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The Shifting Determinants of DefenseSpending Preferences Between 1980and 2008Sencer Ecera & Nicholas J. Veaseyb

a Department of Economics, Istanbul Technical University,Istanbul, Turkeyb Deutsche Bank, New York, NY, USAPublished online: 09 Dec 2013.

To cite this article: Sencer Ecer & Nicholas J. Veasey (2015) The Shifting Determinants of DefenseSpending Preferences Between 1980 and 2008, Defence and Peace Economics, 26:1, 75-88, DOI:10.1080/10242694.2013.848578

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THE SHIFTING DETERMINANTS OF DEFENSESPENDING PREFERENCES BETWEEN 1980 AND 2008

SENCER ECERa* AND NICHOLAS J. VEASEYb

aDepartment of Economics, Istanbul Technical University, Istanbul, Turkey; bDeutsche Bank, NewYork, NY, USA

(Received 16 October 2012; in final form 20 September 2013)

This paper analyzes defense spending preferences using ordered logit regression analysis of American NationalElection Survey data from 1980 through 2008. Our results indicate that as opposed to having the ideology of isola-tionism, political party identification towards the Republican Party or having economic stakes in defense spendingalways play a significant role in increased preference towards defense spending. Demographic groups such asNative Americans, Hispanics, and retired women, a demographic subgroup, display generally positive preferencestowards defense spending. Somewhat surprisingly, another demographic subgroup, ‘security moms,’ do not show apreference. Our analysis also displays lower (higher) preference in the early 1990s (2000s) for defense spendingcompared to the year 2008.

Keywords: Defense spending; Public good

JEL Codes: H53, H41

1. INTRODUCTION

Public opinion on defense spending has changed widely through the years and it has beenshown to have a statistically significant impact on policy outcomes.1 Researchers spent asignificant amount of time studying the determinants of public opinion on defense spendingat the end of the Cold War. While many of these determinants have not changed, the SovietUnion is no longer a major factor in US defense spending preferences – as it was duringthe Cold War. Additionally, in the aftermath of 11 September 2001, the world has enteredinto a new security environment. Hence, public opinion on defense spending has likelychanged since the end of the Cold War and deserves to be revisited.

We use a variation of several modeling frameworks built throughout numerous years ofacademic research on defense spending preferences and we incorporate recent data from theAmerican National Election Studies (ANES) cumulative data file (CDF) along withthe ANES 2008 panel data that was released in 2009. Some academics such as Bartels(1994) concluded that politics had become less of a determining factor on defensespending preferences since the end of the cold war and that ‘willingness to use force’ and

*Corresponding author. E-mail: ecer@itu.edu.tr1Bartels, 1994; Higgs and Kilduff, 1993; Hartley and Russett 1992.

© 2013 Taylor & Francis

Defence and Peace Economics, 2015Vol. 26, No. 1, 75–88, http://dx.doi.org/10.1080/10242694.2013.848578

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‘isolationism’ variables had become the new driving force behind defense spendingpreferences.

2. LITERATURE REVIEW

This paper focuses on public opinion as a main driver of the amount spent on defense, ourliterature review focuses on four strands of arguments: (a) The Political Economy ofMilitary Spending in the USA (b) The American Public’s Defense Spending Preferences inthe Post-Cold War Era, (c) Public Opinion as the predictor of US Defense Spending, andconstituency Opinion and Congressional Policy-Making: We discuss this literature in detailbelow.2

2.1. American Public’s Defense Spending Preferences in the Post-Cold War Era

Bartel (1991) uses public opinion data from the ANES to ascertain the determinants ofdefense spending preferences immediately following the dissolution of the Soviet Union.His results show that ‘defense spending preferences were determined more by a predispo-sition to favor or oppose the use of force in the international arena than by either generalpolitical ideology or attitudes toward the Soviet Union per se.’3 In other words, ‘willing-ness to use force’ along with whether or not an individual considers him or herself to bean ‘isolationist’ was the driving factor in determining an individual’s defense spendingpreference.

Another finding of the Bartels’ paper is that informed voters were quicker to change theirdefense spending preferences following the end of the Cold War than uninformed voters.While attempting to control for the amount of money individuals stand to gain from Depart-ment of Defense budgetary spending in their state, Bartels’ creates an ‘Economic Stake’variable (explained in depth in the Data Section of the current paper), which we also use inour own model. Bartels’ paper pooled semi-annual data from the period 1980–1992 toascertain the determinants of American defense spending preferences and he controls forindividual years with a dummy variable. His model is presented below:

Defense Spending Preferences ¼ b0 þ b1 Conservative ideology

þ b2 Toughness toward Russia ð1984Þþ b3 Willingness to use force

þ b4 Isolationism ð1980� 88Þ þ Economic Stake:

We will now turn to other public opinion polling and defense spending preference issuesthat must be addressed in order to construct our model. The Political Economy of MilitarySpending in the USA – a collection of essays compiled and edited by Alex Mintz –provides a sound background on issues surrounding defense spending.

2It is important to note that we are analyzing defense spending preferences, which is only a factor that effectsdemand. An analysis of defense spending, on the other hand, would require analysis supply and demand factors.See Ali (2012).

3Bartels, 485.

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2.2. Political Economy of Military Spending in the USA

Stoll (1992) takes on the critique that American public opinion on defense spending is‘fickle, changeable and susceptible to wide fluctuations without any underlying rational.’While leaving open the possibility of public opinion being changed by substantial events,Stoll points out that defense spending preferences have held relatively steady throughouttime with extreme opinions on both sides balancing each other out. Additionally, he showsthat any form of strong public consensus on defense spending has had a noticeable impacton the budget process. For his analysis of the proportion of public that feel current defensespending is too little, Stoll uses the following model:

Defense Spending too little

¼ b0 þ b1 Number of visible uses of force involving USSR during war ðper yearÞþ b2 Toughness toward Russia ð1984Þ þ b3 Previous defense spending

þ b4 Previous level of support for increased defense spending

þ b5 Impact of the Reagan administration:

Prior year defense spending and both visible uses of force variables had a statisticallysignificant effect. His results showed that a small number of factors had a large impact onpublic opinion of defense spending and that these factors make it difficult to sustain aperpetual rise in defense spending.4The importance of public opinion on defense spendingis also stressed by Russett, Hartley, and Murray (1994). The authors claim, ‘public opinionis the most substantively important influence on the budget that remains after the ColdWar.’5 Their model looks at the impact of public opinion, the amount of deficit spending,and the declining threat of the Soviet Union. Additionally, the authors find that while publicopinion affects defense spending preferences, the opposite is not true; as defense spendingincreases or decreases, public opinion does not closely track those changes. This finding isimportant for our paper because reverse causalities affect the strength of econometric mod-eling. The direction of causal effect between public opinion and defense spending is alsoevaluated by Higgs and Kilduff (1993).

In addition, Higgs and Kilduff constructed a variable called the ‘Opinion Balance’ bysubtracting the percentage of those who favor less defense spending from the percentage ofthose who favor more defense spending. Using a variable called the residuum, to controlfor the percentage of people who wanted the status quo defense spending, they find that the‘Opinion Balance’ is an accurate predictor of US defense spending. Higgs and Kilduff alsoused a Granger test to evaluate causality between growth in US defense spending outlaysand public opinion on defense spending. They concluded that public opinion – at a fairlyhigh level of confidence – did cause outlay growth, but that the reverse was not true.

Torres-Reyna and Shapiro (2002), ‘The Polls – Trends: Defense and the Military,’ givesthe reader an in depth look at defense spending preferences from the 1960s onward. Thepaper identifies and explains major shifts in defense spending preferences including the lowpoint at the end of the Vietnam War through the high points of the Reagan administration,the subsequent pullback at the end of the cold war and the 31-point swing in defense spend-ing preferences in the wake of 11 September 2001. Torres-Reyna and Shapiro do notattempt to explain defense spending preferences with regression analysis, but it doesinvestigate budget trade-offs for defense spending. Not surprisingly, when the USA is not

4Mintz, 1992.5Russett et al. 1994.

THE SHIFTING DETERMINANTS OF DEFENSE SPENDING PREFERENCES 77

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involved in armed conflict overseas, growing majorities approve cutting defense spendingto reduce the budget deficit. While many Americans benefit from defense spending, thebudget deficit is never far from the mind of most Americans.

Ladd (2007) uses panel data from 2000 to 2002 ANES survey to examine how changesin presidential approval are related to defense policy predispositions and prior politicalawareness. His results show that people with ‘different levels of political awareness willrespond to dramatic messages in distinct ways.’ While Ladd makes the case that politicalparty identification became less of a determining factor in evaluations of President Bushbetween 2000 and 2002, he mentions that political party identification should be includedwhen evaluating defense spending preferences due to the possibility of omitted variablebias. Moreover, Hurwitz and Peffley (1987) argue for including political party identificationin models that examine defense spending preferences. Therefore, although Bartels arguesthat politics will play less of a role in defense spending preferences, we have decided toinclude political party identification in our model.

The academic literature reviewed above guided our thinking when developing a modelfor this paper. Bartels’ model served as our starting point, but his model was designed toevaluate changes in defense spending preferences following the Cold War and includes acontrol variable for the influence Russia had on US defense spending. His paper also pointstowards the declining influence of political party identification on defense spendingpreferences. Ladd’s paper indicates that political party identification does, in fact, have aninfluence on defense spending preferences and he argues for the inclusion of a controlvariable.

3. THE DATA

The CDF taken from the ANES website is a compilation of time series data from the Pre-/Post-Election Study in presidential election years and the Post-Election Study in midtermyears. ‘The CDF is pooled cross- section studies: any respondent for a particular study whois strictly “panel” or “supplement” has been deleted from the Cumulative File.’ For thepurposes of this paper, we have used data from the CDF for the years 1980–2004. Thesampling is random for these years. We went through the process of recoding all the 2008main variables of interest as well as the wealth of demographic and socio-economic datadescribed below. In certain instances, the 2008 ANES question phrasing was different thanthe CDF. In other instances, the list of possible answers from which the respondent couldchoose was different than the list of answers used in the CDF. In all cases, where there werediscrepancies between the 2008 data and the CDF.

Public opinion polling, of course, has its inevitable downfalls. Even though several ofour main variables of interest were asked during all of the years in our study, the surveyquestion placement varied from year to year and could have had an effect on the overallresults. While most respondents pay close attention in the beginning of a survey, it isconceivable that their minds could wonder as the session goes on. Additionally, the ANESsurvey was conducted in person in certain years and via telephone for portions of otheryears. While question placement and type of interview method concerns could be cause forconcern, prior research using the ANES data has concluded that data sensitivity for thesetwo issues does not pose a significant bias.6

6Ladd, 2007.

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3.1. Defense Spending Preferences Variable

The Defense Spending Preferences Variable is constructed from a single question in theANES CDF and two different questions in the ANES 2008 survey. The data ranges from1980 through 2008 and the surveys were taken on presidential election years in the 2000sand congressional election years (every two years) in the 1980s and 1990s (except in1998). In total, 19,043 observations were taken for the defense spending preferencesvariable through the years in our study. The respondent was then given a seven-point scaleto choose from greatly decreased to greatly decreased of defense spending for the years1980, 1982, 1984, 1986, 1988, 1990, 1992, 1994, 1996, 2000, and 2004. In 2008, 963 ofthe respondents were randomly assigned to similar defense spending preference questions.However, the remaining 1120 respondents were given the ‘new’ question and responses tochoose from which are listed below:

Do you think that government should spend more on national defense, less on national defense, or aboutthe same on national defense now?

For the new survey, we recoded the 1–7 defense spending preference scale to match thevariable in the CDF and the old version of the defense spending preferences survey (1 –greatly decrease and 7 – greatly increase). From 1980 through 2008, the responses vary agreat deal. Figure 1 (below) shows the average response on the seven-point scale (respon-dents who have indicated 9 are dropped) and the standard deviation per year. The meanresponse starts high and slowly falls (with one exception) until it hits a low in 1992. Thestandard deviation is relatively high which speaks to the diverse public opinions on levelsof defense spending. In the three years, where the standard deviation is lowest – indicatingthe least disagreement on defense spending preferences – we see a variety of defense spend-ing preferences (also see Table I for US defense spending). First, the 1980 desire forincreased spending preferences swept Reagan into office on a campaign platform ofincreased military spending. Second, in 2000, Bush was elected after 8 years of DemocraticPresidential control that saw the defense budget shrink in comparison with other domesticprograms. Third, in 2008, the defense spending preferences dropped sharply as Americansbecame disenfranchised with being involved in wars in Iraq and Afghanistan. Obama waselected on promises of ending the war in Iraq and focusing on domestic and internationalprograms other than defense (Figure 1).

3.2. Isolationist Variable

The Isolationist variable question has changed throughout the years covered by the ANESCDF. In all years, the statement posed to the respondent was:

This country would be better off if we just stayed home and did not concern ourselves with problems inother parts of the world.

The respondent was then given the scale: 1 – Agree, 2 – Disagree, 9 – DK; depends; notsure; no opinion, can’t say; refused to say.

We recoded all those who disagreed with this statement – non-isolationists – as zero inorder to create a dummy variable to represent the isolationists. All respondents whoanswered 9 were recoded as missing for the purposes of this study and prior research usingthe ANES has determined that potential direction bias does not exist for the variations ofthe isolationist variable (Figure 2).

THE SHIFTING DETERMINANTS OF DEFENSE SPENDING PREFERENCES 79

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TABLE I US spending on national defense

Fiscal year Defense spending ($ in billions) As % of outlays As % of GDP President

1980 $134.0 22.7 4.9 Reagan

1981 157.5 23.2 5.2 Reagan

1982 185.3 24.8 5.7 Reagan

1983 209.9 26.0 6.1 Reagan

1984 227.4 26.7 5.9 Reagan

1985 252.7 26.7 6.1 Reagan

1986 273.4 27.6 6.2 Reagan

1987 282.0 28.1 6.1 Reagan

1988 290.4 27.3 5.8 Reagan

1989 303.6 26.5 5.6 H.W. Bush

1990 299.3 23.9 5.2 H.W. Bush

1991 273.3 20.6 4.6 H.W. Bush

1992 298.4 21.6 4.8 H.W. Bush

1993 291.1 20.7 4.4 Clinton

1994 281.6 19.3 4.0 Clinton

1995 272.1 17.9 3.7 Clinton

1996 265.8 17.0 3.4 Clinton

1997 270.5 16.9 3.3 Clinton

1998 268.2 16.2 3.1 Clinton

1999 274.8 16.1 3.0 Clinton

2000 294.4 16.5 3.0 Clinton

2001 304.8 16.4 3.0 Bush

2002 348.5 17.3 3.3 Bush

2003 404.8 18.7 3.7 Bush

2004 455.8 19.9 3.9 Bush

2005 495.3 20.0 4.0 Bush

2006 521.8 19.7 3.9 Bush

2007 552.6 20.3 4.0 Bush

2008 607.3 20.4 4.2 Bush

1OMB Historical Tables, Table 3.1: Outlays by superfunction and function: 1940–2013.2Budget of the United States Government, Fiscal year 2009.

FIGURE 1 Average defense spending preferences from 1980 to 2008

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3.3. Scale Party Identification Variable

The seven-point scale party identification variable was asked in each year from 1952 to thepresent 2008 in the ANES data. The respondent was the placed on the following scale:

1 – Strong Democrat; 2 – Weak Democrat; 3 – Independent - Democrat; 4 – Independent –Independent; 5 – Independent – Republican; 6 – Weak Republican; 7 – Strong Republican;and 9 – Apolitical (1966 only: and DK)

In all years, if a respondent answered with the mention of a minor party andthen responded ‘neither,’ DK or NA when asked if s/he felt closer to either major party,then s/he has been coded ‘other,’ which was subsequently coded as missing for thepurposes of this study.

In 2008, the respondents were asked two separate questions. The first question asked therespondent to identify whether they were a Republican, Democrat, Independent, or did notknow / care. The second question asked the respondent to indicate the strength of theirpolitical identification. Since the 2008 survey used two separate questions, we had to createa new variable that would include both political identification and strength of political partyidentification. This new variable then had to be recoded to match the CDF.

3.4. Economic Stake Variable

The economic stake variable is included to control for the amount an individual stands togain by having more defense spending in his or her own state. Most likely, a respondentwill have a higher opinion of defense spending if they see federal tax dollars going towardscreating jobs within their home state. The variable is based off the economic stake variableused in the Bartels (1994) paper, but with updated figures for recent years.7

To create the variable, we used the yearly Almanac of American Politics to pull the shareof the federal tax burden for the individual states as well as the share of federal outlays forthe Department of Defense (DOD) per individual state. We then divided the national DODoutlays by the total Federal outlays and arrived at the percentage of taxes spent on Defense.By multiplying this percentage by the amount each state paid in Federal taxes, we were ableto approximate the DOD tax burden per state. Finally, by subtracting the state’s DOD taxburden from the state’s share of the Federal outlay for DOD and dividing by the state’s

FIGURE 2 Average proportion of isolationists from 1980 to 2008

7Bartels, 1994.

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population, we were able to derive the economic benefit variable. The Almanac ofAmerican Politics stopped reporting state level Federal Expenditures for Defense in 1992,so the following year’s data was aggregated from The Tax Foundation and USGovernment-Spending.org. See Appendix 1 and Tables A1 and A2 for further explanation of thisvariable.

4. THE MODEL

Having seen that public opinion has been a strong driver of defense spending and evaluatedseveral models for defense spending preferences, we have constructed our model to includemain variables of interest and, following the discussions in Bartels (1994), a group ofdemographic variables to control for the impact of various other demographic andsocio-economic factors such as age, race, education, marital status, gender, and employmentstatus. The model also includes a group of dummy variables to control for the individualyear of the study as well as two interaction variables of interest:

Defense Spending Preferences = β0 + β1 Isolationist + β2 Scale Political Party Identifica-tion + β3 Economic Stake + β4 Married + β5 Female + β6 Age + β7 Education + β8 Unem-ployed + β9 Retired + β10 Not Working8 + β11 Black + β12 Asian + β13 NativeAmerican + β14 Hispanic + β15 Other Race9 + β16 Dummy Variables to Control for Year(1980–2004 with 2008 as the baseline) + β17 (Married × Female = Security moms) + β18Retired Women.

Based on the literature to date, we expect the Isolationist dummy variable to have a strongnegative correlation with defense spending preferences. In addition, we expect a strongpositive correlation between defense spending preferences and scale political party identifica-tion – a high score on the political party identification is ‘very Republican.’ We also expectthe economic stake variable to have a positive correlation. Finally, previous literature (e.g.Bartels, 1994) has shown that an individual’s education level will have a strong negativecorrelation with defense spending preferences and that being female has a negativecorrelation with the dependent variable. Based on public discussions on ‘security moms,’ wealso expect security moms to have a positive effect on defense spending preferences.10

5. THE RESULTS

The ordered logit on 7 outcomes yields for each determinant a marginal effect on each ofthe probabilities of outcomes. The variables Unemployment, Notworking, Black, Asian,

8‘The “notworking” dummy variable includes homemakers and students, while the unemployed variable coversthose who have been laid off that are seeking work as well as those who are not seeking work.’

9The base race is white and OtherRace is all other than White and those that are explicitly specified in themodel.

10USA Today Editorial, ‘Candidates ignore ‘security moms,’ at their peril,’ 20 July 2004, ‘According to GOPpollster David Winston, ‘security moms’ now make up between 11 and 14% of the electorate. The trend has mani-fested itself in increased concealed-weapons-permit applications among women; the rise of national-security-focused Web logs published by hard-hitting female ‘war bloggers’; and an upsurge in political activism by womenon core homeland-defense issues, such as immigration enforcement.’ http://usatoday30.usatoday.com/news/opinion/editorials/2004-07-20-malkin_x.htm See Also http://www.huffingtonpost.com/news/security-moms Huffington Post,accessed 13 June 2013

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OtherRace, SecurityMoms, and Year 1996 never had statistically significant (at 10%)11

marginal effects on any of the outcomes and hence are omitted from Table II.12 Table IIpresents the statistically marginal effects on each outcome of the remaining determinants ofdefense spending preferences. Note also that no determinant had a significant effect onprobability on outcome 4, which basically indicates a neutral position on defense spending.As expected, Isolationist, Scale Party ID, and Economic Stake were all statisticallysignificant and displayed marginal effects on defense spending preferences in the expecteddirection. To see this conclusion from Table II, consider the Isolationist variable. While themarginal effects of Isolationist are positive for ‘lower’ outcomes 1, 2, 3, which all indicatepreference towards decreasing defense spending, the marginal effects are negative for‘higher’ outcomes 5, 6, and 7. The marginal effects of the other variables can be similarlyread from Table II: Republicans display a preference towards higher spending.

The age and education variables also displayed the expected significance and directionaleffects – increased education decreases the amount one wants to spend on defense holdingall else equal.13 However, increased age increases the amount one wants to spend ondefense. While there is no prior literature to support these findings, Native American andHispanic were also statistically significant and both had positive effects on defense spendingpreferences. Also of interest were the statistical significance of retired women and theunanticipated positive effect on defense spending preferences (except for outcome 3, whichis the weakest level of preference towards less spending).

TABLE II Sign of marginal effect (n/a if insignificant)

Variable: p (1) p (2) p (3) p (4) p (5) p (6) p (7)

Isolationist 0.03 0.02 0.02 n/a −0.03 −0.02 −0.02

Scale party −0.02 −0.01 −0.01 n/a 0.02 0.01 0.01

Econstake −1E-05 −1E-05 −1E-05 n/a 1E-05 1E-05 1E-05

Married −0.01 −0.01 −0.01 n/a 0.01 0.01 0.01

Female 0.02 0.02 0.02 n/a −0.02 −0.02 −0.02

Age −4E-04 −4E-04 −3E-04 n/a 4E-04 4E-04 4E-04

Education 0.02 0.02 0.02 n/a −0.02 −0.02 −0.02

Retired 0.01 0.01 0.01 n/a −0.01 −0.01 −0.01

Native American −0.03 −0.02 −0.02 n/a 0.03 0.02 0.02

Hispanic −0.01 −0.01 −0.01 n/a 0.01 0.01 0.01

Retired Women −0.03 −0.02 0.02 n/a 0.02 0.02 0.02

fy1980 −0.11 −0.09 −0.08 n/a 0.1 0.09 0.09

fy1984 0.02 0.01 0.01 n/a −0.02 −0.02 −0.02

fy1986 0.03 0.02 0.02 n/a −0.03 −0.02 −0.02

fy1988 0.02 0.01 0.01 n/a −0.02 −0.01 −0.02

fy1990 0.05 0.04 0.04 n/a −0.05 −0.05 −0.05

fy1992 0.06 0.04 0.04 n/a −0.05 −0.05 −0.05

fy1994 0.03 0.03 0.03 n/a −0.03 −0.03 −0.03

fy2000 −0.06 −0.05 −0.05 n/a 0.06 0.05 0.05

fy2004 −0.04 −0.04 −0.04 n/a 0.04 0.04 0.04

11p-values of statistical significance were generally much lower than 10%.12Isolationist variable is not preset for the year 1982, so the regression analysis excludes data from that year.13Wong, 2007.

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5.1. The Statistical Insignificance of Security Moms

According to pollsters from both political parties, ‘security moms’ became an important andtargetable voting bloc in the 2004 election. They were defined as middle income, middle-aged mothers who were particularly concerned with the war in Iraq, domestic terrorism andtheir effects on families and children.14 In order to control for this effect in our regressions,we used the ‘security moms’ interaction variable and found it to be insignificant. Some ana-lysts and pundits thought the effect of the security mom voting block was being overplayed.Specifically, they claimed that there were some distinctions in the way women with childrenand women without children vote, but that the distinctions primarily regard domestic socialissues and not national defense.15

The year dummies also help understand how defense preferences changed over time (thebase year is 2008). A clear result is that strong preference towards decreased spending (asindicated by outcome 1) increased in early 90s and decreased in early 2000s. Similarly,strong preference towards increased spending (as indicated by outcome 7) decreased inearly 90s and increased in early 2000s.

6. CONCLUSIONS

The results on both Scale Party Identification and Isolationism show that politics andideology play an important role in defense spending preferences. The data also pointstowards different voter blocks that could potentially be targeted by political candidates seek-ing office or politicians already in office who wish to sell their defense policy ideas. Aswas previously mentioned, the hype about security moms in the 2004 election seems tohave been overplayed. On the other hand, the statistical significance of retired women andthe mainly positive effect this group displays for defense spending preferences couldindicate the rise of the security grandmothers. The difficulty lies in targeting a politician’smessage to the particular group, retired women, without also targeting the individual femalesubset, which displays opposite preferences.

In closing, public opinion on defense spending preferences has gone through manyunpredictable changes throughout the years as the results on year dummies clearly display.Closely following these changes can help policy-makers more effectively enact theirlegislation and can help political candidates stay in touch with the mood of the populacethey endeavor to serve.

ACKNOWLEDGMENTS

The paper has benefited from the comments and suggestions of two anonymous referees onearlier versions. The usual disclaimer applies. Sencer Ecer and Nicholas J. Veasey thankformer colleagues at Georgetown Public Policy Institute. Sencer Ecer’s research was sup-ported by a Marie Curie International Reintegration Grant within the 7th European Commu-nity Framework Programme.

References

Ali, H.E. (2012) Military expenditures and inequality in the middle east and north africa: A panel analysis. Defenseand Peace Economics 23(6) 575–589.

14Klein, 2003.15Elder, 2007.

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APPENDIX 1

TABLE A1 Economic stake compilation example 1984

1984 National Outlays $604,000,000,000 *Federal expenditures amounting to $604 B1984 DOD Outlays $178,248,000,000% spent on Defense 29.5%

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State

Share of federal

person outlays

(DOD)

Share of

federal tax

burden

Fed tax burden

(paid for

DOD) State stake Population

Econ

Stake/

Person

Alabama $2417,000,000 $7583,200,000 $2237,897,738 $179,102,262 3943,000 $45.4

Alaska 861,000,000 1679,100,000 495,523,538 365,476,462 438,000 $834.4

Arizona 2448,000,000 6661,700,000 1965,951,493 482,048,507 2860,000 $168.5

Arkansas 985,000,000 4137,100,000 1220,910,266 −235,910,266 2291,000 −$103.0

California 32,329,000,000 70,716,700,000 20,869,387,983 11,459,612,017 24,724,000 $463.5

Colorado 2419,000,000 8270,700,000 2440,787,638 −21,787,638 3045,000 −$7.2

Connecticut 6309,000,000 10,945,200,000 3230,066,241 3078,933,759 3153,000 $976.5

Delaware 432,000,000 1711,400,000 505,055,674 −73,055,674 602,000 −$121.4

DC 1761,000,000 2145,800,000 633,252,580 1127,747,420 631,000 $1787.2

Florida 7731,000,000 25,895,700,000 7642,146,910 88,853,090 10,416,000 $8.5

Georgia 4106,000,000 11,818,700,000 3487,847,082 618,152,918 5639,000 $109.6

Hawaii 1932,000,000 2595,900,000 766,082,754 1165,917,246 994,000 $1173.0

Idaho 263,000,000 1923,600,000 567,678,564 −304,678,564 965,000 −$315.7

Illinois 1770,000,000 34,788,100,000 10,266,406,041 −8496,406,041 11,448,000 −$742.2

Indiana 2589,000,000 13,386,100,000 3950,406,544 −1361,406,544 5490,224 −$248.0

Iowa 480,000,000 1397,000,000 412,272,278 67,727,722 2905,000 $23.3

Kansas 2168,000,000 6363,700,000 1878,007,943 289,992,057 2408,000 $120.4

Kentucky 1340,000,000 7239,100,000 2136,349,498 −796,349,498 3667,000 −$217.2

Louisiana 3135,000,000 10,313,100,700 3043,525,784 91,474,216 4362,000 $21.0

Maine 1060,000,000 2218,900,000 654,825,310 405,174,690 1133,000 $357.6

Maryland 5104,000,000 12,478,100,000 3682,444,319 1421,555,681 4265,000 $333.3

Massachusetts 5980,000,000 15,960,400,000 4710,114,866 1269,885,134 5781,000 $219.7

Michigan 2217,000,000 24,031,900,000 7092,116,078 −4875,116,078 9109,000 −$535.2

Minnesota 1685,000,000 10,761,400,000 3175,824,548 −1490,824,548 4133,000 −$360.7

Mississippi 2154,000,000 4238,200,000 1250,746,148 903,253,852 2551,000 $354.1

Missouri 6341,000,000 11,762,400,000 3471,232,244 2869,767,756 4951,000 $579.6

Montana 182,000,000 1855,400,000 547,551,886 −365,551,886 801,000 −$456.4

Nebraska 580,000,000 3932,100,000 1160,412,187 −580,412,187 1586,000 −$366.0

Nevada 465,000,000 2549,700,000 752,448,552 −287,448,552 881,000 −$326.3

New

Hampshire

918,000,000 2393,800,000 706,440,501 211,559,499 951,000 $222.5

New Jersey 4099,000,000 23,889,000,000 7049,944,490 −2950,944,490 7438,000 −$396.7

New Mexico 1178,000,000 2705,000,000 798,279,536 379,720,464 1359,000 $279.4

New York 8894,000,000 48,885,700,000 14,426,785,188 −5532,785,188 17,659,000 −$313.3

North

Carolina

3088,000,000 12,148,200,000 3585,086,678 −497,086,678 6019,000 −$82.6

North Dakota 313,000,000 1655,100,000 488,440,836 −175,440,836 670,000 −$261.9

Ohio 4518,000,000 27,794,800,000 8202,595,216 −3684,595,216 10,791,000 −$341.5

Oklahoma 1980,000,000 7650,900,000 2257,876,860 −277,876,860 3177,000 −$87.5

Oregon 507,000,000 6566,700,000 1937,915,797 −1430,915,797 2649,000 −$540.2

Pennsylvania 4928,000,000 30,446,900,000 8985,263,297 −4057,263,297 11,865,000 −$342.0

Rhode Island 562,000,000 2457,800,000 725,327,706 −163,327,706 958,000 −$170.5

South

Carolina

2269,000,000 5945,700,000 1754,650,883 514,349,117 3203,000 $160.6

South Dakota 215,000,000 1397,000,000 412,272,278 −197,272,278 686,000 −$287.6

Tennessee 1349,000,000 9664,200,000 2852,027,023 −1503,027,023 4651,000 −$323.2

(Continued)

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Table A1 (Continued )

State

Share of federal

person outlays

(DOD)

Share of

federal tax

burden

Fed tax burden

(paid for

DOD) State stake Population

Econ

Stake/

Person

Texas 11,690,000,000 41,233,100,000 12,168,406,637 −478,406,637 15,280,000 −$31.3

Utah 1243,000,000 2993,600,000 883,449,028 359,550,972 1554,000 $231.4

Vermont 248,000,000 1068,000,000 315,180,238 −67,180,238 516,000 −$130.2

Virginia 12,128,000,000 13,945,200,000 4115,403,989 8012,596,011 5491,000 $1459.2

Washington 4971,000,000 12,540,200,000 3700,770,811 1270,229,189 4245,000 $299.2

West Virginia 229,000,000 3970,300,000 1171,685,487 −942,685,487 1948,000 −$483.9

Wisconsin 1087,000,000 11,312,600,000 3338,490,604 −2251,490,604 4765,000 −$472.5

Wyoming 174,000,000 1536,700,000 453,499,506 −279,499,506 502,000 −$556.8

Table A2 Economic stake

1980 1984 1986 1988 1990 1992 1994 1996 2000 2004

Alabama $45 $234 $287 $341 $305 $269 $298 $298 $326 $599

Alaska $834 $828 $1052 $1275 $1303 $1331 $1062 $1062 $793 $1159

Arizona $169 $363 $382 $401 $469 $537 $363 $363 $188 $667

Arkansas −$103 $46 −$12 –$69 –$153 –$237 –$353 –$353 –$470 –$628

California $464 $804 $604 $404 $285 $167 –$116 –$116 –$398 –$214

Colorado –$7 $88 $409 $731 $682 $632 $287 $287 –$59 –$139

Connecticut $977 $962 $622 $282 $130 –$23 –$442 –$442 –$862 $680

DC $1787 $1662 $3214 $4767 $3674 $2581 $7660 $7660 $12,740 $15,341

Delaware –$121 –$61 –$209 –$358 –$492 –$626 –$769 –$769 –$913 –$1699

Florida $9 $88 $6 –$76 –$105 –$134 –$323 –$323 –$511 –$686

Georgia $110 $401 $163 –$74 –$70 –$67 –$189 –$189 –$311 –$440

Hawaii $1173 $1569 $1510 $1452 $1437 $1422 $825 $825 $228 $425

Idaho –$316 –$236 –$4 $228 –$54 –$336 –$42 –$42 $252 –$12

Illinois –$742 –$478 –$599 –$720 –$766 –$811 –$802 –$802 –$792 –$911

Indiana –$248 –$21 –$214 –$408 –$382 –$356 –$433 –$433 –$510 –$476

Iowa $23 –$416 –$468 –$521 –$541 –$560 –$510 –$510 –$459 –$548

Kansas $120 $589 $283 –$23 –$74 –$125 –$292 –$292 –$459 –$342

Kentucky –$217 –$81 –$140 –$199 –$210 –$221 –$136 –$136 –$51 –$136

Louisiana $21 $21 $12 $4 –$6 –$15 –$28 –$28 –$41 –$69

Maine $358 $196 $110 $24 $113 $202 $7 $7 –$187 $183

Maryland $333 $690 –$127 –$944 –$276 $393 $640 $640 $888 $1983

Massachusetts $220 $583 $402 $221 $234 $247 –$131 –$131 –$509 –$372

Michigan –$535 –$294 –$518 –$743 –$735 –$727 –$730 –$730 –$733 –$803

Minnesota –$361 –$169 –$281 –$393 –$426 –$459 –$572 –$572 –$684 –$983

Mississippi $354 $740 $763 $787 $587 $386 $231 $231 $77 $20

Missouri $580 $631 $627 $622 $606 $590 $389 $389 $189 $256

Montana –$456 –$285 –$299 –$314 –$317 –$320 –$345 –$345 –$371 –$334

Nebraska –$366 –$201 –$227 –$253 –$248 –$244 –$435 –$435 –$626 –$769

Nevada –$326 –$141 $32 $205 –$57 –$318 –$500 –$500 –$683 –$877

New Hampshire $222 $414 $80 –$255 –$293 –$331 –$559 –$559 –$787 –$744

New Jersey –$397 –$261 –$487 –$712 –$729 –$746 –$845 –$845 –$943 –$1113

New Mexico $279 $384 $1492 $2601 $1502 $403 $1521 $1521 $2639 $2207

(Continued)

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Missing Data for Defense Spending Preferences 1980–2008Variable of interest: Respondent Opinion: Should Government Cut Military Spending?The missing code for this question was:0. Not applicable

Variable of interest: Respondent Placement: Defense Spending Scale(1996,2004: Suppose these people are at one end of a scale, at point 1.)(1996,2004: Suppose these people are at the other end, at point 7.) (2004: And, of course,some other people have opinions somewhere in between, at points 2,3,4,5, or 6).

The missing codes for this question are as follows:0. Not applicable

NOTES:This variable represents seven-point scale data from interviews conducted face-to-face.Respondents interviewed by telephone were asked a branching series and are not repre-sented here.

Variable Years:1980, 1982, 1984, 1986, 1988, 1990, 1992, 1994, 1996, 2000, and 2004

Missing Data for Isolationist Variable 1980–2004MISSING_CODES:————————————

0. NA; no Pre IW; no Post IW; telephone IW (1984: see v.15); form B (1986); form A(1990); noncomparable question format (2000)

Variable years: 1980, 1984, 1986, 1988, 1990, 1992, 1994, 1996, 2000, 2002, and 2004

Table A2 (Continued).

1980 1984 1986 1988 1990 1992 1994 1996 2000 2004

New York –$313 –$137 –$360 –$583 –$643 –$702 –$773 –$773 –$845 –$1119

North Carolina –$83 $22 –$20 –$61 –$103 –$145 –$344 –$344 –$543 –$669

North Dakota –$262 –$4 –$51 –$98 –$81 –$64 –$249 –$249 –$434 –$270

Ohio –$341 –$238 –$207 –$175 –$242 –$309 –$389 –$389 –$469 –$526

Oklahoma –$87 –$34 $4 $42 $26 $10 –$78 –$78 –$166 –$257

Oregon –$540 –$402 –$431 –$459 –$500 –$541 –$626 –$626 –$710 –$838

Pennsylvania –$342 –$213 –$302 –$392 –$448 –$503 –$497 –$497 –$491 -$541

Rhode Island –$170 $44 –$72 –$187 –$125 –$63 –$273 –$273 –$484 –$830

South Carolina $161 $271 $389 $508 $366 $225 $73 $73 –$78 –$13

South Dakota –$288 –$188 –$201 –$215 –$234 –$253 –$326 –$326 –$399 –$561

Tennessee –$323 –$233 –$136 –$40 –$200 –$360 –$142 –$142 $77 –$17

Texas –$31 $181 $159 $136 $107 $78 $28 $28 –$23 $25

Utah $231 $501 $647 $794 $634 $474 $141 $141 –$192 –$15

Vermont –$130 –$228 –$353 –$478 –$531 –$584 –$489 –$489 –$394 –$336

Virginia $1459 $1423 $1991 $2559 $2197 $1834 $1914 $1914 $1993 $3287

Washington $299 $468 $513 $558 $372 $186 –$134 –$134 –$454 –$319

West Virginia –$484 –$405 –$388 –$371 –$391 –$412 –$361 –$361 –$311 –$304

Wisconsin –$473 –$359 –$468 –$577 –$581 –$584 –$638 –$638 –$692 –$738

Wyoming –$557 –$350 –$217 –$83 –$197 –$310 –$397 –$397 –$483 –$995

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