Military Expenditures and Income Inequality among a Panel of OECD Countries in the Post-Cold War Era, 1990–2007

Jeremy C. Graham; Danielle Mueller

doi:10.1515/peps-2018-0016

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Military Expenditures and Income Inequality among a Panel of OECD Countries in the Post-Cold War Era, 1990–2007

Jeremy C. Graham and Danielle Mueller

Published/Copyright: January 8, 2019

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From the journal Peace Economics, Peace Science and Public Policy Volume 25 Issue 1

Abstract

Does military spending exacerbate income inequality? After the Cold War, many developed countries sought to reduce military expenditures in the face of a new security environment without the clear and present threat of large-scale international conflict. The literature has presented mixed evidence on the economic effects of military spending. Moreover, during this era analysts in the OECD have become preoccupied with the economic indicator of income equality. Our study examines the relationship between military expenditures and income inequality. Complementing the established literature on the subject, we find that these two phenomena indeed possess a positive relationship and it is unlikely that this association is due to random chance. Our results are robust to the inclusion of control variables common in the literature. These findings lead us to contemplate the historical and theoretical account of the peace dividend narrative.

Keywords: civilian-military relations; military spending; inequality

Funding source: Division of Graduate Education

Award Identifier / Grant number: DGE-1313583

Funding statement: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program, Division of Graduate Education, Funder Id: 10.13039/100000082, under Grant Number: DGE-1313583. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Appendix

This section contains analyses that may be of interest to readers or reviewers that are not meant to be featured in the study itself. Because they are not simply meant to be inserted into the paper (where an explanation of the results is already present) we will briefly describe each figure or table and where or how it relates to our paper.

Alternative Tables for the Mann Whitney U Test

Inclusion of Portugal as a higher military spender

Portugal’s average military expenditures per GDP over the period of study are 2.07999%. Because it is so close to our average, Table 8 includes Portugal’s membership among these states. As you can see, including Portugal actually lowers the p-value.

Table 8:

Mann-Whitney U Test of Avg. Inequality grouped according to whether Avg. Military Expenditure is Higher than the OECD Avg.

Country	Avg. EHII (1989–2007)	Avg. country mil. exp. > avg. OECD mil. exp.
Australia	36.13	No
Austria	35.55	No
Belgium	38.15	No
Canada	38.02	No
Chile	47.8	Yes
Czech Rep.	28.76	No
Denmark	31.57	No
Estonia	34.89	No
Finland	33.22	No
France	36.74	Yes
Germany	34.73	No
Greece	43.29	Yes
Hungary	38.56	No
Ireland	36.18	No
Israel	43.13	Yes
Italy	36.83	No
Japan	40.05	No
Luxembourg	35.03	No
Mexico	45.39	No
Netherlands	35.94	No
New Zeal.	39.39	No
Norway	35.04	Yes
Poland	36.95	No
Portugal	38.62	Yes
Slovakia	36.16	No
Slovenia	31.74	No
South Korea	37.8	Yes
Spain	39.41	No
Sweden	29.59	No
Switzerland	31.97	No
Turkey	48.18	Yes
UK	36.06	Yes
USA	38.61	Yes

Mann-Whitney U Test Results: Z = −2.468, p = 0.0136.

Above NATO mandated average for Military average

European countries’ propensity to under-contribute to NATO military spending has remained a contentious issue for decades. Framing the presentation of the table in this way, in terms of countries at or exceeding the NATO target agreed to in 2006 and reaffirmed in 2014 of countries to target military spending at 2% of GDP. Although this move is mostly after the dataset, it may be a useful datapoint from a framing perspective (given that an “average of averages” measure may seem to some as convoluted).

Doing so adds Portugal and Australia to the list. As you can see in Table 9, there are no substantive changes from the Mann Whitney U test results presented in the paper.

Table 9:

Mann-Whitney U test of avg. inequality grouped according to whether avg. military expenditure is higher than NATO target.

Country	Avg. EHII (1989–2007)	Avg. country mil. exp. > NATO target
Australia	36.13	Yes
Austria	35.55	No
Belgium	38.15	No
Canada	38.02	No
Chile	47.8	Yes
Czech Rep.	28.76	No
Denmark	31.57	No
Estonia	34.89	No
Finland	33.22	No
France	36.74	Yes
Germany	34.73	No
Greece	43.29	Yes
Hungary	38.56	No
Ireland	36.18	No
Israel	43.13	Yes
Italy	36.83	No
Japan	40.05	No
Luxembourg	35.03	No
Mexico	45.39	No
Netherlands	35.94	No
New Zeal.	39.39	No
Norway	35.04	Yes
Poland	36.95	No
Portugal	38.62	Yes
Slovakia	36.16	No
Slovenia	31.74	No
South Korea	37.8	Yes
Spain	39.41	No
Sweden	29.59	No
Switzerland	31.97	No
Turkey	48.18	Yes
UK	36.06	Yes
USA	38.61	Yes

Mann-Whitney U Test Results: Z = −2.291, p = 0.0219.

Random Effects models w/AR(1) disturbances

Table 10 is essentially applying the model specification of Kentor et al. (2012), intended for scholars who are interested in a similar phenomenon. We opt to present the Driscoll-Kraay SEs due to the close proximity (and therefore virtual certainty of cross-sectional dependence) of our countries, making them a most sensible choice. The Kentor et al. (2012) study is more of a global sample and therefore it may not be as necessary to assume cross-sectional dependence. Nevertheless, we feel re-running our results using a similar model class serves as an appropriate robs.

Table 10:

RE GLS w/AR(1) disturbances

Variables	(1)	(2)	(3)	(4)
	EHII Gini	EHII Gini	EHII Gini	EHII Gini
Military exp. % GDP	0.545**	0.541**	0.589**	0.409
	(0.236)	(0.235)	(0.239)	(0.221)
Net. FDI out. % GDP	0.00311	0.00311	0.00411	0.00246
	(0.00462)	(0.00463)	(0.00468)	(0.00465)
Government consumption	0.0977	0.0998	0.0935	0.111
	(0.0776)	(0.0777)	(0.0784)	(0.0774)
Soc. welfare exp. % GDP	−0.108	−0.109	−0.0988	−0.105
	(0.0555)	(0.0556)	(0.0565)	(0.0573)
GDP growth	−0.0393**	−0.0391**	−0.0363**	−0.0395**
	(0.0181)	(0.0182)	(0.0183)	(0.0183)
Labor force part.	−0.189***	−0.186***	−0.188***	−0.220***
	(0.0396)	(0.0404)	(0.0408)	(0.0386)
Population (millions)	0.00459	0.00424	0.00125	−0.00364
	(0.00804)	(0.00792)	(0.00814)	(0.00714)
GDP per capita (thousands)	0.0128	0.0130	0.00574	−0.00287
	(0.0164)	(0.0164)	(0.0169)	(0.0169)
Trade union density	−0.0507***	−0.0433**	−0.0459**	−0.0544***
	(0.0183)	(0.0210)	(0.0197)	(0.0187)
Taxation % GDP	−0.0552	−0.0537	−0.0491	−0.0599**
	(0.0304)	(0.0305)	(0.0308)	(0.0304)
Debt % GDP	0.0104	0.0104	0.00802	0.00825
	(0.00736)	(0.00737)	(0.00747)	(0.00743)
Imports % GDP	−7.282e + 07	−9.053e + 07	−6.547e + 08	−1.260e + 06
	(1.476e + 08)	(1.485e + 08)	(3.645e + 08)	(1.447e + 08)
Corporatist				0.113
				(1.171)
Liberal				2.528
				(1.309)
Post-communist				−2.960**
				(1.327)
Productivist				−0.126
				(1.917)
SDEM			−1.362
			(1.462)
CDEM			−0.556
			(0.733)
Social democratic		−1.127
		(1.580)
Constant	52.69***	52.34***	53.66***	55.84***
	(2.899)	(2.998)	(3.037)	(3.291)
Observations	402	402	394	402
Number of countries	33	33	32	33
N	402	402	394	402
Overall R-squared	0.552	0.535	0.559	0.663

Standard errors in parentheses. Time dummies not reported.
*** p < 0.01, ** p < 0.05, * p < 0.01.

Prais-Winsten w/correlated PCSEs and panel-specific AR(1)

As an alternative to RE, in Table 11 we use Prais-Winsten regression with panel corrected standard errors (PCSE). If no autocorrelation were specified, this would be an OLS model. We use pairwise selection due to the unbalanced nature of the panel. Although we don’t report the results in this appendix (but do include the code in our replication file), whether we choose to allow the AR(1) coefficients of correlation to vary across panels or not, it does not make a substantive difference. One point we would like to note, regarding possible issue, is the high R2. We witnessed similar R2 even when the AR(1) does not vary. This may deserve further investigation, and at this point we will simply take note of it.

Table 11:

Prais-Winsten regression.

Variables	(1)	(2)	(3)	(4)
	EHII Gini	EHII Gini	EHII Gini	EHII Gini
Military exp. % GDP	0.541***	0.517***	0.602***	0.285**
	(0.133)	(0.142)	(0.128)	(0.133)
Net. FDI out. % GDP	0.00804	0.00815	0.00900**	0.00734
	(0.00454)	(0.00472)	(0.00441)	(0.00487)
Government consumption	−0.00334	−0.0115	−0.0157	0.0203
	(0.0686)	(0.0682)	(0.0672)	(0.0731)
Soc. welfare exp. % GDP	−0.225***	−0.233***	−0.206***	−0.182***
	(0.0386)	(0.0377)	(0.0374)	(0.0548)
GDP growth	−0.0628**	−0.0670**	−0.0580	−0.0545**
	(0.0318)	(0.0332)	(0.0310)	(0.0274)
Labor force part.	−0.260***	−0.275***	−0.256***	−0.308***
	(0.0341)	(0.0344)	(0.0376)	(0.0335)
Population (millions)	−0.00208	−0.00298	−0.00352	−0.00832***
	(0.00253)	(0.00241)	(0.00275)	(0.00270)
GDP per capita (thousands)	0.0328***	0.0342***	0.0347***	0.0244
	(0.0127)	(0.0127)	(0.0129)	(0.0135)
Trade union density	−0.0639***	−0.0912***	−0.0668***	−0.0989***
	(0.0127)	(0.0155)	(0.0133)	(0.0185)
Taxation % GDP	0.0172	0.0167	0.0222	−0.0453
	(0.0367)	(0.0355)	(0.0380)	(0.0339)
Debt % GDP	0.0265***	0.0303***	0.0245***	0.0322***
	(0.00708)	(0.00668)	(0.00705)	(0.00554)
Imports % GDP	−3.347e + 08***	−3.036e + 08***	−5.730e + 08***	−7.252e + 07
	(1.127e + 08)	(1.154e + 08)	(1.971e + 08)	(9.827e + 07)
Corporatist				−2.684**
				(1.151)
Liberal				−0.284
				(0.913)
Post-communist				−4.351***
				(1.152)
Productivist				−4.416***
				(1.310)
CDEM			−1.608
			(1.263)
SDEM			−1.726
			(1.291)
Social democratic		2.136***
		(0.705)
Constant	59.07***	60.89***	60.32***	66.15***
	(2.294)	(2.469)	(2.666)	(3.072)
Observations	402	402	394	402
R-squared	0.960	0.959	0.961	0.966
Number of countries	33	33	32	33
Chi-squared statistic	646.3	751.0	656.7	1114
Chi-squared p-value	0	0	0	0

Panel-Corrected Standard errors in parentheses.
*** p < 0.01, ** p < 0.05, * p < 0.01.

Robust Twostep Estimator “collapse” re-specification w/xtabond2

In Table 12 we present results using our base model of the DPD estimator with the user-created STATA package xtabond, using Windmeijer (2005) corrected standard errors. Despite the fact that using the collapse featuring drastically reduces the number of instruments (38) so that we have likely avoided overfitting, in our opinion, there are a couple reasons to nevertheless remain cautious this estimator. First, the estimator does not calculate the AR(1) statistic. This may not be important since it is not AR(1) violations that cause the mis-specification and the AR(2) statistic is calculated and we do not reject the null hypothesis, which is good for the model. Second, while passing the Sargan test is a good sign, the Hansen test statistic (0.042) yields a p-value of 1.00, which likely indicates inability of detection (though does not necessarily constitute a failure of the test). As we mention in the paper and others have mentioned elsewhere, these models are notoriously sensitive to specification. As with all the inferential models in the paper, we are conservative regarding their individual explanatory power.

Table 12:

System DPD estimation w/collapse feature and twostep.

Variables	(1)
	EHII Gini
EHII Gini (lagged)	0.753**
	(0.306)
Military exp. % GDP	3.180***
	(0.920)
Net. FDI out. % GDP	0.0260***
	(0.00803)
Government consumption	−2.052**
	(0.994)
Soc. welfare exp. % GDP	0.0632
	(0.528)
Debt % GDP	0.0789*
	(0.0447)
MIDS onset	1.033**
	(0.429)
GDP growth	−0.438***
	(0.0986)
Labor force part.	0.187*
	(0.101)
Population (millions)	−0.0646***
	(0.0202)
GDP per capita (thousands)	0.0512
	(0.0606)
Trade union density	0.0771
	(0.0542)
Taxation % GDP	0.00210
	(0.0280)
Imports % GDP	1.868e + 08
	(1.843e + 08)
Constant	26.69***
	(9.161)
Observations	385
Number of countries	33
Chi-squared statistic	104.7
Chi-squared p-value	2.16e−09
AR(1) statistic	.
AR(1) p-value	.
AR(2) statistic	−0.938
AR(2) p-value	0.348
Sargan test statistic	3.895
Hansen test statistic	0.0402

Figure 3 shows the predicted value of Y from the base models – i.e. without political and welfare regime covariates – as they vary with the military spending % GDP variable.

Figure 3:

The predicted value of Y from the base models – i.e. without political and welfare regime covariates – as they vary with the military spending % GDP variable.

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Published Online: 2019-01-08

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Articles in the same Issue

https://doi.org/10.1515/peps-2018-0016

Keywords for this article

civilian-military relations; military spending; inequality