Narrow interests and military resource allocation in autocratic regimes

Justin Conrad, Department of Political Science and Public Administration, University of North Carolina at Charlotte

Hong-Cheol Kim, Chief of ROKAF Force Development Branch, Headquarters of ROK Air Force

Mark Souva, Department of Political Science, Florida State University

Abstract

Why do some autocratic states allocate more resources to the military than others? We contend that as narrow political interests have more influence on a leader, relative to broader political interests, a state’s military burden increases. Further, we argue that two domestic factors are central to explaining the relative strength of narrow political interests for military spending, and therefore variation in state military burden. First, institutions that increase the cost of political participation reduce the influence of the median citizen, increasing the strength of narrow political interests and, concomitantly, military spending. Second, as a regime ages, narrow interests become more entrenched and the regime becomes less concerned about overthrow. In turn, older regimes spend more on their militaries. We test hypotheses from this argument by examining the military burden for all autocracies over the period 1950-2000. We find that variation in restrictions on political participation and the age of the regime are central to understanding differences in military spending among autocracies. Further, once these institutional features are taken into account, we find only modest support for the view that certain types of regimes spend more than others. What matters is not regime type but specific institutional features that affect the strength of narrow interests and vary across, and within, autocratic regimes.

Keywords: military spending, international security, autocracies

Corresponding author: justinconrad@uncc.edu

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Introduction

Why do some autocratic regimes spend more on their militaries than other autocracies?

Scholars have devoted little attention to variation in the military burden—military spending as a

percentage of gross domestic product—among autocracies, and the research that does exist tends

to focus on the difference between military regimes and non-military regimes (Zuk & Thompson,

1982). These studies, as well as research distinguishing between democratic and autocratic

spending patterns (Goldsmith, 2003; Bueno de Mesquita, et. al., 2003; Fordham & Walker,

2005), argue that leaders most often allocate resources to the group(s) keeping them in power.

If military spending is a decision made, in part, to satisfy domestic interests, then we should see a

great deal of variation in autocratic spending (assuming the influence of domestic interests is not

constant across these states). Indeed, there exists a great deal of variation in the military burden

among autocracies. In 1995, for example, Cuba’s military burden was around 1.2% of its gross

domestic product, while North Korea allocated more than 25% of its GDP to the military. This

study identifies domestic sources of variation in military spending across all autocratic

governments.

We argue that the relative strength of narrow interest groups is central to understanding

the military burden, and that the primary factors affecting the strength of narrow military

interests are the costs of political participation and the age of the regime. As institutional

restrictions on political participation increase, the cost of political mobilization increases and the

influence of the median citizen decreases. When the influence of the median citizen decreases,

narrow bureaucratic interests increase. These budget-maximizing bureaucratic interests, in turn,

lead to an increase in government spending, including military spending. We also argue that the

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age of autocratic regimes has a significant effect on the military burden. As a regime ages,

narrow interests become more entrenched and are better able to capture the government for their

own purposes. At the same time, as the government ages and consolidates its power, it becomes

less concerned about violent overthrow and allocates more resources to the military.

We test this argument by examining the military burden for all autocratic states between

1950 and 2000. The empirical analysis shows that our measures of narrow interest group strength

positively affect the military burden. At the same time, we find little empirical support for

differences in military spending across different types of autocratic regimes (e.g. civilian,

military, and monarchical dictatorships). Our findings suggest that what is more important than

regime type are specific institutional features that affect the strength of narrow interests within

the state. We discuss implications of this argument for understanding variation in the military

burden among democracies, the provision of public goods among autocracies, and autocratic

conflict propensities in the conclusion.

Influences on the military burden

Research on the major determinants of the military burden may be usefully classified into

two categories: those that focus on external factors and those that focus on internal factors.

During the Cold War, the influence of external factors on military spending received significant

attention. In a seminal book, Richardson (1960) posits an action-reaction linkage regarding arms

expenditures. When a potential adversary increases its military spending, states respond by

increasing their military spending. Ostrom (1978) finds empirical support for this reactive-

linkage model of defense spending. Others have found that the military burden of rivals

positively affects one’s own military burden (Collier & Hoeffler, 2002; Dunne & Perlo-Freeman,

2003; Dunne, Perlo-Freeman & Smith, 2008). Similarly, Nordhaus, Oneal & Russett (2012)

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demonstrate that states with a higher probability of being involved in militarized disputes are

likely to spend more on their militaries. The military spending of allies may also affect one’s

military spending, with smaller states tending to free ride (Olson & Zeckhauser, 1966; Oneal &

Whatley, 1996).

Recent research addresses cross-national differences in the military burden but focuses on

internal determinants. According to this line of thinking, the threat environment is not the only

factor leading to differences in military expenditures across states. A significant body of research

finds that public opinion exerts a major influence on a state’s defense spending (Hartley &

Russett, 1992; Eichenberg & Stoll, 2003). National income generally has a positive influence on

military spending (Goldsmith, 2003; Nordhaus et al., 2012). De Soysa & Neumayer (2008) point

to the importance of ethnic heterogeneity on military resource allocation. More heterogeneous

regimes are more concerned about the possible dispersal of arms and, accordingly, allocate fewer

resources to the military. As important as these factors may be, they are non-manipulable, at least

in the short-run.

Political institutions are another central feature explaining cross-national differences in

military spending (Goldsmith, 2003; Fordham & Walker, 2005). Specifically, research shows

that democracies allocate fewer resources to their militaries than non-democracies. This line of

research, however, does not address the possibility of variation in military spending among

autocracies.

Nevertheless, some research points to variation in the military burden among autocracies,

primarily differences between military and non-military regimes. These studies, like the research

distinguishing between democratic and autocratic spending patterns, postulate that leaders cater

to their corporate interests; that is, they allocate the most resources to the group keeping them in

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power. Summarizing the early literature on military governance, Jackman (1976: 1079) notes

that the corporate self-interest of military governments should lead to a disproportionate amount

of resources being diverted to the military “even when such efforts conflict with the interests and

aspirations of the wider society.” Thus, regimes led by the military should allocate more to the

military than other regimes. Supporting this argument, Nordlinger (1970) and Thompson (1973)

find that successful military coups lead to an increase in a state’s military burden. Yet these

studies do not examine variation across autocracies. Indeed, Zuk & Thompson (1982: 67) find

that “the growth of military spending of military regimes is not distinctive compared with that of

mixed and civilian regimes.” Further, despite the intuitive appeal of the corporate interest

argument, it has two limitations. First, at best it only accounts for differences between military

regimes and non-military autocratic regimes. Is there a source of variation for the military burden

across all types of autocratic regimes? Second, while military rulers may have a corporate

interest in keeping the military strong, they also have a personal interest in keeping the military

tightly under their control to prevent a coup. A strong military is better positioned to serve as a

rival power within the state and no one is likely to understand this better than a military leader.

To this end, a military ruler, perhaps more than other autocratic rulers, has an incentive to keep

the military relatively weak.

These two arguments about the military burden (democracies spend less than any other

regime type, military regimes spend more than other autocratic regimes) have something in

common: they are ultimately about constituent interests. They assume that leaders make

choices to satisfy their core constituents. According to the first line of research, democracies

devote fewer resources to their militaries because they rely on a broad cross-section of the

population to stay in office. Assuming the public prefers spending on welfare and

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infrastructure (education, transportation, etc.) to spending on the military, democratic leaders

should allocate resources away from the military in an effort to avoid being removed from

office. According to the second line of research, military regimes should spend more on

defense than non-military regimes, since the military is where the leader draws his key

support. Satisfying the corporate interests of the military, like satisfying the public in

democracies, is a way to avoid being thrown out of office.

Drawing insights from these arguments, we contend that variation in autocratic

military spending is primarily a function of the relative strength of narrow military interests

and that the strength of these narrow interests are a function of two domestic factors:

constraints on political participation and the age of the regime. First, institutional constraints

on political participation significantly affect the cost of political participation. As constraints

on participation decrease, leaders pay more attention to the public’s preferences (Lake &

Baum, 2001), while focusing relatively less attention on narrow bureaucratic interests. Second,

as autocratic regimes age, the strength of narrow interests tends to increase (Olson, 1982) and

military resource allocations increase. We elaborate on these points in the next section.

Political participation, regime age, and military spending

Leaders allocate resources with an eye to retaining office. We draw on the argument of

Lake & Baum (2001) to illustrate how office seeking affects military spending. All leaders

aim to maximize rents. The state is a political market, yet the contestability of the market can

prevent a leader from extracting as much rent as s/he desires (Lake & Baum, 2001: 593).

Contestability decreases rent extraction by enlarging a leader’s focus; that is, as contestability

increases, leaders need broader support to remain in power. In turn, as contestability increases,

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rent extraction decreases, leaders pay more attention to the preferences of a broader group of

citizens, and are less bound to narrow bureaucratic interests.

As the influence of narrow bureaucratic interests increases, government spending

increases (Olson 1965, Larkey et al 1981). In fact, the disproportionate influence of narrow

interests often leads governments to make budget allocations in which the costs exceed the

benefits (Aranson & Ordeshook, 1978). Narrow interests that benefit from military spending

include the military and defense contractors.

The relative strength of the influence of narrow interests and broad interests on leader

decision-making is a function on the contestability of the regime. Contestability is largely a

function of restrictions on political participation because such restrictions increase the cost of

assembly. As the cost of assembly decreases, political mobilization increases and individuals

within society are more likely to voice their preferences. Concomitantly, as it becomes easier

for more people in society to participate in the political process, challengers are more likely to

emerge. In other words, as freedom of assembly increases, a state’s leadership is more likely

to be pressured through mass protests and the threat of a peaceful leadership challenge

increases. Therefore, as freedom of assembly increases, a leader’s focus necessarily expands

beyond narrow interests and core supporters. When leaders need broader support to remain in

power narrow bureaucratic interests have less influence on a leader’s decision. Conversely,

when freedom of assembly is strongly curtailed, it is very costly for individuals to organize

and pressure the government, and when individuals have little voice in public policy, their

preferences are less likely to be enacted. In brief, as restrictions on political participation

increase, the cost of assembly increases, and the influence of the median citizen decreases.

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When the influence of the median citizen decreases, leaders have a freer hand in

pursuing policies for narrow interests over the interests of the broader public. Similarly, Gates

et al. (2006) argue that autocracies with strong executive authority and high levels of political

participation are more unstable than other autocratic regimes. The reason for the instability is

that the median citizen tends to have different preferences than the dictator. Autocrats

recognize that there is a tradeoff between allowing political participation and pursuing their

preferred policies (Przeworski, 2000). When possible, they will limit participation to pursue

narrow policies and policies that protect their hold on power. Further, Ghandi & Prezeworski

(2007: 1293) note that the choice to institutionalize, for example, by allowing political parties

in a legislature, “must entail policy compromises.” We contend that one of these policy

compromises is a reduction in military spending. Similarly, Bueno de Mesquita & Smith

(2009, 2010) argue that leaders stave off revolutionary threats by restricting coordination

goods, increasing the cost of political participation. That is, leaders recognize that more open

political participation means the leader does not have as much freedom to pursue narrow

polices since more interests have to be appeased.

Hypothesis 1: Among autocracies, as the cost of political participation decreases,

the military burden decreases.

In addition to the cost of political participation, we argue that a second domestic factor,

regime age, influences the relative strength of narrow interests and therefore military

spending. Olson (1982) contends that time is critical to interest group strength. This is largely

because of “start-up costs in creating any organization” (Olson, 1982: 38). Once a group

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successfully achieves collective action, it “develop[s] strength over time” (Olson, 1982: 40).

Once developed, interest groups persist until there is a major “social upheaval” (Olson, 1982:

40).

Based on these arguments about interest group strength, the logic of collective action

(narrow interests are more likely to organize than broad interests) and the accumulation of

narrow interest groups, Olson (1982) famously concludes that the increasing influence of

interest groups retards economic growth. While a majority of empirical studies find support

for this claim (Choi, 1983; Heckelman, 2000; Coates & Heckelman, 2003), some do not (e.g.

Knack & Keefer, 1997; Knack, 2003). Recent research, however, more precisely examines the

causal logic of Olson’s arguments and finds support for it (Horgos & Zimmerman, 2009;

Coates, Heckelman, & Wilson, 2011). More broadly, Olson’s arguments imply that an

increase in the strength of narrow-interest groups will increase government spending; indeed,

Olson (1982: 37) writes that “the accumulation of distributional coalitions increases…the role

of government…” According to this logic, special interests should become better at organizing

and capturing the government over time. Olson’s discussion and existing tests of his argument

focus on democratic regimes. We contend that the same logic applies to autocratic regimes.

That is, as autocratic regimes age, interest groups become more entrenched and are better able

to capture the government for their own purposes. This should be true of all interest groups,

including the military and defense-oriented groups.

Regime age also affects military spending by decreasing the risk of a coup. Leaders of

new and transitioning regimes face an acute risk of violent overthrow. As a regime ages, the

state’s leadership is less likely to face a coup or other violent challenge to its power.

Research suggests that states which have recently undergone major changes to their political

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institutions are at the highest risk for regime change through civil war (Tarrow, 1994; Hegre

et. al., 2001) and through coup d’état (Belkin & Schofer, 2003; Svolik, 2009). Domestic

violence, according to Hegre et al. (2001), is more likely among states that are in the midst of

regime transition, or have recently experienced such a transition. Even states that have

transitioned to democracy are not immune from such regime instability. Gurr (1993; 2000)

argues that younger democracies are at high risk of political violence because opposition

groups may capitalize on recent political instability to launch attacks against the state. More

generally, Collier & Hoeffler (2007) find that states facing high risks for coups regularly

reduce their military spending. While a long tenure does not guarantee peaceful power

transitions, older regimes are less susceptible to political violence than newer regimes, ceteris

paribus. Older regimes should therefore have less fear of a well-funded military. Ugandan

military reforms provide one example of such logic. After coming to power in 1986, the

reformist leader and current president of the country, Yoweri Museveni, made an immediate

effort to reduce the influence of Uganda’s military in its national politics (Ruzindana, 2010),

cutting the state’s military burden nearly in half. The recent memory of coups and military-led

rebellions may have incentivized the Museveni government to decrease military spending in

an effort to prolong its own time in office.

These arguments motivate our second hypothesis:

Hypothesis 2: Among autocracies, as a regime ages, the military burden increases.

Research design

Dependent variable and unit of analysis

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Scholars have long debated the most appropriate measure to capture military resource

allocations (Goertz & Diehl, 1986; Fordham & Walker, 2005). While a wide range of indicators

have been developed over the years, the most popular has been a measure of total military

expenditures as a percentage of a state’s gross domestic or national product (Goertz & Diehl,

1986, Dunne & Perlo-Freeman, 2003; Goldsmith, 2003; De Soysa & Neumayer, 2008; Fordham

& Walker, 2005). Such a measure, commonly thought of as representing a state’s military

burden, indicates the share of a country’s economy that is dedicated to military pursuits. We

follow this convention by using the military burden as our dependent variable. Data for this

variable comes from Fordham & Walker (2005). Since cross-national data on military

expenditures and GDP have often been criticized for inconsistency and other problems (Brzoska,

1995), in sensitivity analyses, we test our hypotheses using alternative measures of the dependent

variable. These include the absolute level of a state’s military expenditures and the number of

military personnel as a percentage of a state’s population. 1

The unit of analysis for this study is the country-year. The temporal domain of the models

covers the period 1950-2000, while the spatial domain of the study includes all autocracies in the

world for which data are available. We define autocratic regimes as those lacking contestability

for the head of state (Cheibub, Gandhi & Vreeland, 2010). In 1950, there are 32 autocratic states

in our data, 88 in 1970, and 70 in 1997.

Independent variables

The focus of our study is on two domestic factors, the cost of political participation and

regime age. While the cost of political participation will surely vary from individual to

individual, we argue that institutional restrictions on political participation are the central factor

1 Results from these additional analyses are included in the online appendix.

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affecting these costs. As institutional restrictions on political participation increase, individuals

are more likely to be sanctioned for challenging the regime. It is also much more difficult, and

therefore more costly, to solve the collective action problem associated with mobilizing a

challenge to the incumbent. With fewer restrictions on political participation, it is easier to solve

collective action problems because it is easier to assemble and distribute information throughout

society. Further, the easier it is to solve collective action problems, the easier it is to generate

opposition against the incumbent leadership. Restrictions on political participation, then, are

directly related to the cost of political participation. Therefore, as restrictions on political

participation decrease, broader interests have greater influence and military spending decreases,

on average.

To measure the cost of political participation, we use data from the Polity IV project.

Our variable, Political Participation, is the Polity variable, ‘parcomp,’ which “measures the

degree to which this political participation is free from government control” (Polity IV users’

manual: 68). Our variable ranges from 1 to 5. A score of one indicates that political

participation is heavily restricted: “the regime bans the organization of all rival political

parties and oppositional social movements” (p. 69). A score of two indicates that there is some

but very limited political participation. Unlike a score of one, a score of two indicates that the

regime is not willing or not able to effectively ban all political participation. Nevertheless, at

least 20% of the adult population is denied the right of political participation. A score of three

indicates that less than 20% of the adult population is denied the right of participation (p. 75).

In addition, a score of three indicates that participation is highly factionalized. A score of four

indicates that political participation is open to almost all people in society and that the

government interferes in elections, typically through police brutality or some limits on

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political speech and assembly (p. 78. 80). A score of five indicates that “No major social

group or groups are routinely excluded from the political process” (p. 82). Finally, we drop the

Polity scores of zero from our analysis, because this value reflects political participation in

very weak or failed states. In these states, military resource allocation should be relatively

limited (as should resource allocations of any kind).2

To measure Regime Age, we create a variable using data from Cheibub, Gandhi, &

Vreeland, 2010). They extend the Przeworski et al. (2000) conceptualization of democracy to

identify different types of dictatorships. Specifically, they distinguish between autocracies

based on the nature of their “inner sanctums” (Cheibub et al., 2010: 84). This leads them to

identify three types of autocracies: military regimes, in which military officers dominate the

decision-making process, monarchies, in which kin control the reins of government, and

civilian dictatorships, in which a political party or political bureau is dominant. We code a

state as having experienced a major political transition when its regime type (democracy,

military, monarchy, or civilian dictatorship) changes from one year to the next. The variable

Regime Age is the number of years since one of these institutional changes occurred; we take

the natural log of this count since we expect the effects of age to diminish over time.3

For robustness, we also provide results using alternative measures of our key concepts.

The concept of cost of political participation has not received much attention from researchers in

the way of measurement, as most extant data classifies countries by regime type, rather than by

specific characteristics like participation. Gandhi & Przeworski (2007) offer an alternative to our

2 We analyzed all model specifications with an alternative coding of the independent variable where we code the ‘0’s as scores of ‘6,’ with the logic that failed states should spend the least amount of resources. The results are nearly identical to our main results. We also analyzed all models with Polity’s original, unaltered “parcomp” score. The results are consistent with the findings presented in this paper. All models using the alternative coding schemes are available in our online appendix.3 We also estimated the four main models in Table 2 with un-logged versions of the age variables. The significance of these variables is reduced, but all of them remain statistically significant in one-tailed tests.

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measure of political participation for autocratic regimes. They create a trichotomous variable

which describes a state’s party system. The variable equals ‘0’ when there are no parties, or

multiple parties but no legislature. It takes on a value of ‘1’ if there is only one party in the state,

and ‘2’ if there are multiple parties. These distinctions essentially address the level of

restrictions on political participation, as states with one party, no parties, or no legislature have

significantly more restrictions than states where multiple parties are allowed to operate

autonomously. The variable, Multiple Political Parties, equals ‘0’ for states with one or no party

systems (a ‘0’ or ‘1’ on the original scale) and ‘1’ for states with multiple parties and a

legislature (a ‘2’ on the original scale).4 We also examine an alternative measure of regime age.

Using data from Powell & Thyne (2011), we construct a measure that counts the number of years

since the state last experienced a coup. The variable, Years Since Last Coup, is the natural log

of this count, because we expect the effect of stability to diminish over time.5

Control Variables

We include the battery of control variables included in Fordham & Walker’s (2005)

study. Specific coding schemes and sources for these variables are listed in the online appendix,

but we briefly describe the logic for including each. First, military conflict and the threat of

military conflict are historically strong predictors of military resource allocation. The models

therefore include the number of battle deaths as a percentage of the population for both external

wars (Battle Deaths %) and internal wars (Civil War Deaths %). Both of these variables should

be positively associated with military spending. Likewise, the very threat of conflict may drive

4 To check the sensitivity of our measurement choices, we analyzed the models using the original trichotomous measure, and the results are consistent. These additional analyses are available in the online appendix.5 We also measured regime age as the number of years since the government underwent a significant institutional change, as defined by the Polity project, and the number of years since the last coup or revolution with coup data drawn from Banks (2002). Nothing significant changes in our findings, and these results are available in the online appendix.

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up military spending. Indeed, this is the logic of the entire action-reaction/arms race literature.

If a state faces a particularly strong adversary, it may increase its military spending in an effort to

“catch up.” The total capabilities of a state’s rivals are therefore included to capture the level of

threat facing a state in any given year (Rivals’ Power). Conversely, states which have a number

of powerful allies may have less of an incentive to allocate resources to their own militaries.

Collective security may provide an alternative to domestic military spending, so the total

capabilities of a state’s allies (Allies’ Power) is also included. Fordham & Walker (2005) note

that there are additional domestic reasons for a state to increase its military spending. They

include a measure of the population of a state’s empire (Empire Population) to capture the

magnitude of assets the state is responsible for globally. Larger empires should require larger

militaries to maintain control. Finally, since the dependent variable is military spending as a

percentage of GDP, total GDP is included as an independent variable. This addresses the fact

that larger states need fewer resources as a share of their total resources in order to build strong

militaries. In addition, the inclusion of the GDP variable accounts for the possibility that leaders

may use funding allocations for political purposes when the economy is weak (Mintz, 1988).

Empirical analysis

We begin with descriptive statistics illustrating the relationship between the cost of

political participation, regime age, and military spending. Table 1 highlights two relationships.

First, reading across, we observe that as a regime ages, the mean level of military burden

increases significantly. This holds for regimes with very little political participation and

moderate political participation. Second, reading down each column we observe that the mean

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level of military burden is lower in states with more political participation. Overall, the data is

consistent with both Hypotheses 1 and 2.

Next, we examine a multivariate model and employ Prais-Winsten regression to examine

military burden levels within and across nations and use panel corrected standard errors with a

first order autoregressive correlation structure (Beck & Katz, 1995).6 We find support for both

hypotheses across a variety of model specifications (see Table 2). Models 1 and 2 indicate that

as political participation increases, a state’s military burden decreases.7 Using an alternative

measure of participation, Multiple Political Parties, also produces a significant and negative

coefficient (Models 3 and 4). In other words, having a multiple party system, rather than a single

or no-party system, leads to a decrease in a state’s military burden. We also find strong support

for Hypothesis 2: the age of a regime positively affects military resource allocation. In Models 1

and 3, older regimes allocate more resources to their militaries, on average, than younger

regimes. Likewise, when we analyze the time since a state last experienced a coup (Models 2 and

4), higher values of this variable are associated with higher values on the dependent variable.

Each of these models is consistent with the argument that as narrow interests increase their

influence on the government, military spending increases.

In addition to our findings for the key independent variables, the results underscore the

relevance of several control variables. Civil war increases the military burden in some models,

but has no statistical effect in others. Interstate war and an increase in the power of a state’s rival

consistently increase military resource allocation across the four model specifications. These

findings lend support to earlier arguments that external threats and arms races are also key

determinants of military spending.

6 This is the approach used by Fordham & Walker (2005). 7 To examine the possibility that other components of the Polity score could be driving the results, we analyze the effect of executive constraints below.

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Table 3 displays predicted values of the military burden derived from Model 1. The first

row indicates the predicted level of military burden when Political Participation equals ‘1’ and

the regime is two years old. All other variables are held constant at their mean values. In this

scenario, states spend, on average, 2.99% of their GDP on the military. In the second row, when

the regime age is increased to 20 years, military spending rises by 20%. In the final row of the

table, political participation is increased from its minimum value to its maximum. Such a change

in political participation reduces the military burden by 51%.

Several cases are illustrative of the relationships between Political Participation, Regime

Age, and military resource allocation. For example, North Korea has almost no political

participation and a very high military burden, averaging nearly 23% of its GDP in our sample.

Further, North Korean military resource allocation has increased over time. In the 1960s, they

allocated about 14% of their economy to the military. In the 1970s, military spending increased

to about 16%; in the 1980s, it went up to 25%, and then to 26% in the 1990s. In contrast, South

Africa, a non-democratic regime with multiple political parties, allocated just over 1% of its

GDP to the military over the period 1950-1993. Equally important, we observe that South

Africa’s military resource allocation increased over time. In the 1950s, the apartheid regime’s

military spending averaged only about .43%. It nearly doubled in the 1960s to .84%, increased to

1.49% in the 1970s, and increased further to 1.68% in the 1980s. Fiji and Bolivia offer further

evidence of how specific changes in institutional restrictions on participation affect military

spending. From 1970 to 1986, Fiji had multiple political parties and its military spending

averaged about .37%. A military coup occurred in 1987 and the new regime barred all political

parties until 1991. In this period, Fiji’s military spending increased to .95% of its GDP. And

since 1950, when Bolivia has had fewer than two political parties, their military spending has

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averaged about .82%, but whenever they had two or more political parties, their military

spending was less than half that amount, .36%.

Sensitivity analysis: Alternative measures of military spending and estimation procedure

Are our results robust to alternative estimation approaches and alternative measures of

the dependent variable? We initially employ Prais-Winsten estimation with panel corrected

standard errors. However, there are concerns about estimating the autocorrelation structure given

that some states in our sample drop out if they transition to democracy. We re-estimated our

main set of models from Table 2 using a lagged dependent variable and random effects.8 In all

models, the coefficients on our primary independent variables continue to support our

hypotheses: political participation is consistently significant and negative, while the measures of

regime age are always significant and positive. It should be noted, however, that the coefficient

on the regime variables becomes significant and negative.9 Contrary to the corporate argument,

however, we find that monarchies allocate more to the military than civilian or military regimes,

while allocations between civilian and military regimes cannot be statistically distinguished.

Next, we examined two other measures of military resource allocations, military

personnel as a percentage of total population and a regression based index of state militarization

created by Diehl (1985). Again, we find support for our hypotheses with each of these measures.

And with one exception, the regime variables are insignificant in these models.

Finally, we test our hypotheses using an alternative model specification and measure of

the dependent variable. Nordhaus, Oneal & Russett (2012) offer a sophisticated analysis of

military spending as a function of the probability that a state will be involved in a militarized

8 As many studies of military spending suggest (e.g., Batchelor, Dunne & Lamb, 2002), there are often country-specific factors which also influence the level of military spending.9 Full results of this analysis are in the online appendix.

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dispute. This value, Predicted Probability of Conflict, is obtained by regressing a set of

independent variables (the “liberal-realist model” in their terms) on dyadic disputes and

obtaining the probability of conflict within each dyad. These probabilities are then aggregated to

create an overall probability that the state will be involved in any conflict in a given year.

Nordhaus et al also control for each state’s real gross domestic product (logged), its level of

democracy, the log of military expenditures by the state’s allies (Spending-Friends) and the log

of military expenditures by non-allies (Spending-Foes). The dependent variable in their study is

the log of military spending in constant dollars measured with purchasing power parities, and the

temporal period is 1950-2000.

Although Nordhaus et al. (2012: 501) test their models using a number of estimation

techniques, they state that the “preferred” model is one which uses an instrumental variable

approach with a lagged dependent variable and no specified autocorrelation structure.10 We

follow their approach, using two lags of Predicted Probability of Conflict and GDP as

instruments for the lagged dependent variable.

Table 4 displays the results of our variables situated in the Nordhaus et al. model

specification and using their data on military spending and estimation methods. As before, our

hypotheses are supported and are robust to different measures of the independent variables.

Political participation is consistently negative and significant, while regime age is consistently

positive and significant. It is worth noting, however, that the regime type variables (civilian and

military) are now significant, with one exception. The overall conclusions are therefore

unchanged from our previous models: political participation and regime age seem to be

10 We also estimated a model using a lagged dependent variable and a first-order autocorrelation structure. Nothing substantive changes in our results.

19

important determinants of a state’s military expenditures, even after controlling for a robust

measurement of external threat.

Sensitivity analysis: Political participation or executive constraints?

We have argued that political participation is a key institutional feature that drives

autocratic military spending. Notwithstanding the robustness of our findings to this point, it is

natural to ask if other components of the Polity democracy-autocracy index better account for

variation in state military spending? Gleditsch & Ward (1997) show that the Polity executive

constraints component correlates very highly with the Polity democracy index. Do executive

constraints better explain autocratic military spending than political participation? We contend

that executive constraints is a less useful indicator for understanding the military burden

because it says less about the influence of narrow and broad interests on a leader.

Nevertheless, one way to examine the relative explanatory power of these two aspects of the

Polity democracy indicator, political participation and executive constraints, is to include both

of them in the same model. Table 5 shows the results of these estimations. We continue to find

a negative and significant relationship between political participation and a state’s military

burden and, equally important, no relationship between executive constraints and the military

burden.11 These findings lend additional support to our contention that a critical institutional

element that affects military resource allocation in autocracies is the level of political

participation.

Sensitivity analysis: Outliers and endogeneity

11 This finding holds when we employ different estimation techniques, different dependent variables, and drop outliers.

20

Another potential danger in any estimation is the effect of outliers. Our dependent

variable of military spending as a percentage of GDP has a maximum value of 137% in our

sample, while the mean is around 3%. North Korea in 1958 is the state with this large amount of

military spending, and it is also the state with the largest residuals in our analysis. Kuwait in

1992 is the only other observation in which military spending is over 100% (107%), and in 1991,

Kuwait’s military spending is recorded as 66%. These are the only observations in our sample in

which military spending is over 50%. We examined the sensitivity of our results to these outliers.

When we re-estimate our models without the North Korea 1958 observations, our findings are

unchanged. If we also drop the Kuwait 1992 observation, we still find support for our

hypotheses, but the coefficients on the regime variables become statistically significant and

negative.

We next consider the possibility that some regimes may emerge during times of conflict.

In such a scenario, military spending might be unusually high due to the costs of fighting the

conflict, so after the new regime comes to power, spending is bound to decline (biasing the

results in favor of our regime age hypothesis). To account for this possibility, we controlled for

regimes that began either in the same year as a war, or in the year following a war.12 Our main

independent variables are consistent with our expectations, and this new control variable is

insignificant in every model specification. In another set of models we control for the possible

confounding effect of the Cold War. It is possible, for example, that increased military spending

during the Cold War was partially a function of military aid transfers between states. Further, it

is possible that states with lower levels of political participation may have been more likely to

12 We consider a state to be involved in a war if it suffers one or more casualties as a result of a civil or interstate conflict, according to the Correlates of War project (Sarkees, 2000).

21

receive such aid. Controlling for the Cold War, however, does not change the conclusions from

any of our previous models.

Finally, we conducted a test for reverse causality. If increased military spending leads to

decreased participation (reverse causality), then we should also observe that increased military

spending leads to less political unrest in society for unrest is a form of political participation. For

this test, we examine three measures of political unrest. The first is Banks’ Conflict Index. This

is a weighted sum of assassinations, general strikes, guerilla warfare, government crises, purges,

riots, revolutions, and anti-government demonstrations. Pickering & Kisangani (2010) argue that

this Conflict Index should be divided into Mass Unrest and Elite Unrest components. Mass

Unrest is an additive index of anti-government demonstrations, riots, and general strikes, while

Elite Unrest includes government crises and purges.

We regress each unrest variable on military spending. If endogeneity is probable, then we

should find a negative relationship between the military burden and unrest: more resources

allocated to the military leads to less unrest. We estimated this model with a variety of estimation

techniques: (1) time-series cross-section random effects with a first-order autoregressive process,

(2) time-series cross-section random effects with a first-order autoregressive process and a

lagged dependent variable, (3) time-series cross-section fixed effects with a first-order

autoregressive process, (4) time-series cross-section fixed effects with a first-order

autoregressive process and a lagged dependent variable, and (5) time-series cross-section panel

corrected standard errors with a first-order autoregressive process. We do not find much support

for endogeneity. Of the fifteen models we estimated, only once did we find a negative and

statistically significant relationship between unrest and a state’s military burden.

22

Conclusion

Extant research shows that more democratic regimes expend fewer resources on their

militaries than less democratic regimes (Goldsmith, 2003; Fordham & Walker, 2005). It is

increasingly recognized, however, that autocracies are not institutionally identical (e.g. Geddes,

2003; Ghandi, 2008; Ghandi & Prezeworski, 2007; Bueno de Mesquita et al., 2003; Svolik,

2007; Weeks, 2008; Uzonyi et al, 2012). We extend this line of research. The reason democratic

states allocate fewer resources to the military than autocratic states is because narrow military

interests are relatively weaker. We argue that a similar dynamic can account for variation in

military spending across autocratic states. Factors that strengthen the influence of narrow

interests increase military spending while forces that weaken the influence of narrow interests

decrease such allocations. We focus on two factors that affect the strength of narrow military

interests: the degree of political participation and the age of the regime. When participation is

minimal, narrow interests are stronger. Similarly, as a regime ages, narrow interests have more

influence on the leadership.

Tests of the participation and regime age hypotheses find that they are consistent with the

data. Participation decreases overall military spending as well as a state’s military burden, while

regime age increases them. These findings are robust to alternative measures of the independent

variables, the dependent variable, model specification, and estimation approach. In addition, we

find limited evidence that specific regime types influence the military burden, after one controls

for participation and regime age. In some analyses, however, we do find that monarchies allocate

more to the military than civilian or military dictatorships. Extant arguments would seem to

suggest that either military or civilian dictatorships would spend more. While we do not find

23

robust support for it, we believe it calls for further scrutiny and theoretical attention. In addition,

we think it would be valuable to collect bilateral data on military aid.

From a specific policy perspective, an important implication of our research is that more

contestation reduces military spending. Since political participation is a manipulable feature, this

may be a useful to way to reduce worldwide military spending, particularly since we also find

that state military spending is correlated with the spending of potential rivals. From a theoretical

perspective, our findings support the notion that regime age tends to empower narrow interests,

even in autocratic regimes. In other words, the age of a political regime has the potential to tell

us much about political influences on the leadership, the type of behavior we might expect from

a state, and other useful information. Regime age is not just a descriptive, but a useful proxy for

the strength of narrow interests in society. Nevertheless, we believe future research should work

on developing better measures of the strength of military interest groups in society. One place to

start is to look for variation in military spending among states with similar participation and

regime age scores.

Finally, the theoretical framework we advance should prove useful for understanding

other aspects of autocratic government spending, regime survival, and international conflict.

First, research consistently shows that democratic states are better at providing public goods than

autocratic states (Lake & Baum, 2001; Bueno de Mesquita et al., 2003). Our measure of military

spending are not direct measure of public good provision, but it is likely that the correlation

between military spending as a percentage of GDP and military spending as a percentage of

government spending shows a strong correlation. Our theory, then, may shed light on variation in

public good provision among autocratic states. Second, we argue that states spend less on the

military when the military and its corporate supporters have less influence on governmental

24

decision-making. This may provide insight on the occurrence of military coups. Coups are

probably not simply a function of the military wanting power but the military having significant

influence in the government already, as witnessed in the 2011 military coup in Egypt. Third,

when military interests are stronger, militarized conflict may be more likely. In a different

context, Horowitz et al (2005) show that leaders with more hawkish personal characteristics are

more likely to initiate conflict. Similarly, states with more hawkish characteristics may be more

likely to initiate conflict. Importantly, hawkish characteristics are not simply a function of

regime type, but domestic political institutions more broadly. In conclusion, institutions and the

age of a regime influence of the strength of narrow interest groups relative to broader interest

groups; in turn, the relative strength of narrow interests influence leader decisions in general and

military spending in particular.

Replication data

Replication statement: The dataset, codebook, and do-files for the empirical analysis in this article can be found at http://www.prio.no/jpr/datasets.

Acknowledgements

We thank Christopher Reenock, David Lektzian, Brandon Prins and the reviewers and editor for many helpful comments. All errors remain our own.

25

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Table 1. Average autocratic military spending as a percentage of GDP, 1950-1997.

Regime age <= 5

Regime age > 5 & < 24

Regime age >= 24

Participation low (Parcomp = 1)

1.91 3.69 7.45

Participation high(Parcomp = 2-5)

1.17 1.45 2.18

Fordham & Walker (2005) data on military spending

31

Table 2: Openness of political participation and regime age of non-democratic states, 1950-1997

Dependent variable: Military spending as % of GDPFordham &

WalkerModel 1 Model 2 Model 3 Model 4

Political participation - -0.38*** -0.35*** - -(0.08) (0.08) - -

Regime age (logged) - 0.26** - 0.24** -- (0.11) - (0.10) -

Multiple political parties - - - -0.46*** -0.48***- - - (0.10) (0.11)

Years since last coup - - 0.19*** - 0.22***- (0.07) - (0.07)

Civilian leadership - - - -1.39 -1.71*- - - (1.02) (1.00)

Military leadership - - - -1.11 -1.48- - - (1.07) (1.03)

Democracy -0.05*** - - - -(0.02) - - - -

Battle deaths % 11.19** 12.00** 11.74** 15.24***15.46***(4.80) (4.85) (4.80) (4.93) (4.90)

Civil war deaths % 0.07 0.06 0.01 0.13*** 0.09**(0.05) (0.05) (0.06) (0.04) (0.04)

Rivals’ power 5.64** 7.05*** 6.89*** 9.29*** 9.88***(2.52) (2.32) (2.34) (2.17) (2.08)

Allies’ power -0.45 0.40 0.26 0.79 1.15(2.44) (2.36) (2.37) (2.26) (2.22)

GDP -0.01*** -0.01*** -0.01*** -0.01*** -0.01***(0.01) (0.01) (0.01) (0.01) (0.01)

Empire population 0.01*** 0.01*** 0.01*** 0.01*** 0.01***(0.01) (0.01) (0.01) (0.01) (0.01)

Constant 2.71*** 3.04*** 3.20*** 3.55*** 3.92***(0.37) (0.43) (0.40) (1.16) (1.07)

Observations 3414 3397 3397 3416 34160.015 0.020 0.020 0.023 0.025

* p<0.10; ** p<0.05; *** p<0.01 (two-tailed)(Panel-corrected standard errors in parentheses)

32

Table 3: Effects of increased political participation & regime age on military spending (Model 1)

Predicted value Percentage change

Baseline model 2.99 -(political participation=1; regime age=2 years)

Increase regime age 3.59 +20%(from 2 to 20 years)

Increase political participation 1.47 -51%(from 1 to 5)

Values of remaining independent variables held constant at meansPredicted values are expected military spending as % of GDP

33

Table 4: Openness of political participation and regime age of non-democratic states, 1950-2000

Dependent variable: Log of military spending in constant Dollars (PPP)

Nordhaus, et al. Model 5 Model 6 Model 7 Model 8

Political participation - -0.10*** -0.11*** - -(0.03) (0.03) - -

Regime age (logged) - 0.07*** - 0.05*** -- (0.02) - (0.02) -

Multiple political parties - - - -0.12*** -0.12***- - - (0.03) (0.03)

Years since last coup - - 0.04*** - 0.03***- (0.02) - (0.01)

Civilian leadership - - - -0.04* -0.07***- - - (0.02) (0.02)

Military leadership - - - -0.04 -0.06**- - - (0.02) (0.02)

Lagged DV (Instrumented) 0.70*** 0.65*** 0.63*** 0.73*** 0.72***(0.07) (0.10) (0.11) (0.07) (0.07)

Predicted prob. of conflict 0.48*** 0.42*** 0.51*** 0.41*** 0.47***(0.14) (0.14) (0.17) (0.14) (0.16)

GDP 0.30*** 0.34*** 0.37*** 0.27*** 0.28***(0.07) (0.09) (0.11) (0.07) (0.07)

Spending - Foes 0.12*** 0.08** 0.10** 0.07** 0.08**(0.04) (0.04) (0.04) (0.03) (0.04)

Spending - Friends 0.02** 0.01 0.01 0.01 0.01(0.01) (0.01) (0.01) (0.01) (0.01)

Democracy -0.01*** - - - -(0.01) - - - -

Constant -2.92*** -2.38*** -2.75*** -2.05*** -2.19***(0.89) (0.84) (1.01) (0.73) (0.82)

Observations 3449 3289 3289 3095 30950.959 0.956 0.953 0.962 0.961

* p<0.10; ** p<0.05; *** p<0.01 (two-tailed)(Robust standard errors in parentheses)

34

Table 5: Executive constraints and regime age of non-democratic states, 1950-1997

Dependent variable: Military spending as % of GDP

Model 9 Model 10 Executive constraints 0.18 0.18

(0.13) (0.13)Political participation -0.59*** -0.56***

(0.16) (0.16)Regime age (logged) 0.27** -

(0.11) -Years since last coup - 0.20***

- (0.07)Battle deaths % 12.29** 11.98**

(4.86) (4.81)Civil war deaths % 0.06 0.01

(0.05) (0.06)Rivals’ power 7.33*** 7.13***

(2.28) (2.30)Allies’ power 0.67 0.50

(2.33) (2.34)GDP -0.01*** -0.01***

(0.01) (0.01)Empire population 0.01*** 0.01***

(0.01) (0.01)Constant 2.90*** 3.07***

(0.42) (0.41)Observations 3397 3397

0.022 0.021

* p<0.10; ** p<0.05; *** p<0.01 (two-tailed)(Panel-corrected standard errors in parentheses)

35