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Working Paper Series
Rent-Seeking Bureaucracies and
Oversight in a Simple Growth Model
WP 98-03
This paper can be downloaded without charge from:
http://www.richmondfed.org/publications/
Pierre-Daniel G. Sarte
Federal Reserve Bank of Richmond
Working Paper 98-3
Rent-Seeking Bureaucracies and Oversight in a Simple
Growth Model*t
Pierre-Daniel G. Sartez
Federal Reserve Bank of Richmond
April 1998
Abstract
Following recent cross-country empirical work, research on public policy and growth
has come to examine the impact of inefficient or corrupt bureaucracies.
work has emphasized the interactions
contrast,
this paper
and their political
is relatively
focuses
authority.
Most of this
between bureaucracies and private markets. By
on the relationship
We emphasize
between
rent-seeking
two main points.
First,
bureaucracies
when oversight
costly, the political authority exercises little monitoring of its agencies
which reduces the effectiveness of productive government spending. Second, when the
technology used to provide public services is poor, as in many developing economies,
bureaus better succeed
in requesting
toring. Both of these characteristics
overly large budgets
before triggering
any moni-
contribute to reducing the growth rate of already
poor economies.
JEL Classification:
E13, 010
*I am indebted to Andreas Hornstein, Ned Prescott, and John Weinberg for many valuable discussions.
I would also like to thank Michael Dotsey, Sergio Rebelo, seminar participants at the University of Virginia,
and the Fall 1997 Midwest Macroeconomics
Conference for helpful comments. All remaining errors are my
own.
tThe views expressed in this paper are solely those of the author and do not necessarily represent those
of the Federal Reserve Bank of Richmond or the Federal Reserve System.
*Correspondence to: Pierre-Daniel Sarte, Research Department, Federal Reserve Bank of Richmond, P.O.
Box 27622, VA 23261, USA. Fax (804) 697-8255. e-mail: elpdsOl@rich.frb.org.
1
1
Introduction
With the advent of endogenous growth theory, a substantial body of work has been devoted to understanding the impact of public policy on economic growthl. Following recent
cross-country empirical work by Mauro (1995), and Keefer and Knack (1995), this body of
work has expanded to include the analysis of various distortions introduced by inefficient or
corrupt bureaucracies. Along with other case studies such as De Soto (1989), cross-country
regressions generally suggest that rent-seeking and dishonest bureaucracies tend to have an
adverse effect on a country’s growth performance.
Almost without exceptions, the theoret-
ical literature on bureaucratic quality and development has focused on the drawbacks that
arise from the interactions of an inefficient bureaucracy with private markets. As stated by
Hall and Jones (1997), “a corrupt bureaucracy acts as a tax on the productive activities of
the economy. Investors must spend some of their time and resources bribing officials in order
to obtain permits and licenses necessary for the conduct of business.”
While these arguments are no doubt significant, there exists another important avenue
through which bureaucratic quality affects economic growth. In particular, this paper suggests that the empirical results cited above stem, at least in part, from the political authority’s interactions with its executive agencies. It is well known that in many developing
economies, the central government is relatively weak and cannot effectively engage in bureau
oversight. This directly suggests that the growth effects of government spending may be partially linked to an agency problem between the political authority and its bureaucracy. There
exist two lines of ongoing research which, combined together, lend some natural support to
this hypothesis.
First, several authors, notably Barro (1990), and Glomm and Ravikumar
(1994, 1997), h ave suggested that governments provide goods and services that raise the
return to private investment.
In this sense, the construction of highways or the provision
of law enforcement, say, contributes directly to economic growth. Second, public goods and
services do not originate from the central government directly, but rather from a variety of
agencies under its control.
These bureaus are typically better informed than the political
authority concerning the technology they use to provide public services and, furthermore,
generally act in their self interest.
Niskanen (1971), and Migue and Bitlanger (1974), were
among the first to pioneer frameworks where bureaus act to maximize their discretionary
budget. Put together, these two considerations imply that the efficiency of productive public
expenditures is implicitly related to the need for some amount of bureau oversight. This paper, therefore, investigates this issue. In the US, it is precisely the need for bureau oversight
which justifies the existence of the Office of Management and Budget and, to an extent, the
‘See Easterly and Rebel0 (1993) for instance.
2
General Accounting Office.
Figure (la), depicts the relationship between an index of Bureaucratic
Quality of Communication Infrastructures
in a sample of 41 countries.
obtained from Business Environmental Risk Intelligence (BERI),
Delays and the
These indices are
a private investment risk
service. Since the Quality index is meant to capture “facilities for and ease of communication”
as well as the “quality of transportation”
within the country, we may interpret it as a proxy
for the oversight technology available to the political authority. Under this interpretation,
bureaus tend to exhibit lesser delays as the oversight technology improves.
One possible
motivation for this relationship is that greater bureau oversight takes place the better the
oversight t ethnology.
[Insert Figure 1 Here]
On a related note, Figure (lb) suggests that economies with greater bureaucratic delays
grow at slower rates ‘. This paper formalizes two potential explanations for this relationship.
First, and consistent with Figure (la), we show that as the oversight technology deteriorates,
less monitoring of executive agencies occurs which reduces the effectiveness of productive
government spending.
The second explanation relies on the idea that bureaucracies may
inherently differ along technological considerations.
In particular, the provision of public
services is likely to be carried out more efficiently in advanced economies relative to poorer
countries. Under this alternative interpretation,
we show that when the technology used to
provide public services is poor, bureaus better succeed in acquiring excessively large budgets
before triggering any monitoring. We also show that such economies are generally associated
with lower growth rates.
To shed light on the above issues, we adopt a conventional linear neoclassical growth
framework in which productive government spending is financed through distortionary taxation. This framework is, therefore, similar in nature to that of Barro (1990), or Glomm
and Ravikumar (1994, 1997).
Contrary to these papers, however, the provision of public
intermediate inputs occurs through a variety of government bureaus that seek to maximize
their discretionary budget. That is, the difference between the budget they request and the
cost of producing the output required by the political authority. In the analysis below, the
allocation of budget emerges as the outcome of a static optimal contract between a government oversight agency and each of the bureaus. Since bureaus privately observe their cost of
providing public services, this seems a natural approach. More importantly, this particular
2Although Figure 1 depicts unconditional
correlations, Keefer and Knack (1995) show that the BERI
variables are significantly correlated with economic growth, even when included in more fully specified crosscountry growth regressions.
3
modelling strategy allows us to focus on the difficulty of overseeing bureaucratic activity as
a crucial aspect of the environment.
This paper is organized as follows. Section 2 presents a brief outline of the economic
environment. Section 3 describes production and the bureaucratic provision of public goods.
In section 4, we examine the long-run growth properties of the model as well as the equilib
rium degree of oversight that characterizes the bureaucracy. Section 5 addresses the issue of
optimal tax policy. Finally, some concluding remarks are offered in section 6.
2
Structure of the environment
Figure 2 briefly describes the organizational structure of the environment. The economy is
populated by a continuum of households uniformly distributed on the unit interval. House
holds directly operate the technology for final goods and are subject
to taxation.
The
proceeds from taxation can be used in one of two ways. First, tax revenue can be used to
finance a variety of bureaus in the production of public goods and services that are redistrib
uted back to households via distortionary transfers.
Second, tax proceeds can also be used
in the supervision of the bureaucracy through an oversight agency. Bureaus possess private
information with respect to a randomly drawn technology used to produce public services.
Since they also act to maximize their discretionary budget, the monitoring of bureaus serves
to economize on total government outlays.
To the degree that the allocation of budgets
allows for informational rents to be earned by the bureaucracy, we assume that those rents
are rebated back to households as a lump sum transfer3.
[Insert Figure 2 Here]
Given some choice of fiscal policy, and thus tax revenue, our discussion focuses on how to
efficiently allocate public expenditures across bureaus. To keep this latter issue separate from
that of how to set fiscal policy, the budget allocation process is assumed to occur through
an oversight agency which takes policy as given. In the US, the task of bureau oversight,
including budget review and allocation, is largely carried out by the Office of Management
and Budget. As such, the Office of Management and Budget does not decide on policy per se,
which is typically decided in Congress, but rather helps the executive branch of government
3This aspect of the environment builds on Niskanen (1991), who suggests that “neither the members-of
the sponsor group nor the senior bureaucrats have a pecuniary share in any surplus generated by the bureau.
The effect of this condition is that the surplus will be spent in ways that indirectly serve the interests of the
sponsor and the bureau, but not as direct compensation.”
4
with respect to managing public expenditures. According to its mission statement, the role
of the Office of Management and Budget (OMB) is as follows:
“OMB’s predominant mission is to assist the President in overseeing the
preparation of the Federal budget and to supervise its administration
in Ex-
ecutive Branch agencies. In helping to formulate the President’s spending plans,
OMB evaluates the effectiveness of agency programs, policies, and procedures,
assesses competing funding demands among agencies, and sets funding priorities.”
For simplicity, we abstract from labor services in this paper.
This allows us to focus
exclusively on the growth distortions introduced by informational asymmetries between the
political authority and its bureaucracy4.
However, in the model below, we may think of
capital as incorporating a human component as first suggested in Jones and Manuelli (1990))
and Rebel0 (1991).
3
Production and the provision of public goods
Consider a closed economy in which at each date, t, there exists a continuum of intermediate
public inputs indexed by i, ~(t, i), uniformly distributed on [0, N]. Following Barro (1990),
and Alesina and Rodrik (1994)) a single final good, y(t), is produced by combining capital
with government services according to
y(t)
= A/k(t)"
f
I@#-"di,
0 < QI <
1.
In this framework, intermediate public inputs are meant to encompass a range of both
public goods and services. (These may include highways, law enforcement, urban develop
ment, etc...) The variable It(t) stands for capital services. We now examine the environment
in which both the political authority and its bureaucracy interact.
To keep the model rel-
atively simple, the description of the informational framework which follows is assumed
identical in every period.
Let us suppose that each bureau is required to produce a common level of public good
or service, that is z(t,i)
= s(t) for each i, and requests a corresponding budget from a
government oversight agency. In other words, we confine the analysis to that of a symmetric
equilibrium. In this equilibrium, the particular value of z(t) and the requested budget are
4That is, we leave aside the issue of possible scale effects typically associated with labor supply in endogenous growth models.
5
endogenous. In addition, we further assume that the supply of each public input is associated
with a particular bureau. This is in keeping with Niskanen (1991), who sees the relationship
between a bureau and its sponsor as “one of bilateral monopoly which involves the exchange
of a promised output for a budget, rather than the sale of its output at a per-unit price.” The
exact form of the budget making mechanism remains to be described. To do this, however,
first requires us to specify the technology underlying the provision of public goods.
We assume that the provision of z(t) units of public goods by any bureau requires 0x(t)
units of output, measured in terms of forgone consumption5. Here, 0 is a random variable
with support (0, l] which is distributed according to the continuously differentiable probability density function f (0; w). Put another way, the average cost associated with the provision
of government services is itself random.
Let F(B;w)
denote the corresponding cumulative
distribution function, where the parameter w orders distributions by first-order stochastic
dominance. Formally,
aF(8; w)
8W
< 0, for 0 < 0 < 1.
An increase in w, therefore, generally renders the per-unit provision of government services
more costly by shifting the distribution in a first-order sense. While the distribution F(B; w)
is publicly known, the realization of 6’ is costlessly observable only to the individual bureau.
This feature of the model serves to capture an essential aspect of bureau theory. Specifically,
Niskanen (1991) writes that the “bureau’s primary advantage is that it has much better
information about the costs of supplying the service than does the sponsor.” Furthermore,
Blais and Dion (1991) suggest that since bureaus “know much more about the production
process than sponsors, it is easier for them to exploit the situation as monopoly suppliers
than it is for sponsors to exploit it as monopoly buyers.”
Nevertheless, there exists evidence, notably by Aucoin (1991), which indicates that even
if bureaus do tend to press for larger budgets, they do not always obtain them. Consequently,
we assume that a monitoring technology is available to the government whereby AZ(~), X E
(0, l], units of final output can be used to observe a bureau’s realization of 0 when the
bureau’s output is x(t).
As mentioned earlier, a central government’s ability to oversee its
bureaucracy can vary significantly across economies. In poorer countries, bureaus tend to be
geographically dispersed while means of communication remain elementary. One, therefore,
expects developing economies to be associated with relatively high values of X. Observe that
a potential endogeneity problem exists in that persistently lower growth may itself prevent
the adoption of effective oversight technologies.
That is to say, slow growing economies
jIn this sense, the use of output as intermediate goods relies on the assumptions of the one-sector production model.
6
cannot typically afford the in&astructure necessary for a competently run bureaucracy. For
simplicity, we do not address this reverse causation issue here. Nevertheless, it is shown
below that by reducing the effectiveness of productive government spending, the inability to
adequately monitor bureaus can contribute to reducing the growth rate of already struggling
economies.
Since each bureau is required to produce a common level of public good, CC(~),it follows
that
NB(~)~(~) = /,’ ex(tyvdF(e;w),
Bcw) 5 I,
(3)
is the total quantity of resources required, measured in units of output, in the production
of public goods across all bureaus. The fact that there exists a continuum of intermediate
public inputs, or alternatively a continuum of bureaus, implies that this measure is known
with certainty.
At this stage, it should be clear that the informational environment that we have thus
far described is one of costly state verification as in Townsend (1979), or Williamson (1987).
Before turning our attention to the determination of budgets, however, we need to be more
specific as to the assumptions underlying the behavior of bureaus.
In contrast to earlier
work, we have abstracted from the fact that bureaus often interact with firms in awarding
contracts or business and trade licenses. These types of interaction can often lead to economic distortions that severely hinder growth when bureaus are left unchecked.6 In this
paper, our concern is with the growth effects that emerge when distortionary public transfers occur through a rent-seeking bureaucracy. As suggested by Migu6 and Bklanger (1974))
public bureaus are therefore interpreted as impersonal entities that seek to maximize their
discretionary budget. In other words, the difference between total budget and the cost of
producing the output required by the political sponsor7.
Having postulated that bureaus privately observe the quantity of final goods they use to
produce a given level of public input, x(t), we model the budget allocation process as a static
optimal contract, between each bureau and an oversight agency, which serves to economize
on public expenditures.
Since our bureaus are interpreted as surplus maximizing entities,
this contract should allow for monitoring to take place in some states of the world. Indeed, in
a world where monitoring never takes place, bureaus will always announce 6’ = 1 and request
the maximum budget allowed, z(t) units of output.
We then define the optimal budget
allocation mechanism as one which minimizes total government expenditures while ensuring
6See De Soto (1989), or Shleifer and Vishny (1993), for example.
71n adopting this interpretation of bureaus, we also follow Peacock (1978) who points out that “the
essence of the Niskanen/Tullock
approach is that there is a close analogy between the theory of the firm and
the theory of bureaucratic operation.”
7
that the budget allocated to bureaus at least covers their cost of production. In addition, we
require that each bureau’s budget request correspond to its true realization of 8. In other
words, the contract must satisfy incentive compatibility.
As already noted, the oversight
agency we have in mind for the US corresponds to the Office of Management and Budget.
However, the General Accounting Office also plays a significant role in oversight activities.
For the purposes of this model, we can conceptually think of combining these two agencies
into a single institution taking z(t) as given. Under the above conditions, it is optimal for
this government monitoring institution to allocate to each bureau qz(t), measured in units
of output, if 8 < q is reported, and &c(t), if 0 > 77is reported, where 7 E (0, l] satisfies
rnp
J7)
i [&c(t) + k(t)]
NdF(B; w) + r]z(t)NF(r];
(4)
w).
Here, the threshold 7 minimizes total government spending while ensuring that bureaus
obtain a budget which at least covers their cost of production.
Moreover, the contract is
such that bureaus report the truth 8. Bureaus reporting a high cost of production, that is
8 > 7, obtain a relatively large budget in the amount Bz( t). However, these bureaus are also
subject to oversight by the political sponsor. Bureaus reporting a relatively low value of 8,
that is 8 < 7, are not monitored and allocated a flat budget, 7x(t), in excess of the true cost
they incur, 0x(t). As shown in Figure 3, this difference in the amount of (q-0)x(t)
constitutes
a discretionary surplus resulting from informational asymmetries between bureaus and the
oversight agency.
[Insert Figure 3 Here]
Since only bureaus which report 6’ 2 q are subject to oversight, the measure of monitored
bureaus is N[l - F(q; w)]. This directly implies that the quantity of resources used in monitoring activities is NXx(t)[l
- F(q; w)] units of output, which implicitly enters in equation
(4). In particular, observe that the objective function in equation (4) can also be written as
~@(~)~(t)
+ m2(t)
[I - F(~; w)] + IV+)
J,s(q - e)dF(e; w).
(5)
Thus, in this last equation, the first two terms denote the quantity of final goods used
in the production of public services and monitoring respectively.
represents total discretionary
Similarly, the last term
surplus accruing to bureaus, measured in units of output.
Using Leibniz’s rule, we can differentiate equation (5) with respect to 7 which yields
“The form of this contract
is stated without proof since this is a well known result for the case of
verification in pure strategies in this environment. See Townsend (1979), or Williamson (1987).
8
Nz(t)F(rl;
4 - NW)f(rl;4.
(6)
Therefore, as the monitoring threshold 77rises, two opposing forces are in effect. On the
one hand, a higher value of 77 indicates that a smaller fraction of the bureaucracy will be
monitored. This leads to a decline in the quantity of final output used in the oversight of
bureaus. On the other hand, a higher monitoring threshold also means that bureaus’ discretionary budgets from possessing private information will rise. For simplicity, we therefore
assume in this paper that equation (5) is strictly convex in q. Specifically,
It directly follows that the solution to the minimization problem in (4), denoted q*, solves
F(v*; w) - Af(v*; w) = 0.
(8)
This result is quite intuitive in the sense that the oversight agency will monitor the
bureaucracy up to the point where the marginal gain from decreasing bureaus’ informational
rents exactly offset the additional increase in the resources needed in bureau oversight. In this
case, the solution will generally depend on both the monitoring technology, as summarized
by A, and the distribution which characterizes the cost of providing government services, as
summarized by w.
In order to proceed with the model, and in particular the analysis of growth effects
introduced by the agency problem specified above, we now need to describe the make up of
tax revenue. To this end, we assume that all household income is taxed at a constant rate
r E (0,l).
Tax revenue, denoted T(t), is therefore given by
T(t) = 7 b(t) + WI 7
where s(t) = Nz(t) J:* (q*-e)dF(&
(9)
w) is th e measure of total bureaucratic surplus, evaluated
at rl*, and rebated to households as a lump sum transfer. In equilibrium, total government
expenditures in equation (5) must equal tax revenue. Hence, we have
NOB
=
+ ~h(t)p
~lc(t)v~(t)l-*
- F(~*; w)j + k(t)
+ h(t)
Jd”’(77*- e)dF(e; w)
J,“’ (rl* - e)dF(e; w)
or alternatively,
9
,
Go
B(w) + A[1 - F(q*; w)] + (1 - r) [*(q*
[1
- O)dF(B; w) = TA $
(11)
--a .
As first suggested by Barro (1990), and Glomm and Ravikumar (1994, 1997), the ratio
of government services to private capital, therefore, naturally depends on the state of fiscal
policy. However, in this economy this ratio also depends on the cost distribution characterizing the production of public goods and the technology available to monitor a rent-seeking
bureaucracy. It is this latter feature of the model that will allow us to investigate the nature
of the relationships depicted in Figure 1.
Having described the nature of production as well as the provision of public goods, we
can now turn our attention to the demand side of the model and the determination of the
growth rate.
In doing so, we explore the growth implications associated with countries in
which there exist substantial difficulties in oversight. Moreover, we also analyze the growth
effects that are present in countries whose public sector cannot provide public goods and
services very effectively.
As pointed out earlier, both of these cases generally apply to
developing economies.
4
Determination
of the growth rate
To close the model, we assume that the representative
household maximizes its lifetime
utility over an infinite horizon and solves
maxLf=
subject to
e--pt
J
W
4)
cdt
l-O
0
Pl)
with 0 > 0, p > 0,
c(t) + k(t) + 6/c(t) = (1 - T) Ak(t)” iN z(t, i)l-“di
+ s(t)
I
,
02)
c(t) 2 0, k(t) 1 0, k(O) = k0 > 0 given,
where S is the capital depreciation rate and k(t) is the time derivative. The right-hand side of
equation (12) is simply disposable income. Using equation (10) and the household’s budget
constraint, it can easily be shown that the goods market clears. Formally, we have c(t)+i(t)
y(t) -g(t)
where i(t) denotes gross investment, and g(t) = N@w)z(t)
=
+ NXx(t)[l - F(q*; w)]
are the resources actually used in the production of government services and monitoring
respectively.
In equilibrium, the solution to this dynamic optimization problem yields the
familiar formula for the growth rate of consumption,
10
(1 - ~)ANa[z(t)/k(t)]‘-a
- (S + p)
(13)
CT
As in more conventional linear growth models with productive government spending, a
Yc =
higher ratio of public infrastructures to private capital increases the return to capital and,
therefore, contributes positively to economic growth.
This ratio, however, depends on a
variety of parameters that are intrinsic to the agency problem linking the political authority
and its bureaucracy. As a result, it is’ not necessarily surprising that the institutional quality
of government spending emerges as a significant factor in cross-country growth regressions.
In particular, and by using equation (ll), the growth rate in equation (13) ultimately reduces
to
Yc =
[B(l - 7)7?
x
(14)
6
e(w) + x [l - F(q*; w)] + (1 - 7) I”‘(n*
- @)dF(& u>
i
where B = aNA?.
1
p - (6 + P>ll~
Therefore, in addition to fiscal policy, the distribution of costs associated
with the provision of government services, the monitoring technology available in oversight,
and the degree of oversight that actually takes place all affect economic growth.
For notational convenience, let us define the function
qq*; X,w) = qLd> + x [l - F(~*;w)] + (1 - 7) lV*h’
- B)dF(B;w),
(15)
and note that the growth rate in equation (14) is strictly decreasing in h(q*; X,w). This
implies that as a first approximation, the presence of bureaucratic rents in per-unit term.?,
as captured by J:*(q*
- 8)dF(0; w), 1owers the rate of growth in this environment.
The
mechanism underlying this finding is clear in the sense that an increase in informational
rents artificially raises the amount of resources required in the production of public services.
Given unchanged fiscal policy, this distortion reduces the ratio of public infrastructures to
private capital.
Using the resource constraint for the economy as a whole, equation (14) also depicts the
growth rate of all variables including that of output. In the remainder of the paper, we let
the growth rate of the economy be denoted by y. Observe that since total bureaucratic rents
are given by s(t) = Na:(t) J$‘(q* - B)dF(B; w), with z(t) being linear in k(t) in equation
(ll),
this quantity also grows at rate y in this model.
Finally, this framework does not
allow for transitional dynamics. We are now ready to address the issues first set out in the
introduction.
‘Recall
that the total quantity of public intermediate inputs is given by NE(~).
11
4.1
Economic growth and bureau oversight when monitoring is
relatively costly
As suggested by Keefer and Knack (1995), and Ayal and Karras (1996), the institutional
make up of government bureaus differ widely across countries. In some countries more than
others, bureaucracies may be better able to generate bureaucratic rents while being seldom
subject to any monitoring.
Moreover, these country characteristics
with lower rates of growth.
We now explore the possibility that these observations may
are often associated
endogenously emerge in countries where the political authority finds it difficult to oversee
its bureaucracy. As pointed out earlier, we do not address the potential problem of reverse
causation in this paper. However, we do make it clear that inadequate oversight capabilities
can reduce the effectiveness of productive government spending, and thereby adversely affect
the growth rate of already poor economies.
To determine what a less effective monitoring technology implies for economic growth,
we must first derive its impact on the equilibrium oversight threshold q*. Using equation
(8), the effect of a poorer monitoring technology on the equilibrium threshold report v* can
be obtained as
arl*
f (rl*v4
ax = f(7)*;
w)- A@f(q*;
w>/ax>
>O
since, by equation (7), the denominator is strictly positive.
An upwards shift in the mon-
itoring cost function, therefore, naturally leads to less oversight.
the measure of monitored bureaus is given by -Nf(q*;
in fact, consistent with Figure (la).
(16)
w)g
Formally, the decline in
< 0. Note that this result is,
In addition, and since J:* (v* - B)dF(B; w) is strictly
increasing in q*, a less effective monitoring technology directly leads to a rise in the total
quantity of discretionary surplus accruing to the bureaucracy, relative to the total production
of public goods. With these results in hand, we can now trace out the effects of an increase
in X on economic growth.
Since y is decreasing in h(q*; X, w), it follows that 2
2 (>) 0 ++ v
> (<) 0.
Differentiating h(q*; X, w) with respect to X yields
=
where s
[--Xf(q*; X,w) + (1 - r)F(q*;
x, w)] g
> 0 is given by equation (16). It follows that
12
+ [l - F(q*; x, w)] )
(17)
wrl*; x74
dX
2
(<)O
*
(1 - ++I*;
(18)
arl*
A, W)dX + [l-
F(q*;X,w)]
2 (<) Xf(?r’;X,w)%.
As we have just alluded to, an increase in X reduces oversight and therefore allows bureaus
to earn greater informational rents in per-unit terms.
~P(rl*;
A 4%
This distortion shows up as (1 -
> 0 in equation (17) and contributes to lowering the rate of growth. The
second term in equat,ion (17), [l - F(q*; X,W)] > 0, captures the increase in monitoring
costs that occurs as a direct result of the rise in X. Since the tax rate is held fixed in this
experiment, this increase in the amount of final output required to oversee the bureaucracy
lowers the ratio of public goods and services to capital and, consequently, economic growth.
However, since a smaller fraction of the bureaucracy is subject to oversight following the
rise in X, there also exists an offsetting decrease in monitoring costs. This latter effect, as
illustrated by the term -Xf(q*;
X,W)~
< 0 in (17), helps raise the rate of growth.
In general, one might expect the first two distortions in equation (18) to outweigh the
decrease in monitoring costs resulting from the fall in bureau oversight. In this case, a higher
value of X would be associated with lower growth [i.e. g
< O]. Since a higher value of X
is also associated with less oversight, the end result is a relationship that is consistent with
Figure (lb).
It remains that a poor monitoring technology and little bureau oversight do not necessarily lead to lower growth in this framework. Note in equation (18) that the higher the tax rate,
the smaller the effect of the distortion induced by per-unit bureaucratic rents, and hence the
more likely it will be that g > 0. This r esult, which may at first appear counter-intuitive,
in fact directly derives from the simple notion that the interaction of two distortions can
actually help offset each other.
In this case, the presence of informational rents accruing
to the bureaucracy is naturally detrimental to economic growth. However, the effect of this
distortion may be partially reduced by the fact that those rents are ultimately rebated back
to households and, therefore, subject to taxation.
At this point, we find it useful to introduce a simple example in order to make matters
more concrete. In addition, this example will also serve us in the next subsection where the
model loses some degree of analytical tractability.
Suppose that the distribution F(0; w) =
e”, w E (OJ]. lo As required, the parameter w orders distributions by first-order stochastic
dominance since
loThe restriction on w ensures that the convexity assumption in equation (7) actually holds.
13
aF(e; w)
i3W
= O%B
< 0, for 0 < e 5 I.
(19)
Moreover, note that the mean of the corresponding probability distribution, which we have
denoted e(w), is simply --& which indeed increases with w. Given this functional form for
F(8;
w),
it follows that
q* = xw
(20)
dh(q*;A 4 = 1 - (Xw)“(l + TW).
(21)
and, moreover, that
dX
It is then apparent that with this particular distribution function, v
and therefore 2 < 0 (>) 0, whenever T 5
(>) 9.
2 0 (<) 0,
In other words, an increase in the
difficulty associated with bureau oversight generally leads to lower growth except in instances
where the tax rate exceeds some upper bound. Furthermore,
depending on the underlying
technology parameters, it may never be feasible for fiscal policy to undo the adverse growth
effects implied by more costly monitoring and, consequently, greater per-unit bureaucratic
rents. For example, in the special case where X 2 -&a(w), a rise in X unambiguously reduces
economic growth since q
2 1 andr<
1.
The results we have just described are generally consistent with the fact that economies
where a weak central government cannot effectively monitor its bureaucracy often simultaneously display little bureau oversight and lower rates of economic growth. That is not
Shleifer and Vishny (1993) argue that it is
to say that other channels aren’t important.
precisely in those economies that the interactions of rent-seeking bureaucracies with private
markets may be most harmful to growth. According to these authors, “in feudal Europe, in
post-Communist
Russia, and in many African countries, the central government is so weak
that it cannot fire or penalize officials in the provinces . . . for running their own corruption
rackets.”
Nevertheless, our framework does point out that these latter interactions are not
the only sources of distortion one needs to consider.
More importantly, in a world where bureaus operate a technology that cannot be fully
observed by the political authority, our framework puts a strong emphasis on the necessity
for effective oversight.
One could argue that in the US, the Office of Management and
Budget represents an effective way to keep a variety of bureaus, ranging from the Internal
Revenue Service to Customs, under supervision.
In the UK, the equivalent organization,
known as the Government Efficiency Unit, is perhaps just as proficient.
Unfortunately, in
many developing economies, simply ensuring that bureaucracies are faithfully carrying out
14
their required agenda may be quite burdensome.
As shown in this paper, this ultimately
reduces the effectiveness of public policy in fostering economic growth.
4.2
Economic growth and oversight when bureaucracies differ in
the technology used to provide public services
Although the findings that have thus far emerged provide one explanation for the data in
Figure 1, panels (a) and (b), part of this explanation implicitly relies on the assumption that
the index of Bureaucratic
Delays can be interpreted as a sensible proxy for the degree of
oversight. One would indeed expect to observe greater delays under less stringent oversight.
However, this particular interpretation may be far from complete. Indeed, the delays’ index
could just as well be capturing the fact that bureaucracies inherently differ across economies
as a result of technological considerations. In developing countries especially, the technology
available for the provision of government services may be substantially worse than in more advanced economies. In post-Communist Russia for example, the material equipment available
to the public sector is notoriously outdated. In this section, we show that this characteristic
enhances the difficulty of achieving higher growth rates for these poorer economies.
We can capture the idea of an inferior public goods technology by examining an appro
priate shift in F(B; w) through an increase in w. As in the previous subsection, we first derive
the implications of a first-order shift in F(8; w) for the degree of oversight. Focusing on the
simple example introduced earlier where q* = Xw, it directly follows that
While this result does not necessarily generalize to all possible underlying distribution functions, it does have some intuitive appeal. Recall that the oversight threshold in this model
equates the marginal gain from decreasing bureaucratic rents to the marginal increase in the
resources needed for oversight. As shown in Figure (4a), an increase in w leads to a shift in
the average cost distribution associated with the production of government services towards
the higher end of its support. Since a greater fraction of bureaus consequently report higher
values of 8, a higher monitoring threshold q* is generally needed to trigger oversight by the
political authority. In other words, as the technology for providing public infrastructures deteriorates, bureaus get away with reporting substantially large production costs, in per-unit
terms, before any monitoring takes place.
[Insert Figure 4 Here]
15
It should be pointed out, however, that the implications for the overall degree of bureau
oversight are not unambiguous.
To see this, note that a change in w affects the number
of bureaus subject to oversight, N[l - F(q*; w)], both directly, through w, and indirectly,
through q*.
Figure (4b) provides a numerical example to illustrate this last point.
The
parameter values used in this exercise are given in the following table:
Exogenous Parameters:
Q
r~ r
6
p
X
Values:
.65
2
.05
.02
.75
.25
Observe that for low values of w, the fraction of the bureaucracy subject to oversight
actually increases with w. As shown in Figure (4a), this is because in cases where the
public goods’ technology is initially effective, a slight increase in w is sufficient to cause a
relatively large shift in the average cost distribution towards high values of 0. The fraction
of bureaus with relatively poor technology, therefore, increases rapidly with w for low values
of w. It follows that in spite of the increase in the monitoring threshold q*, the measure
of bureaus that are monitored at first increases.
The opposite, of course, is true for high
values of w. Hence, when the average cost of providing public services already tends to
be high, an increase in w leads to less oversight.
These findings imply that in countries
where the technology available to produce public services is of low quality, not only are
particularly large budget requests necessary before any monitoring occurs, but the fraction
of the bureaucracy that is monitored is likely to be small as well. Clearly, these result are
consistent with one might generally expect of less developed economies. It therefore remains
to see, at least with the particular distribution function adopted in this example, if high
values of w are also associated with lower growth.
Figure (4~) shows that the quantity of per-unit bureaucratic rents increases with w. This
result is mainly driven by the fact that z
> 0 while J:*(q*
- B)dF(B) itself increases with
also increases with w as
77** In addition, the mean of the average cost distribution, 5,
pointed out earlier. Since both an increase in per-unit rents and an increase in the average
cost of public goods production lower the fraction of government services to private capital in
equilibrium [i.e. recall equation (ll)], the growth rate tends to fall with w. This is shown in
Figure (4d). It follows that for high values of w, this framework not only suggests that little
oversight occurs, and only when bureaus show themselves to be especially inefficient, but
also
that lower
growth indeed prevails. In addition, if we interpret the index of Bureaucratic
Delays as indicating the degree to which bureaucracies differ across countries on technological
grounds, Figure (4~) stands as the model analogue to Figure (lb).
Figure 5, panels (a)
through (d), show that the relationships we have just described are robust across different
values of both X and r.
16
[Insert Figure 5 Here]
4.3
Economic growth under full information
In this subsection, we briefly relate the analysis above to previous work on the issue of
productive government expenditures and economic growth. More precisely, we show that the
full information case yields the standard expected results. In a full information environment,
the political authority can observe each bureau’s technology for transforming final output
into public services or, alternatively, each bureau’s draw of 0. As a result, there is no need
for monitoring and the government can provide each bureau with a budget precisely equal to
the cost associated with the production of public intermediate inputs. Alternatively, we can
think of the full information scenario as one where oversight is costless so that X = 0. Then,
in the above example, the optimal monitoring threshold satisfies q* = Xw = 0 by equation
(20), and all bureaus are monitored since N[l - F(0; w)] = N.
Moreover, when q* = 0,
J{* (q* - B)dF(B; w) = 0 and th ere are no informational rents accruing to the bureaucracy.
Analogously to equation (11)) it follows that the government budget constraint yields
%4
xc(t)
= TA[-1-Y
w
By solving the representative household’s problem in (Pl),
the resulting rate of economic
growth is still given by equation (13) which, in this case, reduces to
y = &A;(1
- +-8(w)+
- (6 + p)
0
(24)
Under full information, one loses the distortions associated with the fact that bureaucracies often act as rent-seekers.
Evidently, since monitoring is no longer necessary while
bureaucratic rents are reduced to zero, the growth rate in equation (24) exceeds that of
equation (14).
However, note that the quality of the technology used in the provision of
public services may still affect economic growth since y is decreasing in e(w). This result
differs from that of Barr-o (1990). In Barro’s earlier framework, it is implicitly assumed that
final output directly translates into productive government transfers at the rate of one for
one.
5
Growth and welfare maximizing tax policies
At this stage of the analysis, one may find it interesting to address the issue of optimal tax
policy in the set-up we have described.
First, note that in terms of maximizing growth,
17
Barro’s (1990) efficiency condition still holds under full information since the tax rate that
maximizes (24) is given by
7-f
Y = 1 -a.
(25)
This condition balances the adverse impact of distortionary taxation against the fact that
higher taxes raise the ratio of government services to private capital since, from equation
(23), $$ = (TA)%(w)%
In the private information case, however, the ratio of public
intermediate inputs to private capital is given by
3
= (TA)~ [g(w) + A[1 - F( q*;w)] + (1 - 7) jJ+h’
- B)dF(B;w)
+
I
(26)
from equation (11). Thus, taxes play an additional role that contributes to raising growth.
Specifically, they help reduce the distortion associated with per unit bureaucratic rents.
Observe that growth is strictly concave in the tax rate under both full and private information
while the expression in square brackets in (26) monotonically decreases with r. Letting 7;
denote the tax rate which maximizes growth in equation (14), (i.e. the case where bureaus
possess private information) it follows that
In other words, and as shown in Figure (Sa), one might expect countries where bureaucratic
operations lack transparency to adopt more stringent tax policies to make up for the resulting rent distortions. Let us go one step further and ask whether this result still holds when
the political authority is concerned, not with maximizing economic growth, but with maximizing welfare. We may, for instance, imagine a benevolent central government whose aim
is to maximize households’ utility, taking as given the decentralized choices of households
and bureaus.
Since our model under full information collapses to that of Barr-o (1990), it
immediately follows that
[Insert Figure 6 Here]
f
l-u = ryf
= (1 - a).
(28)
where +rLdenotes the welfare maximizing tax rate under full information, When oversight
is costly, however, the benevolent political authority solves
maxU=
O”
e--pt
4va
J
=dt
0
18
with B > 0, p > 0,
m
subject to c(t) + yF;(t) = (1 - ~)[ANk(t)~-~cc(t)~-~
+ s(t)],
(29)
c(t) 2 0, k(t) 2 0, k(0) = Ice > 0 given,
where y is given by equation (14). We can re-write (P2) as
c(op
u = (1 - c7)(p - y(1 - C))
and note that
c(t)
=
xc(t)
(1 - T)AN[~]
where the ratio #
1
--a - y
k(t) + (1 - T)[-
xc(t)
w
q* - qdJv%J)
is constant and given by (26). This problem is rather difficult to solve
analytically since both y and #
are functions of r. Nevertheless, and letting 7pUdenote the
welfare maximizing tax rate under private information, a simulation using the parameter
values introduced in the previous section suggest that
as shown in Figure (6b). Therefore, when oversight is relatively costly, a government concerned about households’ welfare rather than growth chooses an even more stringent tax
policy. To see why this result emerges, note that bureaucratic rents in equation (30), when
expressed in units of capital services, depend on the ratio of government services to capital
which itself increases with T. Thus, the fact that 75 > 7: derives mainly from the idea that
when maximizing welfare, one must take into account initial consumption effects in addition
to growth effects. Finally, Figure (6b) suggests that irrespective of whether one’s concerns
lie with growth or welfare, the less effective the oversight technology, the higher the optimal
tax rate.
6
Concluding remarks
Following the empirical work of Mauro (1995)) and Keefer and Knack (1995)) the notion that
the quality of bureaucratic activity may significantly influence economic growth has recently
received considerable attention.
However, most of the theoretical research in this area has
focused on the interactions of bureaucracies with private markets. By contrast, the purpose
of this paper has been to point out that the interactions of rent-seeking bureaucracies with
their political authority may be equally significant.
19
The reasoning behind this observation relied on two strands of ongoing research. First,
as suggested by Barro (1990), as well as Glomm and Ravikumar (1994, 1997), productive
government expenditures may play a significant role in the process of economic development.
It is presumably this idea that underlies much of the effort exerted by the International
Monetary Fund in lending funds to poorer governments.
Second, the provision of public
goods and services typically occurs through a variety of public agencies. As first suggested by
Niskanen (1970), and Migui, and Belanger (1974), these bureaus possess private information
with respect to their technology and often act in a self-interested fashion. When considered
together, these two arguments directly suggest that the efficiency of government spending is
implicitly linked to the need for some amount of bureau oversight. Thus, in addressing the
issues first set out in the introduction, our framework provides the following observations.
In countries where the central authority cannot monitor its bureaucracy effectively, lower
rates of economic growth tend to emerge along with little bureau oversight. In many African
countries for instance, means of communication and transportation
are rudimentary at best.
Such conditions make it difficult for the relevant political authority to oversee its agencies
in the provinces.
be expected.
Therefore the fact that little oversight takes place in equilibrium is to
Moreover, as the monitoring of bureaus becomes more costly, the amount of
public expenditures-
including bureaucratic
rents- that are necessary for the provision of
public services typically increases. This effect directly results in a lower ratio of government
services to private capital, and hence a lower rate of return to private investment. Economic
growth, therefore, suffers in a way that is consistent with the general trend in Figure (lb).
The findings we have just described, however, do not necessarily hold in all possible cases.
In particular,
the fact that government expenditures
are financed through distortionary
taxation implies that under certain circumstances, the adverse growth effects of bureaucratic
rents may be mitigated by fiscal policy. Under this scenario, it is possible for growth to
increase as the monitoring technology worsens since the fall in oversight implies an offsetting
decrease in monitoring costs.
Finally, we have also argued that in economies where bureaus are limited to inferior
technologies in the provision public services, not only does little oversight occur, but bureaus
generally succeed in requesting especially large budgets before triggering any monitoring.
Furthermore,
these findings directly implied high levels of bureaucratic
rents, in per-unit
terms, as well as lower growth. This is indeed what is suggested by Figure (lb), when the
difference in Bureaucratic
Delays across countries is primarily attributed
to technological
considerations as opposed to oversight.
Although we have tried to provide a reasonable analysis that is consistent with the data
available on the quality of bureaucratic activity across economies, it is clear that the BERI
20
indices are subject to many caveats. One of the more important caveats relates to the somewhat vague definition of the indices which, therefore, allows for multiple interpretations.
We have outlined two such interpretations regarding the index of Bureaucratic Delays. Obtaining better estimates of the way in which bureaucracies differ along specific dimensions,
such as the state of the technology they use or the amount of resources spent on oversight,
would allow us to start sorting out which aspects of bureaucratic activity might matter most
for economic growth. Thus, we hope that our analysis may prove a useful guide in future
empirical work.
21
References
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[2] Aucoin, P. (1991), “The Politics and Management Restraint Budgeting,” in A. Blais and
S. Dion eds. “The Budget-Maximizing
sity of Pittsburgh Press, Pittsburgh,
[3] Ayal, E. and Karras,
G. (1996),
Bureaucrat:
Appraisals and Evidence,” Univer-
Pa.
“Bureaucracy, Investment,
and growth,” Economic
Letters, 51, 233-239.
[4] Barro, R. (1990), “Government Spending in a Simple Model of Endogenous Growth,”
Journal of Political Economy, 98, 103-125.
[5] Blais, A. and Dion, S. (1991),
“The Budget-Maximizing
Evidence,” University of Pittsburgh Press, Pittsburgh,
Bureaucrat:
Appraisals and
Pa.
[6] De Soto, H. (1989), “The Other Path,” Harper & Row Publishers Inc., First Edition.
[7] Easterly, W., Kremer, M., Pritchett, L. and Summers, L. (1993), “Good Policy or Good
Luck,” Journal of Monetary Economics, 32, 459-483.
[8] Easterly, W. and Rebelo, S. (1993), “Fiscal Policy and Economic Growth,” Journal of
Monetary Economics, 32, 417-458.
[9] Glomm, G. and Ravikumar,
B. (1997),
“Productive Government Expenditures
and
Long-Run Growth,” Journal of Economic Dynamics and Control, 21, 183-204.
[lo] Glomm, G. and Ravikumar,
B. (1996),
“Flat-Rate
Taxes, Government Spending on
Education, and Growth,” mimeo.
[ll] Glomm, G. and Ravikumar, B. (1994), “Public Investment in Infrastructure in a Simple
Growth Model,” Journal of Economic Dynamics and Control, 18, 1173-88.
[12] Hall, R. and Jones, C. (1997), “What Have We Learned From Recent Empirical Growth
Research ?,” American Economic Review, 87, 173-77.
[13] Jones, L. and Manuelli, R. (1990)’ “A Convex Model of Equilibrium Growth: Theory
and Policy Implications,”
Journal of Political Economy, 98, 10081038
22
[14] Keefer, S. and Knack, P. (1995),
“Institutions and Economic Performance:
Country Tests Using Alternative Institutional
Cross-
Measures,” Economics and Politics, 7,
207-227.
[15] Mauro, P. (1995), “Corruption, Country Risk and Growth,” Quarterly Journal of Economics, 110, 681-712.
[16] Migue, J.-L. and Belanger, G. (1974), “Towards a General Theory of Managerial Discretion,” Public Choice, 36, 313-322.
[17] Niskanen, W. (1971),
“Bureaucracy
and the Representative
Government,”
Chicago:
Aldine Atherton.
[18] Niskanen, W. (1991), “A Reflection on Bureaucracy and Representative Government,”
in A. Blais and S. Dion eds. “The Budget-Maximizing
Bureaucrat:
Appraisals and
Evidence,” University of Pittsburgh Press, Pittsburgh, Pa.
[19] Peacock, A. (1978), “The Economics of Bureaucracy:
An Inside View,” in “The Eco
nomics of Politics,“, The Institute of Economic Affairs publications, London.
[20] Rebelo, S. (1991), “Long-Run Policy Analysis and Long-Run Growth,” Journal
of Po-
litical Economy, 99, 500-521.
[21] ScuIly. G. W. (1988), “The Institutional Framework and Economic Development,” Journal of Political Economy, 96, 52-62.
[22] Shleifer, A. and Vishny, R. (1993), “Corruption,” Quarterly Journal of Economics, 108,
599-618.
[23] Summers, R. and Heston, A. (1991), “The Penn World Table (Mark 5): An Expanded
Set of International
Comparisons, 1950-1988,”
Quarterly Journal of Economics, 106,
327-368.
[24] Townsend, R. (1979), “Optimal Contracts and Competitive Markets with Costly State
Verification,” Journal of Economic Theory, 21, 265-293.
[25] Turnovsky, S. (1996),
“Fiscal Policy, Adjustment Costs, and Endogenous Growth,”
Oxford Economic Papers, 48, 361-381.
[26] Williamson, S. (1987)’ “Costly Monitoring, Loan Contracts,
Rationing,”
Quarterly J oumal of Economics, 102, 135-145.
23
and Equilibrium Credit
Data Appendix:
Institutional Indicators are obtained from Business Environmental Risk Intelligence. The
description of the variables can also be found in Keefer and Knack (1995). The plots in Figure 1 represent time series averages over the available sample.
Bureaucratic Delays: Measures the “speed and efficiency of the civil service including processing customs clearances, foreign exchange remittances and similar applications.”
Scored O-4, with higher scores for greater efficiency.
Infrastructure Quality:
Assesses “facilities for and ease of communication between
headquarters and the operation, and within the country,” as well as quality of transportation.
Scored O-4, with higher scores for superior quality
Per capita growth rates are averages over the period 1960-89 obtained from the World
Bank tables.
24
Panel
Bureaucratic
Communication
Quality
Index:
Per
Delays
Index:
Scored
Capita
O-4,
Output
Scored
O-4,
(0)
Delays
vs
and
with
of
Transportation
Higher
Panel
(b)
Growth
vs
with
Quality
Score
for
Bureaucratic
Higher
Score
Superior
Quality
Delays
for
Lesser
Delays
Figure 2
HOUSEHOLDS
Bureaucratic Rents,
8
Distortionary Transfers
Distortionary Taxes
I
GOVERNMENT
Budgets
BUREAUS
4-
OVERSIGHT
AGENCY
4
L
Monitoring
J
Figure 3
I
I
Discretionary
Surplus
I
i
I
0
/
T
1
8
Panel
Cumulative
Panel
(a)
Distribution
Function
Fraction
of
(b)
Monitored
Bureaus
N
d
O
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 .o
0.0
0.1
0.2
0.3
0.4
0.5
I9
Panel
Per-Unit
6
0.0
0.1
0.2
0.3
(c)
0.5
w
0.7
w
Panel
Bureaucratic
0.4
0.6
0.6
Rents
0.7
Equilibrium
0.8
0.9
1 .o
(d)
Growth
Rate
I
I
0.8
0.9
,
1 .o
Panel
Per-Unit
(a)
Panel
Bureaucratic
Rents
Equilibrium
(b)
Growth
Rate
t
-
x=.25
lx.5
-
/
A=.75
/
/
//--
,
,
,
__--
,
,
__--
,
,
_---
/
,
__----
,
/
/
0.1
0.2
0.3
0.4
0.5
0.6
/
,
,
0.7
/
/
0
d ’
__--
_---
I
6 0.0
d
/
1
---
_---
I
I
0.8
0.9
-
Panel
r-4
0
1 .o
‘: 0.0
0.1
0.2
0.3
0.4
0.5
6u3
-
7=.1
---
r=.35
rl
-
O 0.0
-
(c)
Panel
Rents
Equilibrium
r=.5
‘/
0.1
0.2
0.3
0.4
0.5
w
0.6
0.7
w
Bureaucratic
0.6
0.7
0.8
0.9
A=.75
--
w
Per-Unit
-
1 .o
(d)
Growth
Rate
0.8
0.9
1 .o
Panel
Growth
(a)
Maximizing
Tax
Policies
0-
:
.
0.
6.A
,
--
---
/
r;=
.35---c
+cJro
26.
co.
2d
0
r
SI
J3j.20
/-
_-.---
0.24
----
0.28
_----
0.32
I
----__
I
&
I
’
0.36
-rp=;85-------Y
0.40
Panel
Welfare
0.44
0.48
0.24
0.28
0.32
Maximizing
0.36
0.52
--
0.56
--
I
0.60
(b)
Solutions
X=0.
0.20
x=.75
--_
0.40
7
0.44
Full
0.48
Information
0.52
Case
0.56
0.60
@FedFRASER