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Earning a Peace Dividend in a Crisis Environment: The Greek Case

  • Zacharias G. Bragoudakis and George A. Zombanakis EMAIL logo


In this paper we attempt to assess the extent to which conversion policies involving the shift of resources from the defence to the non-defence sector may entail some form of peace dividend for the economy of Greece. The issue has become one of increasing importance during the past few years during which the demand for growth-supporting policies has been a top priority requirement as an antidote to the recession. The paper employs a VAR model in order to investigate the interactions among the growth rate of gross domestic product and alternative measures of defence expenditure. We find that defence spending and more so, expenditure on defence equipment, under the present circumstances in which the bulk of the procurement represents import payments is not related in any form of Granger-causal relationship with the economic growth of Greece. Our results point to the fact that there can be no possibility of a peace dividend under the circumstances prevailing, unless defence procurement policies shift to an import – substitution strategy.

JEL Classification: F45; H56; H60


The authors would like to thank Professor Keith Hartley for his constructive comments, Anna Giannopoulou – Merika for valuable econometric assistance and an anonymous referee for constructive criticism. The views expressed do not necessarily reflect those of the Bank of Greece.

A Appendix

Figure 6: Robustness checks for non-defence government spending.
Figure 6:

Robustness checks for non-defence government spending.

B Appendix

B.1 Lag-selection criteria, normality of residuals and stability-test results

In the context of this representation, we can also check for causality using the Granger causality test. Knowing that the estimation of VAR[13] models is “sensitive” to the number of time lags (Banerjee et al., 1993) we use the information criteria[14] LR, AIC, and HQ, and Sim’s Test which indicate that the optimal number of lags included in the VAR is k = 3 (see Table 8). All variables of the system are stationary in their first differences in order to avoid a spurious estimation.

The system equations reveal good fitness while the system passes all diagnostic χ2 tests successfully concerning the hypotheses of no serial correlation, of a residual – normal distribution (see Table 9), of absence of Heteroscedasticity and autoregressive conditional Heteroscedasticity. All equations of the VAR indicate that the residuals are Gaussian as the Johansen method presupposes.

Table 8:

Lag order selection criteria.

VAR lag order selection criteria
Exogenous variables: C
Sample: 1980 2014
Included observations: 40
  1. *Indicates lag order selected by the criterion.

  2. LR, sequential modified LR test statistic (each test at 5% level); FPE: Final prediction error; AIC, Akaike information criterion; SC, Schwarz information criterion; HQ, Hannan-Quinn information criterion.

Table 9:

Normality test of VAR (3).

VAR residual normality tests
Orthogonalization: Cholesky (Lutkepohl)
Null hypothesis: residuals are multivariate normal
Sample: 1980 2014
Included observations: 40

The VAR stability test shows that no root lies outside the unit circle and the VAR (3) satisfies the stability condition (see Table 10).

Table 10:

Stability test: roots of characteristic polynomial roots of characteristic polynomial.

Roots of characteristic polynomial
Lag specification: 1 3
0.764236 −0.549033i0.941007
0.764236 + 0.549033i0.941007
0.804237 −0.210150i0.831240
0.804237 + 0.210150i0.831240
−0.471940 −0.604255i0.766715
−0.471940 + 0.604255i0.766715
−0.308042 −0.628608i0.700027
−0.308042 + 0.628608i0.700027
0.020987 −0.656499i0.656834
0.020987 + 0.656499i0.656834
−0.498841 −0.359930i0.615136
−0.498841 + 0.359930i0.615136
  1. No root lies outside the unit circle.

  2. VAR satisfies the stability condition.

After ensuring on the basis of Table 10 that the stability condition is satisfied, we investigate the potential bidirectional causality among variables. According to Granger (1988) and Granger and Lin (1995), it is argued that military expenditure does not cause growth if the following hypothesis cannot be rejected:


In addition it is also possible to check the hypothesis that growth does not affect military expenditure:


The results of the Granger causality test are presented in Table 11.

Table 11:

Granger causality tests.

Pairwise Granger causality tests
Date: 12/28/15 Time: 23:38
Sample: 1980 2014
Lags: 3
Null hypothesisObsF-statisticProb.
DEQDEF does not Granger cause DRGDP400.858830.4721
DRGDP does not Granger cause DEQDEF0.299500.8255
DDEF does not Granger cause DRGDP400.104330.9570
DRGDP does not Granger cause DDEF3.284930.0328
DNDEF does not Granger cause DRGDP405.718800.0029
DRGDP does not Granger cause DNDEF6.947210.0009
  1. *Denotes rejection of the null hypothesis for the 5% significance.

According to the Granger causality-test results depicted in Table 11 defence spending is not related in any form of causality with the GDP growth of Greece. By contrast all forms of public spending incorporated in the non-defence expenditure variable appear to affect the growth of the economy in a Granger-causality form, the inverse, however, not being the case. This finding has been more or less expected given the particularly pronounced role of the public sector on the functions of the economy.

C Appendix

C.1 Robustness check

To check and ensure the robustness of our model and therefore the validity of our results we estimated our model with the suggested optimal length criteria that is two lags, as proposed by Swartz and Hannan-Quinn. Next we re-estimated taking into account the optimal length criteria as suggested by Akaike and ran a likelihood ratio test. Our results were confirmed. Our technical work is available upon request.

VAR lag order selection criteria.

Endogenous variables: Log(RGDP) Log(EQDEF) Log(DEF) Log(NDEF) 
Exogenous variables: C
Included observations: 40
  1. *Indicates lag order selected by the criterion.

  2. LR, sequential modified LR test statistic (each test at 5% level); FPE, final prediction error; AIC, Akaike information criterion; SC, Schwarz information criterion; HQ, Hannan-Quinn information criterion.

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Published Online: 2017-5-1

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