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Corruption, Military Spending and Growth
d’Agostino G.*, Dunne J. P.**, Pieroni L.***
*University of Rome III (Italy), and University of the West of England (UK)
**University of the West of England (UK), and University of Cape Town (RSA)
***University of Perugia (Italy)
Abstract
This paper considers the complementary effect of corruption and military spendingon economic growth, analyzing both the impact of public spending and the allocationof resources between categories of public spending. The non-linearities emerged fromthe endogenous growth model are due to the links among public spending components,corruption and economic growth. The main findings of the empirical results confirmthe expectation that corruption and military burden lower GDP per capita growthrate. They also suggest that omitting the complementarity effect between militaryspending and corruption the impact on economic performance is underestimated.
Keywords : corruption, military spending, development economics
JEL Classification : O57, H5, D73
1
1 Introduction
Allegations of corruption in the military sector are neither infrequent nor unexpected. It
is argued that the limited competition in the defence sector leads to a relatively high level of
informal contracts and to rent-seeking activities, providing fertile ground for the growth of
corrupt practices. This can have lead to an increase in the cost of military activities and their
burden on the economies rent seeking in the military sector and may crowd out productive
investment in the private second, meaning there are both direct and indirect effects.
We follow the theoretical suggestions of the endogenous growth model originally devel-
oped by Barro (1990) and propose as explanatory key decisions of the model the comple-
mentary effects that arises between private and public sectors and the detrimental effects of
corruption. In our model the government expenditure for military sector and public invest-
ment are defined as potential productive inputs, with different productivity, and corruption
impact on the long run economic growth. The model also incorporates the inefficiencies
of the corruption linked services provided by the public sector (red tape and bureaucratic
corruption), although it does not affect long run growth.
Our research is related to a number of empirical papers that test if the more corruption
enhances the level of military spending (and vice-versa), implying that they are complements
in constraining the economic growth (Mauro, 1995, 1998; Aizenman and Glick, 2003) . Our
work is closed in the spirit to Tanzi (1998) Gupta et al. (2000) who argued that produc-
tive public investments is scaled down in countries with widespread corruption in favour of
government expenditure less verifiable such as the military one.
This paper contributes in this literature in two ways. First, we test if the magnitude
of corruption is associated with a larger size of expenditure of the military sector (as well
as of government spending in investments) and how this externality leads to cut economic
growth. We focus our attention on military-corruption link with economic performance.
These effects of corruption seem particularly relevant when they are associated with higher
shares of military spending in GDP. The confidentialness of some military operations and
privateness in the control of natural resources in developing countries makes difficult any
provisional analysis of costs increasing the discretionary power of the bureaucratic lobby
2
and the corruption level of the public officer (Gupta, 2000). For this reason, we address the
analysis specifically to a large sample of African countries.
Furthermore, we motivate our analysis by estimating from empirical model three types
of elasticities that are able to account for military spending/corruption relationship in the
use of policy analyses (Lee, 2006): i) direct elasticities that arise from the estimations of
the parameters of the model; ii) indirect elasticities that measure the interaction between
corruption and components of government spending, and iii) total elasticities as a sum of
the elasticity i) and ii).
The empirical results, that use a recent panel from 1996 to 2007, confirm the endogenous
model predictions. While government investments enhance economic growth, large military
burden and current government spending cut GDP growth. As expected corruption shows
a negative effect. Significant indirect effects by corruption are found for each components
of government spending in determining economic performance. Specifically we convey an
asymmetric impact between military sector and corruption on economic growth. The neg-
ative effect of military spending on growth is consistently influenced by the indirect impact
of corruption on military burden while the magnitude of the reverse causality although sig-
nificant is less considerable. This result is emphasized when we estimate the elasticities for
countries with high or low levels of corruption and military burden.
The remainder of the paper is organized as follows. In section 2, we briefly review
the relevant literature for our approach. In section 3 we illustrate the endogenous growth
model for the components of government spending extended for the detrimental effects of
corruption. We also derive the expected signs of the model parameters by implementing
the comparative static. Section 4 discusses the methods for the empirical application and
correlated issues. Section 4 illustrates the dataset and provides a discussion of the empirical
results. Concluding remarks are presented in section 6.
2 Preliminaries
Consider an economy where a representative household maximizes an utility function
choosing the optimal amount of private consumption. The agent produces a single com-
3
modity, which can be consumed, accumulated as capital or paid for as an income tax. The
objective is to maximize the discounted sum of future instantaneous utilities:
MAX
∫ ∞0
U(c)e−ρtdt (1)
where c describe the amount of private consumption, and ρ is the subjective discount rate.
Thus, the instantaneous utility function of private consumption is modeled by a constant
elasticity of substitution (CES) function, described as :
U(c) =c1−σ − 1
1− σ(2)
Following Barro (1990) and Devarajan et al. (1996), the production function is modelled
as interaction among private capital k and total public spending G, which is disentangled
among two public productive sectors, that we identify as military spending M , public in-
vestment I and an expenditure for current government consumption, cp. The production
function in aggregate embodies a technology of constant returns to scale under diminishing
returns with respect to each factor,
y = Ak1−α−β−δMαIβcδp (3)
with the household budget constraint given by:
k = (1− τ)Ak1−α−β−δMαIβcδp − c (4)
where τ is the flat-rate of income tax.
Government uses the total amount of collected taxes (τy) in order to finance total public
spending (G), deploying them among public productive sectors (M, I) and current govern-
ment consumption (cp).
The representative household choses the optimal amount of private consumption so as to
maximize (2) subject to (4) and given the initial level private capital k. The steady state
4
growth equation is given by:
γ =c
c=
1
σ
[(1− α− β − δ)(1− τ)A (gmil)
α (ginv)β (gcons)
δ
(G
k
)α+β+δ
− ρ
](5)
where γ measures the consumption growth rate. The government-specific parameters α, β
and δ, are assumed to be complements with the parameter of the private capital. Note that
given the components of government spending, the model also corresponds to the Ramsey
specification where are assumed diminishing returns of the private capital.
The preciding model assumes that financing government spending by income tax is al-
located efficiently. This assumption may be relaxed by assuming that corruptive practises
in the economy induce, at least, allocative distorsions. A different strategy is adopted here,
which consists of assuming that corruption affects the expenditures of the public sectors
evenly so as to avoid any parametric reduction on their effect.
3 Proposed model
The model we propose assumes that the government follows specific rules of financing
described by:
M = h1gmilG (6)
I = h2ginvG (7)
cp = h3gconsG (8)
where gmil, ginv and gcons denote the fraction of public resources allocated for public sector
components whereas h1, h2 and h3 identify the detrimental effects of corruption affects
military spending, public investment and current government consumption, respectively.
Note that modelling the fraction of corruption as a tax captures the idea that the degree of
exposure to corruption is not identical across categories of productive public expenditures
(see, Mariani (2007); Acemoglu and Verdier (1998); Delavallade (2007)). The previous
parametrization (6)-(8) may be cast into (5) such as the steady state growth equation is
5
given by:
γ =c
c=
1
σ
[(1− α− β − δ)(1− τ)A (h1gmil)
α (h2ginv)β (h3gcons)
δ
(G
k
)α+β+δ
− ρ
](9)
Thus, the growth rate in (9) depends on the allocation among government expenditures and
on the magnitude of corruption affecting the performance indicators.
To take the effects of our relevant government variables affected by corruption into ac-
count, we rearrange the previous formulation by exploiting the fact that the tax rate τ (and
hence G/k) is constant in the steady-state. Formally:
γ =1
σ
[j (h1gmil)
α1−α−β−δ (h2ginv)
β1−α−β−δ (h3gcons)
δ1−α−β−δ − ρ
](10)
with j = (1− α− β − δ)(1− τ)A1
1−α−β−δ τα+β+δ
1−α−β−δ .
We now describe in detail the results of comparative static for the model and simulate
the impacts on growth rate of our relevant government variables affected by corruption with
respect to the baseline model.
3.1 Comparative static
Consider gi one component of the government expenditure as in Barro (1990). By differ-
entiating γ with respect to gi we set two cases:
(a)∂γ
∂gi> 0 when gi 6 g∗i (11)
(b)∂γ
∂gi< 0 otherwise
where g∗i is the optimal share of resources of the sector i. Without loss of generality, we
can extend the model to account for the relation between its productivity and that of other
public spending categories. However, the decisions related to the amount of resources to
each sector are not exclusively motivated by its effective needs, but also by the possibility,
for bureaucrats or policy-maker to obtain private gains. From the preceding argumentations,
6
there are therefore some reasonable assumptions that can be included in our model in order
to derive a parametrization for empirical tests:
Assumption 1: the degree of exposure to corruption is not identical across categories of
government spending. As argued by Gupta et al. (2001) there are public sector like defence
that are more susceptible to corruptive practises.
Assumption 2: corruption has a negative impact on growth, i.e. ∂γ∂h< 0. That is, we
exclude that corruption might promote economic growth as it relaxes inefficient and rigid
regulations imposed by government (see for example, Dreher and Herzfeld (2005)).
Assumption 3: modelling corruption as a tax not only makes the impact of compo-
nents of government spending heterogenous on growth rates but focuses the channel of the
interactions (complementary or substitution) of corruption and government components by
the grand (or political) corruption (Bardhan, 1997). This restriction implies the exclusion
of inefficiency current government consumption on growth rate due to the effects of (petty)
corruption. With respect to the equation (8), this negligible impacts leads to h3∼= 1.
Assumption 4: the productivity of the expenditure of the public sectors is not homo-
geneous. In particular, this difference is modelled in the production function by assuming
that β (government investments) is greater than α (military sector).
To highlight the interaction effects between government components and corruption, we
derive mixed derivatives for the case (a) and (b) discussed above which are given by:
(a) gi 6 g∗i ,∂γ
∂gi> 0 | ∂γ
∂hi< 0 =⇒ ∂γ
∂gi∂hi6 0
(b) gi > g∗i∂γ
∂gi< 0 | ∂γ
∂hi< 0 =⇒ ∂γ
∂gi∂hi> 0 (12)
Under the assumption 4, we note that when the share of government spending is higher
(lower) that optimal level, the mixed derivates has a positive (negative) effect on growth rate,
then the relationship between components of government spending and corruption enhances
the negative effects of this variable. Conversely, the a positive impact of more productive
7
sector is constrained by the corruption effect. In Table 1, we present the relationships that
emerge by the theoretical framework and use this expected set-up in the empirical section.
Table 1: Results from the comparative static
dγdg1
dγdg2
dγdh
dγdg1
β > αg1 ≤ g∗1
dγdg1
≥ 0β > α
g1 ≤ g∗1dγ
dhdg1≤ 0
g1 > g∗1dγdg1
< 0 g1 > g∗1dγ
dhdg1> 0
β < α dγdg1
> 0 β < α dγdhdg1
< 0
dγdg2
β < αg2 ≤ g∗2
dγdg2
≥ 0β < α
g2 ≤ g∗2dγ
dhdg2≤ 0
g2 > g∗2dγdg2
< 0 g2 > g∗2dγ
dhdg2> 0
β > α dγdg2
> 0 β > α dγdhdg2
< 0
dγdh
dγdh
< 0
Notes: Given g1 and g2, two productive sector and α and β the productivity, respectively, we remark the partial derivative effects under theassumption of a greater productivity of the sector g2 with respect to g1.
To illustrate the results of the model, in Figure 1 we show simulation panels of growth
rate responses to military spending (gmil) and public investment (ginv), respectively. The
solid line shows the baseline case in which corruption does not affect the growth rate (i.e.,
h1 = h2 = 1). The parameters used in the simulation are mainly extracted by Devarajan
et al. (1996). The result above clarifies the conditions under which the growth rate rises when
the relative government expenditure falls: the growth rate will rise if the share of government
expenditure is less that its optimal share g∗i , while the form of the hump-shaped depends on
the difference of productivity among sectors. Our simulation shows that the share of public
investments (panel b) is able to keep productive this component of government spending also
when, for example, there are extra allocation derived from peace dividends. In the broken
lines are predicted the detrimental contributions of corruption in the relationship between
shares of military and public investments and economic growth (i.e., hi < 1, with i=1,2).
As an expected result, the interactions of corruption with public investments determines a
greater impact on reducing growth rate.
The simulations in Figure 2 of the partial and mixed derivate functions confirms the
expected signs in Table 1. The left hand side of Figure 2 indicates that the sector with higher
productivity is able to shift to the right the optimal level of ginv. Indeed, the derivative of γ,
with respect to ginv, estimates a positive impact for shares of public resources allocated, at
8
Fig. 1: Government spending components, corruption and growth
Notes:We describe with gmil and ginv the shares of resources to military and investment spending respectively, whereas h1 and h2 describethe levels of corruption in the two public sectors. The parameters used for the simulations are: α = 0.1, β = 0.2, η = 0.6, A = 0.7, ρ = 0.02
least until the share of 20 percent. The right panel of Figure 2 plots the interaction effects
of corruption and government spending components on growth rate. From the usual range
of shares of government spending in GDP, this Figure shows that corruption cut slightly the
performance of government investment whereas it is worth noting the significant interaction
of corruption in enhancing the negative effect of military sector on growth rate.
4 Methods
Equation (9) characterizes the growth path of an economy affected by public spending
components, sectoral corruption and the accumulation of private capital. For an empirical
analysis purpose we use the main findings discussed in the previous section and set-up growth
regression, described as:
γit = φγit−1 + ψ1p invit−1 + ψ2Xit + vi + ηt + µit (13)
where i and t characterize each country and time period (with t = 1, 2, ..T ), γit is the
average annual growth rate of GDP for country i during period t. Thus, γit−1 is the lagged
9
Fig. 2: Results of the comparative static
Notes:We describe with gmil and ginv the shares of resources to military and investment spending respectively, whereas h1 and h2 describethe levels of corruption in the two public sectors. The parameters used for the simulations are: α = 0.1, β = 0.2, η = 0.6, A = 0.7, ρ = 0.02
value of the dependent variable, p invit−1 is the lagged value of private capital1, and Xit =
[ginvit , gmilit , gconsit , corrit] is the vector of covariates expressed at time t.
The last equation poses some challenges for estimation. First of all, the presence of
unobserved period (ηt) and country specific (vi) effects have to be treated differently, given
the dynamic nature of the regression. More in detail, the first effect is accounted by the
inclusion of period dummy, whereas the the second one requires to use within group, or first
difference estimators. As a second challenge, for a correct regression specification, we have to
account both for the presence of endogeneity in the regressors, depending on private capital
accumulation function (4), than for the presence predetermined covariates2, such as public
spending components included in the static government budget constraints (6, 7, and 8).
We use generalized method of moments (GMM) estimators developed for dynamic models
of panel data that where originally introduced by Arellano and Bond (1991); Arellano and
Bover (1995). These estimators are based, first, on differencing regressions or instruments
to control for unobserved effects and, second, on using previous observations of explanatory
and lagged dependent variables as instruments (internal instruments). More specifically,
1It is important to stress that the use of the lagged value of private capital is necessary to account forthe delay of the response of the growth rate of GDP to private capital.
2Statistically, we define xit as a predetermined variable when the error term at time t has some feedbackon the subsequent realizations of xit. Hence, for predetermined variables we have that E[xit, εit] 6= 0 fors 6 t and E[xit, εit] = 0 for s > t.
10
following Arellano and Bond (1991); Arellano and Bover (1995), we can rewrite equation
(13) as:
∆γit = φ∆γit−1 + ψ1∆p invit−1 + ψ2∆Xit + ∆µit (14)
where all variables are now expressed as deviations from period mean and t = 3, .., T . Equa-
tion (14) violate the assumption of non correlation between the error term µit − µit−1, and
the lagged dependent variable γit − γit−1. Thus, the use of instruments is required in order
to deal, contemporary, with the likely endogeneity between errors and lagged values of the
dependent variable and with the endogenity in the regressors. The instruments take advan-
tage of the panel nature of the dataset in that they consist of previous observations of the
explanatory and lagged-dependent variables.
Under the assumption of non serial correlation in the the error term and that the explana-
tory variables are weakly exogenous3, our application of the GMM dynamic panel estimator
uses the following moment conditions:
E [γit−2 (µit − µit−1)] = 0 (15)
E[Xit−2 (µit − µit−1)
]= 0 (16)
where X now include in it both the exogenous covariates and private capital p inv. In theory
un unlimited number of moment conditions, which implies the use of a number of instruments
growing with the number of periods T, may increase the efficiency of the estimator, at the
expense of an increasing of the overfitting bias. The trade-off between efficiency and bias
in the estimator becomes not trivial especially when the sample size in the cross-sectional
dimension is limited (Roodman, 2009). Since this is our case, we use as instruments only
the first appropriate lag of each time-varying explanatory variable.
The GMM estimator based on conditions (15) and (16), is known as difference estimator.
Notwithstanding its advantages with respect to simpler panel-data estimators, the differ-
ence estimator has important statistical shortcomings. As point out by Blundell and Bond
(1998) among others, when the explanatory variables are persistent over time, lagged levels
3By definition a variable is weakly exogenous when it is assumed to be uncorrelated with future realizationsof the error terms.
11
of these variables are weak instruments in the first difference specification of the growth re-
gression. Note that, the weakness of instruments influences the asymptotic and small-sample
performance of the difference estimator toward inefficient and biased coefficient estimates,
respectively.
To prevent the potential inefficiency and bias in the difference estimator, we adopt a
strategy which combine the specification in difference (14), with the regression equation in
levels (13) into an unique system of equations (Arellano and Bover, 1995; Blundell and Bond,
1998). Moreover, we use the set of instruments described above for the regression equation
in difference, and the lagged differences of the explanatory variables for the equation in
levels. These are appropriate instruments under the assumption that the correlation between
explanatory variables and the country-specific effect (vi) is the same for all time periods.
That is:
E [γit+pvi] = E [Zit+qvi] and (17)
E[Xit+pvi
]= E [Xit+qvi] for all p and q (18)
Using the stationarity property and the assumption of exogeneity of future shocks, the
moment conditions for the second part of the system are given by:
E [(γit−1 − Zit−2) (µit + vi)] = 0 (19)
E[(Xit−1 −Xit−2
)(µit + vi)
]= 0 (20)
Thus, we use moment conditions (15), (16), (19), and (20) and employ a GMM procedure to
generate consistent and efficient estimates of the parameters of interest and their asymptotic
variance and covariance matrix, which are described respectively by the following formulas:
θ =(X ′ZΩZ ′X
)−1
X ′ZΩZ ′y (21)
AV AR(θ) =(X ′ZΩZ ′X
)−1
(22)
where θ = [φ, ψi] is the vector of parameters, y is the dependent variable stacked first in
12
differences and then in levels, x is the matrix of covariates which includes also the lagged
values of the dependent variable and the private investment stacked first in differences and
then in levels, Z is the matrix of instruments, and Ω is a consistent estimate of the variance-
covariance matrix of the moment conditions. Estimations of the variance-covariance matrix
are obtained through two-step procedure (Arellano and Bond, 1991). This procedure consist
in: i) assuming that the residual µit are independent and homoskedastic both across countries
and over time, to obtain the first step parameter estimations, and ii) using the residual in
the first step estimations to obtain a consistent variance-covariance matrix, and then to
re-estimate the parameters of the model in the second-step.
Since the consistency of the GMM estimators depends on whether lagged values of the
explanatory variables are valid instruments in the growth regression, we set-up different
tests for it. Firstly, we examine the overidentifying restrictions and test the validity of the
instruments by analyzing the sample similar to the moment conditions used in the estimation
process. In particular, Hansen and incremental Hansen tests examine the validity of the full
set of instruments and the instruments included in the level equation, respectively. Failure to
reject the inability of the instrument set gives support to the model specification. Further, a
test for serially correlated errors is implemented. With this dynamic framework, the absence
of a second-order residual correlation is tested for the validity of the estimation under the
null hypothesis.
Finally for a policy-maker perspective, using the estimated parameters from the system
GMM model, we propose different elasticity measures. The objective is threefold: i) to
analyse the direct elasticity of military spending, public investments and corruption on the
growth rate of GDP; ii) to assess the interactions among public spending components and
corruption, by calculating an indirect elasticity measure, and iii) to derive the share of these
elasticities on total elasticities.
Following the suggestion proposed by Lee (2006) we define the direct elasticity as:
ed = πX ′(.,.) (23)
where ˜X(.,.) = [gmil/γ, ginv/γ, corr/γ] is a vector of ratios among military spending, public
13
investments and corruption constructed as country and period means and the growth rate of
GDP constructed as country and period mean, whereas π = [ψgmil, ψginv , ψcorr] is a vector
of parameters estimated by equation (21). For example the direct elasticity of military
spending is then given by:
miled = ψgmilgmil(.,.)γ(.,.)
(24)
Moreover, to estimate indirect elasticity we set-up a fixed effect panel data estimator, which
regress, one by one, the component of X on the other components, and a linear deterministic
trend (T) to control for cyclical effects. For example, the specification of the fixed effect panel
data for military spending is given by:
milit = a+ b1ginvit + b2corrit + T + ui +mit (25)
where ui is the country specific effect, and mit is the error term. The parameters estimated
by (24) and (25), are then used to obtain the indirect elasticity for military spending with
respect corruption, described as:
mil/g correind = (ψcorrb2)gmil(.,.)γ(.,.)
(26)
Finally, total elasticity measure is obtained as the sum of direct and indirect elasticities for
each component. In our example, the total elasticity of military spending is then given by:
miletot =mil ed +mil/corr eind +mil/g inv eind (27)
5 Empirical results
5.1 Data
The dataset used to estimate the system of equations (13) and (14) includes 22 African
countries over the period 1996-2007 and it is extracted from African development indicator
of the World Bank (WBI). The complete list of countries is reported in Appendix 1, along
with the mean of each used variable.
14
Dependent variable (γ) is represented by the percentage annual growth rate of GDP
per-capita, whereas (ginv) is matched by the percentage ratio of gross private investment in
GDP, which includes private nonprofit agencies in addiction to the fixed domestic assets.
Government spending components are characterized by three different variables: i) mil-
itary expenditures which includes all current and capital expenditures on the armed forces
from NATO definition. It includes peacekeeping forces not only defense ministries and other
government agencies engaged in defense projects but also paramilitary forces, if these are
judged to be trained and equipped for military operations and military space activities;
ii)gross domestic fixed investment (i.e., gross fixed capital formation) comprises all additions
to the stocks of fixed assets (purchases and own-account capital formation), less any sales of
second-hand and scrapped fixed assets, by government units and non-financial public enter-
prises. It is worth noting that the variable outlays by government on military equipment are
excluded; iii) government consumption, which includes current expenditures for purchases of
goods and services (including compensation of employees). These components of government
spending are expressed as percentage in GDP.
A measure of corruption is included in the model to account for direct and indirect in-
teractions with growth rate. Although different private or public agencies publish measures
of corruption for countries of the World, we use the control of corruption index (CCI) ex-
tracted from WBI which is based on direct perceptions of firms. This index is interpreted
as the extent to which public power is exercised for private gain, including administrative
and grand corruption, as well as capture of the state by elites and private interests. Because
we are not able to drop-out from the variable the influence of other forms of corruption, we
expect a certain degree of measurement error (in excess with respect to grand corruption
that we specify in the model) in using this variable. The corruption index proposed ranges
from 0 to 100, where 0 means that country is perceived corrupted.
In addition to this set of variables, we define a dummy characterizing the countries in
the sub-Saharan africa (equal to 1), d.sub saharian, and 0 otherwise.
15
5.2 Estimates
Table 2 shows Blundell and Bond (1998) GMM estimation results for system of equations
13 and 14. The first two columns of the table are used as benchmark, including public
spending components and private investments (I) and time dummies (II), respectively;
column (III) introduces corruption index whereas column (IV ) includes the interaction
terms for military sector (gmilt ∗ corrt) and public investments (ginvt ∗ corrt). The lagged
value of private investment is introduced to account for its accumulation function described
in equation 4.
The estimated coefficients are significant, except for the interaction term of public in-
vestments (ginvt ∗ corrt ), and exhibit the expected signs. This outcome confirms the good
ability of the proposed endogenous growth model to explain the nexus among public spend-
ing components, corruption and growth. More in details, from column III, we see that the
share of military spending in GDP exhibits a strong negative impact on the growth rate of
GDP whereas, conversely, public investments are positive relate to it. This result predict
a different productivity level of the two components of public spending, and suggests that
the amount of resources devoted by the government to the military sector exceed its optimal
needs. In this context, corruption does not only entail a decrease in economic performance,
but it also increase the amount of rent-seeking activities, i.e. military spending. As a first
assessment, the theoretical prediction clearly emerges from the estimation (IV ) where the
interaction variable gmilt ∗ corrt affects positively the response variable. On the contrary,
the statistically not significant interaction of ginvt ∗ corrt excludes important effects in the
relationship between the public investment sector and growth rate.
Such a conclusion, corruption, in the African context, is able to favour military spend-
ing, worsening the negative impact of larger military sector on the economy’s growth rate.
Corruption entails the presence of re-allocative effects, which are independent by the pro-
ductivity or efficiency of the sector, but are related to them abilities to generates more rents,
involving bigger amount of money, and so larger bribes (Delavallade, 2005).
Table 3 reports the fixed-effect panel estimation results for the auxiliary regression 25,
useful to measure by (26) the indirect elasticities of military spending, public investments,
16
Table 2: Estimation results: dynamic panel data
I II III IV
γt−1 0.130 *** 0.098 * 0.083 * 0.083 *(0.049) (0.051) (0.051) (0.051)
gmilt -0.320 * -0.439 ** -0.373 * -1.229 **(0.189) (0.202) (0.205) (0.532)
ginvt 0.254 *** 0.215 *** 0.236 *** 0.283 ***(0.060) (0.060) (0.061) (0.070)
gconst -0.124 ** -0.219 *** -0.121 -0.121(0.054) (0.057) (0.079) (0.080)
p invt−1 0.216 *** 0.208 *** 0.217 *** 0.234 ***(0.034) (0.035) (0.035) (0.036)
corrt -0.035 * -0.061 **(0.020) (0.026)
d.sub saharian 5.675 ** 6.603 *** 7.524 ***(2.488) (2.540) (2.608)
Interactions
gmilt ∗ corrt 0.020 *(0.011)
ginvt ∗ corrt -0.000(0.001)
ref t=2002
t = 1997 2.041 ** 2.192 ** 2.326 **(0.901) (0.904) (0.908)
t = 1998 1.435 * 1.484 * 1.830 **(0.862) (0.862) (0.884)
t = 1999 1.588 * 1.635 * 1.975 **(0.898) (0.898) (0.918)
t = 2000 1.230 1.377 * 1.668 *(0.845) (0.849) (0.868)
t = 2001 1.886 ** 2.040 ** 2.384 ***(0.834) (0.838) (0.873)
t = 2003 1.285 * 1.375 * 1.690 **(0.795) (0.797) (0.818)
t = 2004 3.384 *** 3.497 *** 3.729 ***(0.814) (0.816) (0.826)
t = 2005 2.359 *** 2.514 *** 2.790 ***(0.799) (0.804) (0.818)
t = 2006 2.393 *** 2.544 *** 2.865 ***(0.810) (0.814) (0.836)
t = 2007 2.095 ** 2.334 *** 2.618 ***(0.840) (0.850) (0.871)
Second order serial correlation test -0.915 -1.596 -1.549 -1.434(0.359) (0.108) (0.113) (0.151)
Hansen Test 111.23 89.80 90.51 92.88(0.345) (0.456) (0.436) (0.368)
Incremental Hansen Test 4.42 1.90 2.16 3.87(0.345) (0.593) (0.539) (0.276)
N 242 242 242 242
Notes: The dependent variable is the growth rate of GDP, (γ). In parenthesis we report the standard errors, while the asterisksstand for the p-value significance levels. We have that ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.
17
Table 3: Results of the fixed effects panel estimations
gmilt ginvt corrt
ginvt -0.048 *** 0.165(0.014) (0.192)
corrt 0.008 * 0.018(0.004) (0.017)
gmilt -0.896 *** 1.625 *(0.257) (0.850)
Constant 2.073 *** 6.761 *** 46.075 ***(0.315) (1.194) (5.575)
T -0.039 *** -0.010 0.260 *(0.011) (0.049) (0.148)
Breusch and Pagan LM test 762.17 *** 401.72 *** 1136.37 ***N 264 264 264
Notes: The dependent variable are military spending, public investments and corruption, respectively. In parenthesis we reportthe standard errors, while the asterisks stand for the p-value significance levels. We have that ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.
and corruption, respectively. The auxiliary estimations confirm the findings of the Blundell
and Bond (1998) GMM estimations presented above. In particular, there is a significant
association between corruption and military spending but, on the contrary, corruption and
public investments appear not significantly correlated.
Table 4 shows the elasticity measures obtained by equations (24), (26), and (27) and the
share of direct and indirect elasticities in total elasticity in the last two columns. Before
presenting the main outcomes for the analysis, some remarks are necessary: i) indirect
elasticity measures are obtained as the sum of each component, when its estimated parameter
is statistically significant; for this reason, corruption and public investments account only
for the effect of military sector, whereas the indirect elasticity of military spending includes
both corruption and public investments; ii) the direct elasticity measures are obtained using
the estimated parameters form the third column of Table 2.
The estimates show that there are different channels through which corruption and gov-
ernment spending components affect real per capita growth rate. Corruption, for example,
affects real per capita growth rate directly and indirectly, through public sector corruption.
As argued by Gupta et al. (2001), the mechanism of the political decisions that generates
the allocation of government expenditure among the components of public spending uses,
18
Table 4: Estimations of the elasticities
Total elasticity Direct elasticity Indirect elasticity Direct elasticity Indirect elasticityon total elasticity % on total elasticity %
Full-sample
gmil -0.820 -0.273 -0.546 33.345 66.655() () ()
corr -0.644 -0.585 -0.058 90.936 9.064() () ()
ginv 0.560 0.520 0.040 92.879 7.121() () ()
Low military spending sample
gmil -0.175 -0.104 -0.071 40.701 59.299() () ()
corr -0.626 -0.043 -0.583 93.121 6.879() () ()
ginv 0.628 0.034 0.594 94.623 5.377() () ()
High military spending sample
gmil -0.611 -0.223 -0.389 63.552 36.448() () ()
corr -0.631 -0.043 -0.588 93.121 6.879() () ()
ginv 0.451 0.024 0.427 94.623 5.377() () ()
Low corruption sample
gmil -0.425 -0.155 -0.270 63.552 36.448() () ()
corr -0.366 -0.025 -0.341 93.121 6.879() () ()
ginv 0.533 0.029 0.504 94.623 5.377() () ()
High corruption sample
gmil -0.279 -0.158 -0.121 43.307 56.693() () ()
corr -0.865 -0.060 -0.806 93.121 6.879() () ()
ginv 0.564 0.030 0.534 94.623 5.377() () ()
Notes: The specifications reported are referred to estimations presented in Table 2. The asterisks stand for the p-valuesignificance levels. We have that ∗ p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗ p < 0.01. In parentheses we provide bootstrap standard errors.
among the majority of corrupt public officials, prescriptions or regulations that already exist.
However, the incidence in public budget amount is low given the large expenditure generally
invested in these fields.
The total elasticity of the corruption on growth rate (calculated from model III of table
3) is enough large (-0.64). The magnitude of this impact depends on the non-linear effects of
military spending while public investments are negligible. This measure of impact for Africa
countries seems to be realistic at least for two reasons. First, we find a significant and positive
impact on growth rate of an unitary decrease in corruption although the dimension is shorter
than Mauro (1995), Mo (2001) and Pellegrini and Gerlagh (2004) results obtained in large
world datasets. With respect to these authors, GMM estimator allows us to treat consistently
the endogeneity in some explicative variables and avoids issues linked with overestimations
19
of OLS parameters. Secondly, the existence on average of weaker institutions in Africa makes
corruption work as the second-best solution to market distortions imposed by government
procedures and policies at least in the short run. This effect partly counterbalance the
negative impact of corruption on economic performance in the long run.
We find a larger growth rate impact of changes in military spending. Also in this case,
a 10% decrease in military allocation may enhance real per capita growth rate by 8.2% on
growth rate. It is confirmed that rent-seeking sectors as military more than other productive
sectors is dependent from corruptive practices. Indirect elasticity seems to be associated
with the levels of corruption as shown in the sub-sample estimations of the elasticities.
This can support a political intervention to enhance the economic performance that
directly leads to a cut in the share of military spending to GDP, and indirectly reduces
corruptive incentives yielded by the defence sector. From a policy-maker point of view, if
the resources depicted by the military sector are devoted to enhance the share of public
investment, the impact in terms of real per capita growth rate, conditionally to corruption
level, rise. In table 4 we also present the estimated elasticities of sub-samples of countries
selected by distribution of the variable military spending in GDP; the sample ”‘high”’ repre-
sents the countries over the median of the distribution whereas ”‘low”’ represents the group
under the median. The estimated values of corruption in these sub-samples are shown to
be close estimates of those reported for the full sample of African countries, whereas are
growing in countries where the military spending is significantly more higher. The result of
this sensitivity analysis proves the usefulness to disentangle the effect of complementarities
between military sector and corruption as well as the goodness of our previous conclusions.
20
6 Conclusions
Corruption in developing countries is largely accepted to be an important obstacle to
economic growth, but there has been relatively few attempt to identify the specific manner
in which this may occur. In this paper an endogenous growth model that allows corruption
to act on economic growth, through interactions between the military sector and civilian
investments is developed and estimated. The empirical results for a panel of African coun-
tries confirm the predictions that while government investments enhance economic growth,
large military burdens and current government spending reduce it and that corruption has
a negative effect. Significant indirect effects of corruption are found for each components
of government spending, with a particularly strong asymmetric effect for the military com-
ponent. This negative effect of military burden on growth is consistently influenced by
the indirect impact of corruption, while the reverse effect though significant is considerably
smaller. This implies that combatting corruption is likely to directly increase aggregate
economic performances and to indirectly cut the negative impacts of military spending.
Overall, the results provide strong evidence that for the group of African countries studies
high levels of defense spending and corruption have had a damaging effect on economic
performance, both directly and indirectly.
21
Appendix 1 -Descriptive statistics
Country γ p inv g cons g inv mil g corr
Botswana 4.791 11.372 22.768 10.531 3.144 77.458Cameroon 1.698 14.378 9.641 2.367 1.360 11.354Cape Verde 3.406 14.142 17.456 12.329 0.792 62.862Chad 3.928 16.718 6.566 9.058 1.418 13.994Egypt, Arab Rep. 2.972 9.218 11.769 9.485 3.121 45.004Ethiopia 3.603 8.669 12.536 13.378 4.043 29.508Ghana 2.613 13.461 11.400 11.707 0.695 46.275Kenya 0.826 12.480 16.468 4.470 1.491 14.292Lesotho 2.244 32.894 18.296 10.738 2.879 52.063Madagascar 0.701 10.253 8.721 8.139 1.250 54.775Malawi 0.586 8.199 13.357 7.482 0.870 29.742Mali 2.512 14.623 10.695 8.195 2.182 38.771Mauritius 3.633 17.753 13.383 6.106 0.211 68.054Morocco 3.231 21.270 17.933 3.955 3.472 57.554Mozambique 5.360 10.661 9.322 11.244 1.143 30.650Namibia 2.266 16.325 27.372 7.885 2.795 65.671Rwanda 3.101 10.964 11.537 7.258 3.190 39.241Senegal 1.555 17.395 12.340 6.732 1.480 45.788Seychelles 1.808 21.719 26.313 8.482 1.856 63.995South Africa 1.932 13.736 19.013 2.689 1.639 70.333Tanzania 3.002 11.286 12.337 5.550 1.340 20.108Uganda 2.771 14.651 13.848 5.049 2.431 21.679
22
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