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Guns, butter and debt: Sovereign creditworthiness and
military expenditure
Matthew DiGiuseppe
Department of Political Science
University of Mississippi
[email protected]
Abstract
I argue that favorable access to sovereign credit provides governments with greater
autonomy to invest in security by allowing political incumbents to relax fixed-budget
constraints. Borrowing permits leaders to delay and minimize the macroeconomic and
redistributive costs associated with domestic sources of finance. Consequently, leaders
of creditworthy states face fewer political costs when increasing military expenditure
in response to growing demand or maintaining military expenditure when government
revenues fall. A cross-sectional time-series analysis supports two observable implications of the argument. First, creditworthiness is positively associated with military
spending with an effect on par with regime type. Second, creditworthiness conditions
the effect of external threats on military expenditure suggesting that poor credit terms
constrain the provision of security.
Forthcoming in the Journal of Peace Research
Keywords: Military Spending, International Security, International Finance, Sovereign
Credit, Sovereign Debt.
The ‘Great Recession’ has demonstrated the manner in which national security is vulnerable to global economic forces. Governments once privileged with consistent sovereign
credit access have faced serious credit constraints and mounting debt burdens. Consequently,
those states hardest hit by the crisis have cut military budgets, while those that maintained
favorable credit terms saw little reductions in spending (SIPRI, 2010, 177-78). Even in the
United States, which has retained consistent access to credit, policymakers have expressed
concerns that debt burdens and fiscal constraints resulting from a lack of credit may eventually constrain security policy. Former Secretary of State Hillary Clinton expressed that
‘[debt] undermines our capacity to act in our own interests, and it does constrain us where
constraint may be undesirable (Lander, 2010)’ and former Chairman of the Joint Chiefs of
Staff Admiral Mike Mullen has remarked that the ‘biggest threat to our national security is
our debt (Sanger, 2013).’
Are the these concerns valid? Previous research has demonstrated that military expenditures have a significant impact on national debt burdens (Brzoska, 1983, Looney &
Frederiksen, 1986, Dunne & Smith, 2007, Smyth & Narayan, 2009, Heo & Bohte, 2012).
This indicates that many states do incur debt burdens to finance security. However, less
research directly addresses concerns that a government’s credit access and borrowing costs
constrain security.1 Research thus far has largely considered the impact of a state’s credit
terms on material and political constraints within the context of major power war, rivalry,
and other conflict outcomes (Rasler & Thompson, 1983, Schultz & Weingast, 2003, Slantchev,
2012, Shea, 2014, Flores & Kreps, 2013, DiGiuseppe, forthcoming, Poast, 2015). This article
builds on this growing literature by demonstrating a broader relationship between sovereign
creditworthiness and security funding.
I argue that the leaders of creditworthy states can minimize or delay the political costs
imposed by the alternatives to borrowing and thus have greater autonomy to increase and
maintain military expenditure. I test two observable implications of the argument with the
1
Debt burdens are driven by both new debt and accumulated interest on old debts.
1
first cross-sectional time-series analysis of a relationship between sovereign creditworthiness
and defense spending. First, I present strong evidence that sovereign creditworthiness is associated with higher military expenditure. Second, I also demonstrate that sovereign credit has
strategic implications by demonstrating that the growing power of rivals is associated with
an increase in military spending in creditworthy states but has no impact on the spending
of credit poor states.
The argument and analysis have important implications for several areas of academic
research and policy. First, the article demonstrates that a state’s access to credit has a broad
influence on security beyond conflict outcomes. Second, the article furthers understanding
of the determinants of military spending by identifying a variable with an effect on par
with regime type. In doing so, it also adds to the growing understanding that international
political economy, and more specifically international finance, are closely tied to international
security. Next, the theory and findings question the viability of the assumption, common the
both analyses of spending priorities and many prominent theoretical models of world politics,
that all governments face fixed-budget constraints. The findings also support the contention
that the politics of domestic redistribution have significant consequences for the strategic
security interactions between states. Lastly, the empirical analysis has clear policy relevance
that is reflected by recent turbulence in international credit markets that has threatened the
ability of both industrialized and emerging market governments to sustain military spending
after the ‘Great Recession.’
Credit and war
The notion that states use borrowed capital to fund security is not new. Economic history
demonstrates that major powers that held the confidence of international investors have
consistently borrowed to outspend credit poor and fiscally troubled adversaries and rivals
(Kennedy, 1988, Brewer, 1990, Bordo & White, 1991, Ferguson, 2001). States restricted from
2
foreign capital were limited to funds available through domestic extraction and eventually
scaled back military output or suffered economically from unsuccessful efforts to raise capital
through distortionary fiscal or monetary policies. Furthermore, the use of credit promoted
long-run economic growth and power as governments avoided distortionary fluctuations and
decreased economic uncertainty by using borrowed funds to smooth over gaps in revenue
and taxes (Barro, 1979). This focus on the material advantages of credit within major
power rivalry and war is also found in world politics literature. Rasler and Thompson
(1983) propose that great powers partially owe their ascension to the ‘ability to obtain
credit inexpensively, to sustain relatively large debts, and in general to leverage the initially
limited base of their wealth in order to meet their staggering military expenses (490).’ The
losers of hegemonic contests, they contend, failed to ‘maintain a sufficiently competitive
financial capability’ often despite an advantage in traditional sources of capital. Schultz and
Weingast (1998, 2003) add that the advantage democratic and limited governments hold in
raising foreign capital helps explain their success in war fighting and overcoming rivals.
The arguments connecting a state’s credit terms to its material capacity are well suited
to explain outcomes where states approach the limits of their capacity to extract domestic
resources, such as total war. However, states rarely approach this constraint. Under most
circumstances, the political consequences of extracting resources from society prevents incumbents from reaching these limits. Appreciating the political interests and constraints of
the actors actually making budgetary decisions sheds light on credit’s broader influence on
security.
For example, Shea (2014) argues that democratic governments are more sensitive to war’s
economic costs and thus the ability to minimize tax burdens through borrowing exerts a
larger influence on the probability that democratic states, relative to non-democratic states,
win wars. DiGiuseppe (forthcoming) goes further and suggests that the macroeconomic
and sub-national distributional consequences of non-credit financing have implications for
the survival of all leaders that are dependent on a competitive distribution of public and
3
private goods to retain office. His evidence suggests that beyond war outcomes, a state’s
creditworthiness, by facilitating affordable borrowing, shields leaders from the current and
potential costs of conflict giving them a freer hand to initiate interstate conflict. Recent
research also supports the notion that the political consequences of taxes and other nonborrowing tools to finance conflict influence politicians’ behavior. Flores & Kreps (2013)
present evidence that United States politicians who represented those likely to be saddled
with war taxes had a greater propensity to favor alternative methods of war finance.
While recent studies show that the political benefits of creditworthiness influence conflictrelated outcomes, the arguments have broader implications.2 A state’s ability to respond
to internal and external demands and maintain security commitments in peace time are
crucial for deterring threats. In fact, recent research finds losing credit access increases the
probability a government forms an alliance (Allen & DiGiuseppe, 2013). The evidence is
consistent with the notion that states see alliance power as a substitute for arms when the
costs of armaments to deter or fight a war require unpopular fiscal policies. In all, research
demonstrating credit’s influence on a number of discrete foreign policy outcomes is suggestive
of a broader relationship linking the political economy of security with a state’s ability to
raise material resources. As such, evidence that credit financing has a broader impact on
the political economy of security should be apparent in a state’s military expenditure.
Creditworthiness and military spending
Defense spending is often the costliest component of a state’s foreign policy apparatus and
provides a significant amount of information about a state’s position in the world. As such,
understanding what accounts for variation in military spending has numerous implications
for the study of world politics and foreign policy. For practical purposes, military expenditure
provides greater variance upon which to examine the relationship between credit and security
2
In fact, the arguments center around a government’s ability to finance militaries but lack a direct test
of the mechanism.
4
than available in the discrete outcomes previously explored in the literature.
Economic models of defense spending have generally assumed rational welfare maximizing agents make decisions over military spending under fixed budget constraints (Smith,
1995). From this perspective, the primary determinants of military expenditures are external threats, demographics and government resources (Dunne & Smith, 2007, Collier &
Hoeffler, 2007, Nordhaus et al., 2012). Other arguments and models from a variety of disciplines relax the assumptions of a welfare maximizing agent. To varying degrees, this research
considers the incentives facing policy makers and find that sub-national factors relating to
political institutions offer substantial purchase in explaining variation in military expenditure (Garfinkel, 1994, Bueno de Mesquita et al., 2004, Goldsmith, 2007, Albalate et al., 2012,
Conrad et al., 2013, Carter & Palmer, forthcoming). Yet, researchers commonly assume that
fixed budgets constrain decision makers and that institutions influence the relative value of
guns and butter. This assumption persists despite growing evidence that, at least among
industrialized states, many governments pursue both guns and butter (Domke et al., 1983,
Mintz, 1989, Whitten & Williams, 2011). Furthermore, little research addresses the variety
of fiscal tools used to finance defense spending in an attempt to understand how states can
avoid trade-offs (Heo & Bohte, 2012). The literature mentioned in the previous section provides strong reasons to believe that the fiscal tools available to incumbents, notably the cost
of borrowing, strongly influence their ability to provide security while avoiding direct costs
on society (Shea, 2014, DiGiuseppe, forthcoming, Flores & Kreps, 2013).
The idea that borrowing is politically efficient and obfuscates the true cost of expenditure
has a long history in political economy (Hume, 1970 1752, Smith, 1776, Mill, 1984 1994,
Kant, 1795 2003). However, modern economics literature largely rejects this notion as it is
in conflict with the assumption of perfectly informed self-interested rational individuals and
the assumption that governments are benevolent social planners. The concept of ‘Ricardian
Equivalence’ claims that fully informed rational taxpayers recognize that there is no ‘free
lunch’ and that future taxes are required to repay debts (Barro, 1974). If this proposition
5
reflects real life, then those bearing the burden of security spending have little reason to
prefer borrowing over taxes and may even punish incumbents for the additional burden of
interest payments. However, the theorem relies on strong assumptions that even Ricardo
doubted existed in practice (Dooley, 1989). Furthermore, the theorem lacks direct empirical
support (Seater, 1993, Ricciuti & DiLaurea, 2003, Banzhaf & Oates, 2013) and is at odds
with evidence that that individuals hold inconsistent preferences (i.e. wanting both lower
taxes and higher spending) or simply misperceive the costs and benefits of public services
due the complexity of tax systems (Citrin, 1979, Wagner, 1976).
If individuals discount the future or are not fully informed about fiscal policy, survival
minded incumbents have a strong motivation to incur debt to maintain their grip on power
by minimizing the current burden imposed on relevant constituents. This behavior is consistent with some recent research. Empirical evidence does suggest that leaders do ‘buy off
potential rivals and reward supporters’ by borrowing foreign capital (Easterly, 2002, 1680).
Thomas Oatley finds that debt burdens are in fact correlated with the discount rate of
different political regimes (2010). Further, DiGiuseppe et al. (2012) find that sovereign creditworthiness decreases the probability of rebellion and DiGiuseppe & Shea (forthcoming)
find that increases in borrowing costs and credit downgrades shorten leader tenure.
If borrowing is politically beneficial, it is not difficult to see how creditworthiness can
influence military expenditure. As is common in political economy models of foreign policy,
I assume that the desire to remain in power motivates leaders and constituents hold leaders
accountable to their provision of both security and the distribution of public and private
goods (Bueno de Mesquita et al., 2003). As demand for security spending arises under fixed
budget constraints, leaders must make tough choices. They can reallocate resources from
domestic purposes towards military spending or leave the demand for security underfunded.
Leaders can act strategically to place the burden of defense spending on those posing minimal threat to their tenure. However, if they face competition for office, they will likely
have already allocated resources away from less salient constituents. Consequently, it is un-
6
likely that leaders can redirect resources without imposing costs on relevant constituents.
Furthermore, relevant constituents still have clear incentives to place the burden on others
even if defense spending contributes to a public good. This is evident in political debates
over defense spending in the shadow of significant external threats and expectations of war
(Narizny, 2003). Fiscal alternatives to borrowing (spending cuts, new taxes or monetary expansion) also risk macroeconomic stress (Ohanian, 1997) that can further risk a leader’s hold
on power. As previous research demonstrates, poor economic performance has consequences
for leaders of all regime types (Gasiorowski, 1995, Przeworski et al., 2000, Hibbs, 1977). As
such, even if individuals do not discount the future, the macroeconomic consequences of
non-borrowing strategies may risk a leader’s ability to remain in power.
While borrowing can benefit all leaders, not all leaders can borrow. The price and quantity of borrowed funds is determined by its sovereign creditworthiness. As a government’s
creditworthiness increases, borrowing costs fall and the amount it can borrow increases. Consequently, leaders can increasingly relax fixed budget constraints and allocate more resources
towards security without altering the existing fiscal balance or risk macroeconomic liabilities.
The costs and potential macroeconomic harm of the new security spending is thus pushed
forward to an uncertain future. Individuals, not asked to pay additional resources in the
present, are then less likely to support political rivals that challenge a leader’s power from
either inside or outside the ruling coalition.
Beyond avoiding the political unpopularity of taxes, spending cuts or inflation, affordable foreign capital can purchase political support by facilitating additional domestic fiscal
transfers. If policies associated with military expenditure are themselves generating political
opposition that restricts a leader’s preferred policies, political incumbents can use sovereign
credit to ease opposition through fiscal means or purchase political support elsewhere. In
sum, this permits leaders to provide ‘guns and butter’ to achieve their foreign policy goals
and, in the process, increase military resources (Clark & Hart, 2003). This influence is similar
to the effect of other sources of non-tax revenue that have a relationship with more hostile
7
and expensive foreign policies (McDonald, 2007). Lastly, military expenditure itself may
serve domestic ends as it often has welfare externalities, such as employment, that benefit
leftist governments (Whitten & Williams, 2011) or it also may serve as a source of patronage
to military elites (Kono & Montinola, 2012). Those politicians with access to cheap credit
will find it easier to increase military expenditure, without imposing costs on other segments
of society, to further their political careers.
As borrowing costs decrease and sovereign creditworthiness improves, it follows that
leaders will be more willing to take advantage of credit and spend more on their military
than similar states with higher borrowing costs or no access to credit markets. Note that the
opportunity to borrow itself does not cause politicians to spend larger amounts on defense.
However, it does make it more likely that, as their access improves, they will increase or
maintain higher levels of military spending as demand for defense presents itself from both
internal and external sources or existing government revenues fall. Conversely, as credit
costs increase the political costs associated with military expenditure tighten and render
expanding or maintaining military expenditure increasingly difficult. This reasoning leads
to my first hypothesis.
Hypothesis 1 An increase in a state’s sovereign creditworthiness is associated with an increase in military expenditure.
The general argument claims that creditworthiness increases autonomy and relaxes constraints that would otherwise limit the influence of other demands on military expenditure.
While it is difficult to measure the demand for military spending in a single variable, it is
possible to capture the effects separately. Previous research indicates that a state’s external
security environment is an important driver of military spending (Dunne & Smith, 2007,
Nordhaus et al., 2012).3 As such, if the domestic constraints of poor credit terms are suf3
External variables do not entirely account for demand for military expenditure. As other have shown
military spending may serve as a vehicle for welfare spending and thus a domestic source of demand (Whitten & Williams, 2011). I focus here on external threat because it is easily observed within the empirical
framework.
8
ficiently salient, those states that face a mounting external threat will likely spend less on
their military than creditworthy states faced with a similar threat. In the absence of credit,
leaders may choose to do nothing or pursue a cheaper strategy of providing security. In each
case, a state will end up spending less on its military than if it chose to respond to external
threats by arming domestically. The second hypothesis outlines expectations following from
this line of thought.
Hypothesis 2 The effect of external threats on military expenditure will be smallest when
creditworthiness is at its minimum and rise as creditworthiness increases.
Data
To test the hypotheses, I employ a time-series cross-sectional analysis of military expenditure
for all states with available data. I draw on a variety of previous empirical models of military
expenditure to inform my variable selection.
Military expenditure
Cross-national empirical analyses of military expenditure employ one of two dependent variables. Most often, researchers employ a state’s defense burden, the ratio of a state’s military expenditure to its gross domestic product (GDP) (Goldsmith, 2003, 2007, Fordham
& Walker, 2005, Albalate et al., 2012, Phillips, forthcoming). Others instead estimate the
log of a state’s military expenditure while controlling for a country’s economic size on the
right hand side of the equation (Bueno de Mesquita et al., 2004, Nordhaus et al., 2012). For
several reasons, I follow Nordhaus et al. (2012) and adopt the latter approach, in which military expenditure is measured in constant dollars and adjusted for purchasing power parity.4
The defense burden approach is motivated by the idea that states have a fixed amount of
4
Nordhaus et al. (2012) merge the Correlates of War (COW) and SIPRI data. I only employ the recently
updated military expenditure data from the Correlates of War project (Singer, 1987) to keep the data source
consistent over all time periods.
9
resources from which they allocate military expenditure (Dunne & Smith, 2007). As I have
discussed, states are not necessarily constrained by their domestic resources because they
can borrow on sovereign credit markets. As such, the argument concerns the amount states
spend on security that are influenced, but not constrained, by a state’s existing resources.
This is conceptually different from the proportion of resources states allocate toward defense. Consequently, for the purposes of this study, the level of a state’s military spending
more closely reflects the theoretical outcome of interest. While I believe this is the most
appropriate specification for the question at hand, legitimate concerns remain over the measure’s cross-national and temporal comparability (Brzoska, 1995). As such, I demonstrate
in the Supplementary Appendix that the central results are robust to employing the ratio of
military expenditure to GDP as the dependent variable.
Creditworthiness
Researchers have generally relied on credit ratings produced by the private financial services
firms Moody’s, Standard & Poor, or Fitch to measure sovereign creditworthiness (Archer
et al., 2007, Beaulieu et al., 2012). These ratings are the result of risk assessments of a
sovereign government’s likelihood of repayment and default. The rating schemes vary, but
all consist of letter grades ranging no more than 20-values. Overall, these credit ratings
presents several undesirable characteristics for cross-sectional time-series analysis. First,
the data collection process is market driven and excludes states that lack sufficient creditor
interest (Beaulieu et al., 2012). For example, a state can be creditworthy but lack the size to
generate significant interest from a sizable number of international investors. Consequently,
ratings on emerging market sovereigns generally begin in the mid-1990s when investor interest
in sovereign bonds broke a threshold despite that many states borrowed substantial funds
from private banks in decades past. Second, the sample excludes many non-creditworthy
states. Governments that are aware of their poor credit risk are unlikely to hire a rating
agency to assess their standing. As such, significant variation among states with poorer
10
credit prospects is unobserved.
In light of ‘letter-grade ratings’ and other insufficient alternatives, I utilize credit ratings
published by Institutional Investor magazine twice yearly since 1980. The dataset offers a
broad sample of states and greater variance than alternatives and includes states from all
levels of economic development and various regime types. It has been frequently employed
in both economics and political science research to operationalize creditworthiness in crossnational time-series analysis (Ahlquist, 2006, Rose, 2005, DiGiuseppe et al., 2012). The
Institutional Investor rating (IIR) spans from 0-100 (100 representing the most creditworthy)
and reflects the opinion of ‘senior economists and sovereign-risk analysts at leading global
banks and money management and securities firms’ anonymously polled by the magazine
twice yearly and weighted in concordance with the assets of the expert’s firm (D’Ambrosio,
2005). Like the letter grades produce by ratings firms, the polled experts assess each state’s
default risk. As such, the measure serves as a reliable indicator of the market’s perception of
a state’s likelihood of repayment. I take the mean of the two yearly scores to create a yearly
observation for each state. Where they overlap, this variable is highly correlated (0.96) with
each of the more familiar ratings issued by credit ratings agencies.
Additional covariates
Existing empirical models indicate that military expenditure is a function of both external
and internal variables (Goldsmith, 2003, 2007, Fordham & Walker, 2005, Albalate et al.,
2012, Phillips, forthcoming). To capture a state’s external threat, I follow Fordham &
Walker (2005) and include the logged sum of rival capabilities for each state. To construct
the measure, I sum the COW composite index of national capabilities (CINC) of each state’s
strategic rivals (Singer et al., 1972, Thompson, 2001). I also employ this variable to test the
second hypothesis. Next, I include three binary variables indicating a state’s involvement in
ongoing intrastate, interstate or extra-state wars as defined by the Correlates of War project
(Sarkees & Wayman, 2010). Ongoing wars will likely drive demand for military spending as
11
states mobilize against both domestic and external threats to security.5
Beyond ongoing civil conflict, economic size and political institutions are the primary
domestic determinants of military expenditure (Goldsmith, 2003, Albalate et al., 2012). As
I mentioned, I include the log of GDP, adjusted for PPP, to control for the size of a state’s
economy. Because research finds democracy to be a significant predictor of defense effort
(Fordham & Walker, 2005, Goldsmith, 2003, 2007, Albalate et al., 2012), I also include
the 21-point polity scale to control for the effect of regime type (Marshall et al., 2010).
Furthermore, development and regime type are potentially confounding variables because
they are predictors of sovereign creditworthiness. As such, controlling for their effects helps
isolate the variance explained by each indicator and creditworthiness. Lastly, I discuss the
robustness of the results to the inclusion of other variables beyond those employed in the
primary analysis below.6
Estimation and results
With all the relevant variables included, the dataset consists of a pooled cross-sectional timeseries that spans from 1981-2007 and includes 143 states. A data set of this type generates
several complications for a standard linear model.
First, it is possible that unobserved factors influence states to either raise or lower their
military spending in unison. Cross-sectional time-series analysis of military spending is
particularly susceptible to correlations among the panels because the decision of one state to
increase military spending is likely to influence the strategic decisions of rivals or neighboring
states (Sandler & Hartley, 1995, Flores, 2011). Following the recommendation of Beck and
Katz (1995), I address this concern by estimating the models using Prais-Winsten regression
with panel-corrected standard errors.
5
Some scholars include battle deaths as a way to capture the cost of ongoing war (Fordham & Walker,
2005, Nordhaus et al., 2012, Conrad et al., 2013). Because the timespan of this data is limited or does not
indicate yearly deaths during war, I instead follow Goldsmith (2007) and employ dummy variables to capture
the impact of war.
6
Summary statistics are available in the Supplementary Appendix.
12
Next, the analysis of a series, or multiple series, over time requires stationary data. In
other words, the dependent variable must have a constant mean and constant variance over
time. Here, an Augmented Dickey-Fuller test suggests that I can reject the null hypothesis
of a unit root.7 This indicates that the data are likely stationary and standard regression
techniques are suitable.
Another complication arises from serial correlated errors. As others have noted, military expenditure is subject to budgetary inertia because spending is often driven by decisions made in prior periods and bureaucratic institutions can constrain spending reductions
(Goldsmith, 2003, Nordhaus et al., 2012). I include a lagged dependent variable (LDV) to
address this issue with the added benefit of permitting estimation of the dynamic long-term
effects of the independent variables. Still, a Lagrange multiplier test indicates that even with
the presence of the LDV first order autocorrelation remains. Consequently, I also include
panel-specific first-order autoregressive correction.8
Next, research indicates that military expenditure has a strong influence on sovereign
debt burdens (Brzoska, 1983, Looney & Frederiksen, 1986, Dunne & Smith, 2007, Smyth &
Narayan, 2009). Sovereign debt burdens are highly predictive of creditworthiness as creditors
are less willing to lend to states that already have large debt obligations. Consequently, I lag
IIR, and other variables where necessary, one-year to minimize the possibility of endogeneity.9
Linear models
To test the first hypothesis, I estimate the following linear equation:
7
I employed the ‘xtfisher’ command in Stata 12 to test for the presence of a unit root. This is the only
test available for a dataset with unbalanced panels. Because this test is less than ideal, I also report error
correction models in the Supplementary Appendix that are consistent with the results presented here.
8
I estimate the primary models in STATA 13 using the Beck and Katz’s (1995) ‘xtpcse’ command and
correct for panel specific autocorrelation.
9
However, if endogeneity is present, it would introduce bias against my hypothesis because high debt burdens decrease creditworthiness. I also report GMM models below that also address problems of endogeneity
should they be present.
13
ln(M ilExp.)i,t = φln(M ilExp.)i,t−1 + β1 lnGDPi,t−1 + β2 IIRi,t−1 + βXi,t + i
(1)
Where βXi,t is a matrix of control variables and their coefficients, φ is the coefficient
for the lagged dependent variable and i is the error term. Model 1 of Table I presents the
results of this estimation employing the full sample. Here, the IIR coefficient is positive and
significant indicating that creditworthiness has a strong immediate influence on a state’s
military expenditure. A 10-point increase, in IIR is associated with a 1.9% increase in
military spending. This provides initial support for the first hypothesis, which states that
creditworthy countries spend more on defense than those states will less favorable credit
terms.
[Table I Here]
The next three models test the robustness of this finding. Model 2 excludes members of
the Organization for Economic Co-operation and Development (OECD). Inclusion of these
states may potentially drive the relationship observed in Model 1. OECD members receive
highly favorable credit terms and posses levels of wealth conducive to military investment.
Furthermore, non-OECD states’ access to credit markets is more sensitive to economic and
political factors (Mosley, 2003), which potentially tempers the political advantages of credit.
Similarly, Model 3 includes region-fixed effects to control for idiosyncratic regional attributes
that may influence both military spending and creditworthiness unaccounted for by the
covariates. Model 4 adds year-effects to explicitly control for any temporal dynamics or
specific periods and events, such as the Cold War and War on Terror, that might influence
global military spending. In each estimation, IIR remains positive and significant. In fact,
the size of the coefficient almost doubles when excluding OECD states and remains stable
with the inclusion of both region and yearly effects. Contrary to the focus of most previous
research on the relationship between creditworthiness and war outcomes, Model 2 indicates
14
that the effect of creditworthiness is relevant to the foreign policy of those outside the
industrial core and non-major powers. Models 3 and 4 provide confidence that the effect
of creditworthiness is not isolated to regions, like Western Europe, that maybe predisposed
to both high defense expenditure and favorable credit terms or specific to certain periods
within the sample.
Next, Model 5 presents the results of a system GMM estimator suggested by Arellano
& Bond (1991) and Blundell & Bond (1998). The approach addresses several issues present
in the first four models. First, several variables in the model maybe endogenous to military
expenditure. Second, it is possible that the data used are stationary despite initial tests.
Third, the models fail to account for unit heterogeneity. The GMM estimator uses a first
difference transformation that addresses issues of both stationarity and unobserved effects
without the need for fixed-effects.10 It also employs lags and differences of right hand side
variables as instruments to address issues of endogeneity and collinearity.11 Overall, Model
5 demonstrates the results are robust to the issues address by the GMM estimator. Also,
note that the p-value on the Hansen test indicates that the instruments are valid and not
over-identified and that the p-value of the Arellano-Bond test indicates the absence second
order correlation, which is necessary for the estimator to be consistent.
The IIR coefficients in Table I demonstrate only the short-run effect on military expenditure. The long term effect (LTE) of creditworthiness is also relevant because new military
expenditure is often budgeted several years in advance. I estimate the LTE employing the
following equation: LT Ex =
βˆ
.
1−φˆ
Where βˆ represents the coefficient of the independent
variable of interest and φˆ is the coefficient of the lagged dependent variable. Corresponding
with the significant short term effect, the bottom of Table I indicates that the LTE of IIR
is positive and statistically significant at the 95% level in each of the five models.12
10
I also report country fixed-effects specifications in the Supplementary Appendix.
I implement the estimator with the xtabond2 package in STATA 13. I was careful to limit the number of
instruments to avoid inefficient estimates (Roodman, 2009). As such, I use two previous lags and differences
of ln(GDP), IIR and the lagged dependent variable and treat other variables as strictly endogenous.
12
I assessed the statistical significance of each coefficient with simulations employing 10,000 draws of the
coefficient and covariance matrices.
11
15
Figure 1 presents the 95% confidence intervals around the dynamic simulations of four
scenarios based on Model 1’s estimates.13 One set of simulations holds IIR at either its
maximum and minimum while holding all other variables at their mean or modal values.14
The other set holds a state’s Polity score at its minimum (-10) and maximum (10), again
holding other covariates constant. The simulations begin with the log of military expenditure
at the sample mean and allow it to adjust over time.
[Figure 1]
First, the IIR simulations demonstrate statistically significant differences between the
two scenarios (they do not overlap) and also across each scenario. The significance of the
LTE is apparent in both simulations by the third bar (at t+2) as the confidence intervals
no longer overlap with the initial effect. Next, the differences between the two IIR scenarios
increases over time. This demonstrates the importance of considering long term effect,
which is dramatically larger than the initial effect of creditworthiness on military spending.
Furthermore, this is consistent with expectations that bureaucratic inertia influences the
timing of military spending changes.
The simulations are also useful to compare the substantive short and long-run effects of
different variables. Here, I contrast the substantive effects of creditworthiness and regime
type given the latter’s recent attention in the literature (Fordham & Walker, 2005, Goldsmith, 2007, Albalate et al., 2012). The figure clearly illustrates that regime type’s significant
short and long-run relationship with military spending. Figure 1 also demonstrates that the
substantive effect of creditworthiness eventually exceeds that of regime type. This suggests
that economic constraints influence military spending to a greater degree than institutional
constraints.15 Further, creditworthiness’ larger effect on expenditure, relative to regime
type’s often cited influence, provides a strong case for the appreciation of creditworthiness’
13
I adopt a simulation technique advanced by Whitten and Williams (2011, 2012). The simulations employ
10,000 draws of the coefficient and covariance matrices.
14
I hold Polity and the war variables at their modal value and the remaining variables at their mean.
15
Similar simulations based on the Models 2-5 (see Supplementary Appendix) are consistent with this
interpretation.
16
salient and substantial effect on security. Some caution is warranted in drawing this conclusion as previous literature suggests that democratic institutions increase creditworthiness
(North & Weingast, 1989, Schultz & Weingast, 2003). However, the negative coefficient of
regime type and the positive coefficient on IIR suggest that this relationship is not driving
the results here. If regime type and creditworthiness were truly intervening variables in the
manner suggested, their influence would move in the same direction.16 In other ways, this
result is not surprising. While credit rating has a historical relationship with democracy,
the relationship finds mixed evidence in analyses observing the past several decades (Archer
et al., 2007, Saiegh, 2005).
The conditioning effect of creditworthiness
The second hypothesis maintains that creditworthiness conditions the effect of external
threats on a state’s military expenditure. The models in Table II test this hypothesis by
adding a multiplicative term of ln(Rival CINC) and a state’s IIR credit rating (βj (IIRi,t−1 ∗
ln(RivalCIN C)i,t−1 ) to each of the equations estimated in Table I.
[Table II]
Estimation of this model on the full sample (Model 6) indicates that the interaction term
is statistically significant in the hypothesized direction. Because the coefficient provides
limited information about the significance and substantive effects of the interaction, I graph
the marginal effect of ln(Rival CINC) across the values of IIR rating in Figure 2. The
solid lines represent the marginal effect, the dashed lines indicate 95% confidence intervals
around the estimate and the histogram in the background indicates the distribution of IIR
corresponding with the percentage of observations noted on the right Y-axis. Consistent
with the hypothesis, the graph illustrates that the marginal effect of ln(Rival CINC) is
16
Additionally, I explored the potential for an interactive effect between creditworthiness and regime type
based on Shea’s (2014) assertion that democracies are more sensitive to the economic costs of war. I find
that creditworthiness’ effect is positive across regime type with no substantive difference in the effect.
17
statistically indistinguishable from zero at its lowest values. However, the marginal effect is
both statistically and substantively significant as creditworthiness increases. In other words,
ln(Rival CINC) has no effect when states have high borrowing costs but has a positive effect
among states with more favorable credit terms.
[Figure 2]
A similar relationship to the one plotted in Figure 2 is evident when employing the estimates of Models 7-9. However, the GMM model, which eliminates unit heterogeneity, fails
to demonstrate that IRR conditions impact on ln(Rival CINC). This result likely reflects
that changes in rival capabilities are slow changing and invariant for states without rivals.
As such, it is difficult to capture the effects in a model that effectively relies on within panel
differences.17 In all, the results presented in Table II and the corresponding figure are consistent with the second hypothesis. High borrowing costs or the lack of credit market access
makes it difficult to increase spending in response to the growing power of strategic rivals.
This speaks to the constraining influence of poor credit terms even in the face of growing
external threats and further demonstrates the influence of domestic political economy on a
state’s provision of security.
Further robustness checks
For several reasons, I have excluded several potentially confounding variables from the above
specifications that potentially threaten the validity of the findings. To alleviate concerns that
the results are spurious, I identify several such variables and control for them in subsequent
analysis that is available the Supplementary Appendix.
First, many studies of military expenditure consider how a state’s alliances may reduce
the burden of defense. Furthermore, states with strong ties to the international system
may receive more favorable credit terms (Rose, 2005). To address this concern, I include the
17
A country-fixed effects specification (not reported here) presents similar results.
18
logged sum of the capabilities of a state’s defensive and offensive alliance partners, in a similar
manner to the construction of ln(Rival CINC). It is also possible that a state’s governing
capacity influences both the resources it can marshall for security and its ability to repay
debts. With this in mind, I include a measure of state capacity collected by ArbetmanRabinowitz and Johnson (2008) that captures the ratio of a state’s actual tax revenues to its
expected tax revenues. Next, alternative sources of non-tax revenues may reduce a country’s
dependence on foreign capital and also correlate with a state’s credit terms as they may
either increase a state’s ability to repay or harm institutional quality. Further, there are
good reasons to believe that both sources of revenue are associated with higher military
spending or hostile foreign policies as they augment budgets and potentially decrease the
political influence of tax payers (Collier & Hoeffler, 2007, McDonald, 2007). As such, I
include both the log of foreign aid inflows and the log of oil and gas revenue.18 Lastly, a
state’s fiscal health may impact its ability to increase military spending independently of its
creditworthiness. As such, I include a control for a government’s debt burden with the lagged
ratio of public and publicly guaranteed external debt to GNI from the World Bank. In each
of the models, the IIR variable remains positive and statistically significant. The additional
models provide further evidence that a state’s credit terms have an important impact on a
state’s military expenditure. Further, among the five potential variables included, I find that
only debt burden has a statistically (negative) effect on a country’s military expenditure.
Next, I demonstrate that the central results are robust to the use of two alternative
indicators of credit risk. I present four models in Supplementary Appendix that employ
either a binary indicator of credit access in which I code a state as having credit access if
it has an S&P credit rating above ‘junk’ status (greater or equal to BBB) and those that
have no credit rating or a ‘junk’ rating as zero for the period 1990-2007 or bond spreads
(the difference between a country’s yearly average bond price and a benchmark price, the
US 10-year Treasury bond). I find that having credit access and low bond spreads are
18
Aid data is sourced from the World Bank and I employ oil and gas revenue from Ross (2013).
19
both associated with higher military spending. I also use these measures to test the second
hypothesis but only find the expected conditional relationship with the continuous bond
spread variable.
Lastly, my modeling choices depart from previous models of military spending in important ways. To demonstrate the results are not a product of my specification decisions,
I test the how IIR performs in the preferred specifications of Nordhaus et al. (2012) and
Fordham & Walker (2005). As reported in the Supplementary Appendix, IIR is positive and
significant in both cases.
Conclusion
Researchers are beginning to appreciate the variety of ways in which governments fund
security and the implications of different funding strategies for discrete outcomes like conflict
and alliance formation. This article builds on this growing research by exploring how a
government’s access to foreign capital influences the political economy of military spending.
The empirical analysis is consistent with the argument that access to affordable foreign
capital allows leaders to avoid the political consequences of more immediate methods of
finance.
These results build on previous work by demonstrating that sovereign creditworthiness
has a broad influence on security beyond major powers and conflict outcomes and that this
effect is apparent in both the short and long run. Further, the findings provide evidence that
creditworthiness conditions the strategic behavior of states. Those states that must extract
resources for spending primarily through domestic extraction are less likely to respond to
growing external threats with increased military expenditure. As such, the political economy
of international sovereign finance plays a role in limiting the provision of security within states
even in the presence of external threats.
The balance of state resources dedicated to domestic and external purposes lies at the
20
center on many world politics research questions. As such, the findings have clear relevance
for a variety of foreign policy and world politics models that assume, either implicit or
explicitly, that state and political leaders confront fixed-budget constraints. Consequently,
appreciating the influence of sovereign creditworthiness on foreign policy has the potential to
inform our understanding of a variety of outcomes scholars of world politics and policymakers
care about.
Replication data
Replication files for this article can be found at www.prio.no/jpr/datasets.
Acknowledgements
The author would like to thank Michael A. Allen, Susan Allen, Jeff Carter, David H. Clark,
Benjamin O. Fordham, Timothy Nordstrom, Michael J. Reese, Patrick Shea, and members
of the Binghamton University World Politics Workshop.
21
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Table I. Credit ratings & military expenditure
LDV
IIRt−1
log of Rival Cap.t−1
P olityt−1
log of GDPt−1
Intrastate War
Extrastate War
Interstate War
Constant
Observations
R2
pHansen
pAR(2)test
# of Instruments
LTE: IIRt−1
(1)
All States
0.887***
(0.0215)
0.00188***
(0.000491)
0.00640***
(0.00209)
-0.00437***
(0.00117)
0.100***
(0.0212)
0.144***
(0.0272)
-0.0349
(0.0346)
0.0775†
(0.0411)
-0.890***
(0.238)
2864
0.993
(2)
Non-OECD
0.881***
(0.0228)
0.00304***
(0.000705)
0.00573***
(0.00218)
-0.00398***
(0.00129)
0.104***
(0.0230)
0.176***
(0.0332)
-0.0841
(0.0630)
0.149**
(0.0730)
-0.941***
(0.276)
2201
0.991
(3)
Region Effects
0.871***
(0.0240)
0.00172***
(0.000593)
0.00615***
(0.00226)
-0.00205
(0.00149)
0.116***
(0.0230)
0.155***
(0.0268)
-0.0333
(0.0377)
0.0764†
(0.0410)
-0.966***
(0.249)
2864
0.993
(4)
Year Effects
0.887***
(0.0101)
0.00170***
(0.000338)
0.00597***
(0.00172)
-0.00414***
(0.00108)
0.102***
(0.0108)
0.141***
(0.0226)
-0.00901
(0.0348)
0.0619
(0.0402)
-0.841***
(0.159)
2864
0.993
(5)
GMM
0.852***
(0.0308)
0.00339***
(0.00117)
0.0113†
(0.00580)
-0.00315
(0.00217)
0.0836***
(0.0190)
0.180***
(0.0398)
-0.0743
(0.0818)
0.142***
(0.0544)
2864
0.576
0.536
155
0.023***
(0.009)
0.017***
0.026***
0.014***
0.015***
(0.004)
(0.006)
(0.005)
(0.003)
Standard errors in parentheses. † p < 0.10, ** p < 0.05, *** p < 0.01.
Models 1-4 estimated using panel corrected standard errors. Model 5 employs a systems GMM.
28
Table II. Credit ratings, rival capabilities & military expenditure
LDV
IIRt−1
log of Rival Cap.t−1
Rival Cap.t−1 ∗ IIRt−1
P olityt−1
log of GDPt−1
Intrastate War
Extrastate War
Interstate War
Constant
(6)
All States
0.886***
(0.0215)
0.00355***
(0.000833)
0.000938
(0.00313)
0.000143***
(0.0000543)
-0.00430***
(0.00117)
0.101***
(0.0211)
0.153***
(0.0269)
-0.0422
(0.0346)
0.0779†
(0.0402)
-0.954***
(0.241)
2864
0.993
(7)
Non-OECD
0.881***
(0.0227)
0.00406***
(0.00116)
0.00254
(0.00416)
0.0000963
(0.0000925)
-0.00395***
(0.00129)
0.104***
(0.0230)
0.181***
(0.0326)
-0.0933
(0.0647)
0.151**
(0.0730)
-0.974***
(0.279)
2201
0.991
(8)
Region Effects
0.870***
(0.0240)
0.00288***
(0.000771)
0.00191
(0.00321)
0.000110**
(0.0000542)
-0.00214
(0.00150)
0.117***
(0.0229)
0.162***
(0.0266)
-0.0385
(0.0374)
0.0742†
(0.0406)
-1.017***
(0.251)
2864
0.993
(9)
Year Effects
0.886***
(0.0101)
0.00327***
(0.000796)
0.000804
(0.00327)
0.000134**
(0.0000592)
-0.00406***
(0.00108)
0.102***
(0.0108)
0.149***
(0.0226)
-0.0170
(0.0351)
0.0630
(0.0402)
-0.905***
(0.164)
2864
0.993
(10)
GMM
0.858***
(0.0338)
0.00372**
(0.00189)
0.00772
(0.00568)
0.0000977
(0.0000675)
-0.00493**
(0.00204)
0.114***
(0.0420)
0.154***
(0.0400)
-0.0864
(0.0804)
0.116**
(0.0527)
Observations
R2
pHansen
pAR(2)test
# of Instruments
Standard errors in parentheses. † p < 0.10, ** p < 0.05, *** p < 0.01.
Models 1-4 estimated using panel corrected standard errors. Model 5 employs a systems
29
2864
0.179
0.527
108
GMM.
15
14.5
Polity Min.
Polity Max.
14
ln(Military Expenditure)
IIR Max.
13.5
IIR Min.
0
5
10
15
20
Years
Figure 1. Predicting military expenditure over 20 years: Bars indicate the 95%
confidence intervals around the estimated values.
30
4
.02
2
% of Obs.
3
.015
.01
Marginal Effect
1
.005
0
0
-.005
0
50
100
IIR
Figure 2. The marginal effect of log of rival capabilities across IIR
31