0      

Varieties of Democratic Diffusion in Military Alliance Networks

Benjamin Denison

University of Notre Dame

Michael Coppedge University of Notre Dame

DRAFT – Please do not cite without permission

The authors gratefully acknowledge generous funding for data collection and analysis from the Riksbankens Jubileumsfond; the European Commission, the Wallenberg Foundation; the foreign ministries of Sweden, Canada, and Denmark; the research councils of Sweden, Norway, and Denmark; the National Science Foundation; the Universities of Gothenburg and Notre Dame and the Catholic University of Chile; International IDEA; and the Quality of Government Institute at the University of Gothenburg.

1      

Varieties of Democratic Diffusion in Alliance and Neighbor Networks

Abstract:

Numerous studies have reported that countries tend to become more similar to their immediate

geographic neighbors with respect to democracy. We show that a similar process of mutual

adjustment can be found within a very different international network: military alliances. The

causal mechanisms for the diffusion of democracy are notoriously vague, but the existence of

diffusion within alliance networks helps narrow the possibilities. Where these relationships are

significant, the net tendency is overwhelmingly convergence: Allies have tended to become more

similar to one another in their levels of electoral democracy, especially immediately prior to the

onset of a formal military alliance.

2      

Varieties of Democratic Diffusion in Military Alliance Networks

Upon the ratification of the North Atlantic Treaty, President Truman recognized that “the security and welfare of each member of this community depend upon the security and welfare of all. None of us alone can achieve economic prosperity or military security. None of us alone can assure the continuance of freedom.” (Truman 1964). Truman’s claim that a country’s community of peers impacts its own prosperity, freedom, and by extension its level of democracy, is a widely accepted proposition backed by many empirical studies. Yet, stating that a network of interstate relationships in some way affects countries only hints at the many possible pathways through which alliance networks influence other states and their levels of democracy. This paper tests Truman’s claim by examining the impact of one type of interstate community – military alliance networks – on the diffusion and convergence of levels of democracy across alliance networks. We argue that democracy diffuses in part through military alliance networks and that gaps in the level of democracy between alliance members promote convergence toward similar levels of democracy (or non-democracy) in alliance members: the bigger the gap, the stronger the pressure. In addition, we find that there are distinct moments in time that military alliance networks exert the greatest pressure towards democratic convergence. These include in the immediate run up to formal alliance membership in anticipation of the benefits of membership or in order to qualify for membership, as well as during periods of economic or political instability where alliance network members are more concerned about the stability of their alliance partners. However, we also find that membership helps correct deviations from the alliance’s regime norms in the first decade and that militarily powerful members have greater influence on their allies’ regimes.

Democratic diffusion is more than simply geographic clustering of democracies, which has been well documented (O’Loughlin et al. 1998). We understand diffusion as a process in which levels of democracy in some international network – in this case, a military alliance – influence levels of democracy in members of the network – allies. The perennial challenge in quantitative analyses of diffusion is distinguishing true diffusion, defined in this way, from other processes that produce nearly identical outcomes (Franzese and Hays 2008, Simmons and Elkins 2004). Levels of democracy can be clustered in groups of countries because those countries are similar with respect to domestic determinants of democracy (level of development, culture, historical legacies). Levels could also be similar because all of the countries in the network have been exposed to a common external shock, such as a war or a global financial crisis, even though the countries do not directly influence one another. It can also be a challenge to distinguish true diffusion through military alliance from true diffusion through other, overlapping networks such as neighboring countries and trading partners. Finally, it is unclear whether alliance networks channel diffusion or countries that are already similar with respect to democracy tend to form alliances. The models described below address all of these inferential threats.

Brinks and Coppedge (2006) sought to distinguish the internal causes of this clustering from international diffusion by controlling for likely domestic determinants while testing for a specific pattern of democratization that was unlikely to be observed unless countries really did influence one another. Their hypothesis was that gaps in democracy levels between countries drive changes within countries. Their results were consistent with convergence among neighbors: countries surrounded by more democratic countries tended to become more democratic, while those surrounded by less democratic countries tended to become less democratic. We building

3      

upon this intuition of diffusion to test specifically whether gaps in democracy levels among alliance networks similarly drive democratic diffusion.

There are good reasons to suppose that the flow of influence is proportional to the gap in democracy scores. In a military alliance network, for example, if two members have very similar levels of democracy—as in the United Kingdom and the United States, Saudi Arabia and Bahrain, Slovenia and Montenegro today—then we expect that neither country exerts much “pressure” to become more democratic or less so. But the greater the gap, the more we expect the disparity to matter. The gaps are something that people in both societies notice, care about, and comment upon; and sometimes, we think, it moves them to action, and those actions have consequences. The core intuition, therefore, is that differences between countries help explain changes within countries. It is a process of mutual adjustment that drives democratic diffusion and convergence. Our approach is unlike any other in several respects beyond the gap-driven mutual adjustment model and the attention to anticipatory convergence. First, we use new data on electoral democracy from the Varieties of Democracy project. Second, the Electoral Democracy Index we use is constructed from variables measured on an interval scale, unlike most democracy measures, which are ordinal. Interval-level measurement is especially important for calculating democracy gaps between countries, as it is meaningful to subtract equal-interval values but not ordinal ranks—an advantage that ordinal Freedom House data did not afford to Brinks and Coppedge (2006). Thus we are able to measure whether larger gaps in democracy have larger effects on democratic diffusion, providing an initial way to measure the dose-response relationship between the gaps in levels of democracy in the alliance network and democratic diffusion. Moving beyond simple counting of number of democracies in a network to look at specific gaps in the levels of democracy and the level to which alliance members converge is an important improvement in our understanding of diffusion. Finally, our focus on short-term democratic diffusion and convergence allows us to pin down specific mechanisms and timing: when the alliance network pressure has the greatest impact on democratic diffusion. In the rest of this paper we first examine the literature linking military alliances and democracy before presenting our argument for why military alliances help promote democratic convergence. Next, we present how we test the effect of military alliances on democratic diffusion using a set of democracy gap matrix variables constructed from V-Dem data. Finally, we present our results, which are consistent with gaps in levels of democracy inside military alliance networks having strong and diverse pathways for influencing democratic diffusion and convergence to both high and low levels of democracy. Literature Review Democracy, Diffusion, and Military Alliance Networks The relationship between interstate networks, including military alliances, and democracy is a well-tested proposition. As states increase their linkages with other states, the spread of information, economic activity, and political institutions across this network naturally increases as well. This finding has been confirmed in numerous ways. Examining spatial proximity and regional clustering, O'Loughlin et al (1998), Wejnert (2005) and Gleditsch and Ward (2006) illustrate that geographic proximity, both in terms of regional effects and distance between states, has an impact on the long-term diffusion of democracy. They illustrate that states are clustered spatially and often evolve stronger links the closer in proximity they are to each other. Pevehouse (2002) focused specifically on membership in regional organizations, and found that the higher

4      

the proportion of democratic members of the organization, the more likely other members were to become and remain democracies. Using a network based approach, Torfason and Ingram (2010) argue that networks of international governmental organizations (IGOs) serve as conduits to spread democratic diffusion, arguing IGOs are the pathways through which regional diffusion operates. Torason and Ingram also find that the most connected states have the largest effects on the democracy levels of their network members. Goodliffe and Hawkins (2015) argue that states with higher levels of dependence with other states, through security, economic, and international governance networks, spread democracy more readily through their dependence networks. However, determining the direct pathways and mechanisms that lead to democratic diffusion through interstate networks is complicated. While it is clear democracy diffuses through various interstate networks, it is hard to pin down precisely how and when interstate networks promote higher levels of democratic convergence.

A complementary alternative strategy is to infer the mechanisms indirectly from the type of network that channels influence. For example, Woodberry (2012) argues that protestant missions promoted democracy by expanding access to education. Other analyses have found some evidence that countries that are linked by trade and investment (Rudra 2005). Levitsky and Way (2006) argue that competitive authoritarian regimes are more likely to democratize if they have strong trade, finance, transportation, and information linkages to the West (and if the West chooses to exercise its influence). Generalizing the logic of this strategy, each type of network suggests a different mechanism of diffusion: if democracy follows migration patterns, then people probably bring democratic (or non-democratic) ideas, norms, institutions, and practices with them when they cross borders. If it diffuses among trading partners, then actors touched by trade (importers, exporters, and consumers) are probably part of the mechanism.

Military alliances are one possible type of interstate network that can promote regime convergence and democratic diffusion. In fact, there has been a consistent finding confirming the relationship between levels of democracy and alliances across various contexts. Although realists hold that military alliances are responses to security threats rather than domestic characteristics (Walt 1987), democratic states have been shown to ally with other democracies more frequently than they do with non-democratic states, regardless of their security needs. Siverson and Emmons (1991) argued that, increasingly, democratic states have formed alliances at a higher rate than expected, especially since 1945, because of their similar domestic institutions. Leeds (1999) and Lai and Reiter (2000) confirmed this hypothesis, with Lai and Reiter confirming that after 1945, both democracies and autocracies tended to ally with similar regimes. This finding spawned research on the effect of democracy on military alliance choices and a reciprocal interest in the effect of military alliances on democracy and gave rise to a debate over whether democratic states were more likely to form alliances or whether allies were more likely to move towards similar domestic regime types. Some have argued that alliance patterns among democratic regimes are artifacts of the international security environment rather than domestic political preferences for similar type alliance partners. McDonald (2015), on the other hand, argues that the both regime type and alliances are shaped by the international priorities of the United States and the Soviet Union (and other great powers), so that the relationship between alliances and regimes is spurious. Instead, the international security hierarchy forces the birds to have similar plumage.

Others argue the reverse: that military alliances might serve as a pathway for democratic diffusion. Following the democratic transitions in Eastern Europe, Starr (1991) argued that states in interstate networks, such as military alliances, often faced high levels on interdependence that

5      

helped facilitate or inhibited regime change, creating “democratic dominoes.” Thus, after 1945, both democracies and autocracies tended to ally with similar regimes because the alliance created the conditions to allow for regime convergence. Gibler and Wolford (2006) argue that when looking at alliance formation, military alliances allow for settlement of territorial borders between states, creating the peaceful neighborhood necessary for democratic development. Using network analysis, Torfason and Ingram (2010) argue that military alliance networks might promote diffusion of democracy in addition to IGO networks. However they note that military alliances might serve as both a coercive diffusion mechanism, with democratic diffusion forced by a stronger alliance partner, as well as an informational mechanism, in which democratic diffusion is due to mimicking and learning from other alliance partners. Goodliffe and Hawkins (2015) similarly argue that military alliances and security dependence networks create both short-term and long-term trends that push states to become more democratic when they are dependent on more democratic military alliances. They also argue that states in alliances with more autocratic states tend to converge to higher levels of autocracy as well. For all of these, alliances emerge first, followed by shared regime types. Warren (2016), however, argues that the military alliances and domestic political institutions are mutually constitutive, where states simultaneously prefer to ally with states that possess similar levels of democracy and states in dense democratic networks also become more democratic.

It is beyond dispute that alliance networks are comprised of similar regimes. However, it is unclear how this clustering of regimes in alliance arose. We focus on the processes of short-term change that led to the empirical association between alliances and regimes, which gets us closer to causal inference. Other studies have been limited by using binary or categorical measures of democracy; we use an interval-level graded measure, which makes it possible to model diffusion as a pattern of incremental mutual adjustment among countries, leading to convergence around homogeneous higher or lower levels of democracy within alliances. When a country is either more or less democratic than its alliance partners, there is a consistent pull on the country to converge towards the network’s level of democracy. By modeling the interval-level gaps in levels of democracy, we can specifically focus on the pull of alliance networks on divergent members of the network.

Varieties of Democratic Diffusion in Alliance Networks

We argue that gaps between the level of democracy among members of military alliances drive democratic diffusion over the short term: democracy gaps between alliance partners pull them to converge toward the same level of democracy. Furthermore, the larger the gap, the stronger the convergence. Building on Gunitsky (2017), the interstate network can push alliance partners towards regime convergence through three mechanisms: coercion, inducement, and emulation. Coercion, to Gunitsky, refers to imposing new domestic regime types on other states.1 Influence consists of more traditional diffusion processes such as trade and patronage networks used to influence institutional preferences in the materially weaker states in the influence network. Finally, emulation refers to states voluntarily changing their domestic institutions to imitate the success of the newly powerful states, and perhaps attract more investment and other goods from the new hegemon.2                                                                                                                          1  This  is  similar  to  the  literature  on  foreign-­‐imposed  regime  change.  For  more  see  Owen  (2010)  and  Downes  and  Monten  (2013).  2  Similarly,  Miller  (2016)  argues  that  emulation  helps  explain  democratic  diffusion  as  greater  economic  performance  by  democracies  predicts  higher  levels  of  global  democracy  

6      

Alliance networks can promote democratic diffusion through all three of these pathways. First, through coercion, powerful states can propel both prospective alliance members and their long-held allies towards democratic convergence and their preferred regime type. At its most extreme, coercive democratic diffusion finds an alliance member or the alliance network forcefully compelling convergence to a similar regime type. For instance, McDonald (2015) convincingly argues that the Cold War alliance structure was largely a hierarchical network that imposed democratic convergence across Europe. Instead of democratic states forming an alliance against Soviet expansion, he argues, the United States forcibly imposed democratic institutions in the states it dominated throughout Europe, as did the Soviets in their bloc. While his focus is on explaining how this hierarchical alliance structure is responsible for the democratic peace, coercion can serve as one mechanism that forcefully promotes democratic diffusion. Thus, in our model of democratic diffusion, gaps in level of democracy between strong states and their weaker military alliance partners should have a larger effect on democratic convergence. In other words, the larger the power imbalance between a country and its military alliance partners, the larger the coercive impulse the major power will have on promoting democratic convergence. Major powers compel their weaker alliance members towards similar levels of democracy.

At the other extreme, emulation can also occur in alliance networks. However, emulation is mostly aspirational and largely prior to alliance membership. In this form of diffusion, states that seek to join military alliances attempt to emulate the democracy levels of the other military alliance members. Thus, they would seek to close their gaps in level of democracy with other members of the prospective alliance, hoping this will favor their acceptance into the alliance. However, we argue this is the least likely form of democratic diffusion, as Vachudova (2005) and others have argued that some form of inducement from the interstate network is a precondition for a state moving toward an alliance’s level of democracy. Democratic convergence begins only after alliance members actively state that is a precondition for alliance membership.

Although democratic diffusion through coercion and emulation is possible, it is clear that inducement (or influence) is the most common form of diffusion via alliance networks. Inducement or influence pressure both the alliance network and the target country to converge. Influence and inducement mechanisms are either anticipatory or shock-based. Anticipatory convergence occurs when military alliances use their influence with prospective members to encourage democratic convergence before alliance accession. If large gaps in the levels of democracy exist prior to the formal formation of an alliance, we argue that offering prospective alliance commitments could induce and encourage democratic convergence. Prior to the formal agreement to enter into a military alliance, there is often some democratic convergence. In other words, convergence toward a shared level of democracy occurs in the lead-up to formal alliance commitments. “Birds of a feather” flock together because they change their feathers before joining the flock.

Some important alliances make a certain type of regime a formal or informal prerequisite for membership. When they do, countries may evolve toward that kind of regime before joining. For example, NATO expects prospective member countries to move toward democracy (although Portugal, Greece, and Turkey have not always conformed to this expectation). This expectation turned into a formal requirement following the Cold War (Schimmelfennig 2007). In the Warsaw Pact, it was the opposite, as both the eastern alliance structure and the non-democratic regimes faced pressure to converge by the Soviet Union. In both cases, influential groups were comfortable with these international alignments and would have resisted promising to defend a

7      

country with a very different regime (Siverson and Emmons 1991). In addition, this process of anticipatory convergence at times takes the form of political linkage and/or political conditionality. Post-1991 NATO expansion saw NATO members utilizing conditionality programs to entice potential alliance members with membership if they moved their domestic regimes towards higher levels of democracy (Saideman and Ayers 2007, Epstein 2005). In these two cases, respect for minority rights and enshrining civilian control of the military were two examples of democratic reform that prospective NATO membership encouraged in Eastern Europe. Admittance to the NATO alliance network was only possible once potential members converged towards higher levels of democracy.

Second, following the onset of formal military alliance, shock-based convergence can occur when alliance partners exert their influence during periods of regime instability and crisis among their allies. After formally joining an alliance, the gaps between the democracy level of one alliance member and that of the rest of the network matter most during periods of crisis. In recent years, the link between democratic diffusion and political shocks has received increased attention. Both Gunitsky (2014, 2017) and Houle et al. (2016) claim that shocks to the domestic system open up opportunities for waves of democratic diffusion to occur. Building on Miller’s (2012) argument that democratization is largely a two-step process of breakdown and transition to democracy, Houle et al present a two-step theory of democratic diffusion in which some form of shock first weakens the current regime and then the diffusion forces affect levels of democracy during the period of vulnerability.

While Gunitsky focuses on abrupt hegemonic power shifts in the international system, Houle et al. focus exclusively on economic shocks and the breakdown of authoritarian regimes. In their model, economic and hegemonic shocks create periods of uncertainty where democratic diffusion is more likely occur. During periods of stability, regimes coalesce around their existing level and are resistant to change. This logic is confirmed by Houle et al., who examine 125 authoritarian regimes and find that democratic diffusion mechanisms do not, by themselves, produce transitions to democracy. However, when using a two-stage Heckman model to test their theory, they find that after the breakdown of an authoritarian regime for economic reasons, diffusion variables help predict the transition to democracy in a second stage. Although the dependent variable in their study is simply the percentage of states in a given region or neighborhood that are democratic, the fact that this simple diffusion variable retains significance in this two-stage model boosts our confidence that shocks could open the door for diffusion to occur. In additional, Teorell (2010) has tested the effect of political protest as a form of shock that can spark democratization and diffusion, and finds that political shocks could also serve as a trigger for democratization.

These studies suggest that the gaps in democracy levels among members of alliance networks matter more when there are shocks in the system that allow pressure from their network partners to pull them towards convergence. During periods of crisis and instability, alliance members often exert pressure on their alliance partners in the network to bring their political regimes into conformity with their own, whether democratic or nondemocratic, through means ranging from quiet diplomacy, to public admonishments, to military pressure (as in East Germany, Hungary, and Czechoslovakia). While there is an overall trend for democratic convergence to occur, we see the most movement and change when shocks are present in system. Thus, instead of looking at overall trends towards convergence, the shock-based view of democratic diffusion helps explain the timing of various network-based diffusion mechanisms.

8      

Although coercion and emulation mechanisms of democratic diffusion may work for certain alliance members, the bulk of military alliance network diffusion operates through mechanisms of inducement and influence. When gaps are present in the network, the other members of the military alliance network can utilize their influence to promote democratic convergence in distinct ways. In sum, we argue that military alliances have a profound effect on the diffusion of democracy. In addition, we specifically look at when we should expect democratic convergence to occur most heavily and argue that this is in anticipation of formal alliance commitments and afterwards, when long-standing members suffer economic and political shocks. In addition, we expect the strongest military alliance partners to promote democratic convergence most powerfully. In the rest of this paper, we discuss how we test the above arguments and present evidence that military alliances do serve as robust pathways for democratic diffusion. Data, Methods, and Operationalizing Diffusion Hypotheses

To construct our dependent and diffusion variables, we use the Electoral Democracy Index from version 6 of the V-Dem dataset (Coppedge et al. 2016a). The sample encompasses 171 countries from 1900 to 2015, or whenever they existed during this period, for a maximum of 15,227 country-year observations.3 Most of the data come from online surveys of 2,156 country experts, the majority of whom were nationals of or residents in the countries they coded. The online questions were typically answered by five experts whose ratings were aggregated to country-date ratings by a Bayesian latent-variable measurement model.4 The Electoral Democracy Index is an index constructed from 36 fine-grained variables that measure freedom of association, suffrage, clean elections, elected executive, and freedom of expression.5 The V-Dem dataset contains indices of other varieties of democracy—liberal, participatory, deliberative, and egalitarian—but we chose electoral democracy as a first step in order to maximize comparability with other research on diffusion, most of which uses Polity and Freedom House indices, which conform most closely to a concept of electoral democracy. Our index correlates at 0.898 with Polity and .908 with the Freedom House indices, which assures us that we are not measuring an entirely different concept.6 Nevertheless, we believe that V-Dem data are more valid in the sense that they take many more attributes of electoral democracy into account. We also believe that the data are more reliable because they are based on multiple ratings by thousands of raters who know their countries well, and because coder disagreements have been reconciled by a state-of-the-art custom-designed IRT model.

                                                                                                                         3 Most of the omitted countries are microstates in the Pacific, Persian Gulf, and the Caribbean. 4 The measurement model was designed by Daniel Pemstein and others and executed using high-performance computing at Sweden’s SNIC and the Center for Research Computing at the University of Notre Dame (Pemstein et al. 2015). Some variables—the relatively objective ones—were coded by research assistants and thoroughly validated, and the dataset also contains some variables that were recoded from outside sources such as the Comparative Constitutions Project (Elkins et al. 2012). 5 V-Dem data also make it possible to disaggregate electoral democracy into several components and many specific variables, which may help identify more precisely which aspects of electoral democracy diffuse. The components of this index were first transformed to 0-1 interval with a cumulative density function. Thus, high values are high with respect to all country-years from 1900 to 2012. Given the size of the sample, this is an excellent estimate of the full range of possible variation. However, the CDF tends to compress values near the top and bottom of the scale, compared to the point estimates from the measurement model. 6 The Electoral Democracy Index used in this paper is: .5*(1/8*Suffrage Index + 1/4*Clean Elections Index + 1/8*Elected Executive Index + 1/4*Freedom of Association Index + 1/4*Freedom of Expression Index + .5* Suffrage Index * Clean Elections Index * Elected Executive Index * Freedom of Association Index * Freedom of Expression Index. For a detailed description see Coppedge et al. (2016) and Coppedge et al. (2015).

9      

Dependent Variable

Our dependent variable is the two-year change in the V-Dem Electoral Democracy Index. Since we are interested in how gaps in levels of democracy between members of networks impact changes in the levels of democracy in their networks, we use the two-year-differenced value of the Electoral Democracy Index. This allows us to measure how both gaps in democracy level of the alliance network and control variables from two-years prior had an impact in moving the level of democracy over a two-year period. Thus we test for relationships between the democracy gap two years prior and the change in democracy during the two subsequent years. Because the dependent variable is differenced, our models ignore all earlier democracy levels of the target country as explanations for the present. The gap in 1950 still matters for explaining the situation in 1952, but the gap in 1950 is hypothesized to have no direct effect on change in 1953, much less 2012. The V-Dem index allows for interval-level changes in the measure to be meaningful. Diffusion Variables

Most studies of democratic diffusion and alliances operationalize diffusion and regime similarity based on the number of democracies in the state’s network over a period of time. They hypothesize that as the number of democracies in the network increases over time, the likelihood of a country becoming democratic in the near future increases. Many have similarly operationalized diffusion by including regional dummy variables or the mean democracy level in a region among their explanatory variables (Bollen 1983, Hadenius 1992, Lipset et al. 1993, Mainwaring and Pérez Liñán 2013, Przeworski and Limongi 1997). In contrast, we are interested in the impact of the gap between the alliance network’s mean level of democracy and that of each member of the network on subsequent changes in that member’s level of democracy. Simply including counts of democracies or mean levels in the network is not sufficient, as such variables assume that their effects are the same on both democratic and non-democratic countries. Our democracy gap variables enable us to test for a pattern of mutual adjustment within any network of countries. Each country has a level of democracy and each country is both a source of influence on other countries and a target of their influence. There is a gap between each target country’s level of democracy and the level of democracy in the source countries in its network (Most and Starr 1990). These gaps drive the diffusion process in either a negative or a positive direction. If there is positive diffusion, there is convergence because either the more-democratic countries pull the less-democratic countries up toward greater democracy or the less-democratic countries pull the more-democratic countries down to their level, or both. Logically, if there were no disturbances from other dynamic determinants of democracy, this process would continue until all the countries in the network converged at the same level, where the democracy gaps would all be zero.7

We operationalize electoral democracy gaps as the difference between electoral democracy levels in “source” countries and “target” countries. A source country is one that is theorized to have an effect on other countries in the same network. A target country is one that is theorized to be affected by the source country or countries. The electoral democracy gap is simply the source                                                                                                                          7  Negative  diffusion  is  also  possible:  more-­‐democratic  countries  could  push  less-­‐democratic  countries  down  toward  even  less  democracy,  or  less-­‐democratic  countries  could  push  more-­‐democratic  countries  up  toward  greater  democracy.  Negative  diffusion  would  therefore  lead  to  divergence  within  the  network.  However,  we  find  no  negative  diffusion  in  alliance  networks.  

10    

country’s electoral democracy score, Djt, minus the target country’s electoral democracy score, Dit, calculated for every country-year. In the formula for the gap equation, Gijt is the difference in electoral democracy scores between each target and source country at time t:

𝐺!"# = 𝐷!" − 𝐷!" The final steps in the construction of the network variables are to multiply the corresponding electoral democracy gap matrix between each pair of countries by a network weight matrix, yielding a pairwise network gap, and to average them over all source countries j as follows:

𝑛𝑒𝑡𝑤𝑜𝑟𝑘  𝑤𝑒𝑖𝑔ℎ𝑡𝑒𝑑  𝑔𝑎𝑝!" =1𝑛 (𝐺!"#  ×  𝑊!"#)

!

Gijt is the democracy gap between two countries in a given year, n is the number of source countries in the network, and Wijt is the corresponding network weight.

The first diffusion variable we test captures alliance networks. Alliance weights simply reflect alliance membership. For both alliance and neighbor networks described below, “weights” may be a misleading term because they are binary: either a country is an ally or neighbor (weight=1) or it is not (weight=0). If two countries are in multiple military alliances simultaneously (such as the United States and Canada), the network weight remains one. There is no additional weight for membership in multiple, overlapping alliances. We also do not distinguish between bilateral alliances and multilateral alliances, coding all members of multilateral alliances as being, in effect, in a bilateral alliance with every other member of the alliance. To construct our alliance network variables, we use the formal alliances data set from the Correlates of War project [Insert Citation]. To ensure our results are robust, we also use the Alliance Treaty Obligations and Provision dataset from Leeds et al. (2002) to construct a similar variable. In both datasets, countries and their allies are coded on a yearly basis and for every year that the authors code a dyad as being in an alliance, the dyad is given a one in the alliance network variable. When the alliance ends, the dyad reverts back to 0. Importantly, we only code membership in what Gibler (2009) calls defensive alliances and what Leeds et al. (2002) call offensive or defensive alliances. These are military alliances that obligate the members of the alliance to aid in the defense of other alliance members if attacked militarily, and sometimes also compel alliance members to aid an alliance member with offensive military operations. These are distinct from non-aggression pacts, which are coded separately, and do not meet the full definition of military alliances.8 Non-aggression pacts do not operate in the same manner as military alliances, so we do not expect them to affect the diffusion of democracy. As Figure 1 illustrates, alliance networks tend to be regional, although with some significant intercontinental linkages.

The formula for calculating democracy gaps weighted by alliance networks is:

𝑎𝑙𝑙𝑖𝑎𝑛𝑐𝑒  𝑛𝑒𝑡𝑤𝑜𝑟𝑘!" =  1𝑎!"

𝐴!"#  ×  𝐺!"#!

                                                                                                                         8 Leeds et al. (2002) define alliances as “written agreements, signed by official representatives of at least two independent states, that include promises to aid a partner in the event of military conflict, to remain neutral in the event of conflict, to refrain from military conflict with one another, or to consult/cooperate in the event of international crises that create a potential for military conflict.”

11    

Aijt is a matrix of alliance weights that identifies whether countries i and j are in a military alliance at time t, Gijt is the electoral democracy gap at time t, and ait is the total number of country i’s alliance partners at time t. This variable gives us the average democracy gap between country i and all of their alliance partners, j. To test our hypotheses about the timing of convergence and alliance commitments, we construct three similar network variables by separating the alliance networks into three distinct matrices: ten-year prior-to-alliance networks, ten-year later alliance networks, and all other alliance network years. The ten-year prior-to-alliance network uses a network weight Aijt that identifies all countries that will be in a formal military alliance within ten years. The ten-year later alliance network identifies only the immediate ten years after a country’s formal entry into a military alliance. The other alliance network variable includes all other time periods beyond the initial ten years after the onset of formal alliance obligations. These three variables are constructed the same way as the alliance network variable above, with only a subset of alliance observations used in each. To test the extension of our theory about the interaction of economic and political shocks and alliance network gaps, we use two measures of economic shocks and one measure of political shocks, both following on the work of Miller (2012) and Teorell (2010). First for economic shocks we use the two-year lagged level of inflation in the country from Clio-Infra (2014). The logic is that as inflation increases, there is pressure on the national government that can contribute to regime breakdowns and popular disruption. Since the inflation measure is heavily skewed, we transform the measure using a log-modus transformation that expands values close to zero and compresses extreme highs and lows. For positive inflation, the transformation is ln(inflation + 1). For negative growth, the transformation is -ln(abs(inflation -1)). For political shocks, we use the number of non-violent protests in a country-year from the Banks (2016) dataset. Unfortunately, both the economic and political shock measures do not exist before 1950 and truncate our observations by over half. However, this still provides a meaningful test if the results remain robust. Finally, we constructed alliance network variables that are weighted by national material power as a proxy for international state strength. This is to test whether democracy gaps with alliance partners with higher levels of material power have greater impact than those with other alliance partners. To do this, we use the Composite Index of National Capability (CINC) scores from the National Materials and Capabilities (version 4.0) database under the COW project as a measure of national power (Singer 1987). Next, we weight each country i’s electoral democracy gap with country j (𝐺!"#) by its gaps in material power with the same country (𝐶!"#). The CINC-weighted democracy gap matrix is then multiplied by the alliance network matrix as described above. Hence, the formula for calculating CINC-weighted alliance network democracy gaps is:

𝑎𝑙𝑙𝑖𝑎𝑛𝑐𝑒  𝑛𝑒𝑡𝑤𝑜𝑟𝑘(𝐶𝐼𝑁𝐶)!" =  1𝑎!"

𝐴!"#  ×𝐺!"#×𝐶!"#!

𝐶!"# is a matrix that identifies the gap in material capabilities between two countries i and j at time t. This CINC-weighted alliance variable is then the average CINC-weighted democracy gap in country i’s military alliance network at time t. Controls

We include several control variables in the analysis. Presidential and parliamentary election-year dummies are included for years when an election took place, as we know that changes in

12    

democracy are more likely in election years. We also control for year dummies in order to minimize the risk of confounding by period effects. Without them, the risk is that diffusion variables that are relevant only for certain historical periods might capture a trend associated with those periods rather than real diffusion effects. Controlling for mean electoral democracy levels in these years helps keep these processes separate. In some models, we also include region dummies to help control for specific regional effects, especially given the regional clustering of many alliances. Finally, for one of the baseline models, we use the natural log of per capita gross domestic product, which is an interpolation and imputation of data from Maddison (2001) using GDP per capita PPP in constant 2005 international dollars from the World Bank (2013).9

Because the GDP per capita measure reduces our sample by over 7000 country-years, in most models we use the adult literacy rate as a proxy for economic development.10 Many have argued that literacy has a relationship with economic development, as increased levels of literacy and schooling produce higher levels of human capital inside a country (Blaug 1966, Barro 1991, Benhabib and Spiegel 1994). Such countries tend to have higher income, which has long been associated with greater democracy. Our measure of literacy is the adult literacy rate, which measures the percentage of the population age 15 or older who are literate. We use the percent literate variable from Vanhanen (2003) and merge it with the World Bank’s (2016) adult literacy variable for country-years not covered by Vanhanen. Both variables measure the adult literacy rate in the same percentage format. Since both data sources have gaps in their coverage of the literacy rate, however, we interpolated the data after they were combined into one measure.11 After interpolation, the adult literacy variable was divided by 1000 to make the resulting coefficients in the model more legible. The resulting adult literacy variable has 7000 more observations than the GDP per capita measure allowing for expanded use of the V-Dem data and an expanded look at the relationship of interest. Our interpolated adult literacy variable is correlated with the natural log of GDP per capita at .73, suggesting that it is an adequate proxy for economic development.

Finally, building on previous research, we include a neighbor diffusion variable to control for the effect of neighbors as distinct from alliance membership. To define neighbors we use the criteria of proximity used in Brinks and Coppedge (2006). Countries on continents are neighbors if they share a border; Australia is counted as an island, rather than a continent. If an island is close to a continent, its neighbors are the closest neighbor on that continent and any island nations in between. If an island is about equally close to any continent, or to multiple countries on the same continent, it has as neighbors all nearly equally close mainland countries and any islands in between. If an island is not close to any continent it has as neighbors islands within 150 percent of the nearest neighbor. The formula for calculating democracy gaps weighted by neighbor networks is

𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟  𝑛𝑒𝑡𝑤𝑜𝑟𝑘!" =  1𝑛!"

𝑁!"#  ×  𝐺!"#!

                                                                                                                         9  We  obtained  this  data  from  the  included  background  factors  in  the  V-­‐Dem  codebook  (Coppedge  et  al.  2016b).  10 While using the GDP per capita measure reduces our sample size, we ran the baseline model using the adult literacy rate, the natural log of GDP per capita, and both at the same time to illustrate the usefulness of the adult literacy proxy. In the online appendix, we also ran the main mixed effects model substituting GDP per capita for adult literacy rate and our results largely remain the same. 11  These  were  also  obtained  from  the  background  factors  in  the  V-­‐Dem  codebook  (Coppedge  et  al.  2016b).  

13    

Nijt is a dummy variable that identifies whether countries i and j are neighbors at time t, Gijt is the electoral democracy gap at time t, and nit is the total number of country i’s neighbors at time t.

Estimation and Findings The models

Given our interest in the effect of gaps in levels of democracy among alliance networks on short-term democratic diffusion, our estimation techniques make a strenuous effort to approximate causal inference by ensuring that coefficient estimates are based on comparisons of cases that are as similar as possible. First, we utilize fixed-effects estimates, meaning that all variables are transformed into deviations from country means in each model. Building on Weijnert (2013), we also utilized mixed effects models as a form of multilevel modeling that helps predict the variance nested in both countries and regions. The mixed effects models allow us to identify and control for possible random effects in each country as well as any spatial effects due to countries being nested inside regions. In addition, each mixed effect model allows for an independent intercept for each country (or region).When using fixed and mixed effects models, each country is therefore compared only to itself. Given the thousands of attributes that make each country unique, no other country could be equally similar. Although fixed-effects estimation has been a familiar tool in econometrics for decades, it accomplishes much of what is promised by the differences-in-differences tests that are now common in the causal identification literature (Katz 2014; Angrist and Pischke 2014).

Second, to measure short-term change in level of democracy, we use the two-year difference in the electoral democracy index. To model this two-year change, we lag our independent and control variables by two years to capture the effect of those variables at the beginning of the two-year period of change we are interested in. It is precisely these years of change we are interested in explaining. We are asking whether large gaps in levels of democracy among alliance partners two years ago help explain the corresponding change in the level of democracy across the subsequent two years. Because of the aggressive modeling of time (the two-year differenced dependent variable and the year dummies), values of the transformed dependent variable hover close to zero for most country-years, but they are punctuated by periods of change in electoral democracy. We use a two-year difference and a two-year lag because we do not expect a democratization process in one country to be immediately responsive to a democracy gap with another country. It should take time for influential actors in the target country to take stock of a changed situation, to formulate a response, and to organize people and resources to bring about a change. Because there is uncertainty about exactly how long the lag should be, we also tested one-year and three-year reaction times; those results are very similar and are reported in the appendix.

Thus, the coefficients for the diffusion variables should be interpreted as the difference between, on the one hand, belonging to a given network and having a gap of a given magnitude, and on the other hand, not belonging to any of the networks specified in the model. Positive coefficients indicate a convergence effect of diffusion. No matter whether the alliance network has a higher or lower level of democracy as the target country, positive coefficients imply a ‘pull’ or convergence effect. This means that the network is pulling the target country closer to its average level of democracy. Negative coefficients imply the opposite, which is a divergence or ‘push’ effect. This would indicate that the alliance network is pushing the target country to a

14    

higher or lower level of democracy away from the network average. If our argument is correct above, we should see positive and significant coefficients that indicate short-term diffusion is operating and promoting a convergence effect.

In the following section, we test five main sets of models. We first include five baseline models of democratic change that examine the baseline determinants of short-term changes in democracy, including the impact of neighbor networks on democratic diffusion. In the next set of models, we use both fixed effects and mixed effects regression to model the impact of alliance networks on democratic diffusion and short-term democratic convergence. The third set of models test for anticipatory convergence by modeling the effect of alliance networks in the immediate run-up to alliance formation and the immediate post-alliance period. Fourth, we include a set of models that examine the shock-based convergence mechanism by interacting the alliance network gap variable with both the economic and political shock variables. Finally, we include several models testing whether more powerful alliance partners produce more pronounced pressure for democratic convergence. All of these estimates support our claim that alliances help encourage convergence toward a shared level of democracy. Results

Table 1 presents five baseline models. Model 1 uses fixed-effects regression and takes advantage of the largest possible sample. Model 2 controls for the best-known correlate of democracy, per capita GDP (logged), but in the process sacrifices thirteen countries and more than three thousand country-year observations due to listwise deletion. Model 3 includes the neighbor network variables. Model 4 uses mixed effects regression with country-level random effects. Model 5 uses the same mixed effects model with region random effects as well. Across all three models in which they are included, neighbor networks show a consistent positive and significant effect.12 Presidential and parliamentary election year is significant and positive in every model specification, as is adult literacy, indicating that higher levels of adult literacy and presidential election years predict positive changes in democracy levels. GDP per capita is never significant in any model specification, due to collinearity with literacy.13 From this point forward we control for adult literacy rather than per capita GDP in order to take advantage of the maximum sample size. However, models with GDP per capita as a control variable are included in the appendix. All of the other baseline control variables are included in the model below even though some are omitted from the coefficient plots.

Table 2 presents the results for the main alliance network models. The results are also presented graphically in Figure 3. Across all model specifications, the effect of military alliances is positive and significant, indicating that military alliance partners converge to similar levels of democracy. This is true with both the COW alliance member data and confirmed with the ATOP alliance data. The coefficient for the main alliance model is .0593. However when using a mixed effects models, the effect of alliance networks is reduced to between .0484 and .0491, which is still consequential and significant. Similar to Brinks and Coppedge (2006), neighbor networks also are significant and positive across all model specifications, indicating a clear pattern of democratic convergence. This confirms previous work reporting that neighbors converge to the same level of democracy. This is true even though Brinks and Coppedge used Freedom House

                                                                                                                         12 The effect of neighbors is robust across many different model specifications including when omitting the GDP per capita and literacy controls. 13 In the fixed-effects model, the variance inflation factors for GDP per capita, literacy, and urbanization are all greater than 15.

15    

data from 1972 to 1996 and we use V-Dem’s Electoral Democracy Index from 1900 to 2015. The neighbors coefficient of 0.0695 in Model 4 is large enough to produce strong convergence when compounded over several years. For example, this model predicts that the electoral democracy gap between two hypothetical neighbors would shrink by 14 percent in two years, 28 percent in three years, 40 percent in four years, and 50 percent in five years.

Military alliances also predict convergence, but only about 70 percent as fast. In general, two hypothetical alliance partners that are not neighbors would reduce their electoral democracy gap by 36 percent democracy gap in five years. These effects also augment each other. For neighboring allies, Model 4 estimates that 78 percent of the democracy gap would be eliminated in five years, other things being equal. While the model indicates that there are independent and distinct effects of alliance membership and neighbors, combined convergence among neighboring allies is particularly rapid. The neighbor networks variable may get excessive credit because it is the only variable in this model that reflects all the mechanisms that tend to be found disproportionately among neighbors: flows of population, trade, ideas and information, etc. Future modeling of these other networks will reduce the credit given to neighbors, just as the alliance gaps do.

Moving to the next set of models examining anticipatory convergence, we find two results of interest (Table 3 and Figure 4 below). First, when pitting the ten-year-prior-to-alliance-formation variables against the ten-year-post-alliance-formation variable, we find that the prospect of joining an alliance in the next ten years predicts a large, statistically significant convergence effect. However, the 10 years immediately following alliance formation do not have a statistically significant convergence effect after controlling for pre-alliance convergence. In all the model specifications, the impact of anticipating movements towards alliance membership is consistently significant while the immediate post-alliance formation variable is never significant. We interpret this result as indicating that the lead-up to a military alliance is an important pathway through which democracy converges in military alliance networks. In the pre-alliance period, Model 5 estimates the gap in scores to shrink by 24 percent in five years and by 47 percent in ten years. In the first decade of the alliance, the gap would shrink by 19 percent, but this estimate is not statistically significant.

Second, in addition to the ten years prior to the official onset of a military alliance promoting convergence, we find years beyond the initial ten years of an alliance also promote convergence. Throughout all models, the result of the all other years of alliance membership is positive and highly significant. Model 5 estimates that after a decade of alliance, non-neighbors would close 25 percent of the gap in their democracy scores in five years, and 48 percent in ten years; neighbors would shrink the gap by 67 percent in five years and 91 percent in ten. This gives initial evidence that the political and economic shock mechanisms described above operate as well. States that are in longstanding military alliances can respond to political and economic shocks to one of their alliance members’ domestic institutions by working to pull them back to the previous level of democracy, or by being pulled toward the new level of democracy of their alliance partner.

To confirm this result, we move to look at the interaction-based models to test whether the shock-based mechanisms occur. Table 4 and Figure 5 present the results of these interactive models, and show that the shock-based diffusion mechanisms do hold for military alliance networks. When interacting the alliance network gap variables with both inflation (economic shocks) and political protests (political shocks) the interactions are significant. Figures 6 and 7 plot the predicted marginal effects of the interaction of both shock variables with military

16    

alliance gaps, illustrating the predicted effect of the alliance gap variables at different values of economic and political shocks. First, looking at the interaction of inflation and military alliance networks, we find that as the level of inflation increases in a country, the impact of the gaps between them and their military partners grows substantially as well. Conversely, when in inflation is low or approaches 0, alliance networks have a weaker impact on democratic diffusion. Given the non-significant coefficient for alliance network gaps, this implies that an economic shock greatly aids the impact of democracy gaps in the alliance network in promoting convergence. Similar effects hold when plotting the marginal effects of political protests, with the absence of political protest having a very minor effect on the impact of alliance democracy gaps, but 25 protests in a year producing high pressure for democratic convergence from the alliance network. Importantly, these plots both also show that economic and political shocks can promote democratic convergence to both higher and lower levels of democracy, depending on the level of democracy of the alliance network. Thus, if a state is more democratic than its alliance partners, an economic shock in that state can also allow the less democratic alliance network to pull its ally to a lower level of democracy. Combined with the results above, this provides sound evidence that shock-based mechanisms to play a role in alliance democratic diffusion and catalyze democratic convergence. Taken together, these results indicate that as states face economic and political turmoil at home, their long-standing military alliance partners will often exert influence to promote convergence to the same level of democracy.

In the final sets of analysis, we examined whether more powerful alliance partners exert higher levels of convergence pressure on their alliance partners. In Table 5 we examine both CINC-weighted alliance gap variables and US and Soviet/Russia alliances only to see whether powerful alliance members produce convergence in levels of democracy. Initially, the results show modest support for the impact of democracy gaps with stronger alliance partners. In each of the models presented, the CINC-weighted alliance variable is positive and modestly significant, indicating that when gaps in material strength are larger, the gaps in levels of democracy promote faster convergence. The coefficient of the CINC-weighted gap is smaller than the basic alliance gap variable, however. This indicates that while alliances with stronger states might have a distinct effect on diffusion of democracy, military alliances among states of all capability levels promote democratic convergence, regardless on their military strength. To examine more deeply the impact of powerful military alliance partners in promoting democratic convergence, we isolate the American and Russian vectors in the alliance matrix described above and use them as variables in our models instead of the averaged baseline alliance variables. We then re-run the previous analyses using the American and Russian variables in their place. When looking at the results in Figure 5 below, we find that American alliance partners are often pushed towards short-term convergence in keeping with our results above. This holds true for the baseline models and the ten-year models, where American alliance network promote anticipatory convergence as well. Interestingly, there is no effect of Soviet/Russian alliance networks on democratic convergence, indicating most of the results of powerful alliance partners is driven from the American case. American alliance membership and security cooperation then is the much bigger driver of democratic convergence than Russian alliances. It is interesting that Brinks and Coppedge (2006) reported this same difference between superpower networks.

Finally, even when testing such short-term hypotheses about gaps and change, there are still potential confounders. It is possible that major global events lead to short-term spikes or dips in democracy in many countries around the same time, whether they are linked by alliance networks or not. Such events could include the two world wars, the spread of fascism in the

17    

inter-war period, decolonization in the 1950s and 1960s, periods of economic decline such as the Great Depression or the financial shock of 2007-2008, periods of rapid economic growth such as the postwar decade and commodities booms, and general globalization not specific to these networks such as technological innovation and falling trade barriers. Such patterns could also be created by systematic measurement error or specification biases. We would like to rule out the possibility that V-Dem coders share certain historical biases regardless of which country or region they are coding, such as assuming that democracy levels should rise with global peace and prosperity and fall with global war and recession, even if they do not. Furthermore, we want to allay concerns about having measures of polyarchy on both sides of the equation. Even though the dependent variable is a two-year change in polyarchy and the independent variables are based on lagged cross-national differences in polyarchy, one could be concerned that these variables contain some of the same information and may therefore be correlated (Frazese and Hays 2008).14 If there is a necessary mathematical association between dependent and independent variables, or if big global shocks have affected democracy in all alliance partners, or if V-Dem coders assumed that such shocks would affect the countries they coded and biased their ratings accordingly, then we would expect to observe correlated changes in colonizers and colonies. Crucially, however, we would expect to observe such correlated changes regardless of the membership of each network, as most alliance partners would be affected in similar ways.

To allay these concerns and check for robustness in our models, we carry out placebo tests to ensure our variables and models are capturing actual changes in democratic convergence and not coders simply modeling trends in global democracy. To do this, we randomly assign membership in alliance networks by randomizing the country coding through five random draws replicating the same alliance years and number of alliances in the system but with randomly assigned alliances among states. We then replicate the same procedure as above to construct these five sets of placebo alliance networks. After constructing the placebo variables, we re-calculate the baseline models using the placebo network variables and combine the confidence intervals to create the average effect of all five random draws. The results are presented in Figure 9 below. The verdict in all five cases is clear: there is no evidence of short-term confounding due to influential global events, measurement bias, or a bias built into the dependent and independent variables. If there are significant short-term relationships between actual alliance partners, these relationships are due to membership in those specific networks, not to influential shocks affecting many countries regardless of alliance network membership.

Conclusions

It is by now well established that some of the determinants of democratization lie outside a country’s borders. Others have shown that membership in international organizations, including military alliances, are among those determinants and that the direction of regime change depends on the level of democracy found in other member states in the same organization. Our analyses                                                                                                                          14 In a grossly simplified version of our model these changes and gaps are not necessarily correlated. A simple model explains change, Dit – Dit-2, as a function of the difference between source (j) and target (i) countries, Djt-2 – Dit-2. Although Dit-2 is on both side of the equation Dit – Dit-2 = Djt-2 – Dit-2, it cancels out. The left- and right-hand sides are correlated only if democracy in the target country this year (Dit) is a function of democracy in the source country two years before (Djt-2). Any such association would have to be an empirical relationship, not a mathematical artifact. In the more complex actual models, however, because the right-hand-side values are weighted by the coefficients and purged of the covariance they share with other independent variables, the Dit-2 terms do not cancel each other out completely, so correlations are possible.  

18    

specify the nature of these connections more precisely. Alliance members tend to converge toward similar regimes. Powerful states drive convergence to take place more rapidly than less powerful states do, but mostly after members have been in the alliance for a decade or more. Finally, much of the convergence happens in the decade leading up to formal membership in military alliances. This indicates that anticipatory convergence with powerful alliance partners drives the democratic diffusion we see in military alliance networks. We also find that neighbors condition the rate at which alliance partners converge. Because countries tend to become more similar to their neighbors, neighbor networks can accelerate any pressure from neighboring members of a military alliance network. Taken together, these results provide good evidence that military alliances provide one pathway through which democratic convergence can occur. However these results also provide some cautionary results for the ways in which these networks can help promote democratic convergence to a higher level of democracy, but also towards lower levels of democracy as well. As increased instability throughout the globe creates shocks that enable opportunities regime convergence, we should see more movement towards converging regime expectations. This can also lead to convergence to lower levels of electoral democracy as economic instability spreads throughout interstate networks and their alliance partners relate in their assessments in the value of democracy.

19    

References

Angrist, Joshua and Jörn-Steffen Pischke. 2014. Mastering ‘metrics: The Path from Cause to Effect. Princeton: Princeton University Press.

Barro, Robert J. 1992. "Economic Growth in a Cross Section of Countries." The Quarterly Journal of Economics 106(2): 407-443.

Barro, Robert J. 1999. “Determinants of Democracy.” Journal of Political Economy 107 (6): 158-83.

Benhabib, J. and Spiegel, M., 1994. “The Role of Human Capital in Economic Development: Evidence from Aggregate Cross-Country Data.” Journal of Monetary Economics, 34(2), pp.143-173.

Blaug, Mark. 1966. "Literacy and economic development," The School Review 74(4): 393-418. Bollen, Kenneth. 1983. “World System Position, Dependency, and Democracy: The Cross-

National Evidence.” American Sociological Review 48: 468-79. Brinks, Daniel and Michael Coppedge. 2006. “Diffusion Is No Illusion.” Comparative Political

Studies 39 (4): 463-89. Brooks, Sarah and Marcus J. Kurtz. 2012. "Paths to Financial Policy Diffusion." International

Organization. Vol. 66 (1): 95-128. Collier, David and R.E. Messick. 1975. “Prerequisites versus Diffusion: Testing Alternative

Explanations of Social Security Adoption.” American Political Science Review 69 (04): 1299-1315.

Coppedge, Michael. 2012. Democratization and Research Methods. New York: Cambridge University Press.

Coppedge, Michael, Angel Alvarez, and Claudia Maldonado. 2008. “Two Persistent Dimensions of Democracy: Contestation and Inclusiveness.” Journal of Politics 70(3): 335–350.

Coppedge, Michael, John Gerring, Staffan I. Lindberg, Svend-Erik Skaaning, Jan Teorell, and Vlad Ciobanu. 2014. “V-Dem Country Coding Units v3.” Varieties of Democracy (V-Dem) Project. https://v-dem.net/DemoComp/en/reference/version-3-december-2014

Coppedge, Michael, Staffan Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2015. “Measuring High Level Democratic Principles Using the V-Dem Data.” International Political Science Review. Prepublished December 29, 2015, DOI: 10.1177/0192512115622046.

Coppedge, Michael, John Gerring, Staffan I. Lindberg, Svend-Erik Skaaning, Jan Teorell, David Altman, Frida Andersson, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Carl Henrik Knutsen, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Farhad Miri, Pamela Paxton, Josefine Pernes, Daniel Pemstein, Jeffrey Staton, Natalia Stepanova, Eitan Tzelgov, Yi-ting Wang, and Brigitte Zimmerman. 2016a. “V-Dem [Country-Year/Country-Date] Dataset v6.” Varieties of Democracy (V-Dem) Project.

Coppedge, Michael, John Gerring, Staffan I. Lindberg, SvendErik Skaaning, Jan Teorell, David Altman, Frida Andersson, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Carl Henrik Knutsen, Kelly McMann, Valeriya Mechkova, Farhad Miri, Pamela Paxton, Daniel Pemstein, Rachel Sigman, Jeffrey Staton, and Brigitte Zimmerman. 2016b. “V-Dem Codebook v6.” Varieties of Democracy (V-Dem) Project.

Crescenzi, Mark JC, Jacob D. Kathman, Katja B. Kleinberg, and Reed M. Wood. 2012. "Reliability, Reputation, and Alliance Formation." International Studies Quarterly 56(2): 259-274.

20    

Elkins, Zachary, Tom Ginsburg, and James Melton. 2012. Comparative Constitutions Project (CCP). URL: www.comparativeconstitutionsproject.org/.

Epstein, Rachel. 2005. "Nato Enlargement and the Spread of Democracy: Evidence and Expectations." Security Studies 14(1): 63-105.

Franzese, Robert J., Jr. and Jude C. Hays. 2008. “Interdependence in Comparative Politics: Substance, Theory, Empirics, Substance.” Comparative Political Studies 41 (4-5):742-780.

Freedom House. 2015. “Methodology.” Accessed online on August 28, 2015 at https://freedomhouse.org/report/freedom-world-2012/methodology.

Gibler, Douglas M. 2009. International Military Alliances, 1648-2008. Washington: CQ Press. Gibler, Douglas M., and Scott Wolford. 2006. "Alliances, Then Democracy: An Examination of

the Relationship Between Regime Type and Alliance Formation." Journal of Conflict Resolution 50(1): 129-153.

Gleditsch, Kristian Skrede, and Michael D. Ward. 2006. "Diffusion and the International Context of Democratization." International Organization 60(4): 911-933.

González Ocantos, Ezequiel. 2014. “Persuade Them or Oust Them: Crafting Judicial Change and Transitional Justice in Argentina.” Comparative Politics 46 (4): 479-498.

Goodliffe, Jay, and Darren Hawkins. 2015. "Dependence Networks and the Diffusion of Domestic Political Institutions." Journal of Conflict Resolution.

Gourevitch, Peter. 1978. "The Second Image Reversed: The International Sources of Domestic Politics." International Organization 32(4): 881-912.

Gunitsky, Seva. "From Shocks to Waves: Hegemonic Transitions and Democratization in the Twentieth Century." International Organization 68.03 (2014): 561-597.

Gunitsky, Seva. Aftershocks: Great Powers and Domestic Reforms in the Twentieth Century. Princeton University Press, 2017.

Houle, Christian, Mark A. Kayser, and Jun Xiang. "Diffusion or Confusion? Clustered Shocks and the Conditional Diffusion of Democracy." International Organization 70.4 (2016): 687.

Hadenius, Axel. 1992. Democracy and Development. Cambridge: Cambridge University Press. Heckman, James J. 1979. “Sample Selection Bias as a Specification Error.” Econometrica 47(1):

153-161. Katz, Jonathan N. 2014. “Causal Inference with Panel and TSCS Data.” Lecture at the workshop

on “Advanced Causal Inference,” Duke University Law School, August 15. Keck, Margaret and Kathryn Sikkink. 1998. Activists beyond Borders: Advocacy Networks in

International Politics. Ithaca, NY: Cornell University Press. Lai, Brian, and Dan Reiter. 2000. "Democracy, Political Similarity, and International Alliances,

1816-1992." Journal of Conflict Resolution 44(2): 203-227. Layne, Christopher. 1994. “Kant or Cant: The Myth of the Democratic Peace.” International

Security 19 (2): 5-49. Leeds, Brett Ashley. 1999. "Domestic Political Institutions, Credible Commitments, and

International Cooperation." American Journal of Political Science: 979-1002. Leeds, Brett Ashley. 2003. "Do Alliances Deter Aggression? The Influence of Military Alliances

on the Initiation of Militarized Interstate Disputes." American Journal of Political Science 47(3): 427-439.

Leeds, Brett, Jeffrey Ritter, Sara Mitchell, and Andrew Long. 2002. "Alliance Treaty Obligations and Provisions, 1815-1944." International Interactions 28(3): 237-260.

Lipset, Seymour Martin. 1959."Some Social requisites of Democracy: Economic Development and Political Legitimacy." American Political Science Review 53(1): 69-105.

21    

Lipset, Seymour Martin, Kyoung -Ryung Seong, and John Charles Torres. 1993. “A Comparative Analysis of the Social Requisites of Democracy. International Political Science Review 45 (2): 154-75.

Maddison, Angus. 2001. The World Economy: A Millennial Perspective. Paris: OECD. Madrid, Raul A. 2003. Retiring the State: The Politics of Pension Privatization in Latin America

and Beyond. Palo Alto: Stanford University Press. Mainwaring, Scott and Aníbal Pérez Liñán. 2013. Democracies and Dictatorships in Latin

America: Emergence, Survival, and Fall. New York: Cambridge University Press. Marshall, Monty G., Ted Robert Gurr, and Keith Jaggers. 2014. “Polity IV Project Political

Regime Characteristics and Transitions, 1800-2013: Dataset Users’ Manual.” Vienna, VA: Center for Systemic Peace. Accessed online on August 28, 2015 at http://www.systemicpeace.org/inscr/p4manualv2013.pdf.

McDonald, Patrick J. 2015. "Great Powers, Hierarchy, and Endogenous Regimes: Rethinking the Domestic Causes of Peace." International Organization 69(3): 557-588.

Meseguer, Covadonga. 2005. “Policy Learning, Policy Diffusion, and the Making of a New Order.” The Annals of the American Academy of Political and Social Science 598(1): 67-82.

Miller, Michael K. "Economic Development, Violent Leader Removal, and Democratization." American Journal of Political Science (2012): 1002-1020.

Miller, Michael K. "Democracy by Example? Why Democracy Spreads When the World's Democracies Prosper." Comparative Politics 49.1 (2016): 83-116.

Most, Benjamin and Harvey Starr. 1990. “Theoretical and Logical Issues in the Study of International Diffusion.” Journal of Theoretical Politics 2 (4): 391-412.

O’Loughlin, J.,Ward, M. D., Lofdahl, C. L., Cohen, J. S., Brown, D. S., Reilly, D. 1998. “The Diffusion of Democracy, 1946-1994.” Annals of the Association of American Geographers 88: 545-574.

Pemstein, Daniel, Stephen A. Meserve, and James Melton. 2010. "Democratic compromise: A latent variable analysis of ten measures of regime type." Political Analysis 18(4): 426-449.

Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang and Farhad Miri. 2015. “The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data,” Varieties of Democracy Institute: Working Paper No. 21 (December). https://v-dem.net/media/filer_public/67/30/6730d372-7952-4c41-ac15-cc3ad0111830/v-dem_working_paper_2015_21.pdf

Pevehouse, Jon C. 2002. “With a Little Help from My Friends? Regional Organizations and the Consolidation of Democracy.” American Journal of Political Science 46 (3): 611-26.

Przeworski, Adam and Fernando Limongi. 1997. “Modernization: Theories and Facts.” World Politics 49 (2): 155-83.

Rudra, Nita. 2005. “Globalization and the Strengthening of Democracy in the Developing World.” American Journal of Political Science 49 (4):704-30.

Saideman, Stephen M., and R. William Ayres. 2007. "Pie Crust Promises and the Sources of Foreign Policy: The Limited Impact of Accession and the Priority of Domestic Constituencies." Foreign Policy Analysis 3(3): 189-210.

Schimmelfennig, Frank. 2007. "European Regional Organizations, Political Conditionality, and Democratic Transformation in Eastern Europe." East European Politics & Societies 21(1): 126-141.

22    

Singer, J. David. 1987. "Reconstructing the Correlates of War Dataset on Material Capabilities of States, 1816-1985" International Interactions 14: 115-32.

Siverson, Randolph M., and Juliann Emmons. 1991. "Birds of a Feather Democratic Political Systems and Alliance Choices in the Twentieth Century." Journal of Conflict Resolution 35( 2): 285-306.

Simmons, Beth A. and Zachary Elkins. 2004. “The Globalization of Liberalization: Policy Diffusion in the International Political Economy.” American Political Science Review 98(1):171-189.

Starr, Harvey. 1991. "Democratic Dominoes Diffusion Approaches to the Spread of Democracy in the International System." Journal of Conflict Resolution 35(2): 356-381.

Torfason, Magnus Thor, and Paul Ingram. 2010. "The Global Rise of Democracy A Network Account." American Sociological Review 75(3): 355-377.

Truman, Harry S. 1964. “Special Message to the Senate Transmitting the North Atlantic Treaty” in Public Papers of the Presidents of the United States of America, Harry S. Truman 1949. Washington: United States Government Printing Office.

Vanhanen, Tatu. 2003. Democratization and Power Resources 1850-2000 [computer file]. FSD1216, version 1.0 (2003-03-10). Tampere: Finnish Social Science Data Archive [distributor]. (http://www.fsd.uta.fi/en/data/catalogue/FSD1216/meF1216e.html)

Walt, Stephen M. 1990 The Origins of Alliances. Ithaca: Cornell University Press. Waltz, Kenneth Neal. Man, the State, and War: A Theoretical Analysis. New York: Columbia

University Press, 1959. Warren, T. Camber. 2016. "Modeling the Coevolution of International and Domestic Institutions:

Alliances, Democracy, and the Complex Path to Peace." Journal of Peace Research 53(3): 424-441.

Wejnert, Barbara. 2005. "Diffusion, Development, and Democracy, 1800-1999." American Sociological Review 70(1): 53-81.

Wejnert, Barbara. 2013. Diffusion of Democracy: The Past and Future of Global Democracy. Cambridge: Cambridge University Press.

World Bank. 2013. World Development Indicators 2013. Washington, DC: World Bank.  

23    

Table 1: Baseline Models of Electoral Democracy (1) (2) (3) (4) (5)

Baseline GDP Per Capita

Neighbors Model

Mixed Effects

(Countries)

Mixed Effects

(Regions and

Countries) Year Dummies Not Shown

Presidential Election 0.00846** 0.00951** 0.0106** 0.00999*** 0.0103***

(0.00318) (0.00350) (0.00353) (0.00291) (0.00292) Parliamentary Election 0.0167*** 0.0163*** 0.0170*** 0.0173*** 0.0173***

(0.00174) (0.00182) (0.00187) (0.00192) (0.00192) Adult Literacy 0.0941 0.246** 0.231 0.0862 0.148**

(0.0675) (0.0772) (0.118) (0.0507) (0.0564) GDP per capita (Log) 0.130*** 0.0915*** 0.0921***

(0.0147) (0.00483) (0.00484) L2.Neighbor Gaps 0.0180* 0.0482 -0.0543 -0.00856 -0.0211

(0.00777) (0.0311) (0.0306) (0.0210) (0.0217) Constant 0.00846** 0.00951** 0.0106** 0.00999*** 0.0103*** (0.00318) (0.00350) (0.00353) (0.00291) (0.00292) Random Effects Components Countries’ Intercepts Variance

-- -- -- -4.373*** -5.281***

(0.131) (0.467) Regions’ Intercepts Variance

-- -- -- -- -4.410***

(0.134) Residual Variance -- -- -- -2.641*** -2.642***

(0.00749) (0.00749) Observations 13189 9304 9203 9203 9203 N-Countries 165 149 149 149 149 N-Regions -- -- -- -- 9 R2-Within 0.0532 0.0690 0.117 -- -- R2-Between 0.00135 0.0000189 0.0360 -- -- R2-Overall 0.0502 0.0578 0.0674 -- -- Log Lik. 16979.5 11187.7 11354.9 11173.3 11175.5

24    

Wald χ2 -- -- -- 1033.9 1039.5 Prob > χ2 -- -- -- 0.00 0.00 Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

25    

Figure 1: Immediate Geographic Neighbors, 1998 Figure 2: Military Alliance Network, 2000

26    

   

Table 2: Alliance Network Models (1) (2) (3) (4) (5)

Baseline Model

Defense Alliance Model

Mixed Effects Model

(Countries)

Mixed Effects Model

(Region and Countries)

ATOP Model

Year Dummies Not Shown

Presidential Election 0.00914** 0.00954** 0.00909*** 0.00924*** 0.00902***

(0.00323) (0.00323) (0.00249) (0.00250) (0.00250) Parliamentary Election 0.0179*** 0.0176*** 0.0177*** 0.0177*** 0.0177***

(0.00188) (0.00185) (0.00162) (0.00162) (0.00162) Adult Literacy 0.111 0.0872 0.0691* 0.126** 0.115**

(0.0979) (0.0986) (0.0314) (0.0406) (0.0391) L2.Neighbor Gaps 0.112*** 0.0960*** 0.0704*** 0.0704*** 0.0691***

(0.0126) (0.0123) (0.00427) (0.00425) (0.00408) L2.Defense Alliance Gaps 0.0574*** 0.0473*** 0.0481***

(0.0159) (0.00587) (0.00583) L2.ATOP Alliance Gaps 0.0449***

(0.00469) Constant -0.000978 -0.00106 -0.00179 -0.00441 -0.00381 (0.00363) (0.00365) (0.00857) (0.00875) (0.00871) Random Effects Components Countries’ Intercepts Variance

-- -- -4.558*** -5.376*** -5.462***

(0.128) (0.354) (0.351) Regions’ Intercepts Variance

-- -- -- -4.631*** -4.721***

(0.132) (0.136) Residual Variance -- -- -2.722*** -2.723*** -2.723***

(0.00631) (0.00631) (0.00630) Observations 12885 12885 12885 12885 12884 N-Countries 163 163 163 163 163 N-Regions -- -- 10 10 10 R2-Within 0.0955 0.101 -- -- -- R2-Between 0.00379 0.00548 -- -- --

28      

R2-Overall 0.0644 0.0687 -- -- -- Log Lik. 16874.0 16913.1 16710.6 16715.6 16725.6 Wald χ2 -- -- 1248.9 1256.2 1267.9 Prob > χ2 -- -- 0.00 0.00 0.00 Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 Table 3: 10 Year Before and After Alliance Network Models (1) (2) (3) (4) (5)

Baseline Model

Only Ten- Year Model

Ten-Year and All Others

Model

Mixed Effects Model

Mixed Effects Model

(Region) Year Dummies Not shown Presidential election 0.00954** 0.00923** 0.00927** 0.00881*** 0.00908***

(0.00323) (0.00323) (0.00322) (0.00249) (0.00250) Parliamentary Election 0.0176*** 0.0178*** 0.0177*** 0.0177*** 0.0177***

(0.00185) (0.00188) (0.00189) (0.00162) (0.00162) Adult Literacy 0.0872 0.109 0.0932 0.0757* 0.126** (0.0986) (0.0946) (0.0964) (0.0302) (0.0393) L2.Neighbor Gaps 0.0960*** 0.103*** 0.0959*** 0.0676*** 0.0672***

(0.0123) (0.0121) (0.0118) (0.00423) (0.00419) L2.Defense Alliance Gaps 0.0574*** -- -- -- --

(0.0159) L2.10 Year Prior Alliance Gaps -- 0.0312** 0.0272** 0.0298*** 0.0310***

(0.00934) (0.00963) (0.00549) (0.00550) L2.10 Year Later Alliance Gaps -- 0.0119 0.00770 0.00751 0.00842

(0.00950) (0.00981) (0.00557) (0.00557) L2.All Other Alliance Gaps -- 0.0342** 0.0313*** 0.0309***

(0.0130) (0.00565) (0.00561) Constant -0.00106 -0.000957 -0.000645 -0.00145 -0.00387 (0.00365) (0.00355) (0.00360) (0.00856) (0.00872) Random Effects Components Countries’ Intercepts Variance

-- -- -- -4.639*** -5.427***

(0.128) (0.349)

29      

Regions’ Intercepts Variance

-- -- -- -- -4.733***

(0.136) Residual Variance -- -- -- -2.722*** -2.723***

(0.00630) (0.00630)

Observations 12885 12885 12885 12885 12885 N-Countries 163 163 163 163 163 N-Regions -- -- -- -- 10 R2-Within 0.101 0.0982 0.100 -- -- R2-Between 0.00548 0.00316 0.00490 -- -- R2-Overall 0.0687 0.0684 0.0704 -- -- Log Lik. 16913.1 16893.1 16909.1 16719.2 16724.6 Wald χ2 -- -- -- 1254.8 1260.8 Prob > χ2 -- -- -- 0.00 0.00 Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

30      

31      

Table 4: Shock * Alliance Network Models (1) (2) (3) (4) (5) (6)

Inflation Shock

Protest Shock

Inflation & Protest

Shock

Inflation Shock (Mixed Effects)

Protest Shock (Mixed Effects)

Inflation & Protest Shock (Mixed Effects)

Year Dummines Not Shown

Presidential election 0.0101** 0.0125*** 0.0104** 0.0103*** 0.0121*** 0.0107**

(0.00342) (0.00354) (0.00364) (0.00308) (0.00308) (0.00332) Parliamentary Election 0.0147*** 0.0143*** 0.0129*** 0.0149*** 0.0147*** 0.0133***

(0.00193) (0.00197) (0.00209) (0.00206) (0.00213) (0.00237) Adult Literacy 0.284* 0.221 0.446* 0.234*** 0.169** 0.264***

(0.127) (0.153) (0.191) (0.0558) (0.0565) (0.0689)

32      

L2.Neighbor Gaps 0.111*** 0.132*** 0.138*** 0.0763*** 0.0805*** 0.0839***

(0.0144) (0.0150) (0.0168) (0.00543) (0.00598) (0.00671) L2.Defense Alliance Gaps 0.0203 0.0470** -0.00249 0.0126 0.0431*** -0.00262

(0.0160) (0.0179) (0.0226) (0.0105) (0.00773) (0.0136) L2.Inflation -0.00191 -- -0.00337* -0.00183** -- -0.00330*** (0.00098) (0.00162) (0.000604) (0.000791) L2.Defense Alliance Gaps * L2.Inflation

0.0206** -- 0.0253** 0.0185*** -- 0.0231***

(0.00639) (0.00853) (0.00390) (0.00491) L2.Protest -- 0.00317* 0.00284* -- 0.00341*** 0.00322*** (0.00125) (0.00125) (0.000473) (0.000544) L2.Defense Alliance Gaps * L2.Protest

-- 0.00973* 0.00935 -- 0.00976*** 0.0102***

(0.00486) (0.00500) (0.00207) (0.00226) Constant -0.00671 0.0106 0.0113 -0.00565 0.0105 0.0169 (0.00588) (0.0114) (0.0153) (0.0188) (0.00958) (0.0114) Random Effects Components Countries’ Intercepts Variance

-- -- -- -5.374*** -5.497*** -5.402***

(0.482) (0.520) (0.551) Regions’ Intercepts Variance

-- -- --

-4.283*** -4.218*** -4.069*** Residual Variance -- -- -- (0.136) (0.132) (0.132)

Observations 8086 7359 5959 8086 7359 5959 N-Countries 153 163 153 153 163 153 N-Regions -- -- -- 10 10 10 R2-Within 0.122 0.124 0.135 -- -- -- R2-Between 0.00795 0.00000118 0.000413 -- -- -- R2-Overall 0.0731 0.0651 0.0648 -- -- -- Log Lik. 9911.8 9249.9 7468.7 9729.6 9042.2 7270.9 Wald χ2 -- -- -- 938.4 829.1 728.0 Prob > χ2 -- -- -- Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

33      

Table 5: Power and Alliance Network Models (1) (2) (3) (4) (5) (6)

CINC US & Russia

CINC Mixed Effects

US & Russia Mixed Effects

CINC Mixed Effects

(Region)

US & Russia Mixed Effects

(Region) Year Dummies Not Shown Presidential election 0.00917** 0.00925** 0.00878*** 0.00833*** 0.00894*** 0.00881***

(0.00323) (0.00324) (0.00250) (0.00250) (0.00251) (0.00251) Parliamentary Election 0.0178*** 0.0178*** 0.0179*** 0.0179*** 0.0180*** 0.0179***

(0.00187) (0.00188) (0.00163) (0.00163) (0.00163) (0.00163) Adult Literacy 0.104 0.0532 0.0601 0.0449 0.105** 0.0919* (0.0979) (0.0985) (0.0307) (0.0316) (0.0397) (0.0406) L2.Neighbor Gaps 0.112*** 0.106*** 0.0814*** 0.0784*** 0.0816*** 0.0768***

(0.0127) (0.0119) (0.00395) (0.00405) (0.00394) (0.00400) L2.Alliance Gaps (CINC) 0.0512* 0.0421* 0.0473*

(0.0209) (0.0195) (0.0195) L2.USA Alliance Gaps 0.0333* 0.0305*** 0.0340***

(0.0130) (0.00528) (0.00549) L2.Soviet/Russia Alliance Gaps 0.0202 0.00588 0.00843

(0.0272) (0.0103) (0.0102)

Constant -0.000855 0.00101 -0.00128 -0.000387 -0.00345 -0.00266

(0.00364) (0.00361) (0.00859) (0.00859) (0.00874) (0.00877) Random Effects Components Countries’ Intercepts Variance

-- -- -4.604*** -4.564*** -5.463*** -5.327***

(0.132) (0.122) (0.374) (0.340) Regions’ Intercepts Variance

-- -- -- -- -4.663*** -4.690***

(0.135) (0.136) Residual Variance -- -- -2.720*** -2.721*** -2.720*** -2.721***

(0.00631) (0.00630) (0.00630) (0.00630) Observations 12885 12885 12885 12885 12885 12885 N-Countries 163 163 163 163 163 163

34      

N-Regions -- -- -- -- 10 10 R2-Within 0.0960 0.0980 -- -- -- -- R2-Between 0.00355 0.00340 -- -- -- -- R2-Overall 0.0646 0.0659 -- -- -- -- Log Lik. 16877.0 16891.6 16680.6 16695.1 16684.7 16701.2 Wald χ2 -- -- 1175.4 1213.7 1182.5 1216.5 Prob > χ2 -- -- 0.00 0.00 0.00 0.00 Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

35      

-­‐0.06  

-­‐0.04  

-­‐0.02  

0  

0.02  

0.04  

0.06  

0.08  

Defense  Alliances  

10  Years  Prior  

10  Years  Later  

Other  Years   Neighbors  

Figure  9:  Placebo  Tests  for  Alliance  and  Neighbor  Networks  

36      

Appendix

37