indi,t≠t+j , Áit ) = 0, ’j, must hold. We are confident that this is the case, i.e. equations
(a) and (b) do not suffer from an endogeneity problem, given that the wind level in a certain
province is an exogenous factor. Moreover, once the province, the year fixed effects and the
province specific time trends are included, it is difficult to imagine any unobservable factor
correlated with both our dependent variable and the level of wind. In all specifications we use
both clustered standard errors at the province level and bootstrapped (clustered) standard
errors in order to deal with the relatively small number of clusters (34) and the potential
22
presence of serial correlation in our dependent variables.
As a second step we implement a difference in difference analysis, comparing the 8 windiest provinces with the other 26 provinces in the sample over the whole period. We estimate
similar equations to (a) and (b). In particular, let T G denote the treatment group, then in
order to study the behavior of the treatment group over time, we estimate equation (c):
CrimAssoci,t =
ÿ
–t≠t+3 T Gi,t≠t+3
tœT
(c)
The only difference with equation (a) is the variable of interest, T Gi,t≠t+3 , which is a
dummy equal to one for the treated provinces between time t and t + 3, and zero otherwise.
Notice that since the treatment is determined according to the wind level, it has to be
considered exogenous, such that Cov(T Gi,t≠t+jj , Ái,t ) = 0, ’j, holds. Similarly to equation
(b), we focus on the period after the approval of the Green Certificate system and we split
it into several time windows. Therefore we estimate equation (d):
CrimAssoci,t =
ÿ
—t≠t+j T Gi,t≠t+j
t>1999
(d)
As in (c), T Gt,≠t+j is a dummy equal to one for the treated provinces, between time t and
t + j, with t > 1999, and zero otherwise. Also in this case we run two specifications of the
equation (d), depending on the lenght of the time windows under consideration (i.e. j = 3
in equation (d1) and j = 1 in equation (d2)).
One could be worried that we are comparing two unbalanced (8 versus 26 provinces)
and heterogeneous groups, so we further improve our analysis by restricting the comparison among neighboring provinces. Accordingly, we estimate equations (c) and (d) for 19
provinces in total, comprising the 8 windiest provinces of the treatment and the 11 neighboring provinces of the control group11 . In this case we make use of an additional specification
to study the trend of our variables of interest in the treated provinces, as suggested by
Bertrand et al. (2004) to further address the problem of serial correlation which might affect
the dependent variables. In particular, we take the average of all variables before and after
1999 and we estimate the following equation in a panel with a time dimension equal to two:
11
In Appendix B we report the results without including the control variables.
23
CrimAssoci,t = –P OSTi,99≠05 + xi,t ” + ui + vt + Ái,t
(e)
Where P OST is a dummy equal to one for the treated provinces in the second period,
and zero otherwise. To keep the before and after period sufficiently balanced, we restrict the
sample to ten years (1995-2005), such that the averages correspond to the period 1995-1998
and 1999-2005, respectively.
4.2
Results
Descriptive statistics for the 34 provinces in our sample belonging to the South macro area
are reported below.
TABLE 2. Descriptive Statistics
TABLE B. Descriptive Statistics
Variables
Mean
CrimAssoc
2.277
(1.775)
3.034
(2.342)
115.018
(72.496)
0.332
(0.115)
1.227
(0.95)
2.236
(4.848)
18.123
(54.356)
9.490
(0.184)
0.814
(0.12)
609323
(544902.4)
612
TCrimAssoc
Violent crime Index
Clear_Up
(N. Obs.=476)
Wind_Index
Wind Plants
Capacity (MW)
GDP_pc
School
Population
N. Obs.
Standard deviations in parentheses.
In Table 3 we report the regression estimates of equations (a)-(b2) for both criminal
association activity (columns 1-3) and total criminal association activity (columns 4-6).
40
24
TABLE 3. Trend
in simple
andintotal
association
in thein whole
period
forallall
34 provinces.
TABLE
1. Trend
simplecriminal
and total criminal
association
the whole
period(1990-2007)
(1990-2007) for
34 provinces.
VARIABLES
Wind91_94
Wind95_98
Wind99_02
Wind03_07
(1)
(2)
(3)
(4)
(5)
(6)
CrimAssoc
CrimAssoc
CrimAssoc
TCrimAssoc
TCrimAssoc
TCrimAssoc
-0.300
(0.327)
(0.391)
-0.168
(0.361)
(0.470)
0.062
(0.439)
(0.629)
0.770
(0.563)
(0.812)
0.261
(0.285)
(0.267)
0.963
(0.467)**
(0.432)**
-0.063
(0.271)
(0.294)
-0.004
(0.298)
(0.380)
0.346
(0.368)
(0.530)
0.776
(0.432)*
(0.698)
0.347
(0.208)
(0.220)
0.757
(0.279)**
(0.341)**
Wind99_00
0.436
(0.263)
(0.242)*
0.286
(0.229)
(0.271)
0.674
(0.269)**
(0.352)*
0.852
(0.421)*
(0.475)*
Wind01_02
Wind03_04
Wind05_07
0.269
(0.322)
(0.291)
0.399
(0.374)
(0.353)
0.972
(0.476)**
(0.460)**
1.164
(0.627)*
(0.569)**
Controls (School, GDP_pc,
Yes
Yes
Yes
Yes
Yes
Yes
Population,VIO)
Province and Year FE
Yes
Yes
Yes
Yes
Yes
Yes
Observations
612
612
612
612
612
612
R-squared
0.348
0.348
0.349
0.311
0.309
0.310
Number of prov_id
34
34
34
34
34
34
Windit_t+j is the wind level of province i interacted with the time dummy (t_t+j). FE regressions with province specific time-trends.
Clustered standard errors at the province level and clustered bootstrapped standard errors are in parenthesis. Significance levels: *** p<0.01,
** p<0.05, * p<0.1.
All columns report FE estimates with the inclusion of province specific time trends.
Results for both dependent variables show an increasing trend in our coefficient of interests
belonging to {–t≠t+3 }tœT in equation (a), with one of them being positive and significant; in
particular, over the period 2003-2007 we find a significant increase in criminal association
activity in the provinces charaterized by a higher level of wind. In Table A1 in the Appendix
A we report the test of significance of the difference of the coefficients before and after 1999.
The results show no evidence of a pre-trend in our dependent variables; the increase in
both simple and total criminal association activity occurred in windier provinces appears
to be significant only in the post-1999 period. The estimation results for equations (b1)
and (b2) in which we only focus on the post-1999 period, indicate that windier provinces
experienced a significant increase in simple and total criminal association activity especially
between 2003 and 2007. Very similar findings are obtained when we implement a first naive
difference-in-difference exercise comparing the
418 windiest provinces with all other provinces
and we estimate equations (c)-(d2).
25
TABLE
4. 2.
Trend
and
total
criminal
association
in the
whole
periodfor
(1990-2007)
for all 34 provinces.
TABLE
Trendin
in simple
simple and
total
criminal
association
in the whole
period
(1990-2007)
all 34 provinces.
VARIABLES
TG91_94
TG95_98
TG99_02
TG03_07
(1)
(2)
(3)
(4)
(5)
(6)
CrimAssoc
CrimAssoc
CrimAssoc
TCrimAssoc
TCrimAssoc
TCrimAssoc
-0.112
(0.559)
(0.643)
-0.037
(0.656)
(0.848)
0.560
(0.994)
(1.194)
1.907
(1.337)
(1.612)
0.600
(0.533)
(0.486)
1.929
(0.790)**
(0.772)**
0.066
(0.477)
(0.530)
0.009
(0.575)
(0.738)
0.645
(0.828)
(1.074)
1.635
(1.050)
(1.458)
0.638
(0.436)
(0.423)
1.646
(0.527)***
(0.660)**
TG99_00
0.780
(0.533)
(0.486)
0.663
(0.469)
(0.541)
1.535
(0.510)***
(0.655)**
1.952
(0.753)**
(0.911)**
TG01_02
TG03_04
TG05_07
0.630
(0.612)
(0.557)
0.799
(0.604)
(0.661)
1.926
(0.775)**
(0.789)**
2.254
(1.015)**
(1.040)**
Yes
Yes
Yes
Yes
Yes
Yes
Population,VIO)
Yes
Yes
Yes
Yes
Yes
Yes
Observations
612
612
612
612
612
612
R-squared
0.348
0.348
0.349
0.306
0.306
0.306
Number of prov_id
34
34
34
34
34
34
TGi is a dummy equal to 1 for the provinces in the treatment group for the period i, and zero otherwise. In all columns, FE regressions with
province specific time-trends. Clustered standard errors at the province level and clustered bootstrapped standard errors are in parenthesis.
Significance levels: *** p<0.01, ** p<0.05, * p<0.1.
Results in Table 4 show that the group of the windiest provinces displayed a significant
increase in both simple and total criminal association activity over the period 2003-2007
compare to the other provinces in the sample. The tests of significance reported in Table A2
in the Appendix A rule out the presence of any pre-1999 trend.
Since we are actually comparing two heterogenous and unbalanced groups of provinces,
we now turn to a more rigorous exercise and refine the analysis limiting the comparison
between the treated provinces and the 11 neighboring provinces. We start by providing
some inspection and visualization of the behavior of the two groups of provinces in this case.
42
26
12
Figure 3. Criminal Association Activity (number of offences over 100,000 inhabitants)
Figure 4. Criminal Association Activity (number of offences over 100,000 inhabitants)1
Figure 5. Index of Violent Crime
Figure 3 shows the average criminal association activity in the treated provinces (blue
line) compared to the control provinces (red line). Since the beginning of the period the
provinces of the control group experience a higher level of criminal association activity.
Between 1990 and 1993 both the treatment and control provinces display a similar increasing
trend, characterized by a peak around 1993. However in 1997 the gap between the two groups
begins to shrink and completely disappears around 2004. A similar pattern for total criminal
association activity is shown in Figure A1 in the Appendix A.
One might think that the reduction in the gap is due to a general diffusion of criminal
activity in the treatment group13 . Figure 4 displays the pattern of the violent crime index
in the treatment group (blue line) compared to the control group (red line).
12
Treatment Group: Avellino, Benevento, Campobasso, Cosenza, Foggia, Potenza, Sassari, Trapani. Control Group:
eleven neighbouring provinces. Notice that in 1999 the tradable green certificates system (‘Certificati Verdi’, CV)
was introduced.
13
In the robustness section we formally test this alternative explanation.
1
Treatment Group: Avellino, Benevento, Campobasso, Cosenza, Foggia, Potenza, Sassari, Trapani. Control Group: eleven neighbouring provinces. Notice that
in 1999 the tradable green certificates system (`Certificati Verdi', CV) was introduced."
"
43
27
Figure4.5.Index
Index of
of Violent
Figure
ViolentCrime
Crime
1
The graph shows that the gap between the two groups remains roughly constant through-
Treatment Group: Avellino, Benevento, Campobasso, Cosenza, Foggia, Potenza, Sassari, Trapani. Control Group: eleven neighbouring provinces. Notice that
in 1999 the tradable green certificates system (`Certificati Verdi', CV) was introduced."
"
out the period of analysis. Since the method of reporting offenses changed in 2004, in Figure
A2 in the Appendix A we show the pattern of the ratio between the values in the control
and treatment groups, for the three measures of criminal activity.
43
The comparison is quite striking since we do not find the same decreasing pattern in the
ratio of the violent crime index as in the two ratios of criminal association activity which
are characterized by a visible declining trend, from roughly 2 in the first half of the 1990s
(e.g. meaning that the control groups had twice as many charges per person) to parity in
the latest years.
Figure 5 displays the development of the wind energy sector in the treated and control
provinces respectively, in terms of total capacity installed measured in Megawatts.
28
Figure 5. Installed Capacity (MegaWatt)
Figure 6. Installed Capacity (MegaWatt)
TABLE C. Balanced Test
Treated Provinces (Mean)
(1)
Non-Treated Provinces (Mean)
Mean difference (p-value)
As expected, the windiest provinces (blue line) have been(2)characterized by a (3)
higher level
Panel A. Province Characteristics Before 1999
1.371provinces (red line)
2.147in the whole period.
0.10 The most
of CrimAssoc
installed capacity relative to the control
(.592)
(1.153)
TCrimAssoc
1.681
2.735
0.09
64.571
(18.274)
117.773
(71.954)
0.05
(0.121)
(0.108)
2.937
0.136
(15.184)
(.826)
(0.126)
0.814
(0.113)
0.777
Population
531535
(180685.7)
904338.7
(868909.6)
0.251
CrimAssoc
1.896
1.962
0.899
TCrimAssoc
2.355
2.587
0.693
(22.272)
(103.49)
Wind_Index
2.742
1.04
0
Wind Plants
8.678
(7.236)
76.127
(78.579)
9.507
(0.076)
0.907
(0.042)
525643.4
(178791.6)
8
1.233
(1.229)
10.064
(12.891)
9.498
(0.150)
0.874
(0.072)
908245
(868212.6)
11
0.003
(.676) and 2002, which
(1.549)
significant acceleration occurred after 1999
provides further evidence of the
Violent crime Index
structural
break of the CV incentive scheme,
in 1999 and implemented
Clear_Up
0.343 which was established
0.357
0.792
2.742
1.04
0
in Wind_Index
2001. Results in Table 5 are consistent
control provinces
display a
(0.584)with previous figures;
(0.408)
Wind Plants
0
(2.363)
(.258)
significant
higher level of criminal association
activity compared
to treated provinces
before
Capacity (MW)
9.942
0.351
0.04
GDP_pc
9.463
1999,
but the difference shrinks and becomes
insignificant in9.399
the post treatment0.263
period. On
the contrary, the difference in wind plants
and capacity installed
(0.065)
(0.079) between the treatment and
School
0.302
control
provinces
magnifies
Panel B. Province
Characteristics
After 1998after 1999. In addition, control and treatment groups appear
to be similar both before and after 1999,
namely real GDP per
(1.394) in other fundamentals,
(0.838)
(1.392)
(1.126)
capita,
secondary
school enrollment, population
and crucially
rate, which
Violent crime
Index
99.994
149.669 in the clear-up0.202
Clear_Up
0.324
0.319 This evidence rules
0.884 out the
can
be considered a measure of the efficiency
of police activity.
(0.078)
(0.072)
(0.584)
(0.408)
possibility that our results are driven by
an increase in police
effort in the treated provinces
visCapacity
à vis(MW)
the control ones after 1999.
GDP_pc
School
Population
Obs.
Standard deviations in parentheses in columns (1) and (2)
44
29
0.013
0.897
0.270
0.239
TABLE 5. Balanced Test
TABLE C. Balanced Test
Treated Provinces (Mean)
(1)
Non-Treated Provinces (Mean)
(2)
Mean difference (p-value)
(3)
1.371
(.592)
1.681
(.676)
64.571
(18.274)
0.343
(0.121)
2.742
(0.584)
2.937
(2.363)
9.942
(15.184)
9.463
(0.126)
0.814
(0.065)
531535
(180685.7)
2.147
(1.153)
2.735
(1.549)
117.773
(71.954)
0.357
(0.108)
1.04
(0.408)
0.136
(.258)
0.351
(.826)
9.399
(0.113)
0.777
(0.079)
904338.7
(868909.6)
0.10
1.896
(1.394)
2.355
(1.392)
99.994
(22.272)
0.324
(0.078)
2.742
(0.584)
8.678
(7.236)
76.127
(78.579)
9.507
(0.076)
0.907
(0.042)
525643.4
(178791.6)
8
1.962
(0.838)
2.587
(1.126)
149.669
(103.49)
0.319
(0.072)
1.04
(0.408)
1.233
(1.229)
10.064
(12.891)
9.498
(0.150)
0.874
(0.072)
908245
(868212.6)
11
Panel A. Province Characteristics Before 1999
CrimAssoc
TCrimAssoc
Violent crime Index
Clear_Up
Wind_Index
Wind Plants
Capacity (MW)
GDP_pc
School
Population
Panel B. Province Characteristics After 1998
CrimAssoc
TCrimAssoc
Violent crime Index
Clear_Up
Wind_Index
Wind Plants
Capacity (MW)
GDP_pc
School
Population
Obs.
Standard deviations in parentheses in columns (1) and (2)
0.09
0.05
0.792
0
0
0.04
0.263
0.302
0.251
0.899
0.693
0.202
0.884
0
0.003
0.013
0.897
0.270
0.239
Turning to the estimation exercise, Table 6 reports estimates of equations (c) and (d1)
44 columns, and for total criminal association
for criminal association activity in the first two
activity in columns 3 and 4. All columns report FE estimates with the inclusion of province
specific time trends.
30
TABLE 6. Trend in simple and total criminal association in the whole period (1990-2007) for the 19 neighbouring
TABLE 3. Trend in simple and total criminal association in the whole period (1990-2007) for the 19 neighbouring provinces.
provinces.
VARIABLES
(1)
CrimAssoc
(2)
CrimAssoc
(3)
TCrimAssoc
(4)
TCrimAssoc
TG91_94
0.217
0.691
(0.598)
(0.706)
(0.638)
(0.814)
TG95_98
0.291
0.876
(0.680)
(0.683)
(0.871)
(0.998)
TG99_02
1.222
0.857
2.079
0.984
(1.068)* for the 19 neighbouring
(0.507)*provinces.
TABLE 3. Trend in simple and total (0.956)
criminal association in(0.406)**
the whole period (1990-2007)
(1.201)
(0.441)*
(1.355)
(0.491)**
TG03_07
2.167
1.705
3.262
1.891
(1)
(2)
(3)
(4)
(1.256)
(0.488)***
(1.525)**
(0.697)**
VARIABLES
CrimAssoc
CrimAssoc
TCrimAssoc
TCrimAssoc
(1.626)
(0.666)**
(1.820)*
(0.769)**
TG91_94
0.217
0.691
Yes
Yes
Yes
Yes
(0.598)
(0.706)
Population, VIO)
(0.638)
(0.814)
Province
Yes
Yes
Yes
Yes
TG95_98 and Year FE
0.291
0.876
Observations
342
342
342
342
(0.680)
(0.683)
R-squared
0.327
0.326
0.303
0.301
(0.871)
(0.998)
Number
19
19
19
19
TG99_02 of prov_id
1.222
0.857
2.079
0.984
TGi is a dummy equal to 1 for the provinces(0.956)
in the treatment group (0.406)**
for the period i, and zero otherwise.
province specific
(1.068)* FE regressions with(0.507)*
time-trends. Clustered standard errors at the(1.201)
province level and clustered
bootstrapped standard(1.355)
errors are in parenthesis. Significance
(0.441)*
(0.491)** levels:
***
** p<0.05, * p<0.1.
TGp<0.01,
2.167
1.705
3.262
1.891
03_07
(1.256)
(0.488)***
(1.525)**
(0.697)**
(1.626)
(0.666)**
(1.820)*
(0.769)**
Results for criminal association activity show an increasing trend in our coefficient of
Yes
Yes
Yes
Yes
Population, VIO)
interests
belonging
(c),
with two of them
only marginally
Province and
Year FE to {–t≠t+3 }
Yes
Yes
Yes insigniftœT in equation Yes
the Coefficients
Observations
342TABLE 4. Test of342
342
342
R-squared
0.327
0.326
0.303
0.301
icant.
Column
3
displays
a
similar
trend
for
total
criminal
association
activity,
but
we find
Number of prov_id
19
19P-Value
H0
P-Value 19
H19
0
TGi is a dummy equal to 1 for the provinces in the treatment group for the period i, and zero otherwise. FE regressions with province specific
CrimAssoc
TCrimAssoc and 2003-2007 periods.
positive
also
significant
associated
to thestandard
1999-2002
time-trends.and
Clustered
standard
errors at thecoefficients
province level and
clustered bootstrapped
errors are in parenthesis. Significance levels:
Cluster
SE ** p<0.05, * p<0.1.
Cluster SE
***
p<0.01,
In TG
Table
795_98
we report the test of significance
ofTGthe
of the coefficients before
0.8727
TG95_98
0.7663 and
91_94 = TG
91_94 =difference
TG95_98 = TG99_02
after
1999.
TG95_98
=( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
Bootstrapped SE
TG91_94 = TG95_98
0.0563
0.0148
0.0450
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.0549
0.0257
0.0669
TABLE
Bootstrapped
SE
TABLE4.7.Test
Testofofthe
theCoefficients
Coefficients
0.8847
H0
TG95_98 = TG99_02
CrimAssoc
TG95_98 =( TG99_02+TG
03_07)/2
Cluster
(TG
+TG95_98)/2=( TG99_02+TG03_07)/2
91_94SE
TG91_94 = TG95_98
P-Value
0.0692
TG91_94 = TG95_98
0.7450
P-Value
0.0350
0.0382
0.0929
0.8727
H0
TG95_98 = TG99_02
TCrimAssoc
TG95_98 =( TG99_02+TG
03_07)/2
Cluster
(TG
+TG95_98)/2=( TG99_02+TG03_07)/2
91_94SE
TG91_94 = TG95_98
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.0563
0.0148
0.0450
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.0549
0.0257
0.0669
Bootstrapped SE
TG91_94 = TG95_98
0.8847
Bootstrapped SE
TG91_94 = TG95_98
0.7450
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.0692
0.0382
0.0929
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.0350
0.0184
0.0487
0.0184
0.0487
0.7663
The results clearly show no evidence of a pre-trend in our dependent variables; the increase in both simple and total criminal association activity occurred in the treatment compared to the control group appears to become significant only in the post-1999 period. The
31
estimation results for equation (d1) in which we only focus on the post-1999 period and
which was divided into two time windows are shown in Columns 2 and 4 of Table 6; for both
measures of criminal association activity, all coefficients of interest belonging to {—t }t>1999
are positive and significant.
Columns 1 and 3 of Table 8 report the results for equation (d2), where the post-1999
period is divided into four time windows; for both dependent variables, the coefficients
increase over time in significance and magnitude.
TABLE 8. Trend in simple and total criminal association after the introduction of the green certificate system
TABLE 5. Trend in simple and total criminal association after the introduction of the green certificate system (1999-2007)
(1999-2007) for the 19 neighbouring provinces.
for the 19 neighbouring provinces.
VARIABLES
TG99_00
TG01_02
TG03_04
TG05_07
POST99_05
(1)
CrimAssoc
(2)
CrimAssoc
0.963
(0.540)*
(0.519)*
0.941
(0.421)**
(0.594)
1.622
(0.483)***
(0.688)**
2.023
(0.757)**
(0.972)**
(3)
TCrimAssoc
(4)
TCrimAssoc
0.908
(0.631)
(0.571)
1.070
(0.528)*
(0.682)
1.964
(0.640)***
(0.790)**
1.860
(0.983)*
(1.085)*
0.812**
(0.359)
0.951**
(0.409)
Yes
Yes
Yes
Yes
Population, VIO)
Yes
Yes
Yes
Yes
Observations
342
38
342
38
R-squared
0.328
0.535
0.301
0.458
Number of prov_id
19
19
19
19
TGi is a dummy equal to 1 for the provinces in the treatment group for the period i, and zero otherwise. In first and third column, FE
regressions with province specific time-trends. Clustered standard errors at the province level and clustered bootstrapped standard errors are
in parenthesis. In second and fourth column, panel collapsed in two periods as in Bertrand et al. (2004). POSTi is a dummy equal to 1 for the
provinces in the treatment group in the post-1999 period and zero otherwise. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.
In particular, from column 1, it is evident that the treatment group experiences a progressively higher increase in criminal association activity compared to the control group.
Columns 2 and 4 of Table 8 display the results of specification (e), the last at this stage of
analysis. Recall that we collapse the panel in two time periods (1995-1998, 1999-2007), a
method suggested by Bertrand et al. (2004) which represents a simple remedy if variables of
interest are serially correlated. Notice that –, the coefficient of interest, is significant both for
total criminal association and for criminal association. As described in the model, corruption
is driven by the revenues in the wind energy sector. Although the revenues increase sub32
stantially after 1999, their trend oscillates over time both before and after 1999. Therefore
the specifications with the time windows are the most appropriate to be used here, however
looking at longer time periods is a very effective way to provide a synthetic description of
the reality. And the size of the effect which we find is remarkable; after the introduction
of a green certificate system high wind provinces experienced on average an increase of 0.8
(per 100000 inhabit.) of association activity crimes (0.9 for the total criminal association
activity) compared to the neighboring provinces. This is very sizable given that the average
crime level in the sample is of about 2 (per 100000 inhabit.).
All in all, the empirical results in this section are consistent with predictions i) and ii)
of the model, suggesting that after 1999 the windiest provinces experienced a significant increase in both measures of criminal association activity, compared to the neighboring control
provinces. In an area characterized by weak socio-political institutions, the introduction of
the new and more favorable policy regime system had a stronger negative effect (in terms of
corruption), especially in the provinces with the highest potential for efficiency gains.
4.2.1
Robustness
Despite a set of strong empirical results, at this stage we cannot completely rule out the
presence of an omitted variables bias. In particular, the trend in criminal association activity
we observe in the treatment group might be due to a spillover effect from the neighboring
provinces. In the next three sections we provide a robustness analysis to address this issue.
Catching-up between treatment and control? Following Gennaioli et al. (2010), we
change our measure of criminal activity to understand whether we are merely capturing a
general diffusion of criminal behavior which does not depend on the expectation of rents
in the wind energy sector. For this purpose we re-estimate equations (c)-(e), changing our
dependent variable with the index of violent crime.
Results in Table 9 indicate that in all specifications the coefficients of interest are insignificant and eventually negative, showing a similar trend of the violent crime index in the
treated provinces compared to the control provinces.
33
TABLE
Trend
in Violent
Crime
the period
whole (1990-2007)
period (1990-2007)
the19 neighbouring
TABLE
6. 9.Trend
in Violent
Crime
IndexIndex
in thein
whole
for the19for
neighbouring
provinces. provinces.
VARIABLES
TG91_94
TG95_98
TG99_02
TG03_07
(1)
Violent crime Index
(2)
Violent crime Index
12.415
(15.940)
(19.786)
4.845
(18.986)
(23.038)
-2.003
(22.433)
(29.142)
-13.523
(24.196)
(37.550)
-7.436
(12.115)
(10.264)
-17.462
(21.022)
(15.081)
TG99_00
(3)
Violent crime Index
(4)
Violent crime Index
-7.082
(11.764)
(11.096)
-6.860
(20.579)
(13.644)
-17.710
(22.599)
(16.111)
-16.004
(32.718)
(19.526)
TG01_02
TG03_04
TG05_07
POST99_05
3.301
(14.940)
Yes
Yes
Yes
Yes
Population, VIO)
Yes
Yes
Yes
Yes
Observations
342
342
342
38
R-squared
0.786
0.785
0.785
0.450
Number of prov_id
19
19
19
19
TGi is a dummy equal to one for each province in the treatment group in the period i, and zero otherwise. In columns (1)-(3), OLS
regressions with time, province fixed effects, province specific time trends and clustered standard errors at the province level (in
parenthesis). In fourth column, panel collapsed in two periods as in Bertrand et al. (2004). POSTi is a dummy equal to 1 for the provinces in
the treatment group in the post-1999 period and zero otherwise. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.
Another potential explanation for our results could be that the treatment areas are simply absorbing criminal activity away from the other provinces because of the increment of
economic activity that may ensue government intervention. To rule out this channel we
correct the main specifications by allowing GDP, schooling and population to interact with
treatment dummies. In Table 10 we show that our results are robust to the inclusion of
these interaction terms. The corresponding tests of significance are reported in Table A3 in
the Appendix A.
34
47
provinces when the interaction terms between GDP, Population, Schooling and the treatment dummy are included.
(1)
CrimAssoc
VARIABLES
TG91_94
0.054
(0.584)
(0.693)
-0.039
(0.725)
(0.970)
0.717
(0.976)
(1.211)
1.684
(1.299)
(1.659)
TG95_98
TG99_02
TG03_07
(2)
CrimAssoc
(3)
CrimAssoc
(4)
TCrimAssoc
0.639
(0.666)
(0.869)
0.637
(0.787)
(1.115)
1.573
(1.140)
(1.420)
2.803
(1.598)*
(1.886)
0.771
(0.427)*
(0.561)
1.774
(0.479)***
(0.730)**
TG99_00
(5)
TCrimAss
oc
0.843
(0.499)
(0.617)
1.942
(0.659)**
(0.817)**
*
1.026
(0.553)*
(0.573)*
0.663
(0.583)
(0.833)
1.517
(0.532)**
(0.840)*
2.322
(0.779)***
(1.010)**
TG01_02
TG03_04
TG05_07
(6)
TCrimAssoc
0.935
(0.607)
(0.634)
0.759
(0.663)
(0.910)
1.828
(0.657)**
(0.917)**
2.096
(0.994)**
(1.137)*
Controls (School, GDP_pc, Population, VIO)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TG Specific Trend in GDP,Schooling, Pop
Yes
Yes
Yes
Yes
Yes
Yes
Observations
342
342
342
342
342
342
R-squared
0.332
0.331
0.337
0.309
0.308
0.308
Number of prov_id
19
19
19
19
19
19
time-trends. Clustered standard errors at the province level and clustered bootstrapped standard errors are in parenthesis. Significance levels:
*** p<0.01, ** p<0.05, * p<0.1.
TABLE
8. Determinants
of totalallows
installedus
capacity.
Overall
this evidence
to reject with confidence the “spillover effect” or “catching-
up” explanation, suggesting
that in the
consideration,
the treatment
group
did
(1)
(2) period under
(3)
(4)
(5)
(6)
VARIABLES
Capacity (MW)
Lag Capacity
1.011***
(0.032)
1.624***
(0.458)
Wind
Capacity (MW)
Capacity (MW)
not experience a general increase in criminal activity.
Lag CrimAssoc
Using the Actual
inter_ CrimAssoc
the impact of other
1.021***
(0.032)
0.390
(0.661) As
Measure
2.202***
(0.770)
factors characterizing
Capacity (MW)
Capacity (MW) Capacity (MW)
1.092***
1.120***
(0.078)
(0.077)
5.310***
-0.923
(1.598) above, our
(3.381)results
noticed
7.674**
(3.697)
the treatment group.
To
0.925***
(0.079)
1.760*
(0.907)
might
0.940***
(0.081)
0.300
(1.477)
merely
capture
2.217
address this(1.718)
issue we
Controls (School,
Yes
Yes
Yes
Yes
repeat
specifications
(c)-(e) considering
theYesactual levelYesof our index
of windiness;
in
GDP_pc, the
Population,
VIO)
Province and Year
FE
Yes
Yes
Yes T Gi , we Yes
particular,
instead
of using
the treatment
dummy
interact T GYes
value of
i with the Yes
Observations
578
578
136
136
153
153
Number
of prov_id
34
34
8 W ind to run
8 the same9 set of regressions.
9
the
wind
index in province
i, and now
use T G≠
i
In first and second column, all 34 provinces of the Center South, in third and fourth column, provinces of the treatment group. In fifth and
column, the second 9 windiest provinces. inter_CrimAssoc is the interaction between the lagged value of criminal association activity
Insixth
this
case our
identification
assumption
implies InCov(T
G≠ W
indi,t≠ t+3
, Ái,t ) =dummy
0. Invariable
Table
(Lag
CrimAssoc)
and the
dummy for the post-1999
period (1999-2007).
all columns
bias-corrected
least-squares
(LSDV) estimators as originally proposed by Kiviet (1995). Province specific time trends are included and bootstrapped standard errors are
A4-A5
in the
Appendix
Ap<0.01,
we report
the
results under this additional specification.
in parenthesis.
Significance
levels: ***
** p<0.05,
*p<0.1.
Results are consistent with those in Table 6-8, and we find the effects to be even stronger.
35
These findings provide us with sufficient confidence that we are correctly identifying the
impact of the wind level of our treatment group.
Different Treatment We perform additional checks to show the robustness of our findings
changing the treatment group. First, we check whether our results remain valid when we try
other cutoffs for the wind index to select the treatment group. We start dropping Cosenza
from the treatment group since as it is evident from Figure 3, it displays a level of wind
more similar to the provinces belonging to the third quartile of the wind measure and our
findings remain valid14 . Then we consider a larger treatment group, adding the next two
windiest provinces15 . As expected in this case the findings are attenuated16 . To ensure that
our results are not driven by the behavior of a particular province, we repeat our estimation
exercise dropping from the treatment group each province at a time. Despite weakening in
some cases, as when we drop the windiest province, Potenza, our results remain robust17 .
The last test we perform consists of changing the treatment group as a whole. In this
case the results should disappear. As an alternative treatment group we consider the 8 least
windy provinces, which are compared with our original control group18 . As expected, the
trends in criminal association activity of these two groups of provinces do not display any
significant difference. This evidence further demonstrates that our results are not driven by
unobservable features characterizing the control group.
4.3
Wind Energy Sector
We now turn to the second part of the analysis where we model the link between the level of
corruption and the expansion of the wind energy sector (model prediction iii). In this section
we identify the main drivers of the expansion of the wind energy sector. The mechanism
described in the model (and supported by anecdotal evidence) is the following: economic and
political agents, expecting a significant increase in the returns to investments in the wind
energy sector, started to set up illegal networks (criminal associations). In this way, the
entrepreneurs quickly obtained concessions and permits for the installation of wind plants,
14
See Appendix C for the results.
Agrigento, Nuoro.
16
Results are available upon request.
17
Results are available upon request.
18
See Appendix D for the results.
15
36
earning the associated profits as a consequence, while local politicians gained consistent
bribes in exchange. If this has actually been the mechanism at work, we would expect to
find a higher expansion of the wind energy sector in the provinces characterized by a higher
level of criminal association activity.
In the model outlined in Section 2 we have seen that corruption practices spread where
expected returns to investment are higher (i.e. in provinces with a high wind level) and
the quality of institutions is relatively low (complementarity). If that is the case, then one
should observe that: 1) among provinces with the same level of wind, the expansion of
the sector has been greater in provinces with a poor quality of institutions, which fueled
corruption practices and, 2) among provinces with a similar (low) quality of institutions,
the wind sector developed more in provinces characterized by a high level of wind and so
of expected revenues. In addition, this effect should be stronger after 1999, when expected
returns of the investment significantly increased. Despite the lack of data on institutional
quality, we address the second prediction, exploiting the fact that the provinces in our sample
are very similar on institutional dimensions. Analyzing groups of provinces with different
wind level, we then expect to find a significant (positive) correlation between corruption and
the expansion of the wind energy sector for the windiest groups.
Measuring the size of the wind energy sector with the total installed capacity, we analyze
whether the level of criminal association activity at time t≠1 in province i, is correlated with
the total installed capacity (MW) in the same province at time t, and how this correlation
varies for provinces with different wind levels. In the model, the level of corruption is
associated with the expected amount of authorized capacity (number of wind plants), but in
the data we observe the actual capacity installed (or wind plants built). Since it is reasonable
to assume at least a one year period between authorization and installation, we choose to
include the lagged value of criminal association. In this section, the baseline specification is
the following:
Capacity i,t = –Capacity i,t≠1 +—CrimAssoci,t≠1 +“Inter≠ CrimAssoci,t≠1 +xi,t ” + “ i trendi,t +z it
(f)
Where Inter≠ CrimAssoci,t≠1 represents an interaction term between a post-1999 dummy
(=1 for every year between 1999 and 2007), and the lagged value of criminal association
37
activity. zit represents the error term, which in this case is zi,t = ui + vt + ‘i,t , where ui
and vt are province and time effects, and ‘it is an observation specific error. Notice that we
included the lagged dependent variable since Capacity is characterized by high persistency;
indeed our measure of capacity also accounts for the enlargement of an existing wind park,
in terms of Megawatts added. Moreover, given the limited space available in a province for
new plants, it is reasonable to assume that existing installations, both in terms of capacity
and number of wind parks, influence the installation of future ones. The vector of controls
includes GDP per capita and school enrolment as before, which account for the general level
of socio-economic development of the province and the density of population which affects
the space available for new wind energy installations.
Estimating a fixed effect model including the lagged dependent variable on the RHS
leads to the “dynamic-panel bias” introduced by Nickel (1981), since this variable would be
correlated with the error term generating inconsistent estimates. One remedy is to implement
the Arellano-Bond (1991) method, estimating equations in first differences and using lagged
levels of the dependent variable as instruments. However this method is suitable for small T,
large N samples (Rodman 2009), while our sample is characterized by small N and relatively
large T. We therefore adopt another method originally suggested by Kiviet (1995), the
bias-corrected least-squares dummy variable estimation (LSDV) which in cases like ours
proved to be more efficient than instrumental variable type estimators. The coefficients of
interest are — and “; they illustrate whether criminal association activity influences wind
power installations and if this occurred more often after 1999, when the expected returns to
wind energy started to increase and so did the attractivity of the sector. We first estimate
equation (f) for all the provinces in the sample, then we repeat the estimation separately for
the treatment group and for the second 9 windiest provinces, respectively.
To further study the complementarity between windiness and institutional quality, as a
second step, we estimate the following equation:
Capacity i,t =–Capacity i,t≠1 +—T G≠ CrimAssoci,t≠1 +“InterT G≠ CrimAssoci,t≠1 +xi,t ” + “ i trend+z i,t (g)
Where InterT G≠ CrimAssocit≠1 represents an interaction term between the post 1999
38
(1)
CrimAssoc
VARIABLES
(2)
CrimAssoc
(3)
CrimAssoc
(4)
TCrimAssoc
(5)
TCrimAss
oc
(6)
TCrimAssoc
TG91_94
0.054
0.639
(0.584)
(0.666)
(0.693)
(0.869)
TG95_98 and the lagged value of criminal
-0.039
0.637treatment group,
dummy
association activity in the
(0.725)
(0.787)
(0.970)
(1.115)are — and “. As before,
T G CrimAssocit≠1 . Also in this
case, the
coefficients of interest
TG99_02 ≠
0.717
0.771
1.573
0.843
(0.976)
(0.427)*
(1.140)
(0.499)
they illustrate whether criminal association
activity influences wind
installations and
(1.211)
(0.561)
(1.420)power
(0.617)
TG03_07
1.684
1.774
2.803
1.942
whether this occurred more often (1.299)
after 1999.
However, in comparison
to (f), we evaluate
(0.479)***
(1.598)*
(0.659)**
(1.659)
(0.730)**
(1.886)
(0.817)**
*
TG99_00
1.026 group, i.e. high-wind provinces,
0.935
whether
these correlations are stronger for the treatment
(0.553)*
(0.607)
(0.634)
than
for: i) the neighboring provinces of the control(0.573)*
group and, ii) the 9 provinces
TG01_02
0.663
0.759 with
(0.583)
(0.663)
a lower wind level (the ones with a value of wind index
between the second and the
(0.833)
(0.910)third
TG03_04
1.517
1.828
(0.532)**
(0.657)**
quartile). Analyzing the behavior of the two windiest
groups of provinces, we can
assess
(0.840)*
(0.917)**
TG05_07
2.322
2.096
more
precisely the validity of our prediction; indeed (0.779)***
we
are able to draw conclusions
about
(0.994)**
(1.010)**
(1.137)*
the effect of criminal association activity on wind energy installations, for groups of provinces
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TG Specific Trend in GDP,Schooling, Pop
Yes
Yes
Yes
Yes
Yes
Yes
Observations
342
342
342
342
342
342
R-squared
0.332
0.331
0.337
0.309
0.308
0.308
Number
of prov_id
19
19
19
19
19
19
4.4
Results
time-trends. Clustered standard errors at the province level and clustered bootstrapped standard errors are in parenthesis. Significance levels:
*** p<0.01,
p<0.05, *the
p<0.1.estimation results of equation (f)19 .
Table
11 **
reports
Province
and Year FEwind level.
with
a different
11. Determinants of total installed capacity
TABLE 8. Determinants of totalTABLE
installed capacity.
VARIABLES
Lag Capacity
Lag CrimAssoc
(1)
Capacity (MW)
(2)
Capacity (MW)
(3)
Capacity (MW)
(4)
Capacity (MW)
1.011***
(0.032)
1.624***
(0.458)
1.021***
(0.032)
0.390
(0.661)
2.202***
(0.770)
1.092***
(0.078)
5.310***
(1.598)
1.120***
(0.077)
-0.923
(3.381)
7.674**
(3.697)
0.925***
(0.079)
1.760*
(0.907)
Yes
Yes
Yes
Yes
Yes
inter_ CrimAssoc
Controls (School,
(5)
(6)
Capacity (MW) Capacity (MW)
0.940***
(0.081)
0.300
(1.477)
2.217
(1.718)
Yes
GDP_pc, Population,
VIO)
Yes
Yes
Yes
Yes
Yes
Yes
Observations
578
578
136
136
153
153
Number of prov_id
34
34
8
8
9
9
In first and second column, all 34 provinces of the Center South, in third and fourth column, provinces of the treatment group. In fifth and
sixth column, the second 9 windiest provinces. inter_CrimAssoc is the interaction between the lagged value of criminal association activity
(Lag CrimAssoc) and the dummy for the post-1999 period (1999-2007). In all columns bias-corrected least-squares dummy variable
(LSDV) estimators as originally proposed by Kiviet (1995). Province specific time trends are included and bootstrapped standard errors are
in parenthesis. Significance levels: *** p<0.01, ** p<0.05, *p<0.1.
We look at the simple correlation between the lagged value of criminal association and the
total capacity installed, considering all 34 provinces in the Center South region in columns
1 and 2, the treatment group in columns 3 and 4 and, in the last two columns, the second
19
We only present the results for criminal association 48
activity. Tables for total criminal association are available
upon request.
39
9 windiest provinces. In columns 1, 3 and 5, only the lagged value of criminal association
activity is included, while in columns 2, 4 and 6, the baseline specification is enriched with
an interaction term to account for the effect of the lagged value of criminal association in the
post-1999 period. When we consider the whole sample and the treatment group (columns
1-4), the coefficient of the lagged value of criminal association is positive and significant in
the baseline specification but becomes insignificant (or marginally significant) in columns
2 and 4, where the interaction Inter≠ CrimAssoc is always positive and significant. This
implies that the effect of lagged criminal association activity on total installed capacity is
higher after 1999, and this effect appears to be particularly pronounced for the 8 windiest
provinces of the treatment group. On the contrary, coefficients of interest are insignificant
and much lower when the less windy provinces are considered (columns 5-6). Results in
Table 11 show that the level of criminal association at time t ≠ 1 better predicts the total
installed capacity at time t, in the windiest provinces, especially after 1999.
In Table 12 we estimate equation (g), comparing the treatment group, first with the
control group and then with the second 9 windiest provinces.
Determinants of total installed capacity.
TABLE 9. Determinants of totalTABLE
installed 12.
capacity.
VARIABLES
Lag Capacity
LagTG_ CrimAssoc
(2)
Capacity (MW)
(4)
Capacity (MW)
(6)
Capacity (MW)
(8)
Capacity (MW)
1.030***
(0.040)
4.843***
(1.105)
1.037***
(0.039)
0.313
(2.000)
5.906***
(1.907)
1.053***
(0.047)
4.918***
(1.080)
1.061***
(0.047)
0.366
(2.148)
5.897**
(2.511)
Yes
Yes
Yes
Yes
interTG_ CrimAssoc
Population, VIO)
Yes
Yes
Yes
Yes
Observations
323
323
289
289
R-squared
Number of prov_id
19
19
17
17
In first and second column, comparison between the treatment and the control group, in third and fourth column, comparison between the
treatment and the second 9 windiest provinces. LagTG_CrimAssoc is the interaction between the treatment group dummy (TG), and the
lagged value of criminal association activity. interTG_CrimAssoc is the interaction between LagTG_CrimAssoc and the dummy for the
post-1999 period (1999-2007). In all columns bias-corrected least-squares dummy variable (LSDV) estimators as originally proposed by
Kiviet (1995). Province specific time trends are included and bootstrapped standard errors are in parenthesis. Significance levels: ***
p<0.01, ** p<0.05, * p<0.1.
In the first two columns we consider again our original sample comprising 19 provinces;
it is clear that the lagged value of criminal association activity positively (and significantly)
affects the total installed capacity in the treated provinces more than in the control provinces,
40
in the post-1999 period. The same effects, with a similar magnitude, result from columns
3 and 4, where we compare the 8 windiest provinces (treatment group) to the second 9
windiest provinces. Also in this case, the correlation between the lagged value of criminal
association activity and the total installed capacity is higher in treated provinces than in
control provinces (with a stronger effect after 1999, see columns 2 and 4). These results
are quite striking; they indicate that the positive correlation between the level of criminal
association activity at time t ≠ 1, and the total installed capacity at time t, is stronger
in the high wind provinces of the treatment group, with a rise of this effect after 1999.
Recalling complementarity and that provinces in the South of Italy display a similar quality
of institutions, these results document the fact that the wind sector developed more in
provinces with higher criminal association activity fueled by a high wind level and by the
associated expected revenues.
Overall results in Tables 11-12 strongly support the validity of prediction iii) of our
model, showing that the amount of wind energy capacity installed increases with the extent
of corruption, and, the magnitude of this correlation changes when we compare provinces
with different wind level. In particular, in high wind provinces, a greater number of agents,
motivated by the high expected profits, engaged in criminal association activity, favoring the
expansion of the wind energy sector.
Notice that previous evidence might also be driven by the different degree of heterogeneity
in the quality of institutions of different groups of provinces; in particular, there could be a
certain heterogeneity in institutional features among the provinces of the treatment group,
with some provinces having poorer institutions that fostered a higher level of corruption,
while the provinces of the control group should be characterized by a more homogeneous
institutional quality. Unfortunately given the lack of data on institutional quality we cannot
address this issue. However, taking into account that Southern provinces share a similar
institutional background, we strongly believe that our evidence is mainly driven by variation
in the wind level.
Summing up, the estimates from the baseline specification (i.e. without the interaction
term) generally show a positive and statistically significant correlation between the lagged
values of our measures of crime and the level of installed capacity. Once we include the interaction terms, we always find that this relation is stronger after 1999 and among the windiest
41
provinces. We can conclude that, in provinces where economic agents and bureaucrats, motivated by the high expected revenues, have been better “organized” and ready to engage in
criminal association activity (resulting in more permits and concessions), the expansion of
the wind energy sector has been higher. A word of caution is needed regarding the evidence
we find in this section. These results might be biased due to omitted variables or simultaneity; as already pointed out, the first type of bias would arise if there is an unobserved
factor correlated with both the level of criminal association activity and the development of
the wind energy sector, while the second type of bias arises if our variables of interest are
jointly determined, or in other words, each of them exerts an effect on the other. In part,
we address these problems adding the lagged value of criminal association as an explanatory
variable. Moreover, the inclusion of a full set of fixed effects as well as province specific time
trends, and the fact that the treatment group is exogenously determined, mitigate concerns
of endogeneity.
5
Conclusions
Recent anecdotal evidence suggests the diffusion of corruption practices in the renewable
energy sector, especially in the wind energy sector. In Italy, several inquiries have looked into
corrupt practices and bribes conceived with the aim of building wind farms and appropriating
the associated public support. This paper has analyzed the link between public support
schemes for renewable energy and the potential scope for rent seeking and corruption. To
our knowledge, this is the first attempt to study whether public incentives to renewable
energy resources could lead to an increase in illegal activities. This is a policy relevant topic
since over the past years several countries implemented public support policies meant to
promote renewables, which are now being evaluated.
Using a model of corruption, we show that windier provinces are more likely to experience
corruption, especially after the introduction of a more favorable policy regime. Moreover, the
number of wind energy projects in a given province increases with the extent of corruption.
We test these results using a panel data of Italian provinces in the “Mezzogiorno” region for
the period 1990-2007, and are able to find strong empirical evidence that supports our model,
establishing that in the presence of poor institutions, public policy can fuel corruption. The
42
main findings are the following: i) criminal association activity increased more in high-wind
provinces and especially after the introduction of a favorable market-based regime of public
incentives and, ii) the expansion of the wind energy sector is positively correlated with the
extent of criminal association activity; precisely, both the level of wind and the functioning
of social and political institutions play a crucial role, through their effect on corruption. In
particular we find that, comparing provinces with a similar (low) quality of institutions, the
development of the wind sector has been greater in high-wind provinces where economic
agents and bureaucrats, motivated by large expected profits, have engaged more in criminal
association activity.
Overall, the paper points out that even well designed policies can have an adverse impact in heavily regulated environments with poorly functioning socio-political institutions,
especially in places with the highest potential for efficiency gains. The magnitude of our
empirical estimates are significant: for an average wind park (of 10 MW) installed after
1999, and which receives about 1.5 Million euro per year in public support, the number
of criminal association offenses has increased by 6% in the windy provinces with respect
to the less windy ones20 . These results draw clear normative implications, particularly for
developing countries, which are characterized by abundant renewable resources and by weak
institutions.
Besides establishing a clear link between renewable energy subsidies and corruption, our
paper doesn’t allow to quantify additional welfare impacts. From the very rapid expansion
of wind power installations in Southern Italy one can presume that corruption has helped
cut through the red tape, rather than having reduced the equilibrium provision under the
optimum. There could also be corruption-induced welfare costs which are outside of the
model, such as entrepreneurs using “preferred” suppliers of potentially lower quality building
materials, though that would constitute a small share of investment costs compared to the
actual wind turbine. However, given the relatively small share of renewable energy in the
economy, the largest impacts of public subsidies on economic development are likely to
happen through increased corruption, which we have shown to be significant.
Since this analysis has been tested on the case of wind power, the external validity of
our study cannot be generalized to other types of renewables, such as solar or hydropower.
20
This number comes from back-of-the-envelope calculations based on the coefficient of the specification a la
Bertrand.
43
In principle, it could be the case that wind energy, being capital intensive, naturally leads
to oligopolistic markets, where the establishment of connections between few entrepreneurs
and local bureaucrats is easier. Furthermore, when compared to solar, the authorization
process is more difficult, since wind farms necessitate vast areas and generally have a bigger
environmental impact. As a consequence, local politicians have more discretionary power
and the room for corruption is larger. Our research agenda comprises the study of other
types of renewable resources to address this set of issues. In general, assessing the role of
political economy for the case of renewable energy is a fruitful area for future research since
several issues remain unexplored, and at the same time it is becoming a relevant sector of
the economy whose efficient control demands an in-depth analysis of all the main economic
incentives at play.
Acknowledgements
The authors are grateful to Antoine Dechezlepretre, Roberta Distante, Vincenzo Galasso, Reyer
Gerlagh, Humberto Llavador, Tommaso Nannicini, Marcella Nicolini, Jesse Shapiro, Mirco Tonin,
and audience at IMT, Lucca, LSE, Oxford, Stanford, the 1st workshop on organized crime at
Cattolica University, the 9th EEM conference at EUI, Florence and the 19th EAERE conference
in Prague for very helpful comments. We are also grateful to Bianchi M., Buonanno P. and Pinotti
P. for sharing their data on GDP and clear up rate, and to Gennaioli C., Onorato M., Perotti R.
and Tabellini G. for sharing the data their data on criminal activity. A first draft of the paper
was written while Gennaioli was a Post-Doc researcher at FEEM and was circulated with the title:
“Clean or Dirty Energy; Evidence on a Renewable Energy Resource Curse”. The research leading
to these results has received funding from the European Community’s Seventh Framework Program
FP7/2007-2013 under grant agreement number 282846 (LIMITS).
44
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tives, 19 (5), 19–42.
[33] Tanzi, V., (1998), “Corruption Around the World: Causes, Consequences, Scope, and
Cures” IMF Working Paper 98/63; Washington: International Monetary Fund.
[34] Tanzi, V., and Davoodi, H. (1997), “Corruption, Public Investment and Growth”, IMF
Working Paper 97/139; Washington: International Monetary Fund.
[35] Victor, D. (2011) “Global warming gridlock”, Cambridge University Press
[36] Vincente, P., (2010) "Does Oil Corrupt? Evidence from a Natural Experiment in West
Africa", Journal of Development Economics.
[37] Hessami, Zohal (2010), "Corruption and the Composition of Public Expenditures: Evi-
dence from OECD Countries", Munich Personal RePEc Archive.
47
Appendix A.
TABLE A1. Test of the Coefficients when the Wind level is intercated with the time dummy for all the 34 provinces.
H0
P-Value
H0
CrimAssoc
P-Value
TCrimAssoc
Cluster SE
Wind 91_94 = Wind 95_98
0.7276
Cluster SE
Wind 91_94 = Wind 95_98
0.6353
Wind 95_98 = Wind 99_02
Wind 95_98 =( Wind 99_02+ Wind 03_07)/2
0.0858
0.0095
Wind 95_98 = Wind 99_02
Wind 95_98 =( Wind 99_02+ Wind 03_07)/2
0.3859
0.0691
(Wind 91_94+ Wind 95_98)/2=( Wind 99_02+ Wind 03_07)/2
Bootstrapped SE
Wind 91_94 = Wind 95_98
0.0088
0.0470
0.7964
Bootstrapped SE
Wind 91_94 = Wind 95_98
Wind 95_98 = Wind 99_02
Wind 95_98 =( Wind 99_02+ Wind 03_07)/2
0.1558
0.0746
0.1262
Wind 95_98 = Wind 99_02
Wind 95_98 =( Wind 99_02+ Wind 03_07)/2
0.4333
0.1233
0.1484
0.6118
TABLE A2. Test of the Coefficients when the treatment group is compared to all the other provinces in the South
(Sample:34 Provinces)
H0
P-Value
H0
CrimAssoc
P-Value
TCrimAssoc
Cluster SE
TG91_94 = TG95_98
0.8670
Cluster SE
TG91_94 = TG95_98
0.8769
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
0.1712
0.0307
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
0.2979
0.0727
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
Bootstrapped SE
TG91_94 = TG95_98
0.0620
0.1049
0.9030
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
Bootstrapped SE
TG91_94 = TG95_98
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.1959
0.0811
0.1735
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.2767
0.0790
0.1425
48
0.8848
Figure A1. Total Criminal Association Activity (number of offences over 100,000 inhabitants)
Figure A2. Ratios between the level of both measures of criminal association activity and Index of Violent Crime
in the control and treated provinces1
1
"Treatment Group: Avellino, Benevento, Campobasso, Cosenza, Foggia, Potenza, Sassari, Trapani. Control Group: eleven neighbouring provinces. Notice that
in 1999, tradable green certificates system (`Certificati Verdi', CV) was introduced. Vio_Index is the index of violent crime, while CrimAssoc denotes Criminal
Association activity and TCrimAssoc denotes Total Criminal Association activity (sum of simple criminal association and criminal association of Mafia type).
"
49
TABLE A3. Test of the Coefficients provinces when the interaction terms between GDP, Population, Schooling and the treatment
dummy are included (Sample: 19 provinces).
H0
P-Value
H0
CrimAssoc
P-Value
TCrimAssoc
Cluster SE
TG91_94 = TG95_98
0.830
Cluster SE
TG91_94 = TG95_98
0.997
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.118
0.027
0.081
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.117
0.041
0.115
Bootstrapped SE
TG91_94 = TG95_98
TG95_98 = TG99_02
0.870
0.183
Bootstrapped SE
TG91_94 = TG95_98
TG95_98 = TG99_02
0.997
0.149
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.070
0.164
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.048
0.118
TABLE A4. Trend in simple and total criminal association in the whole period using the actual value of the Wind Index
(1990-2007) for the 19 neighbouring provinces.
VARIABLES
TG_Wind91_94
TG_Wind95_98
TG_Wind99_02
TG_Wind03_07
(1)
CrimAssoc
0.154
(0.204)
(0.234)
0.184
(0.216)
(0.325)
0.531
(0.312)
(0.456)
0.921
(0.392)**
(0.619)
(2)
CrimAssoc
(3)
TCrimAssoc
(4)
TCrimAssoc
0.301
(0.150)*
(0.169)*
0.637
(0.185)***
(0.260)**
0.298
(0.238)
(0.292)
0.323
(0.222)
(0.365)
0.746
(0.348)**
(0.503)
1.217
(0.488)**
(0.680)*
0.346
(0.179)*
(0.181)*
0.732
(0.241)***
(0.288)***
Yes
Yes
Yes
Yes
Population, VIO)
Yes
Yes
Yes
Yes
Observations
342
342
342
342
R-squared
0.329
0.328
0.307
0.304
Number of prov_id
19
19
19
19
TG_Windi (interaction term between dummy TG and the actual value of the Wind Index) is equal to the value of Wind Index for each
province in the treatment group for the period i, and zero otherwise. TG_Wind is the treatment group fixed effect which varies in the level of
the Wind Index. In all columns, OLS regressions with time, province fixed effects, province specific time trends and clustered standard
errors at the province level (in parenthesis). Significance levels: *** p<0.01, ** p<0.05, * p<0.1.
50
TABLE A5. Trend in simple and total criminal association after the introduction of the green certificate system using the
actual value of the Wind Index for the 19 neighbouring provinces.
VARIABLES
TG_Wind99_00
TG_Wind01_02
TG_Wind03_04
TG_Wind05_07
(1)
CrimAssoc
(2)
CrimAssoc
0.379
(0.214)*
(0.204)*
0.297
(0.137)**
(0.220)
0.568
(0.172)***
(0.273)**
0.782
(0.304)**
(0.402)
POST_Wind99_05
(3)
TCrimAssoc
(4)
TCrimAssoc
0.359
(0.239)*
(0.216)
0.369
(0.181)*
(0.249)
0.723
(0.215)***
(0.304)**
0.788
(0.368)**
(0.433)*
0.312**
(0.138)
0.371**
(0.151)
Yes
Yes
Yes
Yes
Population, VIO)
Yes
Yes
Yes
Yes
Observations
342
38
342
38
R-squared
0.332
0.315
0.304
0.328
Number of prov_id
19
19
19
19
TG_Windi is equal to the value of Wind Index for each province in the treatment group for the period i, and zero otherwise. In first and third
column, OLS regressions with time, province fixed effects, province specific time trends and clustered standard errors at the province level
(in parenthesis). In second and fourth column, panel collapsed in two periods as in Bertrand et al. (2004). POST_Windi (interaction term
between dummy POST99 and the actual value of the Wind Index) is equal to the average value of Wind Index in the treatment group for the
post-1999 period, and zero otherwise. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.
Appendix B.
Regressions without control variables. Sample: 19 neighbouring provinces.
TABLE B1. Trend in simple and total criminal association in the whole period (1990-2007).
VARIABLES
(1)
CrimAssoc
(2)
CrimAssoc
TG91_94
(3)
TCrimAssoc
(4)
TCrimAssoc
0.237
0.727
(0.636)
(0.726)
(0.630)
(0.803)
TG95_98
0.371
0.972
(0.660)
(0.655)
(0.846)
(0.978)
TG99_02
1.310
0.841
2.191
0.971
(0.930)
(0.416)*
(1.030)**
(0.525)*
(1.190)
(0.440)*
(1.349)
(0.493)**
TG03_07
2.294
1.687
3.412
1.871
(1.230)*
(0.505)***
(1.485)**
(0.729)**
(1.623)
(0.672)**
(1.827)*
(0.777)**
Yes
Yes
Yes
Yes
Observations
342
342
342
342
R-squared
0.321
0.321
0.298
0.295
Number of prov_id
19
19
19
19
TGi is a dummy equal to 1 for the provinces in the treatment group for the period i, and zero otherwise. FE regressions with province specific time-trends.
Clustered standard errors at the province level and clustered bootstrapped standard errors are in parenthesis. Significance levels: *** p<0.01, ** p<0.05, *
p<0.1.
51
TABLE B2. Test of the Coefficients
H0
P-Value
H0
CrimAssoc
P-Value
TCrimAssoc
Cluster SE
TG91_94 = TG95_98
TG95_98 = TG99_02
0.7631
0.0564
Cluster SE
TG91_94 = TG95_98
TG95_98 = TG99_02
0.6832
0.0545
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.0146
0.0370
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.0249
0.0581
Bootstrapped SE
TG91_94 = TG95_98
TG95_98 = TG99_02
0.7896
0.0692
Bootstrapped SE
TG91_94 = TG95_98
TG95_98 = TG99_02
0.6613
0.0344
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.0379
0.0845
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.0182
0.0439
TABLE B3. Trend in simple and total criminal association after the introduction of the green certificate system (1999-2007).
VARIABLES
TG99_00
TG01_02
TG03_04
TG05_07
(1)
CrimAssoc
(2)
CrimAssoc
0.965
(0.543)*
(0.519)*
0.944
(0.420)**
(0.589)
1.629
(0.459)***
(0.701)**
2.028
(0.744)**
(0.973)**
(3)
TCrimAssoc
(4)
TCrimAssoc
0.901
(0.636)
(0.573)
1.063
(0.528)*
(0.674)
1.947
(0.617)***
(0.802)**
1.844
(0.970)*
(1.084)*
POST99_05
0.772*
0.914*
(0.426)
(0.468)
Yes
Yes
Yes
Yes
Observations
342
38
342
38
R-squared
0.328
0.286
0.301
0.288
Number of prov_id
19
19
19
19
TGi is a dummy equal to 1 for the provinces in the treatment group for the period i, and zero otherwise. In first and third column, FE regressions with
province specific time-trends. Clustered standard errors at the province level and clustered bootstrapped standard errors are in parenthesis. In second and
fourth column, panel collapsed in two periods as in Bertrand et al. (2004). POSTi is a dummy equal to 1 for the provinces in the treatment group in the post1999 period and zero otherwise. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.
52
TABLE B4. Determinants of total installed capacity.
VARIABLES
Lag Capacity
Lag CrimAssoc
inter_ CrimAssoc
(1)
Capacity (MW)
(2)
Capacity (MW)
(3)
Capacity (MW)
(4)
Capacity (MW)
(5)
Capacity (MW)
(6)
Capacity (MW)
1.011***
(0.032)
1.624***
(0.458)
1.021***
(0.032)
0.390
(0.661)
2.202***
(0.770)
1.092***
(0.078)
5.310***
(1.598)
1.120***
(0.077)
-0.923
(3.381)
7.674**
(3.697)
0.925***
(0.079)
1.760*
(0.907)
0.940***
(0.081)
0.300
(1.477)
2.217
(1.718)
Yes
Yes
Yes
Yes
Yes
Yes
Observations
578
578
136
136
153
153
Number of prov_id
34
34
8
8
9
9
In first and second column, all 34 provinces of the Center South, in third and fourth column, provinces of the treatment group. In fifth and sixth column, the
second 9 windiest provinces. inter_CrimAssoc is the interaction between the lagged value of criminal association activity (Lag CrimAssoc) and the dummy for
the post-1999 period (1999-2007). In all columns bias-corrected least-squares dummy variable (LSDV) estimators as originally proposed by Kiviet (1995).
Province specific time trends are included and bootstrapped standard errors are in parenthesis. Significance levels: *** p<0.01, ** p<0.05, *p<0.1.
TABLE B5. Determinants of total installed capacity.
VARIABLES
Lag Capacity
LagTG_ CrimAssoc
(2)
Capacity (MW)
(4)
Capacity (MW)
(6)
Capacity (MW)
(8)
Capacity (MW)
1.030***
(0.040)
4.843***
(1.105)
1.037***
(0.039)
0.313
(2.000)
5.906***
(1.907)
1.053***
(0.047)
4.918***
(1.080)
1.061***
(0.047)
0.366
(2.148)
5.897**
(2.511)
interTG_ CrimAssoc
Yes
Yes
Yes
Yes
Observations
323
323
289
289
R-squared
Number of prov_id
19
19
17
17
In first and second column, comparison between the treatment and the control group, in third and fourth column, comparison between the treatment and the
second 9 windiest provinces. LagTG_CrimAssoc is the interaction between the treatment group dummy (TG), and the lagged value of criminal association
activity. interTG_CrimAssoc is the interaction between LagTG_CrimAssoc and the dummy for the post-1999 period (1999-2007). In all columns biascorrected least-squares dummy variable (LSDV) estimators as originally proposed by Kiviet (1995). Province specific time trends are included and bootstrapped
standard errors are in parenthesis. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.
TABLE B6. Trend in simple and total criminal association in the whole period using the actual value of the Wind Index (1990-2007)
VARIABLES
TG_Wind91_94
TG_Wind95_98
TG_Wind99_02
TG_Wind03_07
(1)
CrimAssoc
0.163
(0.216)
(0.231)
0.217
(0.204)
(0.317)
0.564
(0.298)*
(0.452)
0.965
(0.379)**
(0.614)
(2)
CrimAssoc
(3)
TCrimAssoc
(4)
TCrimAssoc
0.292
(0.152)*
(0.168)*
0.622
(0.188)***
(0.261)**
0.312
(0.245)
(0.287)
0.362
(0.207)*
(0.357)
0.787
(0.327)**
(0.499)
1.268
(0.466)**
(0.676)*
0.337
(0.184)*
(0.182)
0.713
(0.250)**
(0.290)**
Yes
Yes
Yes
Yes
Observations
342
342
342
342
R-squared
0.324
0.322
0.301
0.298
Number of prov_id
19
19
19
19
TG_Windi (interaction term between dummy TG and the actual value of the Wind Index) is equal to the value of Wind Index for each province in the treatment
group for the period i, and zero otherwise. TG_Wind is the treatment group fixed effect which varies in the level of the Wind Index. In all columns, OLS
regressions with time, province fixed effects, province specific time trends and clustered standard errors at the province level (in parenthesis). Significance
levels: *** p<0.01, ** p<0.05, * p<0.1.
53
TABLE B7. Trend in simple and total criminal association after the introduction of the green certificate system using the actual value of the
Wind Index
VARIABLES
TG_Wind99_00
TG_Wind01_02
TG_Wind03_04
TG_Wind05_07
(1)
CrimAssoc
(2)
CrimAssoc
0.376
(0.213)*
(0.202)*
0.273
(0.148)*
(0.217)
0.561
(0.176)***
(0.267)**
0.754
(0.309)**
(0.411)*
(3)
TCrimAssoc
(4)
TCrimAssoc
0.353
(0.237)
0.338
(0.198)
0.702
(0.237)***
(0.300)**
0.744
(0.377)*
(0.439)*
POST_Wind99_05
0.267
(0.172)
0.313*
(0.172)
Yes
Yes
Yes
Yes
Observations
342
38
342
38
R-squared
0.326
0.184
0.298
0.237
Number of prov_id
19
19
19
19
TG_Windi is equal to the value of Wind Index for each province in the treatment group for the period i, and zero otherwise. In first and third column, OLS
regressions with time, province fixed effects, province specific time trends and clustered standard errors at the province level (in parenthesis). In second and
fourth column, panel collapsed in two periods as in Bertrand et al. (2004). POST_Windi (interaction term between dummy POST99 and the actual value of the
Wind Index) is equal to the average value of Wind Index in the treatment group for the post-1999 period, and zero otherwise. Significance levels: *** p<0.01, **
p<0.05, * p<0.1.
Appendix C.
Analysis when Cosenza is dropped from the treatment group.
TABLE C1. Trend in simple and total criminal association in the whole period (1990-2007).
VARIABLES
TG91_94
TG95_98
TG99_02
TG03_07
(1)
CrimAssoc
0.404
(0.590)
(0.659)
0.586
(0.628)
(0.907)
1.589
(0.915)
(1.316)
2.764
(1.141)**
(1.729)
(2)
CrimAssoc
(3)
TCrimAssoc
(4)
TCrimAssoc
0.851
(0.434)*
(0.458)*
1.821
(0.511)***
(0.667)**
0.868
(0.698)
(0.760)
1.059
(0.696)
(1.025)
2.387
(1.102)**
(1.499)
3.791
(1.525)**
(1.953)*
1.065
(0.534)*
(0.544)*
2.149
(0.689)***
(0.754)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
324
324
324
324
R-squared
0.333
0.332
0.308
0.305
Number of prov_id
18
18
18
18
Clustered standard errors at the province level and clustered bootstrapped standard errors are in parenthesis.Significance levels:***p<0.01,**p<0.05,*p<0.1.
54
TABLE C2. Test of the Coefficients
H0
P-Value
H0
CrimAssoc
P-Value
TCrimAssoc
Cluster SE
Cluster SE
TG91_94 = TG95_98
TG95_98 = TG99_02
0.690
0.051
TG91_94 = TG95_98
TG95_98 = TG99_02
0.768
0.045
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
Bootstrapped SE
0.006
0.018
TG95_98 =( TG99_02+TG03_07)/2
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
Bootstrapped SE
0.014
0.046
TG91_94 = TG95_98
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
0.736
0.076
0.030
TG91_94 = TG95_98
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
0.757
0.043
0.015
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.074
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.047
TABLE C3. Trend in simple and total criminal association after the introduction of the green certificate system (1999-2007).
VARIABLES
TG99_00
TG01_02
TG03_04
TG05_07
(1)
CrimAssoc
(2)
CrimAssoc
1.023
(0.595)
(0.560)*
0.801
(0.402)*
(0.546)
1.664
(0.505)***
(0.678)**
2.092
(0.830)**
(0.967)**
(3)
TCrimAssoc
(4)
TCrimAssoc
1.066
(0.668)
(0.663)
1.068
(0.542)*
(0.648)*
2.149
(0.637)***
(0.775)***
2.152
(1.009)**
(1.050)**
POST99_05
0.829*
(0.426)
1.053**
(0.445)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
324
36
324
36
R-squared
0.334
0.517
0.305
0.467
Number of prov_id
18
18
18
18
55
Appendix D.
Analysis with a different treatment group (least windy provinces2) and the same control group.
TABLE D1. Trend in simple and total criminal association in the whole period (1990-2007).
(1)
CrimAssoc
VARIABLES
TG91_94
0.332
(0.934)
(0.967)
0.030
(1.223)
(1.251)
0.288
(1.512)
(1.587)
0.047
(1.763)
(2.074)
TG95_98
TG99_02
TG03_07
(2)
CrimAssoc
(3)
TCrimAssoc
(4)
TCrimAssoc
0.277
(0.610)
(0.499)
0.141
(0.734)
(0.762)
1.959
(1.163)
(1.232)
1.463
(1.410)
(1.502)
2.247
(1.517)
(1.967)
0.948
(1.855)
(2.528)
0.482
(0.961)
(0.758)
-0.969
(1.539)
(1.129)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
342
342
342
342
R-squared
0.402
0.401
0.359
0.346
Number of prov_id
19
19
19
19
Clustered standard errors at the province level and clustered bootstrapped standard errors are in parenthesis.Significance levels:***p<0.01,**p<0.05,*p<0.1.
TABLE D2. Test of the Coefficients
H0
P-Value
H0
CrimAssoc
Cluster SE
2
P-Value
TCrimAssoc
Cluster SE
TG91_94 = TG95_98
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
0.6360
0.6758
0.8274
TG91_94 = TG95_98
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
0.6261
0.3449
0.8885
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
Bootstrapped SE
TG91_94 = TG95_98
0.9860
0.9099
0.6517
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
Bootstrapped SE
TG91_94 = TG95_98
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
0.6486
0.8583
TG95_98 = TG99_02
TG95_98 =( TG99_02+TG03_07)/2
0.3414
0.8997
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.9895
(TG91_94+TG95_98)/2=( TG99_02+TG03_07)/2
0.9305
Ragusa, Teramo, Pescara, Caltanissetta, Enna, Reggio di Calabria, Catania, Chieti"
56
0.5343
TABLE D3. Trend in simple and total criminal association after the introduction of the green certificate system (1999-2007).
VARIABLES
TG99_00
TG01_02
TG03_04
TG05_07
(1)
CrimAssoc
(2)
CrimAssoc
0.133
(0.575)
(0.548)
0.700
(0.785)
(0.679)
0.281
(0.816)
(0.679)
0.431
(0.951)
(0.972)
(3)
TCrimAssoc
(4)
TCrimAssoc
0.460
(0.888)
(0.836)
0.202
(1.417)
(0.969)
-0.975
(1.647)
(1.162)
-1.390
(2.040)
(1.456)
POST99_05
0.131
(0.579)
0.143
(0.754)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
342
38
342
38
R-squared
0.403
0.143
0.347
0.130
Number of prov_id
19
19
19
19
57

- CESifo Group Munich

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