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A model of entrepreneurial choices in an economy with a corrupt public procurement sector is built, providing predictions along two main dimensions. First, corruption is more frequent in sectors where public institutions are large buyers. Second, firms favoured with corrupt contracts enjoy extra returns, so that procurement related activities attract the best entrepreneurs. A large scale microeconomic database, including all public procurement operations over a 4 year period in Paraguay, amounting annually to approximately 6% of the country's GDP, is then used to corroborate these predictions.
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Public Procurement and Rent-Seeking: The Case of Paraguay
Toulouse School of Economics (ARQADE, IDEI, and IAST), France
World Bank, USA
Summary. A model of entrepreneurial choices in an economy with a corrupt public procurement sector is built, providing predictions
along two dimensions. First, corrupt public institutions operate by offering contracts without competition and more corrupt entities
channel larger share of their budget in this way. Second, these firms enjoy extra returns, so that procurement related activities attract
the best entrepreneurs. A large-scale microeconomic database, including all public procurement operations over a 4-year period in
Paraguay, amounting annually to approximately 6% of the country’s GDP, is then used to corroborate these predictions.
Ó2015 Elsevier Ltd. All rights reserved.
Key words — procurement, corruption, rent-seeking, development
Public procurement of goods and services is one of the main
areas at risk of corruption in developing countries where reg-
ulations and legal enforcement are weak. On top of the static
cost of corruption and fund embezzlement, systematic depar-
tures from competition in the attribution of public markets
are likely to have a devastating impact on economic agents’
incentives and as a result on these countries’ productive
structure. This paper presents the first large-scale micro-level
evidence on the channels of rent-seeking and its impact on eco-
nomic development, using a unique database of nearly 50,000
public procurement operations in Paraguay, covering the per-
iod 2004–07. In a nutshell, we show that in Paraguay corrupt
behavior in the allocation of public contracts is a key channel
for rent-seeking. This large-scale network of favoritism, some-
times coined ‘‘la patria contratista,
has deeply damaging
economic consequences: public institutions buy goods and
services at inflated prices, and the set of incentives facing
potential entrepreneurs is biased towards unproductive
To guide the analysis, we model the choice of formal entre-
preneurs with idiosyncratic cost levels, between serving private
consumers competitively, or joining a rent-seeking sector,
where they sell to public institutions. In this rent sector, con-
tracts are attributed by corrupt officials, who distort allocation
rules in exchange for bribes. Firms willing to do business with
the Government must therefore be profitable enough to cover
their production costs as well as the bribes. We derive from the
model two main sets of predictions that are sustained by the
data, revealing the following story.
First, we establish that in Paraguay the main channel for
corruption in procurement is the systematic use of an ‘‘excep-
tionalpurchase mechanism, which bypasses legally required
minimum standards of transparency and competition and is
used much more frequently than what should be expected
from international best practice. Using the whole panel dimen-
sion of the data, we show that this type of corruption is used
more by institutions–firms pairs that trade repeatedly and in
large volumes. Moreover, we exploit a natural experiment,
linked to an exogenous change in public monitoring following
the widely publicized release midway through our period of
study of an NGO report flagging up exception as a key
channel for corruption, and show that its use decreased
significantly for these pairs boasting frequent interactions.
Finally, we also provide evidence that this channel dominates
other more ‘‘traditionalways to rig procurement contracts,
such as the breaking down of lots in amounts that escape
the obligation for open tenders.
Second, this implies that firms making more business with
the State, those in the so-called rent sectors, enjoy above nor-
mal rates of return and are the most efficient ones. We provide
evidence of these two aspects, by showing that firms selling
more to the public sector, as well as those selling more through
the exception channel, have higher profit margins, despite the
fact that they trade mostly in standard goods and should face
competition for the market.
As a result, public intervention in markets distort firms’
incentives by inducing additional entry in activities with an
important procurement component. To the extent that this
self-selection process pushes some of the best potential entre-
preneurs towards rent sectors, it generates a misallocation of
talents across the economy. Indeed, we document this strong
selection bias by exploiting an original econometric strategy
using firms’ names.
The paper concludes that in the case of Paraguay, the release
of the 2006 TI report and the subsequent change in exposition
to public scrutiny had an important positive impact on the
overall efficiency of the public procurement process. As it
made obvious the involvement of the civil society in monitor-
ing the use of public funds, and spurred an increase in the
interest of the media, it generated a change in the behavior
of the public sector with respect to the use of exceptional
procedures, especially so where the use of exception was
motivated by corrupt deals.
The rest of the paper is structured as follows. Section 2
reviews the main strands of related literature and spells out
*We are grateful to Marı
´a del Pilar Callizo and Oscar Gavila
´n from
Transparencia Paraguay for making the procurement data available and
for their support during the stay of the third author in Paraguay. We
thank the editor, three referees, and many seminar participants at
Berkeley, CERDI, Edinburgh, Guanajuato, Namur, Oxford, Paris,
Rome, Stockholm, Toulouse, the World Bank and Yale for useful
comments. Financial support from grant RENTSEP ANR-09-BLAN-
0325 is gratefully acknowledged. Final revision accepted: September 5,
World Development Vol. 77, pp. 395–407, 2016
0305-750X/Ó2015 Elsevier Ltd. All rights reserved.
the contributions of the paper. Section 3describes the Para-
guayan institutional environment and reviews procurement
practices over our period of study. Section 4develops the
model and derives empirical predictions. Section 5presents
the data. Sections 6 and 7 present the results related to the
two main sets of theoretical predictions, and Section 8con-
The idea that rent-seeking behavior has important social
and economic costs is a relatively long-standing one in the eco-
nomic and political science literature. Early contributions such
as Tullock (1967), Buchanan (1980), Krueger (1974) and
Bhagwati (1982), were concerned, mostly in a theoretical
framework, with the different types of costs associated with
the transfer of rents and the waste generated by agents engag-
ing time and resources in competing for rents, for example
through political lobbying or corruption.
More recently, some papers have provided explanations for
ways in which rent-seeking entails dynamic costs. Baumol
(1990) and Murphy, Shleifer, and Vishny (1991) focus for
example on the resulting dysfunctional allocation of talents.
In this approach, potential investments in physical or human
capital are directed to rent-abundant sectors (such as those
stemming from political favors, corruption or exploitation of
natural resources), while investments in innovative activities,
which have greater growth potential, become relatively less
attractive and are discouraged. As supporting empirical evi-
dence, Murphy et al. (1991) present cross-country growth
regressions augmented with country-level proportions of engi-
neering and law students, where the former are said to corre-
spond to investments in productive activities while the latter
are considered rent-seekers. Baumol’s evidence, on the other
hand, is based on historical accounts from Rome, Ancient
China, and the Middle Ages.
To date, there is still limited micro-evidence on the actual
channels and consequences of rent-seeking in developing econ-
omies. Following Fisman’s (2001) seminal contribution, some
papers have stressed the performance premium of connected
firms (Hellman, Jones, & Kaufmann, 2003; Fries, Lysenko,
& Polanec, 2003; Slinko, Zhuravskaya, & Yakovlev, 2005).
Other contributions have documented the importance of polit-
ical connections in securing access to key economic inputs,
such as credit (Khwaja & Mian, 2005; Li, Meng, Wang, &
Zhou, 2008), tax advantages or foreign exchange (Hsieh,
Miguel, Ortega, & Rodriguez, 2011), or favorable regulations
(Agrawal & Knoeber, 2001). At a more general level, the large
literature on corruption that developed since the 1990s is also
relevant here, and especially the strand of more recent papers
using microeconomic evidence to directly measure corruption
and its effects on outcomes.
A few contributions have dealt specifically with public pro-
curement. Hyytinen, Lundberg, and Toivanen (2007), who
study the effects of politics on municipal cleaning contracts
in Sweden, show that the lowest bidder does not win 58% of
the time and that the choice of the winner is subject to political
considerations; Goldman, Rocholl, and So (2013) show that
US companies connected, through the composition of their
boards, to the winning party in both legislative and presiden-
tial elections (in 1994 and 2000) are significantly more likely to
have experienced an increase in procurement contracts. Refer-
ences dealing explicitly with corruption include Di Tella and
Schargrodsky (2003), who document the impact of a crack-
down on corruption in Argentinean hospitals, and Bandiera,
Prat, and Valletti (2009), who disentangle the effect of passive
(inefficiency) versus active waste (corruption) in Italy, finding
that the former accounts for about four times the effect of
the latter, and Mironov and Zhuravskaya (2012), who analyze
the link between corrupt procurement and campaign financing
in Russia. Relatedly, Davis (2004) and Deiniger and Mpuga
(2004) analyze the impact of corruption on public service
delivery in South Africa and Uganda respectively.
With respect to this literature, our paper provides two main
contributions. First, we have data not only on the expenses
realized by public institutions, but also on the firms that are
on the selling side. This enables us to capture the effect of
large-scale corrupt practices on the profitability of firms and
hence on the industrial structure of the economy.
We provide
evidence of the distortive effects of rent-seeking in terms of
economic efficiency, by showing that it implies an inefficient
specialization of the more able entrepreneurs in imports and
procurement activities. Second, we document one of the most
prevalent channels of corruption in procurement activity,
namely the use of purchase mechanisms circumventing stan-
dard competitive rules, and uncover the economic characteris-
tics of the institutions and sectors more prone to it.
Paraguay is considered to be one of the most corrupt coun-
tries in the world.
Our period of study is part of a non-
interrupted 61-year spell, including the 1954–89 Stroessner
dictatorship, in which the Colorado party governed the coun-
try. At the heart of the system was the distribution of public
employment and contracts to its supporters, and the exclusion
of its opposition.
An important channel for corruption,
which we focus on here, was the allocation of public contracts
to firms that in most cases were created with the sole purpose
of supplying the state, often by selling a wide variety of
imported goods. As a result of the ample anecdotal evidence
of corruption in public procurement, and under pressure from
international organizations, a law regulating public procure-
ment practices (law 2051/03) was enacted in 2003 by the gov-
ernment of the newly elected president Nicanor Duarte
Frutos, with the announced intention of promoting transpar-
ency and efficiency in public purchases. The most significant of
its provisions were the creation of a public procurement
watchdog (the National Directorate of Public Procurement,
or DNCP), the design of a menu of purchase mechanisms to
regulate procurement procedures, and the compulsion to make
all information (calls, providers, award, etc.) public. This last
proviso was accompanied by the creation of the DNCP web
site where this information is available, but in practice access
is often intermittent and the interface is impractical.
There are strong indications however that improvements in
the regulatory framework did not translate quickly into clea-
ner procurement practices, partly because many officials did
not comply with the new law and the wrongdoings continued.
The main mechanism through which firms were favored is the
use of the exceptional purchase mechanism, by which specific
regulations, such as the obligation to organize public tenders
above certain amounts, were disregarded (see details in Sec-
tion 5below). In 2006, Transparencia Paraguay (TP), the
local chapter of Transparency International, published an
extensive report sponsored by the Inter-American Develop-
ment Bank (IDB) focusing on the excessive use of exceptional
procedures, which was clearly identified as one of the main
irregularities in the procurement process.
The Electricity
State-owned enterprise ANDE has for instance been pointed
out for buying large numbers of electric transformers in this
way over the years, despite the fact that these are routinely
required by the firm for network repairs. Firm officials recog-
nize that this practice usually generates excess pricing of
between 17% and 27%.
Because the report was given ample coverage in the local
media and through public presentations, and its recommenda-
tions were subsequently relayed by the IDB–World Bank Par-
aguayan public procurement evaluation panel, the officials in
charge of procurement in public institutions and firms became
more cautious. Indeed, in 2004 and 2005 purchases made
through the ‘‘exceptionalprocedure amounted to nearly
24% of the total procurement spending. In the period
2006–07 the share of purchases made through the ‘‘excep-
tionalprocedure decreased to 13%. We exploit this ‘‘natural
experiment, by showing that the specific pairs of institutions
and firms that we identify as irregularly using exception in the
first subperiod, subsequently experienced the largest reduction
in its use.
The next section builds a model of corrupt procurement,
from which we derive predictions that we exploit to conduct
an empirical analysis of corruption in public purchases in
The model focuses on the formal sector of the economy as
formal firms are the only ones allowed to compete for public
markets. The production functions involve constant returns
to scale technologies.
The cost function of a producer oper-
ating in the formal sector is CðqÞ¼cq, where by assumption
Assumption A1. cis independently and uniformly distributed
in ½0;c.
Entrepreneurs in the formal sector have the choice between
procuring commodities for the public sector, where, as corrup-
tion prevails, they make rents, or doing business in the private
sector where they serve consumers competitively. As a bench-
mark, we first briefly discuss the corruption-free equilibrium.
In the absence of rent-seeking opportunities, entrepreneurs
serve market demand competitively and make no rents. Com-
petitive pressure helps to select the best available technology
so that in equilibrium the price equates the lowest marginal
cost, p¼0, and quantities traded in the private sector are
DðpÞ. Welfare is maximized.
Rent-Seeking. We now turn to the case, denoted by the
superscript r, where corruption prevails in public purchase.
While in practice corruption and bribes are not observed,
the model based on assuming the existence of corruption will
generate a number of testable predictions discussed below.
Optimal procurement rules specify that for large purchase
above given thresholds it is mandatory to organize a compet-
itive tender (see Auriol, 2006), and to advertise the calls to
encourage submissions by firms. In practice it is not always
possible to organize a competitive tender, for instance because
the commodity is patented, or there is an emergency. To deal
with these specifics cases, public procurement rules include an
‘‘exceptional purchaseprocedure. When they rely on this type
of procedure, public purchasers are able to bypass the compe-
tition phase. This is the easiest way to favor a firm in exchange
of a bribe. Auriol (2006) shows that for large purchases a cor-
rupted procurement official favors limited tendering proce-
dures (i.e., exceptional purchase), thereby maximizing the
price of the purchase and his bribe.
We thus expect corrupt
Paraguayan public institutions to rely on the exceptional pur-
chase mechanism to collect bribes over large purchases.
Firms selection in the rent sector. The public purchaser
chooses how many firms should be included in the rent sector
and creates a lot for each of them out of his/her total budget.
A firm invited to procure one of these lots through exceptional
purchase is in a monopoly position. It can ask for the highest
possible unit price c. In the rent sector a contract of size q>0
hence costs cq. The government officials choose b0;1, the
share of cq they take as a bribe in exchange for giving this
market to a firm without competition.
We deduce that, in
the rent sector, the profit of a firm with cost c0;cand
lot size q>0 is:
PðcÞ¼qðccÞbcq ð1Þ
To access the rent sector the firm must pay bcq. Since bcq is
independent of the cost of the firm, the bribe is equivalent to
a fixed cost which screens out the less efficient firms. Let
crðbÞ2½0;cdenotes the firm that is just indifferent between
the rent and the private sector: PðcrðbÞÞ ¼ 0. Since the bribe
is a fraction of the market value (i.e., it is linear in cq), the
value crðbÞis independent of q. It is straightforward to check
We deduce that if c<crðbÞthen PðcÞ¼qc
rðbÞc½>0. In
the public sector rents are made and different types of firms
coexist because corruption creates artificial barriers to entry.
By contrast entrepreneurs with costs higher than crðbÞwould
make a loss, and so prefer to serve private demand. It is intu-
itive that the share of firms in the rent sector, crðbÞ
decreases with b. The more greedy government representatives
are, the more profitable firms need to be to do business with
them: they need to be able to cover their cost plus the bribes
and still make non-negative profit.
Optimal bribe rate. By choosing b0;1the public pur-
chaser chooses how many firms enter the rent sector. Then
she attributes to each firm with a marginal cost lower than
crðbÞa lot. In our database the lots qij >0 vary depending
on the identity of the purchaser (institution j) and of the seller
(firm i). As shown by Eqn. (2) our results are independent of
the exact size of the lots and on their distribution among the
firms in the rent sector, so we leave them unspecified. The only
constraint is that the lots are positive in value and that their
sum is equal to the available budget. To compute the optimal
bribe rate, public officials internalize the risk of corruption
being detected and punished.
We assume that the greedier
the public purchaser (i.e., the higher bthe bribe rate) the
higher the risk of detection.
That is, the probability of detec-
tion is GðbÞ2½0;1, where GðbÞis a distribution function and
gðbÞ¼G0ðbÞ>0 is the associated density function defined
over ½0;1satisfying the monotone hazard rate property
Assumption A2. 1GðbÞ
gðbÞis decreasing in b.
Consistently with empirical evidence we focus on weak pun-
ishment: in case of detection the bribe is lost to the officials.
For a purchase of total size Q, the net expected rent of the
public purchaser then writes B¼bcQ 1GðbÞðÞ. To avoid
being excluded from the lucrative bribes business, the public
purchaser needs to split the budget among enough firms
(i.e., to choose a low enough b). This captures in a simple
way the fact that Paraguay at the time of the study was a
patronage economy. Indeed, the Colorado party was able to
maintain itself in power for 61 years by sharing among its fol-
lowers the windfall from power through corrupted deals.
We deduce the next result.
Proposition 1. The bribe rate, br0;1Þ, is solution to:
Entrepreneurs choose the rent sector if and only if
Under Assumption A2 it is easy to check that brsolution of
(3) exists and is unique.
Proposition 1 indicates that the most productive entrepre-
neurs choose the rent sector where there is no competition
and commodities are overpriced, so that they make rent
(i.e., PðcÞ¼qc
rðbÞc½>0 for all c<ð1brÞc). The model
captures the essence of the redistribution mechanism among
the Paraguayan elite: only the firms with the lowest cost
are included in the corrupted deals. Firms with high cost
(i.e., those with little physical and social capital, run by
poorly educated and connected managers, etc.) are left to
serve private demand. We deduce that entrepreneurs who
choose to do business with the government are the most
efficient ones and they make rents. Compared to a
corruption-free economy, prices are higher so that the quan-
tities consumed in equilibrium are smaller, leading to lower
aggregate production.
In practice procurement activities are decentralized at the
institution level (ministries, state enterprises, etc.), so for the
empirical analysis bshould be thought of as institution-
specific. Corruption detection varies from one institution
to another. They differ in their level of exposure to public
scrutiny, depending for example on how many people are
harmed by corruption or on how politically sensitive their
activities are. They also differ in their capacity to realize
and hide corrupt acts. A simple way to formalize this in
the context of the model is in terms of hazard rate domi-
nance, which implies stochastic dominance.
We deduce
easily the next result.
Proposition 2. Let Gð:Þand Kð:Þbe two distributions of
probability of corruption detection such that gðbÞ
1KðbÞ8b0;1. Let br
Gand br
Kbe defined in Eq. (3)with
distributions Gð:Þand Kð:Þrespectively. Then br
Institutions characterized by a lower probability of
detection (i.e., lower hazard rate) will be less cautious to hide
corruption and will ask for more bribes. It implies that the
average lot size, q¼Q
1b, which increases with b, will be larger
for those institutions.
It is intuitive that when the public
officials are more greedy there are less firms that are able to
survive in the rent sector and that their lots size is then larger.
At the institution level, we hence expect a correlation between
the frequency of exceptional purchases, which is our marker
for corruption, and the average market shares attributed to
Summary of empirical predictions. Our theoretical results
lead to two main sets of predictions that we take to the data
in the following order, using several complementary empirical
First, according to Proposition 2, institutions character-
ized by a lower probability of detection rely more heavily
on exceptional purchase and have larger lots size attributed
to their providers. In Section 6, we test this prediction in
two steps. In the first one, we discuss in details why in
Paraguay the use of exceptional purchase mechanisms
can be considered a marker for corruption. We then
establish that more corrupt institutions do indeed attribute
larger lots to their providers through the exceptional
purchase mechanism.
Second, following Proposition 1, entrepreneurs who
enter the procurement sector are the most efficient ones
and they are making positive rents. We test this prediction
in Section 7.
Procurement data.
The main data set tracks all the pro-
curement transactions made over the period 2004–07 between
73 public entities (representing over 90% of total Paraguayan
public spending and employment) and 5,517 different private
These 47,615 public purchases include all types
of goods and services, from stationary to machinery, oil pur-
chases, food, services, etc. There are good reasons to believe
that no public procurement operations escape registration
as, under the new system, contracts need to be registered
and executed before the corresponding funds are released.
Total public spending over the whole period amounts to Gs.
12,400 bn. (approx. US$ 2,235 m),
which represents
between 5.5% and 6.9% of Paraguay’s yearly GDP.
The distribution of contract values has a fat left-hand tail
with 84% of purchases costing less than 2000 minimum daily
wage (mdw), while 5.5% of contracts costing over 10,000
mdw make up 86% of the total spending. The sample mean
is approximately US$ 47,000, equivalent to 36 times the
national per capita GDP at the time.
Each observation in the procurement data set contains the
name and type of the public entity, the name and legal regis-
tration number (RUC) of the supplying firm and its owner,
and information on the purchase including the nature of the
good or service categorized in 25 different groups, the total
cost in local currency, and the purchase mechanism used.
Purchase mechanisms are a key provision of the 2003 public
procurement law, regulating the procedures to be followed in
allocating contracts depending on their total value. There are
five legal purchase mechanisms with gradually increasing con-
straints on the minimum number of offers, the mode and
length of publication of the call for offers, and the attribution
Finally, these guidelines can be disregarded in cases of emer-
gency, such as natural disasters or health epidemics, for the
purchase of patented and copyrighted goods, or for purchases
requiring defense secrecy. In those extraordinary circum-
stances, public officials can skip all formal purchase require-
ments through the so-called exceptional purchase
mechanism. In our sample, exceptional purchases are quite
common for certain categories of goods or services, such as
rentals, advertisement, consultancy and transport.
Tax ranking data. We use annual rankings of top taxpayers
published on the Ministry of Finance’s web site. Firms’ ranks
are determined by their total payments on all taxes.
public firms are excluded, we have information for 748 firms
in 2004, 459 firms in 2005, 482 firms in 2006, and 478 firms
in 2007.
Import–export data. We also include annual rankings from
the Customs’ SOFIA official data base. These include the full
universe of importers from 2004 to 2007, including the total
free on board (FOB) value imported, and of exporters for
the same period, including the cost, insurance, freight (CIF)
value exported.
Institution-level corruption indices. We introduce institution-
level corruption indices for a subset of 13 institutions in our
sample. In total, this covers 15,640 of our initial observations,
equivalent to 32.8% of the total. These indices were developed
by the NGO Transparencia Paraguay during 2004–08.
It is useful to discuss the claim that the use of exceptional
purchases is the main channel for corruption in the context
we study. Three aspects are relevant here. First of all, as men-
tioned in Section 3, there is ample anecdotal evidence support-
ing this claim, in particular the report by Transparencia
Paraguay (2005), which has been widely publicized. Second,
the use of such procedure in the Paraguayan context vastly
exceeds comparable figures from around the world. For
instance, in a sample of Brazilian health procurement con-
tracts between 2004 and 2009, exceptions amount to approxi-
mately 9%.
Similarly, from 2006 to 2010, only 7% of EU
procedures were ‘‘negotiated without publication, corre-
sponding to 5% of the value share of total procurement con-
tracts above the EU threshold for mandatory call for
By means of comparison, the equivalent figure in
Paraguay in the two-year period before the publication of
Transparencia Paraguay (2006), looking at contracts above
the 2,000-mdw threshold for mandatory call for tender, is
20.4% of all awards. In terms of share of total contract value,
it climbs to 23.8%.
Finally, it is worth noting that estimations not shown here
to save space provide evidence that none of our results go
through when using as a marker of corruption an other com-
monly mentioned channel for corruption in procurement,
namely the practice of breaking down contracts in smaller lots
so that they can be attributed without competition. This is true
using as dependent variable an indicator of whether the con-
tract lies in a 10% or 20% value band just below the threshold
that implies mandatory open tender. This shows that in the
context of generalized corruption prevailing in Paraguay, pub-
lic purchasers were careless and used the simplest procedure
available to favor firms.
Going to the estimations, note that our unit of observation
is the individual purchase. Corruption being in most cases not
observed, our identification strategy, following the model,
relies on documenting the link between the frequency of
non-competitive procedures (i.e., exceptions), the intensity of
firm–institution relationships (i.e., the lot size in the model),
as well as institution-level detection proneness (i.e., the detec-
tion functions). Each of the 47,615 purchases available corre-
sponds to a pair composed of a firm iand an institution j. Our
sample of 73 institutions and 5,517 firms generates a total of
13,693 different ‘‘activepairs, with an average number of con-
tracts equal to 3.5 (std. dev. 10.5), a minimum of 1 (for 7,215
pairs) and a maximum of 460.
We estimate the following basic model:
excijkt ¼hjþhkþhtþXijtb1þeijkt ;ð5Þ
where exc is a binary variable equal to 1 if the contract over
good kprocured from firm iby institution jin year tis made
through the exception, and h0sare institution (j), good (k), and
year (t) fixed effects. We use a linear probability model to esti-
mate the model above.
The fixed effects allows us to capture any systematic deter-
minants of exceptional purchase that would correspond to
characteristics of the goods (patented or monopolistic goods,
exclusive dealing) and the institutions (specifically dedicated
to attend emergencies, involved in defense deals, etc., possibly
with changes over time), as well as specific time fluctuations or
trend in the use of exceptions. Once these fixed effects are
introduced, we expect no additional features to be significant
if procurement rules are applied correctly.
Our explanatory variables of interest Xijt are firm–
institution-level ones: the total value of each pair’s transac-
tions and the proportion of an institution’s transactions done
with each particular provider, year by year. These are two
measures of the lot size described in the model. Our model sig-
nals as a symptom of corruption the fact that institutions,
which do more exception for that purpose do that through
the attribution of larger lots to their providers (i.e., a more
corrupt public purchaser asks for larger bribes which reduces
the number of firms able to pay them, and increases their aver-
age lot size see Proposition 2).
Table 1 contains the results from (5). In column 1, which
report the estimation with institution, goods and year fixed
effects, the larger the share of an institution’s transactions
done with each particular provider, the higher the probability
that the contract is made through exception, an effect signifi-
cant at the 5% level. This is robust to adding both
institution-year and goods-year fixed effects in column 2.
The marginal effect implies that an institution increasing the
share of its total procurement volume allocated to a particular
firm from 0.9% to 3.8% of its portfolio (that is by one standard
deviation above the sample mean), would increase the share of
its contracts with that particular firm made through the excep-
tion by 36%.
In Column 3, where we add a full set of firm
fixed-effect, the significance is lost, which is not very surprising
considering the number of additional parameters (over 5,000).
Proposition 2 suggests that institutions more exposed to
public scrutiny use less exceptional purchase. We use the
institution-level corruption indices described in the data sec-
tion to proxy for this exposure. We measure corruption with
a synthetic index equal to the arithmetic mean of three original
indices, namely the evaluations based on the Comptroller
General’s report, the number of administrative indictments
in any given institution, and the number of newspaper articles
mentioning corruption in each institution.
We introduce the following specification, where we expect b2
to be positive if the corruption story is relevant
excijkt ¼hjþhkþhtþhjt þXijtb1þXijt Inst Corr jt
The results are in columns 4–6 of Table 1. In column 4, con-
tract value becomes negative, while its interaction with corrup-
tion is positive and significant at the 1% level. This confirms
that the link between frequent interactions and exceptional
purchases is mediated by corruption. This effect remains signif-
icant with institution- and goods-specific time trends in column
5 and, more strikingly, with firm fixed effects, in column 6.
This first set of result support the corruption hypothesis and
gives us more confidence that corruption is the relevant expla-
nation. Of course, despite being more satisfactory than usual
subjective indicators, we cannot completely rule out endogene-
ity of the corruption measure used here, and finding appropri-
ate instruments appears difficult.
Alternative interpretations for the results that frequent pair
interactions lead to more contracts through the exception
include first a ‘‘reputationeffect. In circumstances where pub-
lic institutions need to use exceptional mechanisms, for exam-
ple because of some social emergency, they may turn to firms
they have had frequent interactions with, because they know
these are more reliable. Another explanation involves simple
inefficiency or passive waste, as Bandiera et al. (2009) docu-
ment in the case of public procurement in Italy. Here, the
argument is that procurement officials simply award contracts
to firms they already know, because they do not internalize the
new rules or because they are lazy and it is the solution that
requires less effort.
To sort out the different explanations, our strategy is to
exploit the shock created by the publication of the Transpar-
ency Paraguay report at the beginning of 2006, which
attracted attention to what was considered the excessive use
of exception in public contracting. This prompted debates
and comments in the media and the political arena, as well
as incentives for investigative journalism in the country to
inquire specifically on procurement done through the excep-
tional procedure. Our hypothesis is that if exception is indeed
a mean to make corrupt deals, the institution–firm pairs that
were using exception frequently should display greater reduc-
tions in its use as a result of this shock. In other words, we
hypothesize that where the use of exception was motivated
by corrupt deals, the higher risk of being discovered and sanc-
tioned brought about by increased public scrutiny should have
generated a stronger decrease in its use than where it was moti-
vated by justifiable reasons.
The resulting difference-in-differences strategy thus should
display a positive coefficient based on this differential effect,
even in the case where the report implied an overall reduction
in exceptional purchase. From a theory point of view, this cor-
responds to the monitoring shock shifting the detection func-
tion for a subset of firm–institution pairs, as described in
Proposition 2 above. Alternatively, if the explanation for high
levels of exception is simply inefficiency, or if the decrease
results from a learning process, we should observe a reduction
in its use across the board, and the diff-in-diff estimator should
not be significant.
Table 2, in which we run the basic estimations of Table 1,
columns 1–3, over the separate subperiods 2004–05 and
2006–07, provides a first illustration. Over the period 2004–
05, the variables proxying for frequent/intense firm–institution
relationship, i.e., the total value of pair’s transactions and the
share of institutions’ transactions done with each particular
provider, are significant five times out of six, including in spec-
ifications with firm fixed effects, in column 3. On the contrary,
they are almost never significant in the period 2006–07, and
even become negative with firm fixed effects in column 6.
In the first sub-period, a one standard deviation in value of
pair’s transactions reduces the average use of exception by
between 3% and 4%, while the same effect is more than an
order of magnitude smaller in the second sub-period. The
magnitude of the effect is similarly smaller when looking at
In Table 3, we present the results from a formal differences-
in-differences based on these insights. We estimate the follow-
ing regression, where b3is the diff-in-diff estimator:
excijk ¼b0þb1D0607 þb2DTþb3ðD0607 DTÞþeijk >0;ð7Þ
For the sake of robustness, we consider three alternative ways
to construct our treatment group of institution–firm pairs:
based on high-volume, high share of institutions’ budget,
and high use of exception in the 2004–05 period (see Online
Appendix for details).
The results in Table 3 are striking. In column 1, the treatment
group includes institution–firm pairs with total transactions
value above the median in the first period; the diff-in-diff
estimator is negative and significant at the 1% value, and indi-
cates that the treatment group reduces its average contracting
by exception by close to 4%. The result is more telling when
translated in terms of amounts: for the treatment group, the
value of transactions made by exception goes from representing
23.2% of the total in the first period to only 12.2% in the second
period. This 9 percentage points (pp) reduction should be com-
pared with that of the control group which is only 4.3 pp (from
16% to 11.7% of the total).
A similarly strong result emerges in column 2 when using
instead the share of institutions’ budget as our treatment crite-
ria. There is a decrease between the two periods, significant at
the 1% level. The size of the effect is similar, as the treatment
group experiences a reduction in the value share of exception
of 10.2 pp, while the corresponding control group reduction is
of 3.4 pp.
Table 1. Exceptional purchase determinants and institution-level corruption
(1) (2) (3) (4) (5) (6)
Total value of pair ij 0.002 0.002 0.003 0.123 0.001 0.030
contracts (0.002) (0.002) (0.002) (0.056)
(0.065) (0.030)
Share of institution j0.335 0.360 0.032 1.349 0.690 0.099
contracts with firm i(0.137)
(0.106) (0.621)
(0.656) (0.409)
Total value of pair ij 0.049 0.024 0.026
contracts x corruption (0.012)
Share of institution j0.272 0.145 0.059
contracts with firm icorruption (0.155) (0.170) (0.067)
Instit. F.E. Yes Yes Yes Yes Yes Yes
Goods F.E. Yes Yes Yes Yes Yes Yes
Years F.E. Yes Yes Yes Yes Yes Yes
Instit. years F.E. Yes Yes Yes Yes
Goods years F.E. Yes Yes Yes Yes
Firms F.E. Yes Yes
Observations 47,615 47,615 47,615 15,489 15,489 15,489
R-Squared 0.09 0.12 0.53 0.13 0.17 0.60
Note: In columns 1–6, the dependent variable is an dummy equal to 1 if a contract was made by exception, and 0 otherwise. Corruption is an institution-
level corruption index. Robust standard errors in parentheses, clustered at the institution level. Stars indicate significance at the 10% (*), 5% (**), and 1%
(***) respectively.
In column 3, we experiment with yet another definition of
the treatment group, based on the use of exception in the base
period. The result again strongly support our hypothesis: the
diff-in-diff estimator is negative and significant at the 1% level,
with a probability reduction of 14%. In terms of amounts, the
treatment group value share of exception diminishes from
50.9% to 7.3%, while for the control group it experiences an
increase from 1.4% to 19.9%.
Columns 4 presents the case where the treatment is defined
on the basis of the share of exception in a pair’s transaction
being above the average. Again, the diff-in-diff coefficient is
negative and significant at the 1% level, indicating a probabil-
ity decrease of 21%. The changes in terms of amounts are very
close to those of column 3. Finally, column 5 provides an addi-
tional robustness check, by restricting the base sample to pairs
that have strictly positive exception use in the period 2004–05,
with again very similar results: a probability decrease of 15%,
significant at the 1% level.
Finally, in the last line of Table 3, we also report the results
from similar specifications with a continuous version of the
treatment variables. The complete set of results are on line
with those of the discrete version.
Additional technical details and robustness tests, including
the way treatment groups are constructed, and a comparison
of pre-treatment trends between treatment and control groups,
supporting the idea that the report’s publication can be viewed
as a natural experiment, are also provided in Online Appen-
In the next section, we turn to show how the corrupt prac-
tices documented above distort the profitability of firms.
Table 2. Exceptional purchase determinants – sub periods
(1) (2) (3) (4) (5) (6)
2004–05 2004–05 2004–05 2006–07 2006–07 2006–07
Total value of pair ij 0.037 0.035 0.032 0.001 0.001 0.002
contracts (0.019)
(0.001) (0.001) (0.002)
Share of institution j0.339 0.297 0.085 0.209 0.259 0.097
contracts with firm i(0.178)
(0.146) (0.129) (0.139)
Instit. F.E. Yes Yes Yes Yes Yes Yes
Goods F.E. Yes Yes Yes Yes Yes Yes
Years F.E. Yes Yes Yes Yes Yes Yes
Instit. years F.E. Yes Yes Yes Yes
Goods years F.E. Yes Yes Yes Yes
Firms F.E. Yes Yes
Observations 22,179 22,179 22,179 25,436 25,436 25,436
R-Squared 0.09 0.11 0.51 0.11 0.14 0.62
Note: In columns 1–6, the dependent variable is an dummy equal to 1 if a contract was made by exception, and 0 otherwise. See Table 2 notes for variables
definitions. Robust standard errors in parentheses, clustered at the institution level. Stars indicate significance at the 10% (*), 5% (**), and 1% (***)
Table 3. Exceptional purchase – differences-in-differences
Pair-level treatment criteria (1) (2) (3) (4) (5)
Absolute value
of transactions
Share of transactions
in institution budget
Absolute value
of exception
Share of transactions
done by exception
Share of transactions
done by exception
(only > 0 obs.)
0.012 0.003 0.000 0.023 0.086
(0.011) (0.015) (0.012) (0.015) (0.010)
0.022 0.034 0.216 0.384 0.332
0.037 0.030 0.138 0.211 0.151
Observations 37,453 37,453 37,453 37,453 13,709
R-Squared 0.46 0.46 0.47 0.52 0.52
0.003 0.007 0.080 0.092
(0.018) (0.010) (0.049) (0.046) (0.057)
Continuous 0.187 0.235
treatment (0.365) (0.132) (0.099) (0.047) (0.050)
Note: In columns 1–6, the dependent variable is an dummy equal to 1 if a contract was made by exception, and 0 otherwise. D
is a dummy variable
taking value 1 for the period 2006–07. D
is the treatment group dummy; for each column, the line pair-level treatment criteria indicates the way D
defined at the institution–firm pair level. All estimations include firms, institutions, goods, institutions * years, and goods * years fixed effects, as well as a
control for the amount contracted. The last two lines show the diff-in-diff coefficients and standard errors from similar estimations on the 2004–05 sample
only, where D
is a dummy variable taking value 1 for the year 2005.
Robust standard errors in parentheses, clustered at the institution level. Stars indicate significance at the 10% (*), 5% (**), and 1% (***) respectively.
The model’s second set of predictions is that, as a result of the
corrupt practices unveiled above, entrepreneurs doing business
with public institutions are more profitable than their counter-
parts serving private demand. Moreover, the most able entre-
preneurs are expected to self-select into the more profitable
procurement activities, as only they are efficient enough to
afford both the production cost and the bribes to public officials.
Profitability in the rent sector. We first perform a reduced
form analysis of the effect on firms’ profits of a number of vari-
ables, derived from the results in the previous section. As a
proxy for the share of ‘‘favoredcontracts in the firm’s port-
folio, we use the share of a firm’s contracts made through
the exception, and the weighted average level of corruption
of the institutions it deals with (where the weights are the share
of the sales to these institutions in the firm’s total sales). In
addition, we also use firms’ amount and number of contracts.
The amount of taxes paid provides a reasonable approxima-
tion for profits because the tax rate on gains is flat and uni-
form in each period (30% in 2004, 20% in 2005, 10% in 2006
and 2007). While the inclusion of other taxes (among which
custom duties are by far the largest component) introduces
some noise in the mapping between profits and taxes paid,
we control for total imports in all estimations to minimize this
issue. The model we want to estimate is:
Git ¼aþb1Zit þb2Mit þXitb3þhtþeit ;ð8Þ
where Git denotes the net gains of firm iin year t,Zit is the var-
iable of interest (alternatively, the share of sales through the
exception, average corruption of buyers, total sales to the
state, number of contracts), Mit is the total amount imported,
Xit is a vector of control variables, and htare time fixed effects.
However, the income tax and other taxes are amalgamated
in the tax data, so we only observe:
Tit ¼xtGit þdiMit þmit;ð9Þ
where xt¼0:3 for 2004, xt¼0:2 for 2005, and xt¼0:1 for
2006 and 2007. In order to obtain the firms’ net gains we
therefore divide the total amount paid in taxes by the corre-
sponding tax rates.
We thus test the following specification:
Tit=xt¼aþb1Zit þðb2þdi=xtÞMit þXitb3þhtþeit þmit
under the assumption that Zit is uncorrelated with mit .
The distribution of profits resulting from the available data
is truncated at a strictly positive point. Moreover, the set of
firms for which we have non-zero tax data is not constant over
time. This forces us to restrict the panel to the subset of strictly
positive tax observations.
As a result, we obtain an unbal-
anced panel of 2,167 observations across 4 years for 1,017 pri-
vate firms.
One worry is that unobserved firm characteristics might be
correlated both with the amount of taxes paid and with some
of the Zit variables on the right hand side.
For example, more
efficient entrepreneurs might be more successful in general,
hence pay more taxes, and also win more procurement con-
tracts or be more frequently favored through exception because
of their good reputation. Another concern is related to firm
size. Indeed, bigger firms may have larger overall profits and
also be in a better position to win procurement contracts or
to respond to emergency calls from public institutions. To
address such issues, we add firm-level fixed effects hito (10),
exploiting the panel dimension of the data to wash out any time
invariant firm-level unobserved characteristics.
The results in Table 4 support our hypotheses. Column 1
shows that firm’s profits are significantly increasing in the
share of its contracts made by exceptional purchase. The aver-
age marginal effect implies that a 1% increase in the share of
contracts made by exception corresponds to Gs. 28 millions
(US$ 5,600) additional profits.
In column 2, the correlation between the average level of
corruption of public buyers and firms’ profitability is positive
but only nearly significant at conventional levels, which is not
surprising given that the sample size is reduced to 261 since
corruption indices are not available for all institutions.
Finally, in columns 3 and 4, we look directly at the correla-
tion between firms’ profits and their procurement activity. The
coefficients of both the amounts sold and the number of
contracts are positive and significant. In terms of marginal
Table 4. Procurement and profitability of firms
(1) (2) (3) (4)
Random effects Fixed effects Fixed effects Fixed effects
Imports 0.001 0.003 0.000 0.000
Exports 0.000 0.024 0.001 0.001
(0.021) (0.000)
Exceptional purchase 2.834
Corruption index 1.205
Amount sold 0.293
Number of contracts 0.154
Time F.E. Yes Yes Yes Yes
Firms F.E. No Yes Yes Yes
Observations 476 261 2167 2167
R-Squared 0.46 0.66 0.25 0.25
Hausman chi2 0.40 0.00 0.00 0.00
Standard errors in parentheses. Stars indicate significance at the 10% (*), 5% (**) and 1% (***) respectively.
Note: In all columns the dependent variable is firm-level gains, defined as in Eqn. (10). All data adjusted for yearly price variations. In each case, we test
the appropriateness of the random versus the fixed effect model, using the standard Hausman test. We report only the specification supported by the test.
effects, every additional Gs. sold to the state translates into a
Gs. 0.29 increase in profits, i.e., a rate of return on procure-
ment operations of nearly 29%, while a firm obtaining an addi-
tional contract increases its profits by Gs. 154 millions
(approx. US$ 30,800).
These results, together with those of the previous Sec-
tion showing that firms with bigger procurement portfolios
are more likely to enter in side deals, imply that average
profitability should be higher in procurement than in private
In turn, this is likely to distort firms’ incentives
and induce additional entry of potential entrepreneurs into
these sectors. Next, we provide evidence of this self-selection
Misallocation of talents. An important point of the model is
that firms’ unobserved attributes (entrepreneurial or network-
ing skills, efficiency, etc.) should explain part of their increased
profitability due to a self-selection process. Some of the best
entrepreneurs are attracted to sectors where they can benefit
from the corrupt allocation of procurement contracts,
resulting in a misallocation of talents in the economy.
The following test explicitly addresses the process of
self-selection into the procurement sector, using a procedure
proposed by Wooldridge (2002, p. 631).
This entails
estimating first a probit model to explain the fact that firms
intervene in the procurement sector or not:
where Yiis a dummy variable equal to 1 if the firm sells to
public institutions at any point during the sample period, Xi
is a vector of firm-level observables, and Siis a set of instru-
ments. From (11), we derive b
/, the predicted density and b
the corresponding predicted cumulative density. We then
estimate, for each year, the following tobit model:
Git ¼max 0;aþb1Zit þXitb2þb3Yib
Remember that Git denotes the net gains of firm iin year t,Zit
is either total firm’s sales to the state or its total number of
contracts, and Xit is a vector of control variables. We are inter-
ested in the statistical significance of the two last regressors, as
an indication of self-selection, as well as in how their inclusion
will affect the coefficient b1.
Table 5. Self-selection into procurement and firms’ profitability
(1) (2) (3) (4) (5) (6) (7) (8)
Tobit Tobit Tobit Tobit Tobit Tobit Tobit Tobit
Gains 2004 Gains 2004 Gains 2005 Gains 2005 Gains 2006 Gains 2006 Gains 2007 Gains 2007
Panel 1
Volume of contracts 0.009 0.008 0.041 0.041 0.129 0.105 0.075 0.068
(0.006) (0.019) (0.016)
(0.034) (0.035)
(0.141) (0.026)
Import dummy 3.701 4.101 10.653 11.527 27.689 29.632 30.800 33.454
Import volume 0.000 0.000 0.002 0.001 0.001 0.001 0.001 0.001
Export dummy 1.387 1.440 4.173 4.658 25.662 29.741 17.759 17.168
Export volume 0.000 0.000 0.001 0.000 0.001 0.001 0.001 0.001
(0.000) (0.000) (0.000)
(0.000) (0.001) (0.001) (0.000)
Mills1 1.035 1.552 10.834 10.103
Mills2 3.816 6.741 24.041 25.150
Pseudo R
0.185 0.232 0.275 0.289 0.133 0.162 0.156 0.178
Observations 12,759 12,759 12,759 12,759 12,759 12,759 12,759 12,759
Panel 2
Number of contracts 0.010 0.007 0.032 0.026 0.270 0.187 0.256 0.150
(0.008) (0.009) (0.016)
(0.024) (0.069)
Import dummy 3.696 4.101 10.625 11.511 27.721 29.748 30.746 33.512
Import volume 0.000 0.000 0.002 0.001 0.001 0.001 0.001 0.001
Export dummy 1.381 1.440 4.148 4.653 25.895 29.942 17.438 17.196
Export volume 0.000 0.000 0.001 0.000 0.001 0.001 0.001 0.001
(0.000) (0.000) (0.000)
(0.000) (0.001) (0.001) (0.000)
Mills1 1.020 1.501 10.328 9.694
Mills2 3.817 6.741 24.171 25.232
Pseudo R
0.186 0.232 0.275 0.289 0.135 0.162 0.157 0.178
Observations 12,759 12,759 12,759 12,759 12,759 12,759 12,759 12,759
Note: Tobit specifications with left truncation at the lowest observed profit level in each year. For each specification, explanatory variables correspond to
the relevant year. Robust standard errors in parentheses (bootstrapped with 500 replications when Mills ratios are included). Stars indicate significance at
the 10% (*), 5% (**), and 1% (***) respectively.
The crucial point is the availability of suitable instruments,
that would predict access to the procurement sector, while
being excludable from the second Eqn. (12). To generate these,
we exploit the fact that apart from raising the cost of procure-
ment and changing the identity of sellers, corruption also dis-
torts the sectorial abundance of firms. We capture this bias by
exploiting firms’ names, which are specific to the procurement
categories where a large number of firms are active.
The probit model shows that our names variables are very
strong predictors of firms being active in procurement, while
there is no reason to think that names influence firms’ profit-
ability directly, supporting the excludability requirements.
Table 5 shows the results from estimating (12) on a sample
of 12,759 firms. For each year, we first display the results from
a standard tobit estimation and then provide the results
including self-selection correction terms, with bootstrapped
standard errors. Panel 1 uses the total volume of procurement
contracts as our variable of interest Zit, while panel 2 uses the
total number of contracts.
The correction terms are strongly (jointly) significant (at the
1% level) in all estimations. Moreover, their inclusion system-
atically induces a reduction in the estimated coefficients of the
variables of interest. The marginal effect of firms’ contract vol-
ume on their profitability is reduced by between 9 and 19%
(except in 2005, when it remains constant), and loses signifi-
cance in the last three years. Similarly, the marginal effect of
the number of contracts is reduced by between 19% and
42%, and becomes insignificant in the 2005 sample. There is
thus a strong composition effect, meaning that the profitability
advantage of better entrepreneurs stems from an unobserved
ability differential.
We thus conclude that part of the link between procurement
and firms’ profitability relates to unobserved self-selection of
the best entrepreneurs into activities that offer privileged
access to the procurement sector. This provides the final ele-
ment of our story, in which would-be entrepreneurs are likely
to be disproportionately attracted to sectors in which strong
demand from corrupt public buyers generate opportunities
for rent-seeking.
We have illustrated the fact that rent-seeking is costly to
development, by showing how entrepreneurs’ economic incen-
tives are distorted toward less productive activities as the
result of favoritism in the allocation of public contracts in
Paraguay. After building an entrepreneurial choice model,
we have used a large-scale microeconomic database including
all public procurement operations over a 4-year period to test
the predictions of the model. Firms have a greater probability
of obtaining a contract directly through an exceptional proce-
dure from an institution with which they have a strong con-
tractual relation, both in terms of the total value and
frequency of transactions, particularly when dealing with
more corrupt State entities. This is supported by the evidence
from a natural experiment, which exogenously increased pub-
lic scrutiny over procurement practices, and especially excep-
tion, halfway through our period of study. Firms trading
more with the public sector are more profitable, even when
controlling for their unobserved characteristics. This overall
picture embodies the consequences of a systematic misalloca-
tion of talents a
`la Murphy et al. (1991).
While the results must be qualified because of the intrinsic
limitations of the data, in particular those related to corrup-
tion and to the profitability of firms, we think that the paper
points to two main conclusions. First, rent-seeking is costly
because it destroys the development potential of the best entre-
preneurs. Indeed, the Paraguayan entrepreneurial class is in its
large majority imports-oriented, with over 90% of the top 500
taxpayers being importers. Over the period 1996–2005, the
commercial balance displayed an average deficit of 8.5% of
GDP. Large rents linked to the resale of imported goods to
the State and the historical absence of an import-
substitution strategy have contributed to make Paraguay one
of the least industrialized economy in South America as, apart
from the soybean and meat sectors, its entrepreneurs have sys-
tematically specialized in commercial intermediation, often
with the public sector, rather than in production.
The costs of this productive atrophy and biased specializa-
tion are reflected in the poor record of economic growth. After
a period of significant growth in the 1970s and early 1980s,
linked to the massive construction projects including the
hydroelectric dams, the average rate of growth of per capita
income was only 0.8% over the 1980s and strictly negative
after that (0.1% and 0.6% over the 1990s and 2000s). Over
the last two decades, the Paraguayan Central Bank indicates
that 92% of growth fluctuations were due directly to fluctua-
tion in agricultural production and exports. As a result, per
capita income was lower in real terms in 2005 than it was at
the beginning of the 1980s.
Second, the release on an NGO report on the abuse of
exceptional procedures, appears to have had a significant effect
as it translated in an improvement in the following period. In
that sense, civil society involvement in monitoring the public
sector use of funds appears as a crucial check on corruption.
1. The ‘‘contracting homeland, see for example Alfredo Boccia Paz,
Diario Ultima Hora, Asuncion, March 4, 2009.
2. Authoritative surveys on corruption include Bardhan (1997), Rose-
Ackerman (1999), Svensson (2005), Pande (2008), and Olken and Pande
(2012) among others. There is a large macro literature, staring with Mauro
(1995), while micro-econometric papers include Reinikka and Svensson
(2004), Olken (2007), Bertrand, Djankov, Hanna, and Mullainathan
(2007), Ferraz and Finan (2008), and Sequeira (2014) to mention only a
3. Related papers are Rama (1993), who tracks the number of foreign-
trade rent-seeking regulations over the XXth century in Uruguay and
relates these to political and economic variables, and Fisman and Sarria-
Allende (2010), who present cross-country, industry-level evidence of the
effect of regulatory distortions on the industrial structure.
4. It has lingered in the bottom 4% of surveyed countries included in
Transparency International’s Corruption Perception Index since its
inclusion in 2002. It had for instance a score of 2.1 in 2005, placing it
144th out of 158, and the same score in 2009 (154th out of 180). See Online
Appendix for general information on the country.
5. See Straub (2014) for evidence on the change in the relevance of
political connections for public procurement around the 2008 elections, in
which the Colorado party lost power.
6. For example during the period 2004–05 public firms awarded close to
90% of their advertisement contracts through exceptions. As for specific
institution, the Office of the First Lady spent respectively 40% and 93% of its
budget in these two years using the exceptional mechanism. Some cases have
made headlines, such as the use of this procedure to pay close to US$ 100,000
to a consulting firm formerly owned by the President, for the organization of
the XIIIth conference gathering Americas’ First Ladies in 2005 in Asuncio
(Diario Ultima Hora, Asuncio
´n, June 7, 2007). See Mironov and
Zhuravskaya (2012) for similar stories in the Russian context.
7. Diario ABC Color, Asuncio
´n, January 3, 2010. These figures are
consistent with the estimation by Auriol (2006) and with the results in
Section 7below.
8. This assumption is consistent with existing evidence on manufacturing
and service firms in developing countries, whether they belong to the
formal or the informal sector (see Tybout, 2000). It is also consistent with
the nature of activities included in our procurement database.
9. Empirically Chong, Klien, and Saussier (2013) find a positive
relationship between the use of negotiated procedures without prior
notification and the weakness of governance across the European Union:
in countries more prone to corruption, public purchasers use more often
exceptional purchase. This is consistent with earlier findings by Della
Porta and Rose-Ackerman (2002) who show that in the 1990s in Italy
public authorities were abusing emergency procurement procedures to
bypass competition.
10. For instance Tran (2011), exploiting an Asian trading firm’s records
of the bribes it paid over the year to secure public contracts, shows that the
average kickback was 14.7% of the product cost when auctions were not
required. Ufere, Perelli, Boland, and Carlsson (2012) provides insights
about the supply of bribes by firms.
11. This is a common assumption in the corruption literature, going back
to the Becker and Stigler, 1974 crime-deterrence model. See for example
Besley and McLaren (1993) and Mookherjee and Png (1995).Di Tella and
Schargrodsky (2003) is an empirical application. Wade (1985) is an early
reference on the behavior of corrupt government officials, and
Elbahnasawy (2014) looks at the impact of e-government in deterring
12. Equivalently the probability of detection increases with the share of
firms that are excluded from the rent sector, which by virtue of Eqn. (2)is
equal to ccrðbÞ
c¼b. When the number of firms that are excluded from the
rent sector bis large it increases the probability of outcry and detection
13. The monotone hazard rate property is equivalent to the log-concavity
of the reliability function 1-G(b). It turns out that most reliability
functions of standard random variable are log concave. This is true for
distributions such as uniform, normal, logistic, extreme-value, Chi-Square,
Chi, exponential, Laplace, Weibull, power function, gamma, beta, Pareto,
log-normal, Student’s t, Cauchy and F distributions (Bagnoli &
Bergstrom, 2005; Borzadaran & Borzadaran, 2011).
14. In Paraguay at the time of our study, there had been very few cases of
prosecution or indictment for corruption leading to jail time or fines.
15. Indeed under Assumption A2, 1GðbÞ
gðbÞdecreases in b, while bincreases
in ½0;1. Moreover 1
gð0Þ>0 and 1Gð1Þ
gð1Þ¼0<1 so that these two functions
cross once and only once in ð0;1Þ.
16. In the public segment, corruption implies that unit price is c. In the
private segment price is pr¼cð1brÞas the less efficient firms are left to
serve private consumers. Since pr>p, quantities exchanged in the private
sector fall so that the formal productive sector shrinks.
17. Let Kð:Þand Gð:Þbe two probability functions so that gðbÞ
8b0;1, then it implies that GðbÞ6KðbÞ8b0;1(e.g., see Nanda &
Shaked, 2001).
18. For instance if all procurement contracts are of similar size, we have
cdc, which by virtue of Eqn. (2) is equivalent to Q¼qð1bÞ.
19. See Online Appendix for more details on the different types of data.
20. The data we use were initially painstakingly compiled by Transpar-
encia Paraguay (TP), the national chapter of the international NGO
Transparency International, using the information published on the
DNCP web site.
21. The Guarani–US$ exchange rate over the period fluctuated in the
range 5,021–6,178 Gs. for 1$.
22. As a way to index it to the general evolution of prices, contract values
and thresholds are expressed in minimum daily wage (mdw) units, so for
example a 1,000,000 Gs. contract expressed in multiples of a legal mdw of
50,000 Gs. would amount to 20 mdw. See Online Appendix for more
23. Systematic data on total sales, profits, etc., for the whole universe of
firms could not be accessed due to confidentiality restrictions.
24. FOB is the standard way to report import values without costs of
transport and other taxes, while CIF is the standard way to report export
values, including cost, insurance and freight to the national border.
25. Barbosa and Straub (2014).
26. See European Commission (2011).
27. The inclusion of fixed effects prevents us from using a probit
estimation, while a conditional logit would imply eliminating any pair for
which there is no within variation, therefore reducing the final sample by
approximately half.
28. Here, identification arises from both cross-sectional and time
variation of the amount of exception used across institution–firm pairs,
controlling for a time trend, and institution and goods time-invariant
levels (column 1) as well as institution- and good-specific time trends
(column 2).
29. Note that the identification of the nature of the relationship between
public buyers and suppliers is beyond the possibilities of our data. It
maybe for example that public officials or their family members have
direct stakes in the supplying firms, as the anecdotal evidence suggests, or
that they operate at arm-length and share bribes.
30. Note that institution-year fixed effects absorb the direct effect of
corruption, which is measured year by year. The pairwise correlations
between the Corrjt and the Xijt variables are 0.003 and 0.04,
respectively, so we are not worried by potential multicollinearity.
31. Note that running the specifications of columns 1–3 on the smaller
sample of columns 4–6 confirms a positive (approximately an additional
10% for a one standard value increase) and significant effect for the total
pairs’ contract value.
32. The news index might be particularly subject to caution, as press
coverage of specific institutions, based for example on journalists inquiries
or on denunciations, is likely to be influenced by the nature of the
institutions and their past behavior in procurement or other activities.
Using only the mean of the evaluations based on the Comptroller
General’s report and on the number of administrative indictments in any
given institution yields similar results though.
33. The exception if the coefficient for the share of institutions’ budget
made with the firm, in column 5, which is significant at the 10% level.
34. Note also that the other coefficients support our previous insights:
the control group-specific trend b1is not significantly different from 0,
supporting the idea that the meaningful reduction occurs in the identified
treatment group, while b2is positive and significant, supporting our
previous results on the fact that pairs with frequent/intense interactions
make more use of the exception in the period 2004–05.
35. Using all the observations to measure the variations in net gains, we
would have some positive measurement errors (when a firm’s tax
observation is out of the sample and therefore set at zero for one year
and is positive the following one), some negative ones (in the reverse case),
and more generally errors going either way for firms that do not make it to
the ranking of top taxpayers.
36. Note however that such endogeneity concerns are much less obvious
for variables such as the average level of corruption.
37. We do not have additional firm-level data to control for such general
characteristics. Fixed effects will take care of the size issue as long as it is
reasonably constant over the period of study.
38. Results not shown here to save space indicate that the results in
columns 1 and 2 are robust to systematically controlling for the amounts
of firms’ sales to the State.
39. A technical concern has to do with tax evasion. Indeed, it is
likely that Paraguayan firms do not report all of their sales for tax
purpose, possibly biasing our estimations. One could think that sales
to the State, because they are publicly registered, imply lower rates of
evasion than other sales, in which case we may be facing an upward
bias in our estimations. However, strong anecdotal evidence rather
suggest that well-connected firms use their influence to evade a bigger
share of their tax obligations. This leads us to think that our estimates
should be considered as a lower bound on the true returns of these
firms. In other words, the fact that we still find a positive effect of
public contracts on profits leads us to consider that the true effect is
probably even larger.
40. The procedure, which aims to correct for the failure of the
ignorability-of-treatment assumption, is a kind of extended Heckit,
where sample selection is viewed as an omitted variable bias,
addressed by the inclusion of the Mills ratio as additional
regressors. Fafchamps and La Ferrara (2012) apply this technique
to control for individuals’ self-selection into self-help groups based
on unobservable characteristics.
41. See Online Appendix for the details on how the instruments are
defined, descriptive statistics and first stage estimations.
42. The dummies for the ‘‘constructionand ‘‘consultancycategories
correlate negatively with profits. Moreover, the results are robust to
excluding the import–export category.
43. This has also fueled a flourishing and illegal reexportation business to
the neighbors Brazil and Argentina. See Straub (1998) for more details on
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Available online at
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