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Does Military Spending Crowd OutSocial Welfare Expenditures? Evidencefrom a Panel of OECD CountriesEric S. Lina, Hamid E. Alib & Yu-Lung Luaa Department of Economics, National Tsing Hua University, Hsin-Chu, Taiwanb Department of Public Policy and Administration, AmericanUniversity in Cairo, Cairo, EgyptPublished online: 09 Dec 2013.

To cite this article: Eric S. Lin, Hamid E. Ali & Yu-Lung Lu (2015) Does Military Spending CrowdOut Social Welfare Expenditures? Evidence from a Panel of OECD Countries, Defence and PeaceEconomics, 26:1, 33-48, DOI: 10.1080/10242694.2013.848576

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DOES MILITARY SPENDING CROWD OUT SOCIALWELFARE EXPENDITURES? EVIDENCE FROM A

PANEL OF OECD COUNTRIES

ERIC S. LINa*, HAMID E. ALIb AND YU-LUNG LUa

aDepartment of Economics, National Tsing Hua University, Hsin-Chu, Taiwan; bDepartment of PublicPolicy and Administration, American University in Cairo, Cairo, Egypt

(Received 15 July 2012; in nal form 12 April 2013)

This article examines the relationship between defense and social welfare expenditures using a panel of 29 OECDcountries from 1988 to 2005. It is quite difcult to take into account the simultaneous channels empirically throughwhich the eventual allocation of defense and welfare spending is determined for the guns-and-butter argument.-Taking advantage of our collected panel data-set, the panel generalized method of moments method is adopted tocontrol the country-specic heterogeneity and to mitigate the potential simultaneity problem. The main nding ofthis article suggests a positive trade-off between military spending and two types of social welfare expenditures(i.e. education and health spending). One of the reasons may be that the OECD countries are more supportiveof the social welfare programs; therefore, when the military spending is increased (e.g. military personnel andconscripts), the government may raise the health and education spending as well.

Keywords: Crowding-out effect; Military spending; Social welfare

JEL Codes: H51, H52, H53, H54

1. INTRODUCTION

It has been a widespread view to regard military expenditures as public investment whileothers view it as social costly enterprise that crowds out investment in social welfare. Whenpublic program demand exceeds available resources, the program competes for budgetaryallocations, and one program wins while others lose. In other words, there is a trade-offrelationship among health, education, and military expenditures, as they are the major gov-ernment budgetary components. It is no doubt that understanding the trade-off among thosekey expenditures for a government is crucial for scal sustainability and ability to operateover long term while maintaining a higher standard of living for the public. Consequently,government may therefore reallocate the resources in different sectors where the opportunitycost is lower.The issue on the crowding-out effect between military spending and social welfare can

be traced back to the seminal work by Russett (1969). Using the data from the USA,

*Corresponding author: Department of Economics, National Tsing Hua University, Hsin-Chu, Taiwan. E-mail:slin@mx.nthu.edu.tw

2013 Taylor & Francis

Defence and Peace Economics, 2015Vol. 26, No. 1, 3348, http://dx.doi.org/10.1080/10242694.2013.848576

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France, and the UK, Russett (1969) concludes that a rise in military spending will reducethe expenditures on health and education. The reason is intuitive in the sense that in thepresence of dynamic spillovers from a given government budget, an increase in the militaryexpenditure will crowd out an equivalent amount of all other spending. Thus, education andhealth expenditures will be reduced proportionately (Domke et al., 1983; Scheetz, 1992;Yildirim and Sezgin, 2002). However, according to Keynesian point of view, an increasedmilitary spending is able to promote aggregate demand and in turn stimulate welfareexpenditures indicating a complementarity relationship between military and welfareexpenditures. Due to the lack of a coherent theory to justify the so-called gunsbutter,previous empirical studies tend to obtain mixed results, which are divergent acrossestimation models and data.As summarized in Yildirim and Sezgin (2002), the causal relationship between military

spending and social welfare expenditures is not clear cut in the literature. Ali (2011) alsoargues that the crowding-out of social spending by military spending lacks both theoreticaland empirical justication. Most of the previous studies reveal either the negative trade-offor non-trade-off relationship, while the positive trade-off is found to be limited. Since thereexist a large amount of channels that simultaneously determine the eventual allocation ofdefense and welfare spending, the endogeneity issue should be well taken care of whenconducting the empirical analysis (Deger, 1985). For example, Yildirim and Sezgin (2002)adopt seemingly unrelated regression (SUR) estimation method to investigate the crowding-out effect in Turkey, and Kollias and Paleologou (2011) utilize the vector autoregressiveregression (VAR) approach to examine the presence of budgetary trade-offs betweendefense spending and welfare expenditure in the case of Greece.

FIGURE 1 Military spending vs. social welfare expenditures by country

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The existing empirical studies in relation to the warfare vs. welfare nexus mainly utilizethe time-series data approach, that is, a single country is typically analyzed at one time.1 Adesirable way to investigate the relationship between defense and welfare spending is tocombine the time-series and cross-section data into a panel data-set by observing a bunchof countries for a period of time span. The major advantages for using panel data include:(1) the ability to control for individual heterogeneity (e.g. controlling for country-speciccharacteristics such as culture, weather, location, laws, institutions, and so on) to alleviateomitted variable bias; (2) the increased precision of the regression estimates (using a largesample size relative to the cross-sectional data); (3) a reduction in identication problems(identifying individual dynamics); and (4) the ability to model temporal effects withoutaggregation bias as in time-series studies. To the best of our knowledge, there are verylimited prior studies employing the panel data method to explore the gunsbutter argument.This article collects a panel of 29 OECD countries with time spanning from 1988 to

2005 to empirically investigate the gunsbutter trade-off. With such constructed panel data-set, we are able to not only effectively increase the sample size to evaluate the trade-offrelationship, but also resolve the potential simultaneity problem between military spendingand social welfare expenditures. In particular, we adopt the panel generalized method ofmoments (panel GMM) to allow for endogenous right-hand-side variables, where the paneldata structure provides an excess of moment conditions available for estimation owing toan abundance of instruments (i.e. usually based on the lagged variables). It is also worthnoting that previous studies that inspect the trade-off relationship are focused more on less-developed countries (e.g. Deger, 1985; Hess and Mullan, 1988; Apostolakis, 1992). Anotherfeature of this study is to complement the literature on the gunsbutter argument in terms ofthe developed countries.The remainder of this article is organized as follows. In the next section, we review the

gunsbutter argument and the corresponding empirical ndings. Section 3 describes the data

FIGURE 2 Military spending vs. social welfare expenditures

1Even though there are several papers employing the cross-sectional time-series data, they evaluate the trade-offrelationship for each country in each regression run. For instance, using 12 Asian countries from 1967 to 1982,Harris and Pranowo (1988) conclude that there are three positive and negative trade-offs as well as six indetermi-nant relationships.

EVIDENCE FROM A PANEL OF OECD COUNTRIES 35

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and econometric methods. Section 4 illustrates and discusses the empirical results. The nalsection concludes this article.

2. THE GUNS-AND-BUTTER ARGUMENT

There is a long-lasting debate that the government should choose whether to spend itsmoney on butter (i.e. food or other services) for its citizens or guns which is moneyspent by the government for military defense. That is, the gunsand-butter argument rea-sons that there is a trade-off between military expenditures and other major governmentspending. It can be understood that with the limited overall government budget, to increasemilitary expenditures may result in the crowding-out effect on other components ofgovernment expenditures such as education and health spending.2 From a speech before theAmerican Society of Newspaper Editors on 16 April 1953, the 34th president of US DwightD. Eisenhower addressed a typical guns-and-butter argument:

Every gun that is made, every warship launched, every rocket red signies, in the nal sense, a theft fromthose who hunger and are not fed, those who are cold and not clothed. This world in arms is not spendingmoney alone. It is spending the sweat of its laborers, the genius of its scientists, the hopes of its children.This is not a way of life at all in any true sense. Under the cloud of threatening war, it is humanity hangingfrom a cross of iron.

Many empirical studies obtain the negative (or trade-off) relationship between defenseand welfare using different countries and different types of data (Russett, 1969; Peroff,1976; Dabelko and McCormick, 1977; Peroff and Podolak-Warren, 1979; Deger, 1985;Apostolakis, 1992). As mentioned in Section 1, Russett (1969) nds a reasonably strongnegative relationship between military spending and government spending on health andeducation for the US, France and UK. Dabelko and McCormick (1977) evaluate the impactof changes in military spending on spending levels for public education and public healthin a number of countries in the period of 19501972. Their major ndings include that: (1)opportunity costs do exist for education and health across all countries and all years, butthey are weak in magnitude; (2) levels of economic development have little or no impactupon the opportunity costs for these policy areas; and (3) personalist regimes tend to havehigher opportunity costs of defense than do centrist and polyarchic regimes. Further studiesby Peroff and Podolak-Warren (1979) examine empirically the potential impact of defenseon the private health sector; the results of the analysis give more weight to the hypothesisof a trade-off. Deger (1985) reports a stylized fact that defense has a high physical-resourcecost as well as the exceptionally high human-resources costs. Thus, developing nations canand should divert a minute fraction of their massive armament expenditure as a develop-ment aid for human capital. Apostolakis (1992), in study of 19 Latin American countries,conrms a trade-off between military and spending on health, education, social security,and welfare in the region.For those studies in support of the guns-and-butter argument, they nd that an increase

in military spending generally inuences welfare expenditures in two ways. First, defenseexpenditures may detract from xed investment expenditures, personal consumption, andstate and local government spending, and there will be less public expenditures on humancapital formation, which lowers economic growth and further reduces the welfare spending.

2The guns-and-butter argument can also be understood through the production possibilities curve, that depictsthe trade-off between any two items produced.

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Second, lower income groups are the main people who carry the burden of defense and theheavy defense spending hinders the establishment of more welfare programs.By contrast, some studies argue that military spending may render a positive effect on

investment, economic growth, and welfare (Benoit, 1973, 1978; Lindgren, 1984; Ram,1995). The defense spending is believed to be helpful to human capital formation in educa-tion and health because defense personnel and conscripts are well trained physically andreceive good skills education. Besides, military spending may lead to technological innova-tions and even spin-offs in defense eld. Therefore, the positive trade-off relationship (i.e.complementary relationship) has also been found in the literature; e.g. Verner (1983), Harrisand Pranowo (1988), and Kollias and Paleologou (2011). It is noted that the type of welfarespending may play an important role in the trade-off relationship. Yildirim and Sezgin(2002) and Ali (2011) suggest that the trade-off is positive between education and defensespending, while the relationship is negative between health and military expenditures.Lastly, in addition to the negative or positive gunsbutter argument, no trade-off relation-

ship between military and welfare spending has also been found in the literature as well(Caputo, 1975; Russett, 1982; Domke et al., 1983; Eichenberg, 1984; Hess and Mullan,1988; Mintz, 1989; Davis and Chan, 1990; Frederiksen and Looney, 1994). For instance,Russett (1982) argues that no systematic trade-off between military spending and federalhealth and education expenditures is found, nor there is any signicant depressing effect onhealth and education expenditures. Domke et al. (1983) utilize three-equation model con-trolling for the variety of possible determining factors of public resource allocation, andnds that no patterns of trade-off emerges. Mintz (1989) concludes that, although there maybe years in which R&D or procurement spending increased and welfare spending decreased,the pattern is not common enough to indicate a trade-off between these spending categories.Those studies suggest that the budgetary allocation of defense and welfare expendituresseems to be driven by separate determinants. An increase in defense spending does not nec-essarily lead to a sacrice in health or education expenditure.To sum up, numerous studies have attempted to investigate the trade-off relationship

between defense and welfare spending, while there is no consensus on the empirical nd-ings. As indicated in Section 1, the existing empirical studies are rather limited in terms ofdata and econometric methods, that hampered the efforts on empirical discussion of thevalidity of gunsbutter argument. This study revisits the warfarewelfare trade-off by takingadvantage of a panel of OECD countries to control for unobserved country-specic effectsand employing the panel GMM method to allow for potential simultaneity issue.

3. DATA AND ECONOMETRIC STRATEGY

3.1. Data Description

To make use of a relatively more complete and reliable data source of military and socialwelfare spending, the OECD countries are adopted for our empirical investigation. Thereare many previous studies using OECD data to analyze the relationship between militaryexpenditure and other economic variables (e.g. Smith, 1980; Cappelen et al., 1984; Dunneand Smith, 1990; Lee and Cheng, 2007). In what follows, we describe the data sources thatare taken in this research.The data on (annual) military expenditures are adopted from the Stockholm International

Peace Research Institute (SIPRI), which is an independent international institute for researchinto problems of peace and conict in Sweden, and is considered a reliable source in the

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defense literature.3 In fact, there are several other sources containing country-wise militaryexpenditures, including World Development Indicators (WDI), the US Bureau of ArmsControl, Verication and Compliance, the International Institute for Strategic Studies, andthe IMFs World Economic Outlook. It is noted that WDI uses data on military expendituresand arms transfer from SIPRI so that the military spending based on the two data sources isquite close according to our checkup.4 Even though there are currently 34 members of theOECD, we have included 29 countries in our sample. We excluded four countries (i.e.Chile, Estonia, Israel, and Slovenia) since they had not joined OECD membership until2010. Iceland has also been dropped because it maintains no standing army. The SIPRIdata-set ranges from 1988 to 2006, where we utilize the military expenditures as % of GDPas the main explanatory variable.5

We consider health and education expenditures as the two most important components ofthe social welfare spending following previous studies, e.g. Yildirim and Sezgin (2002), Oz-soy (2002), and Kollias and Paleologou (2011). The health expenditures data are taken fromWDI and Source OECD databases.6 Note that the health expenditures consist of both publicand private health spending, where the data are mainly from WDI while Source OECDcomplements some missing values.In regard to education expenditures, Source OECD provides a relatively complete data

ranging from 1985 to 2002, where we use the public expenditures on education (includingall levels of education) as % of GDP. The United Nations Educational, Scientic andCultural Organization (UNESCO) has been utilized to ll up the data on educationexpenditures in the period of 20032005.7 Owing to the data limitation on the primaryvariables of interest, the country-level military and education expenditures, we thereforerestrict our sample to the period from 1988 to 2005.Additionally, we incorporate the following control variables as suggested in the past

studies (e.g. Russett, 1982; Hess and Mullan, 1988; Yildirim and Sezgin, 2002; Kollias andPaleologou, 2011): tax (total tax revenue of GDP; from Source OECD), GDP (from WDI);8

household consumption (household nal consumption expenditure of GDP; from WDI);population (total population; from Source OECD) and government expenditure (generalgovernment nal consumption expenditure of GDP; from WDI); and Gini coefcient

3The SIPRI military expenditures include all current and capital expenditures on the armed forces, includingpeace-keeping forces; defense ministries and other government agencies engaged in defense projects; paramilitaryforces when judged to be trained, equipped and available for military operations; and military space activities. TheSIPRI data can be downloaded in the following website: http://www.sipri.org/databases/milex.

4The WDI data include the World Bank collection of world-wide development indicators, compiled from of-cially recognized international sources. Please refer to http://data.worldbank.org/data-catalog/world-development-indicators.

5We are grateful to an anonymous referee for the discussion on the difference between share of GDP and shareof central government budget. In fact, many empirical studies use shares of GDP (or GNP) to investigate the trade-off relationship between defense and welfare spending (e.g. Apostolakis, 1992; Ozsoy, 2002). Due to the data limi-tation, we cannot obtain the all the key variables (e.g. education expenditures) in terms of the share of central gov-ernment budget. To keep the consistency of the variable measurement, we stick with the share of GDP in regard tothe military and social welfare expenditures. Nevertheless, it is feasible to calculate the correlation between the mil-itary expenditure share of GDP and share of central government expenditure, which results in a high sample corre-lation (0.7533).

6For Source OECD, please refer to http://miranda.sourceoecd.org/vl=44559650/cl=11/nw=1/rpsv/home.htm.7Please refer to http://stats.uis.unesco.org/unesco/TableViewer/document.aspx?ReportId=143&IF_Language=

eng. Even though UNESCO collects the education expenditures from 1975 to 2007, there are many missing valuesbefore 1999 and after 2005.

8We use the real GDP measuring in billions of US dollars and service year 2000 as the base year.

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(income inequality measure; from the University of Texas Inequality Project (UTIP)).9 Thedescription of the variables and original data sources is listed in Table I.To sum up, our data-set covers a period of 18 years spanning from 1988 to 2005 for 29

OECD countries. The summary statistics are reported in Table II, where the 29 countriesare listed in the rst column of Table II. It is clear to see that on average, Greece has thehighest proportion of military spending in GDP (4.42%) during our sample period 19882005, followed by the second and third highest countries, US (4.07%) and Turkey (3.97),respectively. Nordic countries tend to have relatively higher education expenditures of GDP,e.g. Denmark (6.20%), Sweden (5.72%), and Norway (5.46%). The US average healthexpenditures out of GDP rank the number one in our sample OECD countries.Figure 1 provides the relationship among health, education, and military expenditures in

all of the 29 OECD countries. It is noted that in terms of the ratio of GDP, we observe thatthe military spending tends to have the lowest proportion, heath spending has the largestratio, and the education spending is in between these two expenditures for most OECDcountries. Greece is an exception that military spending out of GDP lies above theeducation counterpart. The rivalry country of Greece, Turkey, reveals a similar trendbetween military and education expenditures before 2003. In most of the countries in oursample, the health and education spendings keep rising while military spending of the GDPratio is relatively stable or declining very slowly. Nevertheless, taking into consideration ofall countries, the correlation between military and health/education expenditures is not clear.When we turn to aggregate the health, education and military expenditures across countriesfor each year, as shown in Figure 2, it seems that the relationship between military expendi-tures and social welfare is negative an increasing trend of health and education spendingbut a down-sizing trend of military expenditures (i.e. in support of the crowding-outargument). Nevertheless, it should keep in mind that these gures are simply descriptivesince no other explanatory variables are controlled. Thus, the preliminary graphicalinspection cannot be regarded as causal. A further analysis taking into account the potentialfactors that are associated with the social welfare expenditures is performed in subsequentsections.

TABLE I Description of Variables

Variables Description Data sources

GDP Gross domestic product (in terms of one billion) WDI

Milex Military spending/GDP SIPRI, WDI

Edu Public and private education expenditures/GDP Source OECD, UNESCO

Health Public and private health expenditures/GDP Source OECD

Tax Total tax revenue/GDP Source OECD

Gini Income inequality measured by Gini coefcient WDI, UTIP

Pop Total population (in terms of one million) Source OECD

Gcon General government nal WDI

Consumption expenditure/GDP

Hcon Household nal consumption expenditure/GDP WDI

9Gini coefcient maintained by UTIP can be found at http://utip.gov.utexas.edu/. Lin and Ali (2009) also adoptthis measure to explore the relationship between military spending and economic inequality.

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TABLEII

Sum

maryof

Statistics

Milex

Edu

Health

Gcon

Hcon

Gini

GDP

Pop

Tax

NM

SD

NM

SD

NM

SD

NM

SD

NM

SD

NS.D.

NM

SD

NM

SD

NM

SD

Australia

181.88

0.12

183.98

0.39

178.23

0.70

1718.26

0.47

1758.67

1.07

1834.78

3.90

18357.77

66.85

1818.43

1.17

1829.22

1.48

Austria

180.87

0.10

185.05

0.57

188.98

1.44

1818.83

0.73

1856.89

0.75

1826.57

2.05

18175.51

21.62

187.93

0.18

1842.11

1.55

Belgium

181.65

0.43

174.92

0.69

188.41

0.98

1821.57

0.87

1854.31

1.14

1827.84

2.48

18212.45

23.33

1810.17

0.17

1843.88

1.27

Canada

181.51

0.35

155.02

0.37

189.21

0.51

1721.04

1.83

1756.43

1.05

1831.59

1.60

18634.41

101.35

1829.68

1.66

1835.20

1.14

Czech

Republic

131.84

0.22

133.56

0.74

166.51

0.84

1621.69

0.99

1651.09

1.67

924.04

3.05

1655.57

5.56

1810.29

0.06

1336.96

1.58

Denmark

181.71

0.21

186.20

1.19

188.52

0.34

1825.65

0.47

1849.80

1.50

1834.42

3.14

18144.67

17.02

185.27

0.10

1848.46

1.25

Finland

181.47

0.24

185.68

0.70

187.45

0.66

1822.26

1.46

1852.31

1.74

1828.36

1.87

18108.49

15.16

185.11

0.09

1844.89

1.49

France

182.96

0.40

175.24

0.54

169.72

0.78

1823.09

0.80

1856.59

0.67

1829.53

2.10

181218.77

131.93

1858.30

1.42

1843.26

1.01

Germany

181.76

0.41

124.33

0.18

179.95

0.78

1819.39

0.53

1858.33

0.75

1827.84

1.82

181749.38

171.70

1881.33

1.37

1836.20

0.84

Greece

184.42

0.25

133.23

0.89

187.68

1.67

1815.25

1.36

1871.41

2.62

1834.19

1.08

18107.20

17.24

1810.66

0.35

1826.06

3.03

Hungary

182.03

0.66

153.72

1.56

147.41

0.48

1810.90

0.93

1866.04

4.35

1827.18

2.39

1844.42

6.52

1810.23

0.14

1539.99

3.17

Ireland

180.90

0.22

183.87

0.45

186.74

0.45

1715.75

1.25

1752.49

5.90

1833.36

3.17

1876.25

28.10

183.71

0.20

1832.06

2.17

Italy

181.98

0.13

184.04

0.57

187.89

0.46

1819.21

0.81

1858.59

0.69

1833.17

1.94

181023.72

81.81

1857.06

0.53

1840.60

2.09

Japan

180.97

0.02

183.57

0.15

177.03

0.74

1715.53

1.71

1755.08

1.94

1830.88

2.01

184452.94

325.03

18125.77

1.62

1827.29

1.19

Korea

Rep

182.92

0.51

123.63

0.76

184.66

0.60

1812.19

0.89

1852.63

1.69

1532.94

1.05

18434.49

123.53

1845.48

2.03

1821.02

2.90

Luxem

bourg

180.76

0.08

153.22

0.37

186.00

0.93

1815.95

0.51

1843.02

2.04

833.38

1.16

1816.94

4.10

180.42

0.03

1837.37

1.87

Mexico

180.50

0.07

139.93

5.34

165.70

0.49

1810.49

1.33

1868.50

2.12

1851.35

2.00

18504.04

81.21

1893.28

7.38

1817.72

0.96

Netherlands

181.91

0.38

154.00

0.35

178.29

0.43

1823.04

0.82

1850.10

0.61

1828.47

1.33

18341.35

46.44

1815.59

0.51

1841.13

2.85

New

Zealand

181.40

0.30

185.38

0.76

187.49

0.67

1718.10

0.73

1759.20

1.05

1835.05

3.23

1848.74

7.97

183.72

0.24

1835.32

1.53

Norway

182.35

0.46

185.46

1.17

188.51

0.82

1821.38

0.93

1847.33

2.92

1830.31

2.52

18147.92

25.02

184.40

0.13

1841.75

1.20

Poland

182.14

0.31

114.45

1.03

165.82

0.38

1619.69

2.05

1661.13

4.10

1830.89

4.08

16151.09

29.83

1838.18

0.12

1534.71

1.80

Portugal

182.23

0.21

184.24

1.36

187.87

1.37

1818.16

1.89

1864.68

1.14

1836.57

1.13

1899.26

13.67

1810.16

0.20

1831.68

2.70

SlovakRepublic

132.18

0.66

74.89

0.74

95.98

0.68

1821.65

1.49

1853.67

2.46

834.14

2.47

1819.53

2.94

185.35

0.05

833.00

1.75

Spain

181.40

0.28

183.70

0.70

187.22

0.58

1817.44

0.74

1859.62

1.18

1832.10

2.78

18522.62

86.22

1840.06

1.37

1833.30

1.15

Sweden

182.10

0.37

185.72

1.19

188.49

0.40

1827.42

0.88

1849.78

1.14

1825.77

1.28

18221.03

26.63

188.79

0.17

1850.20

1.96

Switzerland

181.34

0.31

185.18

0.54

189.99

1.10

1711.51

0.40

1759.69

1.09

1832.79

1.71

18231.78

15.75

187.05

0.25

1828.12

1.56

Turkey

183.97

0.68

83.47

0.56

185.12

1.82

1812.36

1.84

1867.29

1.43

1645.18

2.18

18176.73

33.68

1864.18

5.22

1826.40

5.58

UK

183.10

0.64

184.18

0.77

187.03

0.70

1819.80

1.08

1864.53

1.19

1833.31

1.14

181313.02

180.31

1858.36

0.98

1835.51

1.24

USA

184.07

0.88

184.64

0.65

1813.28

1.26

1715.67

1.05

1768.11

1.47

1843.04

2.61

188621.03

1421.41

18271.10

16.68

1827.60

1.22

Note:N,M,andSDdenotethenumberof

observation,

mean,andstandard

error,respectively.

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TABLE III Panel Unit Root Tests for all Variables

LLC IPS ADF PP

Milex 7.2846 3.1005 99.3570 134.270

(0.0000) (0.0010) (0.0006) (0.0000)

Milex 12.4513 10.5301 216.611 305.2020

(0.0000) (0.0000) (0.0000) (0.0000)

Edu 6.8108 1.5541 86.2057 101.7060

(0.0000) (0.0601) (0.0095) (0.0003)

Edu 12.7242 9.1007 188.9610 174.1070

(0.0000) (0.0000) (0.0000) (0.0000)

Health 1.1155 4.2567 35.3794 27.6888

(0.8677) (1.0000) (0.9917) (0.9998)

Health 14.3196 11.2320 228.0120 245.5000

(0.0000) (0.0000) (0.0000) (0.0000)

Tax 4.7038 1.9022 75.0352 70.1935

(0.0000) (0.0286) (0.0656) (0.1309)

Tax 14.8685 12.7041 265.4250 277.2770

(0.0000) (0.0000) (0.0000) (0.0000)

Pop 2.9575 3.7695 67.3961 152.6050

(0.0016) (0.9999) (0.1866) (0.0000)

Pop 3.3622 3.5996 130.8460 88.4242

(0.0004) (0.0002) (0.0000) (0.0062)

Gcon 3.7310 3.3764 102.1660 66.1805

(0.0001) (0.0004) (0.0003) (0.2154)

Gcon 12.2794 10.9424 220.0210 240.6090

(0.0000) (0.0000) (0.0000) (0.0000)

Hcon 4.8137 3.3393 107.0130 117.5900

(0.0000) (0.0004) (0.0001) (0.0000)

Hcon 15.3279 13.5635 270.8680 329.4690

(0.0000) (0.0000) (0.0000) (0.0000)

Gini 9.5494 6.7573 158.5390 171.1570

(0.0000) (0.0000) (0.0000) (0.0000)

Gini 20.2839 17.5672 392.2940 717.9210

(0.0000) (0.0000) (0.0000) (0.0000)

GDP 5.37183 10.4765 10.2369 13.9213

(1.0000) (1.0000) (1.0000) (1.0000)

GDP 5.3865 5.9892 136.0130 154.0430

(0.0000) (0.0000) (0.0000) (0.0000)

Note: LLC, IPS, ADF, and PP represent the Levin et al. (2002), Im et al. (2003), augmented Dickey-Fuller, and Phillips and Perronpanel unit root tests, respectively.The p-values are in parentheses. indicates the rst-difference operator.

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3.2. Panel GMM

As noted in Section 1, evaluating the trade-off relationship between the military spendingand welfare spending using the typical OLS or SUR regression may suffer from the endoge-neity problem, e.g. Deger (1985). Therefore, this study applies the panel data to get rid ofthe country-specic effect to alleviate the omitted variable bias. We further consider toresolve the potential simultaneity problem (e.g. it could be a bi-directional crowding-outeffect between military spending and welfare expenditures) by adopting the panelgeneralized method of moments, which is designed to allow for endogenous right-hand-sidevariables. The panel GMM takes advantage of the panel data structure by providing anexcess of moment conditions available for estimation based on an abundance of instruments(lagged variables) without seeking for external instrumental variables (IV). Since thewelfare expenditures studies in this article consist of health and education expenditures, weconsider two separate panel data models to explore whether the military spending willcrowd out the health and education spending. The health and education equations are there-fore dened as follows:

Healthit ao a1Milexit a2Eduit W 0itg di mit (1)

Eduit bo b1Milexit b2Healthit W 0itc li eit (2)

where Healthit, Eduit, and Milexit denote the health, education, and military expenditures ofGDP for country i (i = 1,...,N) at time t (t = 1, , T), respectively. Wit is a vectorincluding other control variables such as tax of GDP, household consumption of GDP,government consumption of GDP, population, GDP, and Gini index. i and i represent thecountry-specic effects. a, b, g, and c are parameters to be estimated. mit and eit areidiosyncratic error terms. If we obtain a negative a1 (or b1), it may conclude that there is a(negative) trade-off between military spending and health (or education) spending. Whilewe yield a positive a1 (or b1) , it may indicate that military spending and health (or educa-tion) spending are complementary.It is worth noting that Russett (1982) considers the rate of change in all variables in his

empirical model. Yildirim and Sezgin (2002) further claim to utilize all variables in termsof the rate of growth in order to prevent the non-stationary problem. We follow theseprevious studies by taking the rst difference of the level economic variables to avoid thespurious regression problem. We then conduct the panel unit root tests to check whether thedifferenced variables under consideration are indeed stationary. Several popular panel unitroot tests in the literature are implemented, including LLC test (Levin et al., 2002), IPS (Imet al., 2003), Fisher-type tests based on the ADF regression (Maddala and Wu, 1999), andPhillips/Perron-type individual unit root test (Choi, 2001). The four types of panel unit roottests reported in Table III show that after taking the rst difference, all variables in ourmodel reject the null hypothesis ensuring all the variables to be stationary. Therefore, weuse the rst-difference variables in our model.A standard approach to dealing with the simultaneity problems is the IV estimation

method. For cross-sectional data, it is difcult to nd suitable instruments. However, withpanel data under some mild exogeneity assumptions, lagged variables can serve as validinstruments for panel GMM estimation. Our panel GMM specication in principle canallow all right-hand-side variables to be endogenous (e.g. the military spending and

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education expenditures in the health equation), indicating that endogeneity issue can be welltreated to obtain consistent estimates (see Cameron and Trivedi (2005) for more details). Todescribe our panel GMM approach, we rst introduce the generalized method of momentestimator by Hansen and Singleton (1982) in the context of linear panel data model:

Yi Xih ui;

where Yi and ui are T 1 vectors, Xi is a T K matrix with tth row x0it , and K is the dimen-sion of explanatory variables. We use the exogenous variables in the previous period(s) otherthan the current period as an instrument (Zi), and Zi is a T r matrix, where rK is thenumber of instruments. This is in the spirit of Anderson and Hsiao (1982) and Arrelano andBond (1991) methods. The resulting unconditional r moment conditions are:

EZ 0i ui 0:

The GMM estimator based on these moment conditions minimizes the associated quadraticform:

TABLE IV Empirical Results for Health Equation

Variables POLS FE RE GMM 1 GMM 2 GMM 3 GMM 4 GMM 5 GMM 6

Milex 0.143 0.196** 0.153 0.920*** 0.534*** 0.441*** 0.687*** 0.875*** 1.055***

(0.118) (0.097) (0.095) (0.245) (0.135) (0.122) 0.151 (0.210) (0.287)Edu 0.004 0.005 0.004 0.052 0.061** 0.056** 0.004 0.175*** 0.141***

(0.013) (0.015) (0.015) (0.130) (0.024) (0.023) (0.020) (0.061) (0.023)Tax 0.016 0.015 0.016 0.084* 0.006 0.018 0.017 0.060***

(0.013) (0.012) (0.012) (0.049) (0.012) (0.024) (0.034) (0.023)Pop 0.142*** 0.064 0.147*** 0.221*** 0.143*** 0.136*** 0.245*** 0.181***

(0.030) (0.122) (0.034) (0.055) (0.027) (0.034) (0.054) (0.028)Gcon 0.250*** 0.248*** 0.249*** 0.087 0.047 0.143*** 0.097* 0.210***

(0.037) (0.023) (0.023) (0.097) (0.050) (0.055) (0.051) (0.034)Hcon 0.044** 0.042*** 0.044*** 0.138*** 0.144*** 0.110*** 0.110*** 0.005

(0.017) (0.013) (0.012) (0.032) (0.016) (0.021) (0.019) (0.026)Gini 0.005 0.005 0.005 0.001 0.011* 0.014** 0.012* 0.01 0.016**

(0.005) (0.005) (0.005) (0.009) (0.006) (0.006) (0.006) (0.010) (0.008)GDP 0.001 *** 0.002 *** 0.001 *** 0.002 *** 0.001 *** 0.001 *** 0.000 0.002 *** 0.002 ***

(0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.000) (0.001) (0.000)Constant 0.111*** 0.145*** 0.113*** 0.167*** 0.140*** 0.136*** 0.154*** 0.122*** 0.137***

(0.015) (0.037) (0.017) (0.020) (0.012) (0.012) (0.014) (0.023) (0.017)

N 389 389 389 334 306 306 306 306 306

First stageF statistics

66.760 78.230 49.980 27.780 52.640 47.320

HansensJ statistics

7.303 21.748 21.552 15.438 17.587 19.088

p-value 0.504 0.152 0.088 0.349 0.226 0.162

Notes: Standard errors are in parentheses.* denote statistical signicance at the 10% level.** denote statistical signicance at the 5% level.*** denote statistical signicance at the 1% level.The F-statistics are obtained by regressing the endogenous regressor (Milex) on the instruments.

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QN b XNi1

Z 0i ui

" #0WN

XNi1

Z 0i ui

" #;

where WN denotes an r r weighting matrix. The panel GMM estimator can be obtainedas follows:

b^PGMM argbQN b:

Since we are considering a linear panel data model, our panel GMM estimator is thus givenby the following closed-form solution:

TABLE V Empirical Results for Education Equation

Variables POLS FE RE GMM 1 GMM 2 GMM 3 GMM 4 GMM 5 GMM 6

Milex 0.200 0.244 0.200 0.951*** 1.192*** 0.891*** 1.063*** 0.791*** 0.577**

(0.145) (0.335) (0.315) (0.360) (0.169) (0.316) (0.250) (0.275) (0.270)

Health 0.046 0.056 0.046 0.980** 0.661*** 0.285* 0.364* 0.077 0.231

(0.170) (0.183) (0.169) (0.443) (0.209) (0.152) (0.211) (0.166) (0.251)

Tax 0.027 0.024 0.027 0.142** 0.104*** 0.085*** 0.060** 0.023

(0.037) (0.041) (0.038) (0.068) (0.022) 0.030 (0.024) (0.029)

Pop 0.103 0.917** 0.103 0.205*** 0.106*** 0.060* 0.066*** 0.073**

(0.261) (0.418) (0.106) (0.072) (0.037) (0.031) (0.019) (0.035)

Gcon 0.090 0.073 0.090 0.361 ** 0.398 *** 0.478 *** 0.366 *** 0.177

(0.055) (0.092) (0.086) (0.163) (0.115) (0.111) (0.123) (0.123)

Hcon 0.001 0.008 0.001 0.143* 0.018 0.049** 0.025 0.085***

(0.027) (0.045) (0.041) (0.083) (0.022) (0.020) (0.035) (0.024)

Gini 0.010 0.008 0.010 0.028 0.036** 0.036*** 0.029** 0.044*** 0.030**

(0.017) (0.018) (0.017) (0.021) (0.016) (0.010) (0.014) (0.013) (0.013)

GDP 0.001 0.001 0.001 0.002** 0.001** 0.000 0.000 0.001*** 0.001**

(0.002) (0.001) (0.001) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000)

Constant 0.141*** 0.094 0.141*** 0.264*** 0.234*** 0.184*** 0.189*** 0.151*** 0.158***

(0.043) (0.130) (0.051) (0.065) (0.032) (0.034) (0.037) (0.030) (0.035)

N 389 389 389 344 321 321 321 321 321

First stage F

statistics

50.430 691.930 54.850 23.100 94.340 191.940

Hansens J

statistics

8.084 12.592 10.644 8.926 10.620 13.169

p-value 0.425 0.702 0.714 0.716 0.716 0.513

Notes: Standard errors are in parentheses.* denote statistical signicance at 10% level.** denote statistical signicance at the 5% level.*** denote statistical signicance at the 1% level.The F-statistics are obtained by regressing the endogenous regressor (Milex) on the instruments.

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b^PGMM XNi1

X 0i Zi

!WN

XNi1

Z 0iXi

!" #1 XNi1

X 0i Zi

!WN

XNi1

Z 0i Yi

!:

It is convenient to express the panel GMM estimator in matrix from:

b^PGMM X 0ZWNZ 0X 1X 0ZWNZ 0Y ;

where X 0 X 01. . .X 0N , Z 0 Z 01. . .Z 0N , and Y 0 Y 01. . .Y 0N :The panel data structure allows us to make use of the lagged variables to serve as the

instruments, which usually results in an over-identied model. To test the correct modelspecication, the over-identifying restriction tests are performed to test whether the instru-ments are valid. Moreover, to avoid the weak instruments problems, we adopt the partial F-Statistics to check if the instruments are strong or not. Other estimation methods, such aspooled OLS (POLS), random effect (RE), and xed effect (FE) estimators, will also beimplemented for comparison purposes.

4. EMPIRICAL RESULTS

Tables IV and V report the estimation results of the health and education Equations in (1)and (2), respectively. The POLS estimators indicate that military spending is nothing to dowith both health and education expenditures. FE and RE specications lead to a notrade-off result, where we control the country-specic heterogeneity to mitigate theomitted variable bias problem. On one hand, it is observed that the coefcient estimatesusing POLS, FE, and RE methods generally give rise to the same signs as those in terms ofvarious panel GMM estimations. On the other hand, the POLS, FE, and RE may obtainmany insignicant parameter estimates (e.g. see Table V). Because POLS, FE, and RE donot treat the right-hand-side variables as endogenous, the estimation results are hence notreliable. In the following discussion, we will concentrate on the results of the consistentparameter estimation using panel GMM method.As pointed out in Cameron and Trivedi (2005), the panel GMM method can be con-

ducted by choosing different lagged variables as instruments. We rst consider to includelag one-period (e.g. Milexit1andWit1 in Equations (1) and (2)) and two-period (e.g.Milexit2andWit2 in equations (1) and (2)) variables as the IV (see GMM1 model in TablesIV and V).10 The lagged variables up to the three periods are also performed in our analy-sis, indexed by models GMM2GMM6 in both tables. Overall, instruments based on laggedvariables up to two (model GMM1) and three (model GMM2) periods lead to very similarresults.When inspecting Table IV, it is clear to see that military spending has a positive impact

on the health expenditure (see the results of models GMM1 and GMM2). The positivecrowding-out effect implies that an increase in military spending also promotes the health

10We also consider to use only the lag one-period variables as the IV. However, the rst-stage F-statistic is smal-ler than the rule of thumb cut-off value 10 (Staiger and Stock, 1997), indicating a weak instrument problem. There-fore, we do not report the set of estimation results.

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spending as % of GDP, suggesting a complementary relationship between the two types ofgovernment spending. As for the education equation in Table V, we nd that militaryspending also has a signicantly positive impact on education expenditures once againconrming a positive crowding-out effect of milex vs. welfare spending relationship. Thisphenomena has been found in previous studies such as Verner (1983), Harris and Pranowo(1988), Frederiksen and Looney (1994), and Kollias and Paleologou (2011). According toFrederiksen and Looney (1994), government is willing to cut from the infrastructureprograms instead of curtailing the social welfare expenditures in response to an increase ofmilitary spending. In addition, the defense spending can be benecial to human capital for-mation since defense personnel and soldiers are appropriately taken care (in terms of healthspending) and well trained physically, and receive good skills (in terms of education spend-ing). This is particularly the case since we focus on the well-developed OECD countries.Moreover, even though the military expenditures are mainly paid for new weapons ratherthan military personnel, the education training programs must be offered for army person-nel. Our empirical results are robust across different model specications in terms ofGMM3GMM6 in Tables IV and V.It is worth noting that our nding that military spending and two social welfare

expenditures (i.e. health and education) are complementary is slightly different from thoseof Yildirim and Sezgin (2002) and Ali (2011) using the data in Turkey and Egypt, respec-tively. Their nding that the trade-off is positive between education and defense spending isconsistent with our result, while the negative crowding-out effect between health and mili-tary expenditures is opposite to our crowding-in effect.The relationship between health and education expenditures in both health and education

equations is distinct. In Table IV, education spending has a positive impact on health spend-ing even though this relationship is not always signicant (e.g. models GMM1 andGMM4). However, health expenditures tend to shrink the size of education expenditures asindicated in Table V. Once again, it is not signicant in each model specication (e.g. mod-els GMM5 and GMM6). The asymmetric nding between the two social spendings may bedue to that the health spending seems to constantly account for a higher rate as % of GDPthan education (see Figure 2 and Table II). However, it needs more work to gure out thedetailed link between the two variables in the future research.In regard to other control variables, the effect of population on education and health

expenditure is signicantly positive in both equations. The evidence may imply that thesocial welfare expenditures depend on the number of welfare recipients. The positive impactof the tax revenue on education expenditure suggests that the increasing tax revenues canbe utilized to nance more social welfare expenditures. The effect of household consump-tion and government consumption is positive and signicant in both equations. However,GDP has a negative effect on the social welfare expenditures, which might indicate thatother components (e.g. gross investment and net export) in GDP increase with a faster ratethan government spending.To test whether our model specication is right, the Hansens over-identifying restric-

tion tests are performed across GMM1GMM6 in both Tables IV and V. It shows thatthe p-values are all large enough to support the over-identifying restriction. The weakinstrument problem is an issue in an over-identied model. To address this problem, weapply the F-statistic criterion by Staiger and Stock (1997) from the rst-stage regression.Tables IV and V show that the panel robust F-statistics (treating military spending asendogenous) are sufciently large enough to avoid the weak instrument problem in allcases.

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5. CONCLUSION

In this article, we aim to empirically evaluate the guns-and-butter argument using a paneldata of OECD countries from 1988 to 2005, which provides much more complete and accu-rate data sources. Earlier empirical studies on the relationship between defense and socialwelfare expenditures tend to use single-equation models or cross-section data analysis,ignoring the potential endogeneity problem. This study adopts the panel GMM method tocontrol for unknown country-specic heterogeneity, to overcome the simultaneity problem,and thus to reach a more reliable empirical result.Our empirical nding suggests that there is a positive crowding-out effect between

defense spending and the two types of social welfare expenditures, namely, health and edu-cation expenditures, in terms of the panel GMM estimator. This result is along the line ofseveral previous studies (e.g. Benoit, 1973, 1978; Lindgren, 1984; Harris and Pranowo,1988; Ram, 1995; Kollias and Paleologou, 2011), even though we employ a different data-set and different empirical method. The positive effect (or complementary effect) in ourstudy may be due to the fact that the developed OECD countries are more supportive of thesocial welfare programs. Government is willing to cut from the infrastructure programsrather than sacricing the social welfare expenditures in response to an increase of militaryspending. Additionally, the defense spending can be benecial to human capital formationbecause defense personnel and soldiers are appropriately taken care and well trained physi-cally, and receive good skills. Therefore, if the government increases military spending, thehealth and education spending may be raised as well.Since this study focuses on merely OECD countries, it is still unclear whether there is a

signicantly positive crowding-out effect in non-OECD countries or other regions in theworld. It is likely that the guns-and-butter argument holds in a panel of the developing orless-developed countries. However, it is out of the scope of the current article. We leave thisdirection to our future research.

ACKNOWLEDGMENT

We wish to acknowledge very useful and constructive comments from two anonymousreferees and the participants at the 15th Annual Conference of the African EconometricSociety, American University in Cairo, Egypt for many useful comments and suggestionson earlier drafts of this article. Financial support from the Monsoon Asia and Multi-CulturesProgram, Research Center for Humanities and Social Sciences at National Tsing HuaUniversity is gratefully acknowledged. Any remaining errors are our own.

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Abstract1. INTRODUCTION2. THE GUNS-AND-BUTTER ARGUMENT3. DATA AND ECONOMETRIC STRATEGY3.1. Data Description3.2. Panel GMM

4. EMPIRICAL RESULTS5. CONCLUSIONAcknowledgmentReferences