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Defence expenditure and economic growth in the European Union
Journal of Policy Modeling 26 (2004) 553–569
Defence expenditure and economic growth in the European Union A causality analysis Christos Kollias a,∗ , George Manolas b , Suzanna-Maria Paleologou c a Department
of Economics, University of Thessaly, Volos 382 21, Greece General for Economic Policy, Ministry of Economy & Finance, Greece c Department of Economics, University of Ioannina, Greece
b Directorate
Accepted 1 March 2004 Available online 25 May 2004
Abstract This paper examines the relationship between military expenditure and growth among the EU15 members using co-integration and causality tests for the period 1961–2000. Although the results reported herein do not reveal a uniformity among the 15 countries, the apparent prevalence of the direction of causality from growth to military expenditure as well as the absence of the reverse causal ordering may be an indication that an important number of governments in the European Union (EU) make defence spending policy decisions based on the state of their economy with the concomitant implications for the objective of a Common European Security and Defence Policy (CESDP). © 2004 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved. JEL classification: H56; C22 Keywords: Military expenditure; Causality; European Union
1. Introduction In recent years there has been a move towards the development of a Common European Security and Defence Policy (CESDP) in the European Union (EU). ∗
Corresponding author. Tel.: +30 24210 74771. E-mail address: [email protected] (C. Kollias).
0161-8938/$ – see front matter © 2004 Society for Policy Modeling. doi:10.1016/j.jpolmod.2004.03.013
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In the Thessaloniki 2003 summit, a document by the EU High Representative for the Common Foreign and Security Policy (CFSP) outlining the EU security principles and policy was adopted by the 15 members of the Union. In it, it is stated that a more active, coherent and capable EU is needed in order to pursue its strategic objectives, play its role as a global actor and meet old and new threats and challenges (Solana, 2003). The development of a Common European Defence Policy and perhaps eventually the creation of a European Defence Union (EDU) raises a number of important policy issues from an economics perspective (Fontanel & Smith, 1991; Guyot & Vranceanu, 2001; Hartley, 2003; Hoffman, 2000; Wolf & Zycher, 2001). In terms of defence spending, the EU15 member states present a very diverse pattern. For example, in 2000 the defence burden, i.e., military expenditure as a share of GDP, among the EU member states, ranged from 0.7% in the case of Ireland and Luxemburg to as high as 4.9% in the case of Greece. Similarly, in terms of development and growth performance the pattern is just as diverse with the growth rate in 2000 ranging from 7.5% in the case of Ireland to 2% in the case of Denmark. The causal relationship between economic growth and military spending has been the subject of extensive empirical work such as, inter allia, Abu-Bader and Abu-Qarn (2003), Chowdhury (1991), Dakurah et al. (2001), Dunne et al. (2001), Heo (1998), Joerding (1986), Kollias and Makrydakis (2000), Kusi (1994). Examining the pool of the empirical findings reveals that the relationship in question cannot be generalised across countries and over time. In fact little consensus exists either on the presence of such a relationship nor on its nature and direction between growth and military spending since among other things it depends on the level of socio-economic development of the country(ies) involved, the sample period as well as the methodology employed. This paper hopes to contribute to the existing pool of literature by providing further empirical evidence on this issue from the EU15 group of countries given the move towards a CESDP in order for the EU “. . . to assert its identity on the international scene, in particular through the implementation of a common foreign and security policy including the eventual framing of a common defence policy which might in time lead to a common defence”. As pointed out by Guyot and Vranceanu (2001), this constitutes a very ambitious and complex objective set out by the authors of the Maastricht Treaty (1992) with far reaching implications for European integration as well as the world scene.
2. Economic growth and defence spending in the European Union The EU countries represent a fairly diverse picture both in terms of level of development measured in per capita GDP terms, as well as in terms of growth performance and defence burden. As it can be seen from Tables 1 and 2, for the period for which the tests are conducted here, i.e., 1961–2000, the average GDP growth rate was 3.4% (maximum 13.2%, minimum −6.6%, S.D. 2.7). For the same period
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Table 1 GDP growth rates in the European Union-15
Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg The Netherlands Portugal Spain Sweden UK EU15
1961–2000
1961–1969
1970–1979
1980–1989
1990–1999
2000
3.2 3.1 2.7 3.4 3.3 2.8 4.1 4.8 3.3 4.0 3.3 4.3 4.1 2.6 2.4 3.4
4.5 4.8 4.8 4.5 5.6 4.4 8.5 4.4 5.8 3.8 5.0 6.4 7.7 4.4 3.0 5.2
4.1 3.6 2.5 3.7 3.7 3.1 5.5 4.7 3.8 2.7 3.4 5.1 3.8 2.5 2.4 3.7
2.1 2.0 1.8 3.6 2.2 1.8 0.8 3.1 2.4 4.4 1.9 3.2 2.8 2.1 2.4 2.4
2.2 2.0 2.1 1.6 1.7 2.2 1.9 6.6 1.4 5.0 2.9 2.6 2.4 1.4 1.9 2.5
4.1 3.5 2.0 3.6 3.7 2.9 3.9 7.5 2.7 2.7 5.6 3.2 3.8 4.9 3.9 3.9
Source: European Commission’s European Economy.
the average defence burden was 2.8% of GDP (maximum 7.4%, minimum 0.7%, S.D. 1.5). Over time, for each member state, both time series present often significant deviations from the EU mean. In the case of growth rates most countries’ growth performance has generally tended to oscillate above or below the EU mean depending on the time period examined with no strong consistent pattern emerging. For example, for the whole period in question, the average EU growth rate Table 2 Military expenditure as a share of GDP in the European Union-15
Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg The Netherlands Portugal Spain Sweden UK EU15
1961–2000
1961–1969
1970–1979
1980–1989
1990–1999
2000
1.1 2.7 2.2 1.7 4 3.1 5.2 1.3 2.5 1 3.1 4.2 2 3 4.6 2.8
1.2 3.2 2.8 1.8 5.3 4.3 4.1 1.3 3.2 1.2 4.1 6.7 2 4 5.8 3.4
1.1 3 2.3 1.5 3.9 3.4 5.7 1.5 2.6 0.9 3.3 5.4 1.9 3.4 4.8 3.0
1.2 3 2.3 1.9 4 3.2 6.2 1.5 2.3 1.1 3.1 3.2 2.8 2.7 4.9 2.9
0.9 1.7 1.8 1.6 3.2 1.9 4.5 1 2 0.8 2.1 2.5 1.5 2.2 3.2 2.1
0.8 1.4 1.5 1.3 2.6 1.5 4.9 0.7 2.1 0.7 1.6 2.1 1.3 2.1 2.5 1.8
Source: SIPRI Yearbooks, various issues.
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was 3.4% with the UK rate of 2.4% being the lowest among the 15 members while the highest is that of Ireland — an average of 4.8% for 1961–2000. Similar variations for different countries are recorded in the various sub-periods (1961–1969, 1970–1979, 1980–1989, 1990–1999) with no consistent pattern emerging in terms of under/over-performers as far as growth rates are concerned. However, more consistent is the pattern that emerges when it comes to the deviations in terms of defence spending as share of GDP (Table 2). In the case of countries such as Greece, France, the UK and Portugal their defence burden has consistently been higher than the EU average whereas in cases such as Luxemburg, Finland, Ireland and Austria their defence burden has consistently been lower than the EU average. For example, during 1961–2000 the average defence burdens of the former were 5.2%, 4%, 4.6% and 4.2%, respectively while for the latter they were 1%, 1.7%, 1.3%, 1.1% and 2.2%, respectively. For the same period the average for the whole EU was 2.8%. More or less, the same picture emerges if one looks at the different sub-periods. To a large extent, this reflects different external security needs and significant differences in defence policy both during the bipolar as well post-bipolar eras as studies that have estimated demand for military expenditure models for various countries indicate (see inter allia Hartley & Sandler, 1990; Kollias & Paleologou, 2003; Murdoch & Sandler, 1990; Sandler & Hartley, 1995; Smith, 1990). For example, the UK and France maintain a nuclear component, are members of the UN Security Council and play a global role in international politics; Greece has long faced country-specific defence priorities vis-à-vis Turkey, while countries such as Ireland, Sweden, Finland and Austria are not members of the NATO alliance and in principle were not directly influenced by the East-west arms race during the Cold War. Significant differences also emerge if one looks at the indigenous defence production capabilities (see inter allia Barros, 2002; Hartley, 1998; Inbar & Zilberfarb, 1998; Kollias & Rafailidis, 2003; Maneval, 1994; Mollas-Gallart, 1997; Struys, 2002). Countries such as France, Germany and the UK and perhaps to a lesser extent Sweden, Italy, Spain, Austria and the Netherlands have a developed domestic defence industry with a strong export orientation while other countries such as a Greece and Portugal have comparatively little indigenous defence production capabilities with very weak defence industrial sectors thus relying on imports for their military hardware. Military spending can affect the economy through various channels. On the one hand, such expenditure may stimulate growth through Keynesian-type aggregate demand stimulation. Increased demand induced by higher military spending leads to increased utilisation of capital stock and higher employment. Increased capital stock utilisation may lead to increases in the profit rate which in turn may lead to induce higher investment thus generating short-run multiplier effects and higher growth rates. On the other hand, such spending has been shown to have growth retarding effects mainly through investment crowding-out, inflationary pressures and the reduction of available public funds for spending and investment in other, potentially more productive and growth inducing, areas. All these channels through which military spending can influence, promote of retard, growth, assume that such
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expenditures are causally prior to economic growth. However, it is possible that growth may be causally prior to defence spending in the sense that a country with high growth rates may allocate more resources to defence. In this context, it is also possible that countries with higher growth rates may divert resources from defence to more productive uses. A number of policy implications can arise from understanding the direction of causal ordering between growth and defence expenditure. If, for example, the direction of causality is found to be from growth to military spending then this could be interpreted as an indication that countries are trying to protect their wealth and people from external threats; advance their interests in the international or peripheral scene; strengthen their role and assert their identity in world affairs; pursue their strategic objectives. Security threats can of course emanate from other nations or group of nations or indeed they may take the form of asymmetrical threats. In fact, recent years have seen the rise of new security challenges such as terrorism and major crises, both international (Iraq, Rwanda, Afghanistan) as well as on the European continent (Bosnia, Kosovo), requiring the use of military power. Such crises have confirmed US military superiority and strengthened the case for the need to build an EU military capability in order to support its role in the world scene protecting and advancing its interests and its security and strategic objectives. The emergence of such threats has resulted in pressures for changes in defence structure and equipment as well as increased security spending in a number of countries and in particular the US while similar trends may appear among EU members after years of reductions in defence budgets following the end of the Cold War. Insofar as an EU defence capability is seen as a pure public good then an issue that arises is that of the allocation of the costs that this entails among the EU members that will participate and benefit from CESDP. If on the other hand, the direction of causality is from military expenditure to growth this may be indicating the presence of aggregate demand and employment effects that to a large extent may be attributed to domestic arms production and spin-offs from military R&D. Again, in the context of a CESDP, issues that arise concern the procurement policy for military hardware, the restructuring of the European defence industrial base, the creation of a single EU defence market, the division of labour among the EU defence industries and European defence R&D policy (Fontanel & Smith, 1991; Guyot & Vranceanu, 2001; Hartley, 2003). In particular, given the need for new military hardware to undertake Petersberg type missions, enhance and upgrade existing EU military capability is such areas as strategic airlift, satellite surveillance, smart weapons the role of the existing EU defence industrial base is of cardinal importance.
3. Methodology and empirical findings From the preceding discussion, four possible causal relationships may be established empirically: uni-directional causality from growth to military spending
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and vice-versa, bi-directional causality and finally no causality. The annual data for the 15 EU members used in the tests conducted, as described in the following part, were obtained from the European Commission’s European Economy, directorate-general for economic and financial affairs in the case of GDP growth rates (GDPt ) and from SIPRI Yearbooks (various issues) in the case of military expenditure as a share of GDP (milext ). As already mentioned, the period covers the years 1961–2000 and a battery of different causality tests are used. We began our analysis with F tests that were used to examine on a preliminary level the presence and direction of causality between the two variables. Furthermore, the standard Granger causality test, i.e., Eqs. (1) and (2), were estimated for all 15 countries. Milext = a0 + a1 GDPt−i + a2 milext−i + vt
(1)
GDPt = b0 + b1 milext−i + b2 GDPt−i + t
(2)
Overall, from the results of our preliminary causality analysis (that for reasons of brevity are not reported here) it appeared that the causal ordering between the two time series involved could not be generalised across all 15 EU members. On the basis of the F test for causality, presence of bi-directional causality was evident in the case of three countries — Austria, Belgium and Portugal, no causal ordering was present in Denmark, France and Luxembourg while for the rest uni-directional causality running from growth to military spending was detected. The F test did not yield evidence of causality running from defence spending to growth. On the basis of the Granger causality test it appeared that, out of the 15 countries uni-directional causality from growth to military expenditure was present in the case of seven countries — Germany, Greece, Italy, the Netherlands, Spain, Sweden and the UK; bi-directional in the case of Austria and Luxembourg and no causality was detected from estimating (1) and (2) in the case of six countries — Belgium, Denmark, Finland, France, Ireland and Portugal. Again no causal ordering from military spending to growth could be detected. These results differed to those of the F test in the case of Belgium Finland, Ireland, Luxembourg and Portugal. However, as Dakurah et al. (2001) point out the weakness associated with tests such as the above is that the issue of spurious regression in the presence of non-stationary variables is ignored. To allow for this, the causality issue is tested using the co-integration and error correction methodology (Dakurah et al., 2001; Dunne et al., 2001; Kollias & Makrydakis, 2000). In this context, the first step is to examine the timeseries properties of the two variables. This is done via the estimation of the ADF and Phillips–Perron unit root tests (Table 3). If both of these variables are found to be I(1), we then look for a co-integrating relationship between them. If there is no co-integrating relationship, we make the variables stationary by first differencing and test for non-causality in a VAR context. Following the examination of the time series properties of the two variables we then proceed by applying various causality tests. If the variables of interest were found to be co-integrated then we estimated the VECM given by Eqs. (3) and (4).
Table 3 Results of unit root tests Augmented Dickey–Fuller (ADF)
Phillips–Perron (PP) Levels ( )
First difference ( )
Levels ( )
First difference ( )
Levels ( )
First difference ( )
Austria Milex Growth
−1.287 −2.879
−5.660∗ −8.777∗
−2.786 −3.888∗
−5.631∗ −8.649∗
−1.545 −4.778∗
−6.400∗ −12.507∗
−3.087 −5.597∗
−6.490∗ −12.301
Belgium Milex Growth
0.098 −2.741
−3.803∗ −6.878∗
−0.938 −3.218
−3.918∗ −6.816∗
0.241 −2.741
−5.586∗ −14.637
−1.092 −5.797∗
−5.615∗ −14.677∗
Denmark Milex Growth
−0.989 −4.465∗
−6.739∗ −9.088∗
−2.657 −5.028∗
−6.638∗ −8.931∗
−0.176 −5.507∗
−7.550∗ −12.747∗
−3.314 −6.035∗
−7.370∗ −8.930∗
Finland Milex Growth
−2.280 −3.867∗
−4.594∗ −6.127∗
−2.241 −4.002∗
−4.524∗ −6.064∗
−2.601 −3.614∗
−9.181∗ −6.348∗
−2.649 −3.534
−9.024∗ −6.232∗
France Milex Growth
−2.304 −2.393
−3.512∗ −6.161∗
−2.806 −2.689
−3.326 −6.300∗
−2.516 −2.777
−4.450∗ −4.716∗
−2.685 −3.887∗
−4.633∗ −9.937∗
Germany Milex Growth
−0.938 −4.322∗
−6.594∗ −7.403∗
−2.227 −4.924∗
−6.444∗ −7.295∗
−0.131 −4.131∗
−7.254∗ −8.489∗
−3.052 −4.465∗
−7.030∗ −8.362∗
Greece Milex Growth
−1.809 −2.424
−4.429∗ −7.688∗
−1.682 −3.490
−4.496∗ −7.659∗
−1.722 −4.621∗
−5.528∗ −13.038∗
−1.602 −5.558∗
−5.526∗ −12.793∗
Ireland Milex Growth
−0.572 −2.940
−3.698∗ −6.624∗
−1.070 −3.305
−3.940∗ −6.577∗
−0.633 −3.649∗
−5.884∗ −9.238∗
−1.126 −4.038∗
−6.025∗ −9.130∗
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First difference ( )
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Levels ( )
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Table 3 (Continued ) Augmented Dickey–Fuller (ADF)
Phillips–Perron (PP)
First difference ( )
Levels ( )
First difference ( )
Levels ( )
First difference ( )
Levels ( )
First difference ( )
Italy Milex Growth
−1.512 −3.381∗
−4.694∗ −7.315∗
−2.009 −5.003∗
−4.837∗ −7.257∗
−1.205 −4.294∗
−5.949∗ −10.338∗
−2.028 −5.660∗
−6.018∗ −10.227∗
Luxembourg Milex Growth
−1.820 −4.142∗
−4.072∗ −6.696∗
−2.196 −4.415∗
−4.012∗ −6.597∗
−1.524 −5.171∗
−5.902∗ −12.758∗
−2.170 −5.441∗
−5.807∗ −12.521∗
The Netherlands Milex −0.681 Growth −2.735
−4.206∗ −6.572∗
−1.659 −2.993
−4.151∗ −6.486∗
−0.443 −3.412∗
−7.155∗ −9.375∗
−1.784 −3.746∗
−7.067∗ −9.283∗
Portugal Milex Growth
−0.935 −3.671∗
−5.119∗ −7.780∗
−1.880 −4.455∗
−5.044∗ −7.688∗
−0.725 −4.027∗
−7.221∗ −8.642∗
−2.416 −4.603∗
−7.117∗ −8.503∗
Spain Milex Growth
−1.053 −3.613∗
−2.525 −6.127∗
−1.149 −2.774
−2.565 −6.332∗
−1.270 −3.244∗
−5.213∗ −7.435∗
−1.328 −3.170
−5.223∗ −7.594∗
Sweden Milex Growth
−1.053 −3.613∗
−5.475∗ −6.985∗
−3.576∗ −4.008∗
−5.460∗ −7.050∗
−0.735 −3.577∗
−5.601∗ −7.787∗
−3.003 −3.676∗
−5.545∗ −7.836∗
UK Milex Growth
−0.452 −4.896∗
−3.134∗ −6.794∗
−1.502 −4.884∗
−3.099 −6.695∗
−0.187 −4.354∗
−5.361∗ −7.705∗
−1.513 −4.292∗
−5.287∗ −7.564∗
Notes: The critical values for the and the of the ADF test, with 39 observations at 5% significance level is −2.940 without a trend and −3.53 with a trend. The same values hold for the Phillips–Perron tests. ∗ Statistically significant at 5%.
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Levels ( )
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If no long-term co-integrating relationship was established the standard Granger causality test was estimated. Milext = a0 + a1 GDPt−i + a2 milext−i + a3 ECTt−1 + vt
(3)
GDPt = b0 + b1 milext−i + b2 GDPt−i + b3 ECTt−1 + t
(4)
The tests for co-integration, reported in Table 4, show that the variables are co-integrated in the case twelve EU countries except for Belgium, Greece and Ireland. Thus, for the former a VECM model, i.e., (3) and (4), was estimated in order to determine the presence and direction of causality (Table 5). For the latter, the standard Granger causality regressions (1) and (2) were estimated (Table 6). On the basis of the findings reported in Table 5 it would appear that in the case of the causality tests based on the error correction models bi-directional causality was detected in three cases — Austria, Denmark and Luxemburg, and absence of any causal ordering in another three namely France, Finland and Portugal. In six countries causality appeared to run from growth to military spending. These are Germany, Italy, the Netherlands, Spain, Sweden and the UK. In Table 6, in the case of the three countries were no co-integration was established — Belgium, Greece and Ireland no causality was detected in the case of Belgium and Ireland and uni-directional from growth to military spending in the case of Greece. The prevalence of the direction of causality from growth to military expenditure, in seven countries out of the 15 EU members, along with the absence of a reverse causal ordering, i.e., from military expenditure to growth, may be suggesting that the military burden is primarily determined by economic factors rather than geopolitical and security considerations while bi-directionality in three suggests a degree of interdependence between the two as well as the possible presence of Keynesian type aggregate demand effects. It would appear therefore, that several EU governments make defence spending policy decisions based on the state of their economy. To the extend that this observation accurately describes the defence spending policy process vis-à-vis the state of the economy, this implies that the initial increases in the defence budgets required to support a fully independent EU defence pillar depend upon the growth performance of the European Economy(ies) from where the resources to support CESDP will be drawn. In particular, this direction of causality may be indicating that as the economy grows resources are channelled towards increased security and protection and/or that defence needs are catered by governments only when the state of the economy permits the diversion of resources to generally unproductive uses such as defence. If indeed this is the case, then a policy implication is that the increases in the European defence budgets required in order to meet the capital equipment and operational needs of a fully independent EU defence capability depend upon the growth performance of the European Economy(ies). However, it should be noted here that the move towards a common defence will eventually bring costs savings
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Table 4 Co-integration results Maximal eigenvalue Austria GDP & milex (VAR lag = 2) Ho: r = 0 33.180∗ Ho: r ≤ 1 6.583 Ho: r = 0 Ho: r ≤ 1
Trace statistic 39.763∗ 6.583
Belgium GDP & milex (VAR lag = 2) Ho: r = 0 14.579 Ho: r ≤ 1 1.801 Ho: r = 0 Ho: r ≤ 1
Trace statistic 16.380 1.801
Denmark GDP & milex (VAR lag = 2) Ho: r = 0 35.986∗ Ho: r ≤ 1 6.737 Ho: r = 0 Ho: r ≤ 1
Trace statistic 42.724∗ 6.737
Finland GDP & milex (VAR lag = 2) Ho: r = 0 19.812∗ Ho: r ≤ 1 5.217 Ho: r = 0 Ho: r ≤ 1
Trace statistic 25.829∗ 5.217
France GDP & milex (VAR lag = 2) Ho: r = 0 24.319∗ Ho: r ≤ 1 5.452 Ho: r = 0 Ho: r ≤ 1
Trace statistic 29.772 5.452
Germany GDP & milex (VAR lag = 2) Ho: r = 0 22.450∗ Ho: r ≤ 1 0.956 Ho: r = 0 Ho: r ≤ 1
Trace statistic 23.407∗ 0.956
Critical value 5%
Critical value 1%
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
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Table 4 (Continued ) Maximal eigenvalue Greece GDP & milex (VAR lag = 2) Ho: r = 0 7.845 Ho: r ≤ 1 2.703 Ho: r = 0 Ho: r ≤ 1
Trace statistic 10.549 2.703
Ireland GDP & milex (VAR lag = 2) Ho: r = 0 13.868 Ho: r ≤ 1 0.313 Ho: r = 0 Ho: r ≤ 1
Trace statistic 14.181 0.313
Italy GDP & milex (VAR lag = 2) Ho: r = 0 26.396∗ Ho: r ≤ 1 2.481 Ho: r = 0 Ho: r ≤ 1
Trace statistic 28.877∗ 2.481
Luxembourg GDP & milex (VAR lag = 2) Ho: r = 0 16.829∗ Ho: r ≤ 1 3.448 Ho: r = 0 Ho: r ≤ 1
Trace statistic 20.278∗ 3.448
The Netherlands GDP & milex (VAR lag = 2) Ho: r = 0 21.416∗ Ho: r ≤ 1 3.063 Ho: r = 0 Ho: r ≤ 1
Trace statistic 24.480∗ 3.063
Portugal GDP & milex (VAR lag = 2) Ho: r = 0 20.435∗ Ho: r ≤ 1 2.559 Ho: r = 0 Ho: r ≤ 1
Trace statistic 24.995∗ 2.559
Critical value 5%
Critical value 1%
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.230 9.750
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.230 9.750
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Table 4 (Continued ) Maximal eigenvalue Spain GDP & milex (VAR lag = 2) Ho: r = 0 21.640∗ Ho: r ≤ 1 2.336 Ho: r = 0 Ho: r ≤ 1
Trace statistic 23.976∗ 2.336
Sweden GDP & milex (VAR lag = 2) Ho: r = 0 20.260∗ Ho: r ≤ 1 6.614 Ho: r = 0 Ho: r ≤ 1
Trace statistic 23.870∗ 6.614
UK GDP & milex (VAR lag = 2) Ho: r = 0 26.367∗ Ho: r ≤ 1 2.696 Ho: r = 0 Ho: r ≤ 1
Trace statistic 29.063∗ 2.696
Critical value 5%
Critical value 1%
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.320 9.750
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.230 9.750
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.230 9.750
Notes: The tests are based on the critical values obtained from Johansen and Juselius (1990). ∗ Reject the null hypothesis at the 5% significance level.
through the avoidance in duplication, equipment standardisation and harmonisation, economies of scale and perhaps even specialisation among participating nations. In other words, joint forces provide more power than the sum of individual forces potentially at a lower cost (Fontanel & Smith, 1991; Hartley, 2003). As noted above, defence is generally regarded as an unproductive use of scarce resources. Thus, channelling resources to defence may prove a burden on the economy and its performance, slowing down growth which in turn will adversely affect the flow of resources to the defence sector with the concomitant impact on the ambitious objective of a common European defence with full independent capabilities to support operations. Perhaps, the only channel through which a feedback mechanism from defence to the economy can be set in motion is that of the defence industrial sector and military R&D. Both can generate aggregate demand and multiplier effects from increased capacity utilisation and technological advances that spill over to the rest of the economy through the development and production of the hardware and assets a CEDSP would require. This raises the issue of the division of labour in defence production, in the context of the juste-retour principle, and of course the need to further restructure on a pan-European level the defence
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Table 5 Causality tests based on the error correction term Hypothesis tested
t statistic
Austria Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.146∗ 2.225∗ −3.482∗ −3.132∗ −3.944∗ −3.555∗
Denmark Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−0.734 0.633 −2.043∗ −0.887 −0.286 −2.596∗
Finland Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−1.200 −0.217 −1.540 −1.900∗ −0.958 −1.290
France Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
0.828 −0.102 −1.060 −0.519 0.797 −0.732
Germany Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.698∗ 3.316∗ −3.150∗ −1.340 −0.600 −0.950
Italy Sample period: 1960–2000 ␣1 = 0 ␣2 = 0
−1.467∗∗ 3.171∗
Wald statistic
4.607∗ 9.798∗
0.539
0.786
1.415
1.530
0.683
0.269
7.281∗
1.796
2.152∗
566
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Table 5 (Continued ) Hypothesis tested
t statistic
␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.340∗ −0.389 0.840 −1.485
Wald statistic 0.151
Luxembourg Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.784∗ 1.807∗ −2.333∗ −3.045∗ −2.483∗ 2.548∗
7.555∗
The Netherlands Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.310∗ −2.554∗ −2.404∗ −0.251 −0.887 −0.117
5.321∗
Portugal Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−0.593 −0.537 −1.112 0.398 0.231 −0.073
1.030
Spain Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−4.917∗ 5.961∗ −5.922∗ −2.541∗ 1.054 −0.147
24.124∗
Sweden Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.466∗ 1.008 −3.191∗ −0.100 0.504 −1.115
4.150∗
9.272∗
0.060
0.158
6.460∗
0.999
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Table 5 (Continued ) Hypothesis tested
t statistic
Wald statistic
UK Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−3.675∗ 2.217∗ −4.687∗ −2.266∗ 1.604∗∗ −0.401
13.510∗
∗ ∗∗
5.139∗
Statistically significant at 5%. Statistically significant at 10%.
industry improving its efficiency and competitiveness and creating a single European market for defence equipment. Associated with this, from the demand side, is the need to create an EU armaments and procurement agency through for example the upgrading of OCCAR (acronym for: Organisation Conjointe de Cooperation en matiere d’ Armement) that has been operating since 1998 with the participation of France, Germany, UK and Italy (Guyot & Vranceanu, 2001; Hartley, 2003). Finally, pooling of resources is also necessary when it comes to defence related R&D given the high costs associated with the development of new weapons systems. Table 6 Granger causality tests Hypothesis tested
t statistic
Wald statistic
Belgium Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 b1 = 0 b2 = 0
−0.778 0.813 −1.381 −4.463∗
0.605
Greece Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 b1 = 0 b2 = 0
−2.479∗ −4.259∗ −1.354 1.073
Ireland Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 b1 = 0 b2 = 0
−1.281 −0.329 −1.290 −1.812∗∗
∗ ∗∗
Statistically significant at 5%. Statistically significant at 10%.
1.907
6.150∗ 1.835
1.641 1.467
568
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4. Concluding remarks The quest for a common European foreign policy and indeed a security and defence identity dates back to the 1950s with the aborted attempt to establish a European Defence Community that eventually led to the further strengthening of NATO as the main vehicle and institution in the foreign and security policies of all EEC members. The recent moves towards a CEDSP raise a plethora of important policy issues that go beyond the political and strategic aspects associated with the potential creation of a common defence and perhaps a European defence union. The fruition of this ambitious objective rests, among other things, on whether commonality of national interests and strategic objectives exists among the individual states since defence is closely associated with national sovereignty. Nevertheless, the move towards a CEDSP has a significant economic dimension attached to it. It concerns the relationship between the defence sector and the economy from where the resources to support a common defence policy will be drawn. In this context, this paper set out to examine the relationship between defence expenditure and growth among the 15 members of the EU. Although the results did not reveal a uniformity among the 15 countries, the apparent prevalence of the direction of causality from growth to military expenditure may be an indication that an important number of governments in the EU make defence spending policy decisions based on the state of their economy. Of course, this observation must be tempered by the fact that in five countries no causality was established while in another three bi-directional causality was detected. On the basis of this, and perhaps going beyond our findings, it was argued here that an important policy issue arising in the effort to build a CEDSP is that of the role of the European defence industrial base. Although eventually there are potential efficiency gains in a common defence, in the short to medium term increases in defence budgets may be necessary in order to meet the costs associated with CEDSP. Since this depends upon the state and the performance of the European Economy and given the unproductive nature of defence, the only feedback mechanism available is that of the European defence industry. Defence production with its aggregate demand and multiplier effects through the backward and forward linkages can at least partly offset the burden that increased military spending will pose on the economy. Acknowledgments The authors gratefully acknowledge valuable comments and suggestions on an earlier version of the paper. References Abu-Bader, S., & Abu-Qarn, A. (2003). Government expenditures, military spending and economic growth: Causality evidence from Egypt, Israel and Syria. Journal of Policy Modeling, 25, 567–583.
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Barros, C. (2002). Small countries and the consolidation of the European defence industry. Defence and Peace Economics, 13, 311–320. Chowdhury, A. R. (1991). A causal analysis of defence spending and economic growth. Journal of Conflict Resolution, 35, 80–97. Dakurah, H., Davies, S., & Sampath, R. (2001). Defense spending and economic growth in developing countries. A causality analysis. Journal of Policy Modeling, 23, 651–658. Dunne, P., Nikolaidou, E., & Vougas, V. (2001). Defence spending and economic growth: A causal analysis for Greece and Turkey. Defence and Peace Economics, 12, 5–26. Fontanel, J., & Smith, R. (1991). A European defence union? Economic Policy, 13, 393–425. Guyot, M., & Vranceanu, R. (2001). European defence: The cost of partial integration. Defence and Peace Economics, 12, 157–174. Hartley, K. (1998). In E. Inbar & B. Zilberfarb (Eds.), UK defence industries. Hartley, K. (2003). The future of European defence policy: An economic perspective. Defence and Peace Economics, 14, 107–115. Hartley, K., & Sandler, T. (Eds.) (1990). The economics of defence spending. An international survey. Routledge. Heo, U. (1998). Modelling the defence-growth relationship around the globe. Journal of Conflict Resolution, 42, 637–657. Hoffman, S. (2000). Towards a common European foreign and defence policy? Journal of Common Market Studies, 38, 189–198. Inbar, E., & Zilberfarb, B. (Eds.) (1998). The politics and economics of defence industries. Frank Cass. Joerding, W. (1986). Economic growth and defence spending. Journal of Development Economics, 21, 35–40. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on co-integration-with applications to the demand for money. Oxford Bulletin of Economics, 52, 169– 210. Kollias, C., & Makrydakis, S. (2000). A note on the causal relationship between defence spending and growth in Greece: 1955–1993. Defence and Peace Economics, 11, 173–184. Kollias, C., & Paleologou, S. (2003). Domestic political and external security determinants of the demand for Greek military expenditure. Defence and Peace Economics, 14, 437–445. Kollias, C., & Rafailidis, A. (2003). A survey of the Greek defence industry. Defence and Peace Economics, 14, 311–324. Kusi, N. K. (1994). Economic growth and defence spending in developing countries: A causal analysis. Journal of Conflict Resolution, 38, 152–159. Maneval, H. (1994). Defence spending in West-Germany. Defence and Peace Economics, 5, 221–246. Mollas-Gallart, J. (1997). Country Survey IX: Spain. Defence and Peace Economics, 8, 267–306. Murdoch, J., & Sandler, T. (1990). In Hartley & Sandler (Eds.), Swedish military expenditure and armed neutrality (pp. 148–176). Sandler, & T., Hartley, K. (1995). The economics of defence. Cambridge University Press. Smith, R. (1990). In Hartley & Sandler (Eds.), Defence spending in the United Kingdom (pp. 76–92). Solana, J. (2003). A secure Europe in a better world. EU high representative for the CFSP, European Council, Thessaloniki Summit, June. Struys, W. (2002). Country Survey XV: Defence policy and spending in Belgium. Defence and Peace Economics, 13, 31–53. Wolf, C., Jr., & Zycher, B. (2001). European military prospects, economic constraints, and rapid reaction force. RAND Publications.
Defence expenditure and economic growth in the European Union A causality analysis Christos Kollias a,∗ , George Manolas b , Suzanna-Maria Paleologou c a Department
of Economics, University of Thessaly, Volos 382 21, Greece General for Economic Policy, Ministry of Economy & Finance, Greece c Department of Economics, University of Ioannina, Greece
b Directorate
Accepted 1 March 2004 Available online 25 May 2004
Abstract This paper examines the relationship between military expenditure and growth among the EU15 members using co-integration and causality tests for the period 1961–2000. Although the results reported herein do not reveal a uniformity among the 15 countries, the apparent prevalence of the direction of causality from growth to military expenditure as well as the absence of the reverse causal ordering may be an indication that an important number of governments in the European Union (EU) make defence spending policy decisions based on the state of their economy with the concomitant implications for the objective of a Common European Security and Defence Policy (CESDP). © 2004 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved. JEL classification: H56; C22 Keywords: Military expenditure; Causality; European Union
1. Introduction In recent years there has been a move towards the development of a Common European Security and Defence Policy (CESDP) in the European Union (EU). ∗
Corresponding author. Tel.: +30 24210 74771. E-mail address: [email protected] (C. Kollias).
0161-8938/$ – see front matter © 2004 Society for Policy Modeling. doi:10.1016/j.jpolmod.2004.03.013
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In the Thessaloniki 2003 summit, a document by the EU High Representative for the Common Foreign and Security Policy (CFSP) outlining the EU security principles and policy was adopted by the 15 members of the Union. In it, it is stated that a more active, coherent and capable EU is needed in order to pursue its strategic objectives, play its role as a global actor and meet old and new threats and challenges (Solana, 2003). The development of a Common European Defence Policy and perhaps eventually the creation of a European Defence Union (EDU) raises a number of important policy issues from an economics perspective (Fontanel & Smith, 1991; Guyot & Vranceanu, 2001; Hartley, 2003; Hoffman, 2000; Wolf & Zycher, 2001). In terms of defence spending, the EU15 member states present a very diverse pattern. For example, in 2000 the defence burden, i.e., military expenditure as a share of GDP, among the EU member states, ranged from 0.7% in the case of Ireland and Luxemburg to as high as 4.9% in the case of Greece. Similarly, in terms of development and growth performance the pattern is just as diverse with the growth rate in 2000 ranging from 7.5% in the case of Ireland to 2% in the case of Denmark. The causal relationship between economic growth and military spending has been the subject of extensive empirical work such as, inter allia, Abu-Bader and Abu-Qarn (2003), Chowdhury (1991), Dakurah et al. (2001), Dunne et al. (2001), Heo (1998), Joerding (1986), Kollias and Makrydakis (2000), Kusi (1994). Examining the pool of the empirical findings reveals that the relationship in question cannot be generalised across countries and over time. In fact little consensus exists either on the presence of such a relationship nor on its nature and direction between growth and military spending since among other things it depends on the level of socio-economic development of the country(ies) involved, the sample period as well as the methodology employed. This paper hopes to contribute to the existing pool of literature by providing further empirical evidence on this issue from the EU15 group of countries given the move towards a CESDP in order for the EU “. . . to assert its identity on the international scene, in particular through the implementation of a common foreign and security policy including the eventual framing of a common defence policy which might in time lead to a common defence”. As pointed out by Guyot and Vranceanu (2001), this constitutes a very ambitious and complex objective set out by the authors of the Maastricht Treaty (1992) with far reaching implications for European integration as well as the world scene.
2. Economic growth and defence spending in the European Union The EU countries represent a fairly diverse picture both in terms of level of development measured in per capita GDP terms, as well as in terms of growth performance and defence burden. As it can be seen from Tables 1 and 2, for the period for which the tests are conducted here, i.e., 1961–2000, the average GDP growth rate was 3.4% (maximum 13.2%, minimum −6.6%, S.D. 2.7). For the same period
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Table 1 GDP growth rates in the European Union-15
Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg The Netherlands Portugal Spain Sweden UK EU15
1961–2000
1961–1969
1970–1979
1980–1989
1990–1999
2000
3.2 3.1 2.7 3.4 3.3 2.8 4.1 4.8 3.3 4.0 3.3 4.3 4.1 2.6 2.4 3.4
4.5 4.8 4.8 4.5 5.6 4.4 8.5 4.4 5.8 3.8 5.0 6.4 7.7 4.4 3.0 5.2
4.1 3.6 2.5 3.7 3.7 3.1 5.5 4.7 3.8 2.7 3.4 5.1 3.8 2.5 2.4 3.7
2.1 2.0 1.8 3.6 2.2 1.8 0.8 3.1 2.4 4.4 1.9 3.2 2.8 2.1 2.4 2.4
2.2 2.0 2.1 1.6 1.7 2.2 1.9 6.6 1.4 5.0 2.9 2.6 2.4 1.4 1.9 2.5
4.1 3.5 2.0 3.6 3.7 2.9 3.9 7.5 2.7 2.7 5.6 3.2 3.8 4.9 3.9 3.9
Source: European Commission’s European Economy.
the average defence burden was 2.8% of GDP (maximum 7.4%, minimum 0.7%, S.D. 1.5). Over time, for each member state, both time series present often significant deviations from the EU mean. In the case of growth rates most countries’ growth performance has generally tended to oscillate above or below the EU mean depending on the time period examined with no strong consistent pattern emerging. For example, for the whole period in question, the average EU growth rate Table 2 Military expenditure as a share of GDP in the European Union-15
Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg The Netherlands Portugal Spain Sweden UK EU15
1961–2000
1961–1969
1970–1979
1980–1989
1990–1999
2000
1.1 2.7 2.2 1.7 4 3.1 5.2 1.3 2.5 1 3.1 4.2 2 3 4.6 2.8
1.2 3.2 2.8 1.8 5.3 4.3 4.1 1.3 3.2 1.2 4.1 6.7 2 4 5.8 3.4
1.1 3 2.3 1.5 3.9 3.4 5.7 1.5 2.6 0.9 3.3 5.4 1.9 3.4 4.8 3.0
1.2 3 2.3 1.9 4 3.2 6.2 1.5 2.3 1.1 3.1 3.2 2.8 2.7 4.9 2.9
0.9 1.7 1.8 1.6 3.2 1.9 4.5 1 2 0.8 2.1 2.5 1.5 2.2 3.2 2.1
0.8 1.4 1.5 1.3 2.6 1.5 4.9 0.7 2.1 0.7 1.6 2.1 1.3 2.1 2.5 1.8
Source: SIPRI Yearbooks, various issues.
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was 3.4% with the UK rate of 2.4% being the lowest among the 15 members while the highest is that of Ireland — an average of 4.8% for 1961–2000. Similar variations for different countries are recorded in the various sub-periods (1961–1969, 1970–1979, 1980–1989, 1990–1999) with no consistent pattern emerging in terms of under/over-performers as far as growth rates are concerned. However, more consistent is the pattern that emerges when it comes to the deviations in terms of defence spending as share of GDP (Table 2). In the case of countries such as Greece, France, the UK and Portugal their defence burden has consistently been higher than the EU average whereas in cases such as Luxemburg, Finland, Ireland and Austria their defence burden has consistently been lower than the EU average. For example, during 1961–2000 the average defence burdens of the former were 5.2%, 4%, 4.6% and 4.2%, respectively while for the latter they were 1%, 1.7%, 1.3%, 1.1% and 2.2%, respectively. For the same period the average for the whole EU was 2.8%. More or less, the same picture emerges if one looks at the different sub-periods. To a large extent, this reflects different external security needs and significant differences in defence policy both during the bipolar as well post-bipolar eras as studies that have estimated demand for military expenditure models for various countries indicate (see inter allia Hartley & Sandler, 1990; Kollias & Paleologou, 2003; Murdoch & Sandler, 1990; Sandler & Hartley, 1995; Smith, 1990). For example, the UK and France maintain a nuclear component, are members of the UN Security Council and play a global role in international politics; Greece has long faced country-specific defence priorities vis-à-vis Turkey, while countries such as Ireland, Sweden, Finland and Austria are not members of the NATO alliance and in principle were not directly influenced by the East-west arms race during the Cold War. Significant differences also emerge if one looks at the indigenous defence production capabilities (see inter allia Barros, 2002; Hartley, 1998; Inbar & Zilberfarb, 1998; Kollias & Rafailidis, 2003; Maneval, 1994; Mollas-Gallart, 1997; Struys, 2002). Countries such as France, Germany and the UK and perhaps to a lesser extent Sweden, Italy, Spain, Austria and the Netherlands have a developed domestic defence industry with a strong export orientation while other countries such as a Greece and Portugal have comparatively little indigenous defence production capabilities with very weak defence industrial sectors thus relying on imports for their military hardware. Military spending can affect the economy through various channels. On the one hand, such expenditure may stimulate growth through Keynesian-type aggregate demand stimulation. Increased demand induced by higher military spending leads to increased utilisation of capital stock and higher employment. Increased capital stock utilisation may lead to increases in the profit rate which in turn may lead to induce higher investment thus generating short-run multiplier effects and higher growth rates. On the other hand, such spending has been shown to have growth retarding effects mainly through investment crowding-out, inflationary pressures and the reduction of available public funds for spending and investment in other, potentially more productive and growth inducing, areas. All these channels through which military spending can influence, promote of retard, growth, assume that such
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expenditures are causally prior to economic growth. However, it is possible that growth may be causally prior to defence spending in the sense that a country with high growth rates may allocate more resources to defence. In this context, it is also possible that countries with higher growth rates may divert resources from defence to more productive uses. A number of policy implications can arise from understanding the direction of causal ordering between growth and defence expenditure. If, for example, the direction of causality is found to be from growth to military spending then this could be interpreted as an indication that countries are trying to protect their wealth and people from external threats; advance their interests in the international or peripheral scene; strengthen their role and assert their identity in world affairs; pursue their strategic objectives. Security threats can of course emanate from other nations or group of nations or indeed they may take the form of asymmetrical threats. In fact, recent years have seen the rise of new security challenges such as terrorism and major crises, both international (Iraq, Rwanda, Afghanistan) as well as on the European continent (Bosnia, Kosovo), requiring the use of military power. Such crises have confirmed US military superiority and strengthened the case for the need to build an EU military capability in order to support its role in the world scene protecting and advancing its interests and its security and strategic objectives. The emergence of such threats has resulted in pressures for changes in defence structure and equipment as well as increased security spending in a number of countries and in particular the US while similar trends may appear among EU members after years of reductions in defence budgets following the end of the Cold War. Insofar as an EU defence capability is seen as a pure public good then an issue that arises is that of the allocation of the costs that this entails among the EU members that will participate and benefit from CESDP. If on the other hand, the direction of causality is from military expenditure to growth this may be indicating the presence of aggregate demand and employment effects that to a large extent may be attributed to domestic arms production and spin-offs from military R&D. Again, in the context of a CESDP, issues that arise concern the procurement policy for military hardware, the restructuring of the European defence industrial base, the creation of a single EU defence market, the division of labour among the EU defence industries and European defence R&D policy (Fontanel & Smith, 1991; Guyot & Vranceanu, 2001; Hartley, 2003). In particular, given the need for new military hardware to undertake Petersberg type missions, enhance and upgrade existing EU military capability is such areas as strategic airlift, satellite surveillance, smart weapons the role of the existing EU defence industrial base is of cardinal importance.
3. Methodology and empirical findings From the preceding discussion, four possible causal relationships may be established empirically: uni-directional causality from growth to military spending
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and vice-versa, bi-directional causality and finally no causality. The annual data for the 15 EU members used in the tests conducted, as described in the following part, were obtained from the European Commission’s European Economy, directorate-general for economic and financial affairs in the case of GDP growth rates (GDPt ) and from SIPRI Yearbooks (various issues) in the case of military expenditure as a share of GDP (milext ). As already mentioned, the period covers the years 1961–2000 and a battery of different causality tests are used. We began our analysis with F tests that were used to examine on a preliminary level the presence and direction of causality between the two variables. Furthermore, the standard Granger causality test, i.e., Eqs. (1) and (2), were estimated for all 15 countries. Milext = a0 + a1 GDPt−i + a2 milext−i + vt
(1)
GDPt = b0 + b1 milext−i + b2 GDPt−i + t
(2)
Overall, from the results of our preliminary causality analysis (that for reasons of brevity are not reported here) it appeared that the causal ordering between the two time series involved could not be generalised across all 15 EU members. On the basis of the F test for causality, presence of bi-directional causality was evident in the case of three countries — Austria, Belgium and Portugal, no causal ordering was present in Denmark, France and Luxembourg while for the rest uni-directional causality running from growth to military spending was detected. The F test did not yield evidence of causality running from defence spending to growth. On the basis of the Granger causality test it appeared that, out of the 15 countries uni-directional causality from growth to military expenditure was present in the case of seven countries — Germany, Greece, Italy, the Netherlands, Spain, Sweden and the UK; bi-directional in the case of Austria and Luxembourg and no causality was detected from estimating (1) and (2) in the case of six countries — Belgium, Denmark, Finland, France, Ireland and Portugal. Again no causal ordering from military spending to growth could be detected. These results differed to those of the F test in the case of Belgium Finland, Ireland, Luxembourg and Portugal. However, as Dakurah et al. (2001) point out the weakness associated with tests such as the above is that the issue of spurious regression in the presence of non-stationary variables is ignored. To allow for this, the causality issue is tested using the co-integration and error correction methodology (Dakurah et al., 2001; Dunne et al., 2001; Kollias & Makrydakis, 2000). In this context, the first step is to examine the timeseries properties of the two variables. This is done via the estimation of the ADF and Phillips–Perron unit root tests (Table 3). If both of these variables are found to be I(1), we then look for a co-integrating relationship between them. If there is no co-integrating relationship, we make the variables stationary by first differencing and test for non-causality in a VAR context. Following the examination of the time series properties of the two variables we then proceed by applying various causality tests. If the variables of interest were found to be co-integrated then we estimated the VECM given by Eqs. (3) and (4).
Table 3 Results of unit root tests Augmented Dickey–Fuller (ADF)
Phillips–Perron (PP) Levels ( )
First difference ( )
Levels ( )
First difference ( )
Levels ( )
First difference ( )
Austria Milex Growth
−1.287 −2.879
−5.660∗ −8.777∗
−2.786 −3.888∗
−5.631∗ −8.649∗
−1.545 −4.778∗
−6.400∗ −12.507∗
−3.087 −5.597∗
−6.490∗ −12.301
Belgium Milex Growth
0.098 −2.741
−3.803∗ −6.878∗
−0.938 −3.218
−3.918∗ −6.816∗
0.241 −2.741
−5.586∗ −14.637
−1.092 −5.797∗
−5.615∗ −14.677∗
Denmark Milex Growth
−0.989 −4.465∗
−6.739∗ −9.088∗
−2.657 −5.028∗
−6.638∗ −8.931∗
−0.176 −5.507∗
−7.550∗ −12.747∗
−3.314 −6.035∗
−7.370∗ −8.930∗
Finland Milex Growth
−2.280 −3.867∗
−4.594∗ −6.127∗
−2.241 −4.002∗
−4.524∗ −6.064∗
−2.601 −3.614∗
−9.181∗ −6.348∗
−2.649 −3.534
−9.024∗ −6.232∗
France Milex Growth
−2.304 −2.393
−3.512∗ −6.161∗
−2.806 −2.689
−3.326 −6.300∗
−2.516 −2.777
−4.450∗ −4.716∗
−2.685 −3.887∗
−4.633∗ −9.937∗
Germany Milex Growth
−0.938 −4.322∗
−6.594∗ −7.403∗
−2.227 −4.924∗
−6.444∗ −7.295∗
−0.131 −4.131∗
−7.254∗ −8.489∗
−3.052 −4.465∗
−7.030∗ −8.362∗
Greece Milex Growth
−1.809 −2.424
−4.429∗ −7.688∗
−1.682 −3.490
−4.496∗ −7.659∗
−1.722 −4.621∗
−5.528∗ −13.038∗
−1.602 −5.558∗
−5.526∗ −12.793∗
Ireland Milex Growth
−0.572 −2.940
−3.698∗ −6.624∗
−1.070 −3.305
−3.940∗ −6.577∗
−0.633 −3.649∗
−5.884∗ −9.238∗
−1.126 −4.038∗
−6.025∗ −9.130∗
559
First difference ( )
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Levels ( )
560
Table 3 (Continued ) Augmented Dickey–Fuller (ADF)
Phillips–Perron (PP)
First difference ( )
Levels ( )
First difference ( )
Levels ( )
First difference ( )
Levels ( )
First difference ( )
Italy Milex Growth
−1.512 −3.381∗
−4.694∗ −7.315∗
−2.009 −5.003∗
−4.837∗ −7.257∗
−1.205 −4.294∗
−5.949∗ −10.338∗
−2.028 −5.660∗
−6.018∗ −10.227∗
Luxembourg Milex Growth
−1.820 −4.142∗
−4.072∗ −6.696∗
−2.196 −4.415∗
−4.012∗ −6.597∗
−1.524 −5.171∗
−5.902∗ −12.758∗
−2.170 −5.441∗
−5.807∗ −12.521∗
The Netherlands Milex −0.681 Growth −2.735
−4.206∗ −6.572∗
−1.659 −2.993
−4.151∗ −6.486∗
−0.443 −3.412∗
−7.155∗ −9.375∗
−1.784 −3.746∗
−7.067∗ −9.283∗
Portugal Milex Growth
−0.935 −3.671∗
−5.119∗ −7.780∗
−1.880 −4.455∗
−5.044∗ −7.688∗
−0.725 −4.027∗
−7.221∗ −8.642∗
−2.416 −4.603∗
−7.117∗ −8.503∗
Spain Milex Growth
−1.053 −3.613∗
−2.525 −6.127∗
−1.149 −2.774
−2.565 −6.332∗
−1.270 −3.244∗
−5.213∗ −7.435∗
−1.328 −3.170
−5.223∗ −7.594∗
Sweden Milex Growth
−1.053 −3.613∗
−5.475∗ −6.985∗
−3.576∗ −4.008∗
−5.460∗ −7.050∗
−0.735 −3.577∗
−5.601∗ −7.787∗
−3.003 −3.676∗
−5.545∗ −7.836∗
UK Milex Growth
−0.452 −4.896∗
−3.134∗ −6.794∗
−1.502 −4.884∗
−3.099 −6.695∗
−0.187 −4.354∗
−5.361∗ −7.705∗
−1.513 −4.292∗
−5.287∗ −7.564∗
Notes: The critical values for the and the of the ADF test, with 39 observations at 5% significance level is −2.940 without a trend and −3.53 with a trend. The same values hold for the Phillips–Perron tests. ∗ Statistically significant at 5%.
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Levels ( )
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If no long-term co-integrating relationship was established the standard Granger causality test was estimated. Milext = a0 + a1 GDPt−i + a2 milext−i + a3 ECTt−1 + vt
(3)
GDPt = b0 + b1 milext−i + b2 GDPt−i + b3 ECTt−1 + t
(4)
The tests for co-integration, reported in Table 4, show that the variables are co-integrated in the case twelve EU countries except for Belgium, Greece and Ireland. Thus, for the former a VECM model, i.e., (3) and (4), was estimated in order to determine the presence and direction of causality (Table 5). For the latter, the standard Granger causality regressions (1) and (2) were estimated (Table 6). On the basis of the findings reported in Table 5 it would appear that in the case of the causality tests based on the error correction models bi-directional causality was detected in three cases — Austria, Denmark and Luxemburg, and absence of any causal ordering in another three namely France, Finland and Portugal. In six countries causality appeared to run from growth to military spending. These are Germany, Italy, the Netherlands, Spain, Sweden and the UK. In Table 6, in the case of the three countries were no co-integration was established — Belgium, Greece and Ireland no causality was detected in the case of Belgium and Ireland and uni-directional from growth to military spending in the case of Greece. The prevalence of the direction of causality from growth to military expenditure, in seven countries out of the 15 EU members, along with the absence of a reverse causal ordering, i.e., from military expenditure to growth, may be suggesting that the military burden is primarily determined by economic factors rather than geopolitical and security considerations while bi-directionality in three suggests a degree of interdependence between the two as well as the possible presence of Keynesian type aggregate demand effects. It would appear therefore, that several EU governments make defence spending policy decisions based on the state of their economy. To the extend that this observation accurately describes the defence spending policy process vis-à-vis the state of the economy, this implies that the initial increases in the defence budgets required to support a fully independent EU defence pillar depend upon the growth performance of the European Economy(ies) from where the resources to support CESDP will be drawn. In particular, this direction of causality may be indicating that as the economy grows resources are channelled towards increased security and protection and/or that defence needs are catered by governments only when the state of the economy permits the diversion of resources to generally unproductive uses such as defence. If indeed this is the case, then a policy implication is that the increases in the European defence budgets required in order to meet the capital equipment and operational needs of a fully independent EU defence capability depend upon the growth performance of the European Economy(ies). However, it should be noted here that the move towards a common defence will eventually bring costs savings
562
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Table 4 Co-integration results Maximal eigenvalue Austria GDP & milex (VAR lag = 2) Ho: r = 0 33.180∗ Ho: r ≤ 1 6.583 Ho: r = 0 Ho: r ≤ 1
Trace statistic 39.763∗ 6.583
Belgium GDP & milex (VAR lag = 2) Ho: r = 0 14.579 Ho: r ≤ 1 1.801 Ho: r = 0 Ho: r ≤ 1
Trace statistic 16.380 1.801
Denmark GDP & milex (VAR lag = 2) Ho: r = 0 35.986∗ Ho: r ≤ 1 6.737 Ho: r = 0 Ho: r ≤ 1
Trace statistic 42.724∗ 6.737
Finland GDP & milex (VAR lag = 2) Ho: r = 0 19.812∗ Ho: r ≤ 1 5.217 Ho: r = 0 Ho: r ≤ 1
Trace statistic 25.829∗ 5.217
France GDP & milex (VAR lag = 2) Ho: r = 0 24.319∗ Ho: r ≤ 1 5.452 Ho: r = 0 Ho: r ≤ 1
Trace statistic 29.772 5.452
Germany GDP & milex (VAR lag = 2) Ho: r = 0 22.450∗ Ho: r ≤ 1 0.956 Ho: r = 0 Ho: r ≤ 1
Trace statistic 23.407∗ 0.956
Critical value 5%
Critical value 1%
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
19.220 12.390
17.180 10.550
Critical value 5% 25.770 12.390
Critical value 1% 23.080 10.550
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
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Table 4 (Continued ) Maximal eigenvalue Greece GDP & milex (VAR lag = 2) Ho: r = 0 7.845 Ho: r ≤ 1 2.703 Ho: r = 0 Ho: r ≤ 1
Trace statistic 10.549 2.703
Ireland GDP & milex (VAR lag = 2) Ho: r = 0 13.868 Ho: r ≤ 1 0.313 Ho: r = 0 Ho: r ≤ 1
Trace statistic 14.181 0.313
Italy GDP & milex (VAR lag = 2) Ho: r = 0 26.396∗ Ho: r ≤ 1 2.481 Ho: r = 0 Ho: r ≤ 1
Trace statistic 28.877∗ 2.481
Luxembourg GDP & milex (VAR lag = 2) Ho: r = 0 16.829∗ Ho: r ≤ 1 3.448 Ho: r = 0 Ho: r ≤ 1
Trace statistic 20.278∗ 3.448
The Netherlands GDP & milex (VAR lag = 2) Ho: r = 0 21.416∗ Ho: r ≤ 1 3.063 Ho: r = 0 Ho: r ≤ 1
Trace statistic 24.480∗ 3.063
Portugal GDP & milex (VAR lag = 2) Ho: r = 0 20.435∗ Ho: r ≤ 1 2.559 Ho: r = 0 Ho: r ≤ 1
Trace statistic 24.995∗ 2.559
Critical value 5%
Critical value 1%
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
14.880 8.070
12.980 6.500
Critical value 5% 17.860 8.070
Critical value 1% 15.750 6.500
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.230 9.750
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.230 9.750
564
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Table 4 (Continued ) Maximal eigenvalue Spain GDP & milex (VAR lag = 2) Ho: r = 0 21.640∗ Ho: r ≤ 1 2.336 Ho: r = 0 Ho: r ≤ 1
Trace statistic 23.976∗ 2.336
Sweden GDP & milex (VAR lag = 2) Ho: r = 0 20.260∗ Ho: r ≤ 1 6.614 Ho: r = 0 Ho: r ≤ 1
Trace statistic 23.870∗ 6.614
UK GDP & milex (VAR lag = 2) Ho: r = 0 26.367∗ Ho: r ≤ 1 2.696 Ho: r = 0 Ho: r ≤ 1
Trace statistic 29.063∗ 2.696
Critical value 5%
Critical value 1%
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.320 9.750
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.230 9.750
18.330 11.540
16.280 9.750
Critical value 5% 23.830 11.540
Critical value 1% 21.230 9.750
Notes: The tests are based on the critical values obtained from Johansen and Juselius (1990). ∗ Reject the null hypothesis at the 5% significance level.
through the avoidance in duplication, equipment standardisation and harmonisation, economies of scale and perhaps even specialisation among participating nations. In other words, joint forces provide more power than the sum of individual forces potentially at a lower cost (Fontanel & Smith, 1991; Hartley, 2003). As noted above, defence is generally regarded as an unproductive use of scarce resources. Thus, channelling resources to defence may prove a burden on the economy and its performance, slowing down growth which in turn will adversely affect the flow of resources to the defence sector with the concomitant impact on the ambitious objective of a common European defence with full independent capabilities to support operations. Perhaps, the only channel through which a feedback mechanism from defence to the economy can be set in motion is that of the defence industrial sector and military R&D. Both can generate aggregate demand and multiplier effects from increased capacity utilisation and technological advances that spill over to the rest of the economy through the development and production of the hardware and assets a CEDSP would require. This raises the issue of the division of labour in defence production, in the context of the juste-retour principle, and of course the need to further restructure on a pan-European level the defence
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Table 5 Causality tests based on the error correction term Hypothesis tested
t statistic
Austria Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.146∗ 2.225∗ −3.482∗ −3.132∗ −3.944∗ −3.555∗
Denmark Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−0.734 0.633 −2.043∗ −0.887 −0.286 −2.596∗
Finland Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−1.200 −0.217 −1.540 −1.900∗ −0.958 −1.290
France Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
0.828 −0.102 −1.060 −0.519 0.797 −0.732
Germany Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.698∗ 3.316∗ −3.150∗ −1.340 −0.600 −0.950
Italy Sample period: 1960–2000 ␣1 = 0 ␣2 = 0
−1.467∗∗ 3.171∗
Wald statistic
4.607∗ 9.798∗
0.539
0.786
1.415
1.530
0.683
0.269
7.281∗
1.796
2.152∗
566
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Table 5 (Continued ) Hypothesis tested
t statistic
␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.340∗ −0.389 0.840 −1.485
Wald statistic 0.151
Luxembourg Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.784∗ 1.807∗ −2.333∗ −3.045∗ −2.483∗ 2.548∗
7.555∗
The Netherlands Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.310∗ −2.554∗ −2.404∗ −0.251 −0.887 −0.117
5.321∗
Portugal Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−0.593 −0.537 −1.112 0.398 0.231 −0.073
1.030
Spain Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−4.917∗ 5.961∗ −5.922∗ −2.541∗ 1.054 −0.147
24.124∗
Sweden Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−2.466∗ 1.008 −3.191∗ −0.100 0.504 −1.115
4.150∗
9.272∗
0.060
0.158
6.460∗
0.999
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Table 5 (Continued ) Hypothesis tested
t statistic
Wald statistic
UK Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 ␣3 = 0 b1 = 0 b2 = 0 b3 = 0
−3.675∗ 2.217∗ −4.687∗ −2.266∗ 1.604∗∗ −0.401
13.510∗
∗ ∗∗
5.139∗
Statistically significant at 5%. Statistically significant at 10%.
industry improving its efficiency and competitiveness and creating a single European market for defence equipment. Associated with this, from the demand side, is the need to create an EU armaments and procurement agency through for example the upgrading of OCCAR (acronym for: Organisation Conjointe de Cooperation en matiere d’ Armement) that has been operating since 1998 with the participation of France, Germany, UK and Italy (Guyot & Vranceanu, 2001; Hartley, 2003). Finally, pooling of resources is also necessary when it comes to defence related R&D given the high costs associated with the development of new weapons systems. Table 6 Granger causality tests Hypothesis tested
t statistic
Wald statistic
Belgium Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 b1 = 0 b2 = 0
−0.778 0.813 −1.381 −4.463∗
0.605
Greece Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 b1 = 0 b2 = 0
−2.479∗ −4.259∗ −1.354 1.073
Ireland Sample period: 1960–2000 ␣1 = 0 ␣2 = 0 b1 = 0 b2 = 0
−1.281 −0.329 −1.290 −1.812∗∗
∗ ∗∗
Statistically significant at 5%. Statistically significant at 10%.
1.907
6.150∗ 1.835
1.641 1.467
568
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4. Concluding remarks The quest for a common European foreign policy and indeed a security and defence identity dates back to the 1950s with the aborted attempt to establish a European Defence Community that eventually led to the further strengthening of NATO as the main vehicle and institution in the foreign and security policies of all EEC members. The recent moves towards a CEDSP raise a plethora of important policy issues that go beyond the political and strategic aspects associated with the potential creation of a common defence and perhaps a European defence union. The fruition of this ambitious objective rests, among other things, on whether commonality of national interests and strategic objectives exists among the individual states since defence is closely associated with national sovereignty. Nevertheless, the move towards a CEDSP has a significant economic dimension attached to it. It concerns the relationship between the defence sector and the economy from where the resources to support a common defence policy will be drawn. In this context, this paper set out to examine the relationship between defence expenditure and growth among the 15 members of the EU. Although the results did not reveal a uniformity among the 15 countries, the apparent prevalence of the direction of causality from growth to military expenditure may be an indication that an important number of governments in the EU make defence spending policy decisions based on the state of their economy. Of course, this observation must be tempered by the fact that in five countries no causality was established while in another three bi-directional causality was detected. On the basis of this, and perhaps going beyond our findings, it was argued here that an important policy issue arising in the effort to build a CEDSP is that of the role of the European defence industrial base. Although eventually there are potential efficiency gains in a common defence, in the short to medium term increases in defence budgets may be necessary in order to meet the costs associated with CEDSP. Since this depends upon the state and the performance of the European Economy and given the unproductive nature of defence, the only feedback mechanism available is that of the European defence industry. Defence production with its aggregate demand and multiplier effects through the backward and forward linkages can at least partly offset the burden that increased military spending will pose on the economy. Acknowledgments The authors gratefully acknowledge valuable comments and suggestions on an earlier version of the paper. References Abu-Bader, S., & Abu-Qarn, A. (2003). Government expenditures, military spending and economic growth: Causality evidence from Egypt, Israel and Syria. Journal of Policy Modeling, 25, 567–583.
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