Peace Economics, Peace Science andPublic Policy

Volume 14, Issue 3 2008 Article 4

Military Spending, Corruption and EconomicGrowth

Luca Pieroni∗ Giorgio d’Agostino†

∗University of Perugia, lpieroni@unipg.it†University of the West of England, dagostino.giorgio@gmail.com

Copyright c©2008 The Berkeley Electronic Press. All rights reserved.

Military Spending, Corruption and EconomicGrowth∗

Luca Pieroni and Giorgio d’Agostino

Abstract

The purpose of this paper is to identify the complementary effect of corruption and militarysector on economic performance and to test the magnitude of their impact separately. Unlike themethod generally used in the economic literature, we estimate a cardinal corruption index ex-pressed as a percentage of GDP per capita through the multiple causes multiple indicators model(MIMIC).

The cross-country results show a negative impact of military spending and corruption indicatoron economic performance, which are in line with previous findings. However, the negative effectis mitigated by a significant positive, though asymmetric, relationship between these two factorsin affecting per capita growth rate.

KEYWORDS: military spending, corruption, economic growth

∗We are grateful to participants of the conference “Patterns of Corruption in the 21st Century,”Athens, 6-7 September 2008, for their suggestions. All errors are our own.

I. INTRODUCTION

Empirical studies have revealed a significant relationship between corruption andeconomic growth (Mauro, 1995, 1997; Davoodi and Abed, 2000, a). Without deny-ing the positive role that corruption has played in the history of some specific coun-tries, corruption was found to be endemic and pervasive, yielding lower growth,hampering private investment as well as productive public investment and inhibit-ing the efficiency of public services (Bardhan, 1997). These effects of corruptionseem particularly relevant when they are associated with higher shares of militaryspending in GDP. The confidentialness of some military operations and private-ness in the control of natural resources in developing countries makes difficult anyprovisional analysis of costs increasing the discretionary power of the bureaucraticlobby and, on average, the corruption level of the public officer (Gupta et al., 2000,a). However, in general, the limited competition in the defence sector yields an in-crease of informal contracts and rent-seeking activities where corruption may takeroot. This implies that the profitability on productive investments may be crowdedout by the rent-seeking military sector.

The purpose of this paper is to test if corruption opportunities are associ-ated with a larger size of expenditure of the military sector and if this ”dangerousexternality” has a significant impact on economic growth. In attempting to validatethe hypothesis at work, we derive a latent index of corruption based on the causesand variables that are known to be good proxies of corruption. The estimated pa-rameters are obtained from a Multiple Indicators Multiple Causes (MIMIC) model,as a special case of structural equation models (Joreskog and Goldberger, 1975;Joreskog, 1977). This allows ranking each country according to a cardinal index ofcorruption expressed as a percentage of GDP per capita. Thus, we can appropri-ately use these estimations for policy analysis. By introducing an interaction termbetween the corruption index and each countrys military burden, we can estimategross and indirect elasticities of the military and corruption with respect to eco-nomic growth.

The empirical results confirm our conjectures that corruption and mili-tary burden lower GDP per capita growth rate, especially in those sub-samples ofcountries where the share of military expenditure in GDP is high. Nevertheless,omitting complementarity effect between these variables may mislead their net im-pact on economic performance.

The remainder of the paper is organized as follows. In section II we focuson the empirical conjectures to test. We offer a complete description of the variablesused in the estimations and we explain the procedure to estimate the cross-countrycardinal index of corruption. Section III presents the empirical results while insection IV we provide some concluding remarks.

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II. THE FRAMEWORK FOR THE EMPIRICAL ANALYSIS

The theory provides support for testing the hypothesis that the net impact of mil-itary expenditure and corruption on the differentials in real per capita growth rateare affected by the externalities of the relationship between governments’ sectoralexpenditures and the goodness of the institutions. We formulate the following em-pirical specification,

(1) ! = a1mil +a2corr +a3milcorr +"X

where (!) is the real per capita growth rate, a1 and a2 the parameters associated withthe share of military spending in GDP (mil) and the government corruption index(corr). milcorr is an interaction term between military spending and corruption.Finally, X includes a complete set of control variables. The equation (1) assumesthat military spending and corruption are complement factors and their impact ongrowth is a non-linear function. We start writing the net effect of the military oneconomic growth as

(2) ! !(mil) = a1 +a3corr, a1 < 0,a3 " 0

where a3 is zero for insignificant effect of the interaction term. In this case, thedirect impact of military spending is measured by the negative size of a1. Then, wederive the effect of corruption on growth rate as

(3) ! !(corr) = a2 +a3mil, a2 < 0,a3 " 0

It worth noting that also a2 < 0.Disregarding the special case in which a3 is notsignificant (i.e. only a direct effect of military spending and corruption affects thegrowth performance), we note that this symmetry in equation (2) and (3) yieldsan identifying assumption on a3 and it conveys an indirect impact on economicgrowth. In spite of the fact that increases in military spending in GDP are some-times cycle-dependent and not necessarily linked with the quality of institutions andthe government, the significance of the interaction variable should account for themagnitude of this externality and obtaining a measure of the net elasticity when theestimations are used for policy analyses (Lee, 2006).

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(a) Data

To find empirical support for our hypotheses, the strategy is to estimate the growthequation (1) in two steps where the corruption index is exogenously generated. Forthe growth equation we use the variables from the cross-section database createdby Aizenman and Glick (2003) also used in Pieroni (2008) covering 90 countriesover the period 1989-1998. To account for the impact of corruption on growth,we estimate a cardinal corruption indicator for these countries through the multiplecauses multiple indicators model (MIMIC) (Joreskog and Goldberger, 1975). Ourapproach is different from previous works that use corruption perception indices.The advantage of this corruption measure is twofold. Firstly, the cardinal measureof corruption can be dealt with the intertemporal-optimizing endogenous growthmodel and consistent with the measures of the elasticities. Secondly, corruptioncan be endogenously derived by the mechanisms that generate corruption (causes)and where corruption acts (endogenous effects). As argued in Dreher et al. (2007),the role of the casual factors as determinants of the cardinal measure of corruptionavoids spurious regressions that may arise from the corruption perception indices(i.e.country risk guide, transparency international, ect.). These factors are not al-ways directly responsible for causing corruption. Sometimes ” perception-basedindex” reflects the quality of country’s institution rather than its actual degree ofcorruption (Weber, 2005).

The economic literature suggests a large number of political, historical,sociological and economic factors to characterize corruption and the relationshipwith the economic performance1. As pointed out by Mauro (1995), corruptiondistorts the resource allocation mechanism not only in relation to the public sec-tor, but also private investments contracts. Corruption affects the propensity of thefirm to accumulate private capital, which may result in a delay in development.Comprehensive descriptions of the corruption factors and variables definitions anddescriptive statistics used to estimate the cause and/or consequences of corruptionare reported in Table 1.

(b) The multiple causes multiple indicator model (MIMIC)

In this section we describe the statistical methodology that enables to quantify thelatent variable of corruption using the exogenous causes and the endogenous indi-

1For example, corruption affects the political structure of a country and produces a distortioninto the normal development process, reducing the transparency of government action Treis-man (2000), the certainty of the judicial action North (1990), and the protection of propertyrights (La Porta et al., 1999). On the other hand, the origins of the legal system and the qualityof the governance have been characterized by the history of the country and relations betweencolonies and colonizers (Treisman, 2000).

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TABLE 1 DESCRIPTION OF VARIABLES AND SOURCES

VARIABLES DESCRIPTION MEAN SOURCES

GROWTH MODEL! The logarithmic difference between GDP1998 and GDP1989 1.37 World Penn Table

6.2lgdpt#1 Logarithm of GDP1989 8.40 World Penn Table

6.2inv gdp Mean of the annual growth rate of private investment in GDP

(1989-1998)14.73 World Penn Table

6.2mil Mean of military spending in GDP (1989-1998) 2.56 World Bankgov Mean of non-military spending in GDP (1989-1998) 14.82 World Bankgpop Mean of annual growth rate of total population (1989-1998) 1.77 World Penn Table

6.2Africa Dummy variable for Africa

CORRUPTION MODEL (MIMIC)Political factors

pol cont C The political regime, the effectiveness of his action and thetransition between different regimes. Index from -10 (tyranny)to 10 (democracy)

2.12 Marshall and Jag-gers (2000)

dur dem C The number of years since the most recent regime change 26.76 Marshall and Jag-gers (2000)

leg ger C Quality of legal system and property right protection degree.Index from 0 to 10

5.00 Gwartney et al.(2003)

Historical factorsleg right C Origins of the legal system of the country. Dummy variable for

British, German and Socialist country origins *0.00 Gwartney et al.

(2003).Sociological factors

mil pol C Military intrusion into the public life and into the public deci-sion mechanism. Index from 0 to 10.

6.70 Marshall and Jag-gers (2000)

freepress C Degree of freedom press. Index from 0 to 100. 42.17 The FreedomHouse Project

educ C Secondary school enrolment as percentage of GDP. 55.14 World Bankmil per C Share of military personnel in GDP. 1.89 World Banklatitude C Degree of distance from the equator *. 0.00

Economic factorsPriv cre I Amount of deposit and other financial assets as share of GDP. 0.41 Beker et al.

(1999)GDP I Gross domestic product per-capita. 6037.7 World Bankmil exp C Military exportations as share of GDP. 0.57 World Bank

Note: In the third column we use the letter ”C” if the variable have been used as exogenous cause of corruption an ”I” if is an endogenous indicator of corruption.$The variables leg ger and latitudew are used in the normalized form (mean zero and standard deviation equal to one).

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cators in Table 1. The MIMIC is the resultant of two statistical models: a structuralequation that directly estimates the latent variable (#) starting from its causes (xq)and a measurement equation that captures indirectly the effects of the unobservedvariable through the use of endogenous indicators (yp).

(4) # = $ !x+ %

(5) y = &# +'

where % and ' are respectively the structural disturbance and the measurement er-ror vector, which are normal distributed, mutually independents with zero mean.By assuming that E (%' !) = 0 and by defining the variances of the disturbances asE(%2) = (2 and E(' ' !) = ! , where ! is a (pxq) diagonal matrix, the model canbe solved for the reduced form as function of the observed variables. By inserting(4) into (5) we can write,

(6) y = "!x+ v

where " = $& ! and v = &% +' . The covariance matrix of this reduced form is thenobtained as,

(7) # = E(vv!) = E!(&% +' )(&% +' )!

"= (2&& !+!

Through the use of the covariance matrix #, we are able to set-up the necessary andsufficient conditions to identify the parameters of the structural equation. Hence,we restrict the rank of the covariance matrix to be equal to the number of the indi-cators minus one, Rank = (E (vv!)) = p#1, holding the covariance of the structuraldisturbance as free 2. This condition implies that when we estimate parameters ofthe MIMIC by the maximum likelihood needs normalizing one or more elementsof the vector & . Finally, the latent variable (#) is given as,

(8) # = $ !x

2 There are several conditions that have been used into the statistical literature to identify theparameters of the structural equation. The most used one are the “t-rule”, “order-condition”and“rank-rule”. However, only the last rule represents both a sufficient and necessary condition(Bollen and Davis, 1994).

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where $ is the estimation of the structural parameters of the equation (4).

(c) Estimation of the corruption index

In Table 2 we show the estimated parameters of the MIMIC model in which thestructure of the model is identified. We use the )2# test of the exact fit in order toanalyse the identification adequacy of the latent variable and the root mean squareerror approximation (RMSEA) that account for the fit of the estimated model basedon the differences between estimated and actual covariance matrices (Dreher et al.,2007).

The parsimonious estimation of the identifying models in column 3 in-volves five exogenous causes of corruption and two endogenous indicators (MIMIC5,1,2). The significance of their coefficients allows to be confident about the ro-bustness of the analysis. To compare our measure of corruption with the existingcorruption (qualitative) indexes, we have plotted the estimated data with respect tothe fit of the regression between our corruption measure and the country risk guidecorruption index. Figure 1 shows that it exists a good relationship between the twoindexes (coefficient of correlation * = 0.74) although the tails are too thick3. Thisimplies that while perception and cardinal corruption indices seem strictly relatedin the median part of the empirical distribution, relevant biases can arise from theextreme values such that the use of a perception index of corruption for sub-samplesmay become questionable. Extensions of this issue represent an aim for future re-searches.

III. RESULTS

Table 3 shows the cross-country regression results for equation (4). We present theestimations for the full sample of countries (columns 1-4) and for two restrictedsub-samples with respectively, high and low levels of military spending in GDP(columns 5-10). Parsimonious specification in the first column, that includes aperception measure of corruption, is extended in the colums 2-4 by introducingthe interaction variable (milcorr) to capture the (non-linear) relations between cor-ruption and military spending by the estimated cardinal index of corruption. It isworth noting that, among the set of control variables largely used into the so called”Barro regression” (Barro, 1997), the model specification in column 2 includes aswell a specific interaction term (govcorr) that may account for the nonlinearities

3Our complete estimation results and the detailed ranking of the countries are available upon re-quest.

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TABLE 2 ESTIMATIONS OF THE MIMIC MODEL

Variables Mimic Mimic Mimic(8,1,2) (6,1,2) (5,1,2)

pol cont -0.0043 0.0065(0.045) (0.043) --0.096 0.15

dur dem -0.14 $ -0.13 $ -0.13 $(0.052) (0.056) (0.048)-2.73 -2.78 -2.78

leg ger -0.17 $ -0.16 $ -0.16 $(0.046) (0.045) (0.045)-3.73 -3.56 -3.56

leg right -0.16 $ -0.14 $ -0.13 $(0.059) (0.061) (0.055)-2.71 -2.43 -2.45

educ -0.14$ -0.16 $ -0.15 $(0.062) (0.048) (0.057)-2.25 -2.57 -2.68

latitude -0.060 -0.074$ -0.075$(0.044) (0.042) (0.042)-1.38 -1.74 -1.79

mil per - 0.072(0.042) # #-1.73

mil exp -0.0011(0.042) - #-0.026

)2($) 5.98 2.26 1.42(p#value) (0.54) (0.81) (0.84)RMSEA($$) 0.000 0.000 0.000(p#value) (0.69) (0.72) (0.89)Degreeoffreedom 7 5 4

Notes: the standard errors are given in the round parenthesis, while below are quoted the t-statistic.In the first column we report the estimation of MIMIC model using eight causes and two indicatorvariables. In the 2nd and 3rd columns we include in the model specification those variables that arestatistically significant. The diagnostic tests ($) of adaptability of the model based on chi-squareand the goodness of the fit ($$) based on root mean square error of approximation (RMSEA) arereported.

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FIGURE 1 COMPARISON BETWEEN INDEXES OF THE COUNTRY RISK GUIDEFITTED AND OUR ESTIMATION DATA OF CORRUPTION

Note: This graph is obtained through a correlation analysis between the fitted value of the countryrisk guide index calculated by our measure of corruption and our cardinal corruption index. Theasterisk represents the 5% significance level of the correlation coefficient.

yielded by a non-military misallocation of resources. The magnitude of the im-pact of milcorr is quite similar to the one obtained by Gupta et al. (2000) and it isstable in the specifications where the parameters are found significant. This sug-gests that there are complementary effects among these variables which affect thenet impact on economic growth. On the contrary, corruption (corr), the share ofnon-military government spending in GDP (gov) and (govcorr) are not significantand they exhibit unexpected signs. Although the impact of corruption on alloca-tion of government resources depends non-linearly on the level of corruption, inhigher corruption environment these effects may not be significant for the supplyof government goods and services, which favour the public sectors characterizedby significant market failures (Davoodi et al., 2000, b). For this reason, in column3, we replace govcorr with a more specific variable built as an interaction dummybetween the share of non-military government spending to GDP and the level ofcorruption exceeding the mean (column 4 is the most compact regression specifi-cation, without gpop, to remove some problems of correlation with others controlvariables).

Supporting the conjectures described in section II, the model specifica-tions in column 3 and 4 are able to capture the non-linearities related to the connec-tion between non-military spending and corruption (d govcorr); this specification

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TABLE 3 RESULTS OF THE CROSS-COUNTRY MILITARY EXPENDITURE - GROWTHRELATIONSHIP FOR FULL SAMPLE AND SUB-SAMPLES OF COUNTRIES

E1 E2 E3 E4 E5 E6 E7 E8 E9 E10lgdp -1.620 *** -1.182 ** -1.426 ** -1.252 ** -2.285 *** -2.355 *** -2.049 ** 0.047 -0.145 -0.094

(0.597) (0.581) (0.570) (0.565) (0.678) (0.727) (0.800) (0.994) (0.947) (0.887)mil 0.104 -0.725 ** -0.777 ** -0.927 *** 0.451 0.784 0.752 -1.076 * -1.106 * -1.126 **

(0.194) (0.360) (0.346) (0.336) (1.407) (1.418) (1.583) (0.592) (0.559) (0.539)gov -0.102 0.004 -0.135 * -0.138 * -0.286 -0.141 -0.114 -0.015 -0.187 -0.189

(0.079) (0.123) (0.078) (0.079) (0.167) (0.110) (0.122) (0.193) (0.116) (0.113)inv gdp 0.086 0.116 ** 0.127 ** 0.134 ** 0.000 0.035 0.005 0.116 0.121 * 0.121 *

(0.057) (0.057) (0.055) (0.055) (0.125) (0.127) (0.142) (0.074) (0.068) (0.067)gpop -1.011 ** -0.890 * -0.753 -1.900 *** -1.661 ** -0.058 -0.136

(0.486) (0.490) (0.476) (0.596) (0.656) (0.843) (0.790)corr -0.028 -0.029 -0.071 ** -0.062 ** -0.019 0.055 0.066 -0.065 -0.130 ** -0.126 **

(0.024) (0.054) (0.030) (0.030) (0.088) (0.062) (0.069) (0.104) (0.060) (0.055)goodgov 0.599 **

(0.244)africa -3.193 *** -2.433 *** -2.609 *** -2.891 *** -2.429 ** -2.667 ** -3.557 *** -2.641 ** -2.852 ** -2.891 **

(0.837) (0.818) (0.792) (0.780) (1.044) (1.078) (1.138) (1.242) (1.174) (1.134)milcorr 0.023 ** 0.024 ** 0.024 ** -0.013 -0.022 -0.026 0.027 0.029 * 0.029 *

(0.010) (0.009) (0.010) (0.038) (0.038) (0.042) (0.016) (0.015) (0.015)govcorr -0.001 0.005 -0.002

(0.003) (0.004) (0.005)d govcorr 0.143 ** 0.155 ** 0.043 0.144 0.163 * 0.162 *

(0.064) (0.064) (0.102) (0.104) (0.085) (0.084)Constant 15.080 *** 13.440 *** 16.882 *** 14.099 *** 26.701 *** 24.202 *** 18.443 *** 4.019 8.209 7.563

(4.545) (4.855) (4.666) (4.372) (6.419) (6.268) (6.521) (8.203) (7.804) (6.744)R2 0.314 0.284 0.335 0.319 0.398 0.361 0.204 0.298 0.370 0.389N 71 73 73 73 31 31 31 41 41 41

Note: The dependent variable is the growth rate of GDP (!). We report in parenthesis the standard errors, while the asterisks stand for p-value significance levels. We have that$ p < 0.1, $$ p < 0.05, $$$ p < 0.01

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makes the corruption coefficient significant. Moreover, from the models 3 and 4,it is possible to forecast that a 15% decrease in our corruption index determines arise of the real growth rate of 1%. Columns 5-10 report the estimation results forthe sub-samples of countries with low and high military spending in GDP. We havechosen these sub-samples by an endogenous threshold in line with the Hansen’sprocedure (Hansen, 2000). The two sub-samples include respectively 39 countriesfor low level of military burden and 50 countries for high level (Pieroni, 2008). Theregression results confirm the existence of a strong relationship between corrup-tion and the military for countries with a higher share of military spending to GDP.In Table 4 the estimated gross elasticities of military sector and corruption showa significant impact on per capita growth rate. However, to disentangle their neteffect, we estimate their indirect elasticity. Non-linear effects of military spendinginfluence the magnitude of the impact of corruption on growth and they stronglyaffect the net elasticity. On the other hand, complementary effects of corruption,though significant seem to influence less the negative inelastic relationship betweenthe share of military spending and economic growth. This supports the hypothesisthat, irrespective of the corruption influences, a reduction in the share of militaryspending to GDP could noticeably enhance the economic performance, while poli-cies addressed to reduce corruption should take more into account the magnitude ofthe indirect effects of the defence sector.

TABLE 4 GROSS AND INDIRECT ELASTICITIES OF THE ESTIMATED MODELSIN THE FULL SAMPLE

Endogenous Exogenous variablesvariable Military Sector Corruption

Gross elasticity Indirect elasticity Gross elasticity Indirect elasticity!(Model1) -1.35** 0.041** -0.67 0.53**

(0.641) (0.016) (0.832) (0.221)!(Model2) -1.43** 0.044** -1.65** 0.56**

(0.622) (0.017) (0.691) (0.259)!(Model3) -1.71** 0.044** -1.44** 0.56**

(0.631) (0.017) (0.682) (0.259)

Note: We estimate first the gross elasticity effects that derive by the marginal effect of the variable(obtained form table 3) multiplies by the ratio between the mean of the variable under investigationand the mean of the growth rate of GDP. For example the gross elasticity of military spending onGDP is demil = "mil(M(mil)/M(!)), where M represent the mean value of the variable and " is theestimation parameter of table 3. The interaction (or indirect) elasticity effects are obtained by theproduct of the direct effect of the interaction variable and the mean of the analysed variable. Hence,in the case of the share of military spending in GDP, we can write the indirect effect as iemil ="milcorr(M(mil)/M(!)). $$ The asterisks show the significance at the 5% level while asymptoticbootstrapped standard errors are shown in round brackets.

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IV. CONCLUDING REMARKS

Our results confirm that the share of military spending to GDP and corruption de-teriorate per capita growth rate. Also, it clearly emerges the presence of comple-mentary effects between these two explicative variables. We use such result tosuggest some policy implications. The estimated elasticities convey an asymmetricimpact of corruption and military sector on economic growth. The negative effectof corruption on growth is consistently influenced by the indirect impact of militaryburden on corruption. On the other hand, the indirect elasticity of corruption in themilitary spending-growth relationship is also significant though we estimate that itsmagnitude is less constrained from a policy point of view. Thus, specifically fordeveloping countries, a complementary policy anti-corruption addressed to fostereconomic performance may determine a consistent reduction of military spending.

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