A Network of Thrones: Kinship and Conflict in

Europe, 1495-1918

Seth G. Benzell and Kevin Cooke∗

Boston University

Preliminary and Incomplete

August 11, 2016

Abstract

We construct a database linking European noble kinship networks,monarchies, and wars. We show that monarchs who share kinship ties,such as through the marriages of their children, fight fewer wars. To estab-lish causality, we exploit randomness in the timing of deaths of individualsimportant to the kinship network. Decreases in kinship between rulers,resulting from these deaths, are associated with an increased frequencyand duration of war. Additionally, over our period of interest, we docu-ment a three-fold increase in the percentage of European monarchs withkinship ties. Together, these findings help explain the well-documenteddecrease in European war frequency.

1 Introduction

“Although marriages may secure peace, they certainly cannot make it perpetual; for assoon as one of the pair dies, the bond of accord is broken...”

Desiderius Erasmus, Education of a Christian Prince (1516)

Bella Alii Gerant, tu felix Austria nube. Nam quae Mars aliis, dat tibi Dıva Venus.1

Unofficial Habsburg Motto

War has high costs - military budgets, injuries, disease, lives lost, and disruptiveeffects on long run economic growth. Fortunately, the frequency of war has greatlydeclined over the past several centuries (Levy, 1983; Gat, 2013). One popular classof explanations for this trend is increased interdependence between states. These

∗We would like to thank Bob Margo and the wonderful seminar participants at BostonUniversity and the EH-Clio Conference at Facultad de Ciencias Economicas y Administrati-vas PUC-Chile for their advice and encouragement. This work would have been impossiblewithout the diligent aid of a team of research assistants, especially Holly Bicerano, Chen Gao,Nadeem Istfan, Anna Liao, Ardavan Sephyr and Shawn Tuttle. Finally, we thank George R.R. Martin and the developers at Paradox Interactive for the paper’s inspiration.

1Let others wage war; you, happy Austria, marry. For what Mars awards to others, Venusgives to you.

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connections can take a variety of forms. They include economic interdependence,membership in international organizations, increased cultural exchange, and personalpolitical relationships. We focus on this last type of connection and establish a causalrelationship to conflict in the context of Europe between 1495 and 1918.

To this end, we combine genealogical records on European nobles with conflictdata. During our sample period, we observe a nearly 50% decline in the prevalenceof war. During the same time, conditional on a war occurring, the average number ofparticipants per war increases substantially. We also document a new finding: Euro-pean rulers had increasingly close kinship ties during this same period. We proposeand empirically demonstrate that these deepening kinship networks contributed to thedecreasing frequency of conflict between European monarchies.

Early Modern Europe was characterized by ever-more centralized and absolutemonarchies. This form of government places the personal relationships of monarchsand their families at the center of politics. Monarchs were also the heads of royalfamilies. They were expected to arrange the careers and marriages of close familymembers. Dynastic marriages were negotiated strategically, in order to benefit theroyal family and the state. The Hapsburg dynasty is a well-known (and well-studied)example. The family gained control of Spain, the Netherlands, and Hungary throughtheir marital prowess.

Dynastic marriages ultimately knitted together rulers across the continent. It isour hypothesis that these kinship bonds were effective in decreasing the prevalence ofwar. However, these same bonds also formed the basis of military alliances. There-fore, the decreased odds of war came with a dangerous tradeoff. Additional partiesentered into the wars that did occur. To motivate these hypotheses, we present asimple game-theoretic framework in which rulers care about their reputations withtheir close relatives. As two rulers share tighter kinship ties, war between their statesis increasingly likely to upset a ruler’s immediate family. Essentially, kinship ties raisethe cost of war. High costs of war decrease the prevalence of war. In the case ofalliances, closer kinship ties raise the cost of refusing another ruler’s call to war. Thus,closer kinship ties both decrease war frequency and increase war breadth.

The data set we construct to test these hypotheses has three main components.First, we generate a list of sovereign Christian monarchies. For each monarchy, wedocument the reign of each ruler. Second, we build a dynamic kinship network be-tween the nobles of Europe based on Tompsett (2014)’s genealogical data. Finally,we carefully combine and expand existing data sets on European conflict and theircovariates during this period.

Our data set allows us to apply the tools of social network theory to test our hy-pothesis. Raw correlations seem to indicate, contrary to our hypothesis, that moreclosely connected rulers are more likely to engage in war. However, as mentionedabove, the formation of kinship ties is often strategic. In particular, diplomatic mar-riages were seen as a way to prevent or end conflicts. Thus marriages may have beendisproportionately formed between families with a high propensity for war. To avoidthis endogeneity issue and recover causal effects, we seek exogenous variation in thenetwork structure.

We adapt an identification strategy previously employed in labor economics (Wang,2013). We leverage variation in the kinship network generated by deaths exogenous tothe international political situation to estimate the effect of weakening kinship ties onconflict behavior. We show that increased kinship network distance leads to a declinein war probability. The effect on alliance behavior is inconclusive. Our findings suggestthat 30%2 of the observed decrease in war frequency can be explained by increasedfamily ties.

While the relationship between kinship networks and conflict is the focus of our

2The derivation of this estimate is presented in the conclusion.

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study, a range of other possible causes of the decreased frequency of war have beenput forth. One is increasing rule of law generated by the rise of powerful states. Thishypothesis largely seeks to explain the decline in human violence relative to primitivesocieties. However, the decreasing trend has continued into modern times. Thus,it cannot be wholly explained by the emergence of the state. A second theory isthat increased economic development led to peace. However, Voigtlander and Voth(2013), provide evidence against both these hypotheses. Noting the same patternsin European warfare, they argue that war led to increased land to labor ratios andper capita incomes. This in turn led to more surplus for governments to tax awayto support additional wars. Other explanations include democratic peace theory andchanges in norms.

We do not attempt to refute these or any other theory. However, the increase in

monarch kinship ties is well timed to explain part of the post-Napoleonic increase in

peace. We view our study as a step toward estimating the empirical importance of

the various proposed mechanisms.

2 Historical Context and Related Literature

We restrict our analysis to the monarchies of Christian Europe from 1495-1918.3

During this interval, Europe underwent dramatic changes in its internationalpolitics.

The period from the end of the 15th century to the middle of the 18thcentury - typically referred to as ‘early modern Europe’ - was characterizedby absolute monarchies with hereditary succession. However, in regions withstronger nobilities (such as Poland), the king might be elected for life by acouncil of nobles. Importantly, even in these regions new kings were typicallyselected from a single great family.

Stability in leadership suggests stability in networks. This arguably in-creased the long-term returns to inter-dynastic relationships. Forming rela-tionships with the great family was potentially more important than formingrelationships with any state organ. Ambassadors as well as military and admin-istrative leaders were often close family of the ruler. Marriages of members of theroyal family were typically arranged or approved by the monarch.4 To the ex-tent that a monarch was absolute he could wage war on a personal whim. Evenin Britain, during periods when the Parliament and Cabinet decided whetherto declare war, the King was in charge of the war’s conduct (Hoffman, 2011).

The Habsburg Holy Roman Emperor and Austrian Archduke Maximilian I(r.1486-1519) was especially adept at marriage arrangements. Marrying Maryof Burgandy in 1477, he gained control of her dukedoms in the Low Countries.To secure an ally in the interminable Valois-Habsburg struggle with France in

3Geographically, this roughly corresponds to continental Europe, the British Isles, theMediterranean Islands, and Russia. For a more detailed explanation of inclusion criteria, seethe appendix.

4Sometimes this principle was legally codified. For example, King George III, upset atthe nonstrategic marriage of his brother, passed the Royal Marriages Act (1772) throughparliament, which reads in part “That no descendant of the body of his late majesty KingGeorge the Second, male or female, (other than the issue of princesses who have married, ormay hereafter marry, into foreign families) shall be capable of contracting matrimony withoutthe previous consent of his Majesty, his heirs, or successors... and that every marriage, ormatrimonial contract, of any such descendant, without such consent first had and obtained,shall be null and void, to all intents and purposes whatsoever.” This law was only repealedin 2015.

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Italy, he married his son Philip ‘the Handsome’ to Joanna ‘the Mad’ of Castile in1498. To reduce border tensions with East European neighbors, granddaughtersand grandsons were married to Hungarian and Bohemian rulers. This series ofmarriages set the groundwork for one of the largest empires in history, which atvarious points controlled lands from the Philippines to Budapest.

This and other Habsburg expansions via marriage into lands adjacent toFrance set the stage for a prolonged dynastic conflict. The Valois and, after1589, Bourbon dynasties waged a series of wars with the Habsburg dynasty forcontrol of Italy and the Netherlands. Both sides worked hard to enlist allies,often using marriages of state.

Fichtner (1976) uses the marriage negotiation letters of 16th Century Habs-burgs to craft a broader anthropological theory of European royal marriage. Shefinds that royal marriages often entailed marathon negotiations over dowries,inheritance rights, and international political obligations. The size of dowriesinvolved (usually bi-directional) could rival the yearly maintenance of standingarmies. These marriages allowed the Habsburgs to place spies and influencersin a foreign court and place Habsburgs in lines of succession.5 The marriageswere also important opportunities for two dynasties to perform ceremonies ofhonorary kin-making. As in Levi-Strauss’ (1969) theories of ‘Marriage Alliance’among primitive tribes, these mutual exchanges of gifts and wives allowed twodynasties to become honorary kin rather than rivals.6 She also theorizes thatthe norm of only a pair of royals being able to produce legitimate heirs mayhave served to solidify the power of the upper nobility of all countries againstthe lower nobility.

The importance of dynastic ties was not limited to the continent. Henry VIII(r.1509-1547) found flexibility in marriage so important that it in part drove himto found the Anglican Church. His daughter, Queen Elizabeth (r.1558-1603)was known as the ‘Virgin Queen’ because she used the possibility of betrothalas a carrot to influence foreign leaders. Her successor, King James (r.1603-1625), spent a decade unsuccessfully negotiating the marriage of his daughterto Spain. This would have ended a series of conflicts and brought in a largedowry. However, the ‘Spanish Match’ fell through because of domestic anti-Catholic sentiment.

Examining a sample of about 800 individuals descended from King GeorgeI of England (r.1714-1727) Fleming (1973) argues that international and do-mestic politics played a large role in marriage choices. Statistically, she findsthat members of the higher nobility were more likely to marry foreigners, othermembers of the high nobility and close relatives. They were also less likely tomarry commoners than the lower nobility.

The eve of the Thirty Years War (1618-1648) helps illuminate the relation-ship of kinship networks to conflict. In the preceding century, Lutheranism hadspread across the Holy Roman Empire. Perceived persecution of Protestantsled the Lutheran princes of the Empire to form a Protestant Union for mutual

5emph“In an age when accurate information from abroad was at a premium, a child at aforeign court could keep one apprised of events there. Ferdinand’s daughter Catherine reportedto her father regularly about dealings between her husband, King Sigismund Augustus ofPoland, and Muscovy...”. Ibid.

6For example, Levi-Strauss remarks that “Marriage alliance always involves a choice be-tween those with whom one is allied and on whom henceforth one relies for friendship andhelp, and those with whom an alliance is declined or ignored and with whom ties are severed.In one way, then, one is the slave of one’s alliance...”

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defense. A series of wars of religion rocked the continent.The Catholic Habsburgs were able to broker an uneasy peace through con-

cessions at the Peace of Augsburg (1555). This treaty established the principle‘cuius regio, eius religio’, legally legitimizing Protestantism in at least someparts of the Empire. However, lingering issues set the tables for future con-flict. Importantly, with religion so politically charged, inter-confessional royalmarriages became very rare. An important tool for the deescalation of dynasticconflict was eliminated.

Protestant Bohemian nobles, concerned about the erosion of Protestantrights, brought the lingering conflict to a head. They did so by throwing theHabsburg ruler’s representatives out a window in 1618 (in the Second Defen-estration of Prague), and calling for the election of a protestant prince. Theruler they chose was Frederick V, elector of The Palatinate (r.1610-1623). Thisoutcome was unacceptable to the Habsburgs (Bohemia being a pivotal voter inthe electoral college), and a steadily escalating conflict ensued.

Figure 1 shows the family and ancestral relationships between the states ofEurope in 1618. From this figure alone, one could almost perfectly predict thetwo primary blocks during the Thirty Years War! On the left note three mainblocks of connections: the Catholics of France, Spain and Southern Italy; asecond block of Catholic States in Austria, Bohemia, and Poland, and a Protes-tant block, containing England, the Netherlands, Denmark and the Protestantelectorates of the empire (Prussia, Saxony, and the Palatinate). The divisionbetween these camps is clearly centered in Germany, which was to be the bat-tlefield for the conflict. The second figure emphasizes the strength of Habsburgties between the family’s Austrian and Spanish branches.

Arguably, it was Frederick V’s centrality in the international system that led‘The Bohemian Revolt’ to escalate into a century-defining war. The lands con-trolled directly by Frederick V were relatively weak, but he was at the centerof protestant politics. Frederick V was the son of the founder of the Protes-tant Union, which contained many other protestant-leaning principalities of theHoly Roman Empire. He was closely connected by blood and marriage to themost powerful protestant states in Europe. King James I of England (r.1567-1625) was his father-in-law, William the Silent of Orange (r.1544-1584) (firstStadtholder of the independent Netherlands) was his grandfather, the electorGeorge William of Brandenburg (r.1619-1640) was his brother-in-law, and Chis-tian IV of Denmark (r.1588-1648) was his uncle-in-law. All of these states wouldeventually be drawn in to the war. The Habsburgs too drew in familial allies.Phillip III (r.1598-1621) of the Spanish Habsburgs quickly rallied to his cousins’cause. Catholic France, despite being long-time rivals of Austria, were connectedto Spain by marriage. They demurred for most of the conflict from direct in-tervention, choosing to subsidize Austria’s opponents. Sweden, connected tothe Protestant camp by blood, but not a living connection, also waited untilhalfway through the conflict to enter ‘in defense of Protestantism’.

International politics continued to develop after the Peace of Westphaliaended the Thirty Years War. Yet, family ties retained a central role. Themajor wars of the early Eighteenth Century were those of the Spanish, Aus-trian, and Polish Successions. The realignment of Spain from the Habsburg to

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Blood Connections in 1618

15 ° W

0 ° 15° E

30° E

45° E

30 ° N

45 ° N

60 ° N

75 ° N

Living Connections in 1618

15 ° W

0 ° 15° E

30° E

45° E

30 ° N

45 ° N

60 ° N

75 ° N

Figure 1: In the lead up to the Thirty Years War, kinship connections displayed aclear Catholic/Protestant division. In the above maps, black dots represent capitals.In the left figure, lines represent living kinship connections between rulers, while in theright lines represent blood connections within 3 generations (i.e. the two rulers sharea great grandparent or more recent ancestor). Modern political boundaries included forreference.

Bourbon camp, which ended the War of Spanish Succession (1701-1714) wasa fundamental shift in Continental politics. The War of Austrian Succession(1740-1748) (during which Bourbon Spain fought against Austria), was overwhether Maria Theresa (r.1740-1780) might inherit. This war was in turn oneof several (from the Seven Year’s War (1754-1763) to the Seven Week’s War(1866)) in the Habsburg-Hohenzollern conflict (or “Deutscher Dualismus”) thatcharacterized central European international politics until German unificationin 1871.

During this period religion became less important and nationalism and ide-ology increasingly important as sources of conflict.7 Yet even conflicts thatwere primarily ideological or nationalist could have important kin-network el-ements. The French Revolutionary Wars were preceded by the ‘Declaration ofPillnitz’ (1791) in which Holy Roman Emperor Leopold II (r.1790-1792) warnedof military consequences should King Louis XVI (r.1774-1791) and his family(including Leopold II’s sister, Marie-Antoinette) not be freed. This missive wastaken by the French Revolutionary government as tantamount to a declaration

7The decline in religion as a cause of war is usually dated to the end of the Thirty Year’sWar. The Peace of Westphalia, which ended the conflict, is pointed to as creating a modernsystem of sovereign states, engaged in primarily non-ideological conflict (though the impor-tance of this treaty in creating a new ’Westphalian’ system is disputed). The French Revo-lutionary wars are perhaps an important exception, which had important ideological causes.The rise of modern nationalism is usually associated with the Italian and German unificationmovements in the early to mid-19th century. Certainly inter-confessional marriage becamemore common after the Thirty Years War. In (Orthodox) Russia, following Peter the Great’s(r.1682-1725) drive to Westernize, marriages with both Catholic and Protestant nobility werecommon.

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Figure 2: Share of states connected is hollow circles, left axis. Share of states at warare filled circles, right axis. Ten year bins. Important political periods are indicated.Trend lines are 3rd degree polynomials.

of war, and led to their invasion of the Habsburg Netherlands shortly after. Af-ter Napoleon’s victories in the War of the Fifth Coalition, he sealed his imposedalliance with Austria by marrying the daughter of Austrian Emperor Francis II(r.1772-1835).8

The years between the Napoleonic Wars and World War I, sometimes knownas ‘The Concert of Europe’ were atypically peaceful. The ‘Holy Alliance’ of themajor monarchs of Europe was dedicated to defending royal prerogatives andconservative values against the new ideas sweeping Europe. This alliance ofthe Monarchs, which was explicitly a fraternity, may have only been possiblebecause of their increasing sense of kinship.9 Figure 2 displays the time trends inwar and connectedness. It suggests an inverse relationship, driven by a decreasein conflict and increase in connections in the 19th century.

In World War I, this system of personal relationships between the rulersfinally failed. King George V of England (r.1910-1936), Tsar Nicholas II ofRussia (r.1894-1917), and Kaiser Wilhelm II of Germany (r.1888-1918) were allfirst cousins, and descendants of Queen Victoria of England (r.1819-1901).10

On the eve of the war, the German and Russian rulers exchanged a series ofpersonal telegrams signed ‘Willy’ and ‘Nicky’, desperately trying to deescalatethe conflict. However, since their Grandmothers’ death, the two had grown intomutual distrust and suspicion.11 This last grasp towards brotherhood proved

8The HRE had been dissolved in the intervening period, and Habsburg patriarchal landsreorganized into the Austrian Empire

9From the text we have ”...the three contracting Monarchs will remain united by the bondsof a true and indissoluble fraternity, and, considering each other as fellow-countrymen, theywill, on all occasions and in all places, lend each other aid and assistance ; and, regardingthemselves towards their subjects and armies as fathers of families, they will lead them, inthe same spirit of fraternity with which they are animated, to protect Religion, Peace, andjustice.”

10George V and Wilhelm II were her biological grandchildren. Nicholas II was a grandson-in-law, having married Victoria’s granddaughter Alix of Hesse in 1894.

11While signing the mobilization order, Kaiser Wilhelm II remarked “To think George and

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too little too late, and with the war came the end of a Europe dominated byKings and Tsars.

To our knowledge there are no papers that econometrically study the con-nection between royal family networks and war. However, the study of socialnetworks in economics has expanded dramatically in the last several years. Thisprovides a theoretical framework for our study. Network techniques have beenused to help understand trade flows (Cheney 2014), venture capital outcomes(Uetake 2012), microfinance (Banerjee et. al. 2013), and even the spread ofgossip (Banerjee et. al. 2014). “Social and Economic’ Networks” (Jackson,2008) provides an overview of the methods and measures used in this literature.Jackson (2014), in a review of the literature, forcefully argues for the importanceof network connections in understanding economic outcomes.

The seminal paper, applying network methods to historical political out-comes, is Padgett and Ansell (1993). They use network centrality to explainhow the Medici, a noble family of no particular note in 1400, rose to the pinnacleof Florentine politics in 1434. Their thesis is that Cosimo de’Medici forged aseries of marriages that placed his family ‘between’ the other great families ofFlorence. This allowed the Medici the opportunity to be involved in nearly alldecisions of consequence.

Spolaore and Wacziarg (2009) find that a measure of genetic distance be-tween nations is strongly correlated with international conflict. More ‘related’populations are more likely to fight wars. They suggest this is a consequence ofpopulations of similar cultures being more desirable for conquest. In contrast,we find that more closely related rulers are less likely to engage in war.

Maoz et al. (2004) and Maoz (2010) show that bilateral trade relationsand mutual membership in international organizations are negatively correlatedwith bilateral conflict (though find mixed results when using alternative mea-sures of network connectivity). Jackson and Nei (2015) advance an argumentthat alliance networks without a ‘peace surplus’ from trade are inherently un-stable. They attribute the post 18th century decline in war frequency to a risein international trade.

Two recent papers use data similar to our own. Iyigun (2008) uses Brecke’s‘Conflict Catalogue’, and finds strong evidence that Ottoman invasions aidedthe spread of Protestantism in Europe in the 16th century. Dube and Harish(2015) study the effect of ruler gender on conflict. Like us, they construct adata set matching Wright’s (1945) war data to (an earlier version of) Tompsett’sgenealogical data. Dube and Harish use the geneological data to identify thegender of rulers’ close relatives. They use this information to make a compellingcase that female rulers were more likely to engage in wars than men. Theysuggest this results from female rulers being more likely to delegate domesticresponsibilities to their spouses.

While our analysis also relies upon Wright (1942) and Tompsett, we exertsignificant effort to expand, reconcile, and improve upon these sources. More-over, our re-construction of the genalogical data as a dynamic kinship networkdistinguishes our methodology. Our data construction efforts are detailed inAppendix A. We cannot directly compare our procedures, as Dube and Har-ish have yet to release the details of their data and its construction. However,

Nicky should have played me false! If my grandmother had been alive, she would never haveallowed it.”

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as the Appendix discusses in detail, our data seems to be more comprehensiveand detailed, including more countries, rulers and additional covariates (such astime-varying country adjacency and ruler religions).

3 Network Concepts

We contend that kinship connections have a causal relationship with interstateconflict. In order to test this hypothesis, we employ a suite of tools from networktheory. This section offers a brief introduction to the concepts we employ.12

A kinship network consists of a set of living individuals, I, and the kinshiprelations between them. Individuals are nodes of the network, and their imme-diate kinship relations are links. Specifically, two individuals are said to havean immediate kinship relation if they are spouses, siblings, or parent and child.We construct a kinship network for the European nobility in every year from1495-1918.

Each year’s network can be represented by an adjacency matrix, At. At isan |It|× |It| square matrix, where |It| is the number of living individuals in yeart. The (i, j)th entry in the matrix is 1 if individuals i and j are linked and 0otherwise. Formally, these are undirected, unweighted graphs.

From At, we derive two other matrices. These are the degree matrix, Dt,and the Laplacian matrix, Lt. Dt is the diagonal matrix where Dt,(i,i) =∑

j∈I At,(i,j). The Laplacian matrix is given by Lt = At −Dt.The network of kinship relations evolves from year to year. The set of nodes

varies as individuals are born and die. Links can either exist from birth or beformed through marriage. They can be dissolved through divorce. In order tostudy this changing kinship network, we need to introduce summary measuresof the network structure. We focus on four such measures. These are shortestpath length, resistance distance, harmonic centrality, and distance to commonancestor.

Shortest path length is a simple measure of the kinship distance betweentwo individuals. Consider two nodes i and j. The shortest path length betweenthem is the minimum number of links that must be traversed to move fromnode i to node j.13 If a path exists, we say the pair is connected. When nopath exists, shortest path length is undefined. The longest path observed is of30 degrees.

Resistance distance also measures kinship distance, but considers all pathsbetween two nodes. This measure reinterprets the network as an electricalcircuit where each link is a resistor of unit resistance. The resistance distancebetween nodes i and j, denoted Ri,j , is the effective electrical resistance betweenthe nodes. Formally, when a path exists, resistance distance is defined by

Ri,j = Γi,i + Γj,j − 2Γi,j ,

where Γ is the Moore-Penrose pseudo-inverse of L.

This measure was popularized by Klein and Randic (1993) who prove it is ametric in the technical sense.

12For a more thorough introduction to network methods in economics, see Jackson (2008).13We give examples of the calculation of this measure and the resistance distance in the

following section.

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Resistance distance, Ri,j , is less than or equal to the shortest path distance.When there is no shortest path, the resistance distance will similarly be unde-fined. The resistance distance is decreasing in the number of paths between twonodes. Resistance distance will also be shorter when these paths are shorterand have fewer nodes in common. As the distance metric takes into accountall paths between two nodes, it is a natural counter-part to the shortest pathdistance.

Resistance distance has been widely used in the physical and applied sci-ences. In addition to its obvious importance in electrical engineering, the mea-sure has been widely used in molecular chemistry to describe the strength ofchemical bonds. It is closely tied to the expected travel time between nodes fora random walker on the network (Chandra et al. 1996). It also appears as a keyparameter in synchronizing system clocks in distributed sensor nets (Elson etal. 2004), as well as in algorithms that detect clusters in networks (Fortunato2010). Despite its popularity in other fields, resistance distance has been muchless prevalent in economics.

The above two concepts measure the bilateral connectedness of two indi-viduals. However, an individual’s global importance in the network may alsoplay a role in international affairs. To measure this, we use harmonic centrality.Harmonic centrality can be calculated as follows:

h centi =∑j 6=i

1

dist(i, j)(1)

where dist(i, j) is the shortest path distance. When no shortest path exists weset this distance to be ∞ and define 1/∞ = 0. With this convention, the har-monic centrality measure can handle networks with several distinct componentsin a natural way. This is important in our context, when there are often two ormore disconnected clusters of nobles.

We calculate shortest path distances using the full set of individuals in ourdata. However we only sum over the inverse distances to other (contempora-neous) rulers. Thus this centrality measure is a measure of importance in thenetwork of rulers rather than the broader noble network. This also has thedesirable consequence of eliminating any bias due to our genealogy containingan oversampling a region’s nobles.

Axiomatic foundations for harmonic centrality are presented in Boldi andVigna (2013). They show this measure is more well behaved then any othergeometric centrality measure. In particular, it does not suffer from some of thebiases which affect the closely related concept of closeness centrality.

Finally, we measure the blood relationship of a pair of individuals withdistance to common ancestor. For a pair of individuals, this measure is constantfrom year to year. To calculate it, we construct a single directed network of allindividuals in the data. Unlike the (undirected) network above, in this networklinks only run in one direction - from children to their parents.

For individuals i and j, we find all individuals who can be traveled to fromboth i and j. Distance to common ancestor for i and j is defined to be theminimum of the maximum of paths from i and j to a common ancestors. So,if a pair of rulers’ closest common ancestor is a mutual great grandparent,their blood distance is 3. If one ruler’s grandparent is another ruler’s greatgrandparent, their blood distance is still 3. If a pair do not share a common

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ancestor, this measure is undefined. If a common ancestor exists, we say thepair is blood connected. The maximum distance searched is 7 generations back.

In the data section, we provide summary statistics of our data utilizing eachof these concepts. Our main analysis relies on shocks to shortest path length andresistance distance. The theoretical connection between these distance measuresand conflict outcomes is explored in the next section.

4 Conceptual Framework

To help fix ideas we develop a simple model. The goal of this model is to showhow kinship network structure can influence conflict outcomes. We begin withthe following stylized facts:

(i) Parties in a war tend to personally dislike their opponents,

(ii) An individual’s opinion of others can be influenced by the opinions ofthose he is close to, and

(iii) It is costly for a ruler to be disliked by close family members.

Consider two identical nations, called England and France, whose rulers areconnected via living family members. Suppose a contentious situation has arisenin which the rulers perceive an expected benefit, b, from attacking the opponent.This is offset by a cost which has to be paid by the ruler if the war results in aclose relation disliking him. c is the expected disutility a side faces for launchinga war.14 This is summarized in the following simple game:

France

EnglandPeace War

Peace 0,0 0,b-cWar b-c,0 b-c,b-c

Figure 3: The War Game

Generically, this game has a unique equilibrium.15 Whenever b > c, the uniqueNash Equilibrium is war. Conversely, when c > b then peace is the uniqueequilibrium. Therefore, as the expected reputation cost of attacking increases,we should expect wars to be less frequent.

14Throughout this section, we focus on the interpretation of c as the cost to a ruler of havingpersonal relations upset about a conflict, and b as an unrelated stochastic invasion dividend.However, very similar arguments obtain for related interpretations of c and b. For example, theexpected invasion dividend b might be decreasing in the strength of the connection between apair rulers. This might be due to a higher peace surplus (perhaps because of greater economicor cultural compatibility) or because of less divergence in beliefs about the relative balance offorces (which would tend to prevent negotiation failures). Similarly, c might be interpreted asan administrative or reputation cost a ruler must pay to secure the loyalty of close relatives(who may have important domestic or military roles) who would be tempted to defect to theopposing ruler.

15In this simplified model, the return to being invaded is the same as the return to peace.Adding a positive peace dividend or negative defense cost will not change the qualitativeimplications of the model for b and c. Adding these creates a range for b − c where the WarGame is a ‘Stag Hunt’ with war and peace equilibria.

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We propose that c is a decreasing function of the kinship distance betweenthe rulers. Suppose there is a unique path of living family members betweenthe two rulers. Figure 4 is an example of such a network. Here the Queen ofEngland’s son is married to King of France’s daughter.

English Queen English Prince French Princess French King

Figure 4: The shortest path distance from the English Queen to the French King is 3.

The Queen knows attacking France will result in the King being upset withthe her. With some probability, this negative opinion of the Queen will betransmitted to the King’s daughter. Upon transmission it becomes possible thatthe Princess could negatively influence her husband’s opinion of the Queen. Ifthe Princess successfully causes the English Prince to become upset with hismother, the Queen will suffer disutility from this estrangement. This expecteddisutility is c.

Suppose the transmission of this negative opinion (from one upset agent toher immediate family member) occurs with a fixed probability at each link. Thenclearly c would be decreasing in the kinship distance of between the rulers.16

Thus, we predict that an exogenous increase in network distance should increasethe probability of war.

In more complicated networks there may be many paths between the rulers.Additional paths increase the ways in which negative opinion can be transmit-ted. To address this, we complement shortest path with resistance distance.17

Figure 5 shows why this can be important:

English Queen English Prince French Princess French King

Queen’s Sister King’s Brother

Figure 5: The shortest path distance from the English Queen to the French King is 3.However, the resistance distance between them is 1.5. In figure 2, it was equal to theshortest path distance.

16Our model draws inspiration from the classic children’s game “Telephone”. This analogybecomes even closer if we reformulate the model in terms of a communication story wherenegotiation costs are increasing in path length due to potentially corrupted messages.

17Beyond computational tractability, we do not provide a formal justification for the useof this particular all-path measure. However, we conjecture that resistance distance varieslinearly with the probability of negative opinion transmission when a sophisticated individualattempts to transmit the opinion through costly persuasion. (ed. There may be more to sayhere; we’re thinking about it)

12

A similar logic can also explain alliance behavior. Just as attacking someone islikely to give that person a negative opinion of you, refusing an alliance requestis likely to anger the requesting party. Thus we should expect the cost of refusal,c′, to vary with the kinship network.

Consider the game in which one country, the Fighter, chooses whether ornot to (costlessly) ask a second country, the Bystander, for help in a war. TheFighters’s actions are ask (A) or not ask (NA), while the Bystander can eitherhelp (H) or not help (NH). If the Bystander helps he pays an effort cost e.Refusing a request for help bares the expected cost c′. If help is provided theFighter benefits. This game is summarized in the following diagram:

Fighter

Bystander

1,−e

H

0,−c′

NH

A

Bystander

1,−e

H

0, 0

NH

NA

Figure 6: The Alliance Game

Whenever c′ > e, the unique subgame perfect Nash Equilibrium of this gameresults in help being asked for and given. As in the war game, the cost param-eter, c′, is a decreasing function of kinship distance. Thus we expect kinshipdistance to not only have a positive relationship with war frequency, but alsoto have a negative relationship with observed alliances.

5 Data

5.1 Data Construction

Our analysis is based on a newly constructed data set on royal kinship networksand wars. To construct royal kinship networks as well as the identities of thoseruling various states at particular points in time, we rely primarily on Spuler(1977), Tapsell (1984), and the most recent update of Tompsett’s (2014) “RoyalGenealogy” collection.

For each country-year in our data set we identify if the country is at war and,if so, with whom. The primary source for the conflict data is Wright (1942). Ad-ditional information is taken from Langer (1972), Phillips and Axelrod (2005),Levy (1983), Sarkees and Wayman (2010) and Brecke (2012). Information onformal alliances between states was gleaned from Levy and Thompson (2005)and Gibler (2009).

Our analysis is restricted to sovereign Christian European monarchies from1495-1918. This limitation aids in collecting a comprehensive data set, whilefocusing on the types of states for whom dynastic connections might be partic-ularly important.

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count mean sd min maxCountry Dyad 1 858War 78613 .034778 .1832181 0 1Any War 78613 .4606363 .4984513 0 1Revealed Alliance 36212 .1584281 .365147 0 1Capital Distance 78613 7.037528 .7249732 .58829 9.4666Same Religion 78613 .4480302 .497295 0 1Neither Landlocked 78613 .5582664 .4965966 0 1Adjacent 78613 .1320901 .3385909 0 1Shortest Path Length 30418 7.166579 4.634233 1 30Resistance Distance 30418 2.742834 1.98191 .2 15.752Blood Distance 48103 4.616053 1.681341 1 7Centrality (Higher) 78613 2.534504 1.63201 0 7.7917Centrality (Lower) 78613 1.139043 1.315805 0 6.6039

Table 1: Summary Statistics

For a complete description of the data construction, and data collected thatwas not used in our analysis, see the appendix.

5.2 Data Description

Our raw data set consists of 90,653 country-pair years. Of these, 3,864 pairs arepersonal unions, where the same ruler controlled two crowns simultaneously. Byconstruction, personal unions are never at war, so these pairs are not includedin the analysis.

The granularity of our data means that if a ruler changes in the year a warbegins or ends, it is impossible to say which of the rulers made the decision tobegin or end the war. Therefore, for our main analysis we also drop dyad-yearsin which there is a ruler change. This eliminates an additional 8,176 dyad-years. Finally, for our main regressions we also drop dyads with a shortest pathof length one (as these pairs’ shortest path network cannot be shocked). Thisdrops an additional 962 dyad years.

There are are 858 country pairs in our sample. In table 1, we report on3 classes of variables. The first class (War, Any War, Revealed Ally) makeup our measures of conflict activity. War is a dummy variable which indicateswhether a pair of countries are at war in a given year. Any War indicateswhether we observe either country in the pair fighting a war (against anyone) inthe given year. Revealed Ally is an indicator for whether the pair of countriesare fighting a common enemy (and not each other) conditional on at least oneof them being at war with some one (i.e, Any War= 1). Revealed Allianceconveys whether countries fight together given an opportunity exists. Therefore,Revealed Alliance is undefined when neither country is at war.

The second class of variables are pairwise covariates. The variables ‘NeitherLandlocked’ and Adjacent are self-explanatory dummy variables. We also reportthe natural log of the distance between two countries’ capitals in kilometers anda dummy for whether the pair of rulers are members of the same religious group

14

(Catholic, Protestant, or Orthodox).The final class of variables are network measures that are discussed thor-

oughly in section 3. Shortest Path, Resistance, and Blood Distance are eachbilateral measures of kinship.18 High and low centrality give the global net-work importance of the more and less central member of the dyad. For a givencountry pair, the harmonic centrality of the more and less central country arereported separately. Other information collected but not used, such as on formalalliance formation, is not reported.

5.3 Correlation Analysis

We are primarily interested in the relationship between network measures andwar. In the following figures we explore trends in these variables. Figure 2 givesthe proportion of states at war and connected in 10 year bins.

Table 2 explores how our different explanatory variables correlate with war.(Table 13 in the appendix conducts a similar analysis on revealed alliance).All regressions are estimated on our dyad-year panel utilizing variations on thefollowing linear probability model:

war(i,j),y = α+β Conn+γ Conn∗Net Dist+δ X(i,j),y+θi+θj+θfloor(y/50)+ε(i,j),y(2)

Here war(i,j),y is a dummy for whether countries i and j are at war in yeary. We regress this on Conni,j (whether a network connection exists between iand j), Conn ∗Net Disti,j (shortest path between i and j conditional on beingconnected.), a vector of dyad-year controls X(i,j),y (including blood connection,adjacency, same religion, neither landlocked, and log of capital distance), andfixed effects for country (θi, θj) and 50-year period (θfloor(y/50)). In table 2,regressions (1) and (2) shortest path distance is used as the measure of networkdistance while (3) and (4) utilize resistance distance. Regressions (1) and (3)drop the fixed effects and controls from the above model. All of these specifica-tions drop personal unions and rulers changes.

In this table and throughout the paper are calculated using two-way countryclustering. We believe this to be a conservative method since it does not exploitthe long time horizon of our data. Thus our criteria for statistical significance isa high bar.19 Given our long yearly panel (with approximately 90 observationsper pair of states), downward bias from autocorrelation in war is likely to besmall, as the bias is proportional to 1/T (Nickell, 1981).

These simple regressions seem to indicate that close family ties are associatedwith war. Significance disappears after adding additional controls. Unsurpris-ingly, we also report a large positive and significant relationship between warand sharing a land border.

18If a pair is not connected in a given year (by living relatives for Shortest Path andResistance or ancestry in the case of Blood Distance) then these measures are undefined.

19Jackson and Nei (2015) use the same clustering technique. While we feel this method isconservative relative to the literature, it does not allow for spatial correlation of errors acrossnearby countries and misses some potential correlations between country pairs. This secondissue can be addressed by a dyadic clustering procedure (see Cameron and Miller (2015)). Wehave been unable to implement this dyadic clustering in our full sample. This is because thenetwork has multiple components due to the dropping of unconnected pairs. However, wehave not found large differences in calculated standard errors on small subsamples.

15

Table 2: War and Connections

(1) (2) (3) (4)war war war war

Connected 0.00629 0.00799 -0.00548 0.00883(0.0101) (0.00716) (0.00850) (0.00701)

Shortest Path Length -0.00213∗ 0.000373(0.000985) (0.000522)

Resistance Distance -0.00122 0.000536(0.00138) (0.00113)

3 Gen. Blood 0.00911 0.00891(0.00960) (0.00945)

Adjacent 0.0384∗ 0.0384∗

(0.0171) (0.0171)

Same Religion -0.00624 -0.00635(0.00723) (0.00721)

Neither Landlocked -0.000412 -0.000397(0.00418) (0.00418)

Capital Distance 0.00597 0.00599(0.00503) (0.00504)

Observations 78613 78613 77651 78613∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Standard errors are robust to two-way country clustering.

Regression of dyadic war on contemporaneous network characteristics following equation 2.Connected indicates whether a pair of countries are connected via kinship. Shortest pathlength and resistance distance are measures of kinship distance multiplied by a dummy forwhether a connection exists. 3 Gen. Blood indicates whether the rulers share a commonancestor within 3 generations (i.e. a great grandparent or closer). Capital distance is in logkilometers.

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Table 3: Blood Connections and Conflict

(1) (2) (3) (4)war R. Ally war R. Ally

Blood Degree 1 0.0367∗∗∗ 0.0142 0.0348∗∗∗ 0.00122(Shared Parent) (0.0129) (0.0372) (0.00867) (0.0340)

Blood Degree 2 0.0761∗∗∗ 0.0628∗∗∗ 0.0694∗∗∗ 0.0110(Shared Grandparent) (0.0270) (0.0176) (0.0193) (0.0293)

Blood Degree 3 0.0158∗ 0.0812∗∗∗ 0.0202 0.0142(Shared Great Grandparent) (0.00898) (0.0219) (0.0125) (0.0219)

Blood Degree 4 0.00524 0.0315 0.0104 -0.0542∗∗∗

(0.00530) (0.0217) (0.00772) (0.0200)

Blood Degree 5 0.00182 0.0300∗ 0.00924 -0.0421∗∗∗

(0.00700) (0.0171) (0.00711) (0.0147)

Blood Degree 6 -0.000702 0.0564∗ 0.00338 -0.00814(0.00382) (0.0327) (0.00611) (0.0164)

Blood Degree 7 -0.00653∗ 0.0609∗∗∗ 0.000797 0.00485(0.00383) (0.0160) (0.00344) (0.0168)

Adjacent 0.0382∗∗∗ 0.0541∗∗∗

(0.0123) (0.0142)

Same Religion -0.000588 -0.0104(0.00751) (0.0183)

Capital Distance 0.00440∗ 0.0111(0.00266) (0.00869)

Constant 0.0207∗∗∗ 0.0619∗∗∗ -0.0353∗∗ -0.0892(0.00503) (0.0100) (0.0168) (0.0638)

Country FE X X50 year FE X XN 78613 28028 78613 28028∗p < .10,∗∗ p < .05,∗∗∗ p < .01

Standard errors are robust to two-way country clustering.

Regression of dyadic war and revealed alliance on the genetic relationships of the rulers. Ifrulers are of different generations, the maximum of the pair’s number of steps to a commonancestor is used. For example, if a pair’s closest common ancestor is one monarch’s fatherand the other’s grandfather, the pair have a blood degree of two. Capital distance is in logkilometers.

17

Table 3 gives more detail on how the propensity for conflict varies withgenetic distance. Recall that brothers would have a blood distance of 1, andthat first cousins would have a blood distance of 2. War is more likely for thevery closely genetically related and lower for the more distantly related relevantto the baseline. This nonlinearity is likely driven by succession crises betweenthe most closely related. Some of the most important conflicts of the 18thcentury were succession crises. To quote Erasmus,

Why, then, is there most fighting among those who are most closelyrelated? Why? From [dynastic inheritants] come the greatest changesof kingdoms, for the right to rule is passed from one to another:something is taken from one place and added to another. From thesecircumstances can come only the most serious and violent conse-quences; the result then, is not an absence of wars, but rather thecause of making wars more frequent and more atrocious...

While the network measures (other than blood connections) do not appearto have a strong relationship to war in these preliminary regressions, this shouldbe expected. Kinship relations might come along with competing (unobserved)claims to a throne. Often, they are formed (in the case of marriages) betweencountries who are explicitly trying to forestall an impending conflict or end anongoing one. In other words, network formation may be more likely betweenpairs of countries that are more predisposed to conflict. This will tend to createa positive relationship between kinship and war. However, history and theorysuggest that living kinship connections should have a causal impact in the op-posite direction. In the following section, we will explore ways of getting aroundthese and related endogeniety concerns in order to estimate the true effect ofkinship connections on conflict.

6 Results

We employ two strategies to help establish a causal relationship between livingkinship connections and conflict outcomes. Our first approach relies on the factthat deaths break links in the network. Using these death shocks we show thatincreasing the network distance between a pair of rulers tends to increase thelikelihood of war. Our second strategy uses an instrumental variable approachbased on the randomness of a ruler’s firstborn child’s gender. While our marriageresults face a weak instrument issue, our result is consistent with the theory thatbeing more closely connected results in fewer wars and more alliances.

6.1 Death Shocks

When an individual along the shortest network path between rulers dies, theshortest path length between them mechanically increases (weakly). We lever-age this variation in network structure generated by these ‘on-path’ deaths todetermine the effect of shortest path length on conflict. Our main results arederived using the following linear probability model:

war(i,j),y =α+ β1 death(i,j),y + γ (Shortest Path)(i,j),y+

δ X(i,j),y + θi + θj + θfloor(y/50) + ε(i,j),y(3)

18

Here death is a dummy variable for death shocks (either on-path or off-path), Shortest Path is the length of the shortest path before any death shocks,and the rest are controls, including country and time fixed effects. IncludingShortest Path as a control is necessary because the probability of receiving anon-path death shock is increasing, roughly linearly, in the length of the shortestpath. In this model the main coefficient of interest is β1. This is the increasedlikelihood of a war in the same year a death occurs. We control for shortestpath in all specifications, because the likelihood of an on path death is linearlyincreasing in the length of the shortest path.20

Estimates of this model are reported in table 4. It shows that on-path deathsare associated with a 1.25 percentage point increase in contemporaneous war.Given an overall war hazard of 2.7 percent, this is a large effect. These resultsare robust to the inclusion of covariates and fixed effects.

Moreover, off-path deaths21 are shown to have no significant impact. To theextent that deaths of people within a degree of a ruler do impact conflict, theyseem to decrease its likelihood. This placebo should help assuage any concernsthat deaths are more likely during times of war., while not reported here theseresults are strengthened when a lagged dependent variable is included.

On-path death shocks occur in 11.4 percent of dyad-years. These shocksare driven by the deaths of 263 important individuals (one death can occur onmany shortest paths). Placebo deaths occur in 17.9 percent of dyad-years. Inyears after the death shock rulers are completely disconnected 41.8 percent ofthe time. When the rulers remain connected, they are 2.03 degrees more distantin the subsequent period on average. Our result is robust to excluding deathsof individuals who were known to have died violently or suspiciously.22

We can decompose the impact on contemporaneous war. On-path deathscould either increase the probability of war starting or delay a war’s conclusion.To investigate this, we use the following specification.

war(i,j),y =α+ β1 death(i,j),y + β2 death(i,j),y × war(i,j),y−1+

η war(i,j),y−1 + γ (Shortest Path)(i,j),y + δ X(i,j),y+

θi + θj + θfloor(y/50) + ε(i,j),y

(4)

Unlike Equation 3, this model allows for a differential effect of death shocksdepending on whether the pair was at war in the previous year.

Here, the main coefficients of interest are β1 and β2. The first is the effectof a death shock on the probability of a war starting. The sum of the two is theeffect of a death on a war lasting an additional year. Estimates of this modelare reported in table 5. It shows that on-path deaths are associated with a.25 percentage point increase in the probability of war starting. It also shows

20Some discussants have suggested we use deaths as an instrument for shortest path length inregression 2. Unfortunately using this as an instrument in a traditional way is impossible. Theissue is that our exogenous variation is only present for pairs that begin a period connected. Sowe can only present results on that subset of the data. In this and all subsequent regressionsincluding deaths, we only include observations where a network path exists (so deaths mightoccur).

21These are deaths of one of the two ruler’s immediate kin (i.e. individuals with a shortestpath of one from the ruler) who are not on the shortest path.

22For many of the individuals in our sample we are able to observe the actual cause of death.As we only observe one on-path death directly related to battle, this is not driving our result.Descriptive statistics on deaths are reported in the appendix.

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Table 4: War and Death Shocks

(1) (2) (3) (4) (5) (6)war war war war war war

On-Path Death 0.0107∗∗ 0.0125∗∗∗ 0.0125∗∗∗

(0.00360) (0.00368) (0.00359)

Off-Path Death -0.00572 -0.00307 -0.00324(0.00317) (0.00241) (0.00242)

Shortest Path Length -0.00243∗ -0.00232∗ -0.000237 -0.0000704 -0.0000710 0.0000821(0.00105) (0.00105) (0.000489) (0.000484) (0.000357) (0.000346)

Adjacent 0.0286∗∗ 0.0287∗∗

(0.0106) (0.0107)

3 Gen. Blood 0.00546 0.00537(0.00613) (0.00608)

Neither Landlocked 0.00149(0.00640)

Same Religion -0.00437 -0.00464(0.00479) (0.00482)

Capital Distance 0.00531∗ 0.00539∗

(0.00251) (0.00263)

Country FE X X X X50 year FE X X X XObservations 29456 29456 29456 29456 29456 29456

Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Regression of dyadic war on a dummy for deaths of people on the shortest path betweenthem. Off-path death is a dummy indicating whether an individual with an immediatekinship connection with the ruler died within the year. 3 Gen. Blood indicates whether therulers share a common ancestor within 3 generations (i.e. a great grandparent or closer).Constant included but not reported. Capital distance is in log kilometers. Adjacent is adummy for whether the nations shared a land border.

20

that on-path deaths are associated with a 14 percentage point increase in thechance that existing wars continue. Each of these represent an approximately20 percent increase in the average survival rates of peace and war respectively.

Dube and Harish (2015) provide evidence that the gender of rulers maymatter to international conflict. It may also be the case that the gender ofpeople along a kinship network may be important to its function in internationalrelations. Table 6 explores these possibilities. It breaks down the impact of onpath deaths by the gender of the person who died, and the gender of thoseon the shortest path. Shortest paths composed entirely of women would havean index of zero, and a shortest path composed entirely of men would have asex index of one. The results indicate that the sex of the person dying haslittle effect on war. Specification four provides limited evidence that deathsalong male dominated shortest paths are less likely to cause wars. However,this disappears when additional controls are added.

In table 7 we look at leads and lags in the impact of on and off-path death.We use the full specification employed in regression (5) of table 4. However, theoutcome variable is not contemporaneous war, but war up to five years in thepast or future. The table reports the point estimate of the impact of an on-pathor off-path death dummy on future and lagged wars.

The table shows that an on-path death increases the chance of war up to twoyears before and up to a year after. However, the peak effect is contemporaneous.It is not particularly surprising that an on-path death should have an impact inpreceding years. The most important reason for this is that both deaths and warstart dates are measured imprecisely. An individual might not actually be dead,but might be totally incapacitated in the year or two before his or her death.Similarly, a minor skirmish that might have been forgotten (and therefore neverrecorded as a war) if a kinship network was stable might escalate into a fullwar after an important death. However, the initial skirmish might be recordedin our data as the start of the conflict. It makes sense for the impact of thedeath to diminish over time as the political situation endogenously adjusts tothe deaths (e.g. through strategic marriages of state).

Table 7 also indicates that off-path deaths of closely related individuals mayhave an effect on war. However, this impact is in the opposite direction, andtends to decrease the chance of war. The decreased chance of war in the twoyears after the death may be due to the monarch losing an important admin-istrator, being more careful with his own life after the loss of an heir, or thereduction in belligerence may be due to grief. It is less clear why such a deathmight make war less likely five years in advance. Monarchs with sickly wives,brothers, parents or young children may be inherently less likely to fight.

In table 8, we report estimates of the analogous model for revealed alliancesestimated on the subset of our data in which one member of the dyad is atwar with someone. The sample size is reduced to about 8,000 dyad years,because revealed alliance is only possible if at least one of the pair of states is atwar. Since our sample size is much smaller we do see a corresponding decreasein power. While point estimates are consistent with the hypothesis that onpath-deaths decrease the chance of reveled alliance, none of our estimates havesignificance.

In Appendix B, we perform additional robustness checks to confirm theseresults. First, we use an alternate type of placebo, and verify that it has noimpact on conflict behavior. Then we investigate the cause of deaths for those

21

Table 5: War Start, Continuance and Death Shocks

(1) (2) (3) (4) (5) (6)war War War War War War

On-Path Death -0.000276 0.00257∗∗ 0.00248∗

(0.000285) (0.00128) (0.00129)

Wart−1×On-Path Death 0.130∗∗∗ 0.139∗∗∗ 0.140∗∗∗

(0.0483) (0.0501) (0.0502)

Off-Path Death -0.00229∗ -0.00225 -0.00235(0.00139) (0.00154) (0.00152)

Wart−1×Off-Path Death 0.0101 0.00988 0.00974(0.0613) (0.0603) (0.0604)

Wart−1 0.782∗∗∗ 0.796∗∗∗ 0.754∗∗∗ 0.770∗∗∗ 0.753∗∗∗ 0.769∗∗∗

(0.0252) (0.0272) (0.0232) (0.0257) (0.0233) (0.0258)

Shortest Path Length -0.000259 -0.000127 -0.000258 -0.000134(0.000185) (0.000164) (0.000167) (0.000146)

Adjacent 0.00983∗∗∗ 0.00943∗∗∗

(0.00221) (0.00207)

3 Gen. Blood 0.0000908 0.000458(0.00184) (0.00177)

Same Religion -0.00152 -0.00195(0.00159) (0.00165)

Neither Landlocked 0.000140 0.000277(0.00157) (0.00164)

Capital Distance 0.00254∗ 0.00250∗

(0.00131) (0.00133)

Constant 0.00688∗∗∗ 0.00721∗∗∗ -0.00542 -0.0271 -0.0375∗∗∗ -0.0367∗∗∗

(0.00156) (0.00155) (0.00340) (.) (0.00738) (0.00765)

Country FE X X X X50 year FE X X X XN 29456 29456 29456 29456 29456 29456∗p < .10,∗∗ p < .05,∗∗∗ p < .01

Standard errors are robust to two-way country clustering.

Regression of dyadic war on a dummy for deaths of people on the shortest path betweenthem. Off-path death is a dummy indicating whether an individual with an immediatekinship connection with the ruler died within the year. Also included are a term for whetherthe pair was at war in the previous year, and the interaction between this and both types ofdeath. 3 Gen. Blood indicates whether the rulers share a common ancestor within 3generations (i.e. a great grandparent or closer). Capital distance is in log kilometers.

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Table 6: Death Shocks by Sex

(1) (2) (3) (4) (5)War War War War War

On-Path 0.00543 0.0136 0.0136∗

Male Death (0.00765) (0.00831) (0.00802)

On-Path 0.00234 0.0109∗ 0.0107∗

Female Death (0.00719) (0.00620) (0.00621)

On-Path 0.0562∗∗ 0.0386Death (0.0261) (0.0243)

Path -0.00365 -0.0258Sex Index (0.0481) (0.0378)

Index×Death -0.0830∗∗ -0.0480(0.0415) (0.0399)

Shortest Path -0.000238 -0.0000710 -0.00253∗∗∗ -0.000301Length (0.000488) (0.000363) (0.000888) (0.000297)

Adjacent 0.0286∗∗∗ 0.0288∗∗∗

(0.0106) (0.0107)

3 Gen. Blood 0.00547 0.00621(0.00613) (0.00565)

Same Religion -0.00436 -0.00430(0.00476) (0.00477)

Neither Landlocked 0.00149 0.00234(0.00643) (0.00598)

Capital Distance 0.00530∗∗ 0.00525∗∗

(0.00251) (0.00245)

Constant 0.0289∗∗∗ -0.122 -0.109∗∗∗ 0.0488∗ -0.155∗∗∗

(0.00862) (.) (0.0324) (0.0264) (0.0323)

Country FE X X X50 year FE X X XN 29456 29456 29456 29456 29456∗p < .10,∗∗ p < .05,∗∗∗ p < .01

Standard errors are robust to two-way country clustering.

Regression of dyadic war on a dummy for deaths of people on the shortest path betweenthem by gender. Path sex index is an index indicating the gender mix of those on theshortest path. A shortest path of entirely men would take a value of one and a shortest pathof entirely women would have an index of zero. Index×Death is the interaction of the sexindex with a dummy for within year on-path death. 3 Gen. Blood indicates whether therulers share a common ancestor within 3 generations (i.e. a great grandparent or closer).Capital distance is in log kilometers. Adjacent is a dummy for whether the nations shared aland border.

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Table 7: Effect of On and Off-Path Death Over Time

Years since On-Path Death Off-Path DeathDeath Coefficient Coefficient

5 -0.0018 -0.0012(.0045) (.0040)

4 0.0029 -0.0032(.0057) (.0045)

3 0.00090 -0.0071(.0057) (.0047)

2 0.0038 -0.0061∗

(.0055) (.0034)1 0.0078∗∗∗ -0.0050∗∗

(.0055) (.0020)0 0.0125∗∗∗ -0.0032

(.0036) (.0024)-1 0.0079∗∗ -0.0015

(.0033) (.0023)-2 0.0079∗∗ 0.0005

(.0032) (.0033)-3 0.0023 -0.0012

(.0032) (.0024)-4 -0.0020 -0.0040∗∗

(.0034) (.0021)-5 0.0036 -0.0053∗∗∗

(.0022) (.0020)∗p < .10,∗∗ p < .05,∗∗∗ p < .01

Standard errors are robust to two-way country clustering.

Regression of dyadic war on a dummy for deaths of people on the shortest path betweenthem by gender. Specification follows equation 3, except the outcome is not justcontemporaneous war, but war in future and past years. Standard errors are reported inparentheses. All the controls in specification five of table 4 are used.

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Table 8: Allies and Death Shocks

(1) (2) (3) (4) (5) (6)R. Ally R. Ally R. Ally R. Ally R. Ally R. Ally

On-Path Death -0.00265 -0.0118 -0.0103(0.0217) (0.0239) (0.0227)

Off-Path Death -0.0107 -0.00579 -0.00572(0.0170) (0.0122) (0.0119)

Shortest Path Length -0.00799 -0.00808∗ 0.00372 0.00346 0.00591 0.00568(0.00408) (0.00410) (0.00217) (0.00211) (0.00340) (0.00328)

Adjacent 0.0865∗∗∗ 0.0969∗∗∗

(0.0203) (0.0233)

3 Gen. Blood 0.0401∗ 0.0377(0.0202) (0.0204)

Neither Landlocked 0.200∗∗

(0.0718)

Same Religion 0.000700 -0.00152(0.0241) (0.0230)

Capital Distance 0.0184 0.0161(0.0187) (0.0185)

Country FE X X X X50 year FE X X X XN 8039 8039 8039 8039 8039 8039∗p < .10,∗∗ p < .05,∗∗∗ p < .01

Standard errors are robust to two-way country clustering.

Regression of dyadic revealed alliance on a dummy for deaths of people on the shortest pathbetween them. Off-path death is a dummy indicating whether an individual with animmediate kinship connection with the ruler died within the year. 3 Gen. Blood indicateswhether the rulers share a common ancestor within 3 generations (i.e. a great grandparentor closer). Constant included but not reported. Capital distance is in log kilometers.Adjacent is a dummy for whether the nations shared a land border. Constant included butnot reported. Revealed Ally is an indicator for whether the pair of countries are fighting acommon enemy (and not each other) conditional on at least one of them being at war withsome one (i.e, Any War= 1).

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who died on the shortest path. We are able to record deaths for over two thirdsof these individuals, and only one died in battle. A handful died in a way thatwas arguably endogenous to the political situation. However, dropping theseincidents from the sample makes no difference to any of our results.

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6.2 Royal Marriage

The previous approach focused on variation (death shocks) which increased thenetwork distance between rulers. Now we examine the opposite type of variation,marriage, in which rulers are brought closer together.

Marriages are often strategic. For example, marriage decisions are oftenmade in an effort to end or prevent wars. Therefore, we would expect a simpleregression of war on marriage to be biased. This type of strategic marriage willtend to form between pairs of countries with greater propensities for war. Thusthe raw correlation will be biased towards a finding that marriage is correlatedwith war. To address this concern we employee an instrumental variable strategyexploiting the randomness of the gender of firstborn children.

Specifically, when a pair of rulers have firstborn children of opposite genders,marriages of state are easier to conduct. Thus we can use opposite firstborn childgender as an instrument for the existence of a marriage between the ruler’s chil-dren. A disadvantage of this approach is that we can only use it to analyze theapproximately 100 marriages between the direct children of rulers. Addition-ally, the sample is reduced to those dyads in which both rulers have at least onechild. 23

However, because marriages mechanically connect rulers and thus we mightthink of opposite firstborn child gender as an instrument not only for marriage,but also for whether a network path exists between a pair of rulers. This fa-cilitates a more direct comparison to the results of the previous section. Therelevant results are reported in the following two tables. The directions of allresults are broadly consistent with the idea that closer kinship ties decreasethe probability of war. However, with the two-way country clustered standarderrors this regression suffers from a lack of power. Additionally, notice that thepoint estimate of the coefficient associated with being connected is large (-.067)relative to the estimates of the previous section. This is potentially a result ofbias introduced by a weak first stage (indicated by the small F-stat).

23We refrain from using the gender of second born children as the decision to have additionalchildren is potentially endogenous to the gender of the first child.

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Table 9: IV First Stage

(1) (2) (3) (4)marr exist marr exist conn conn

iv 0.0396∗∗∗ 0.0404∗∗ 0.220∗∗∗ 0.0561∗∗

(0.0147) (0.0153) (0.0420) (0.0262)

3 Gen. Blood 0.0216∗∗ 0.162∗∗∗

(0.00929) (0.0384)

Adjacent 0.0125 0.0302∗∗∗

(0.0125) (0.00947)

Capital Distance -0.00137 0.00183(0.00273) (0.0110)

Same Religion 0.0120∗∗ 0.187∗∗∗

(0.00466) (0.0242)

Country FE X X50 year FE X XK-P F-Stat 7.297 7.013 27.51 4.565N 53975 53975 53975 53975∗p < .10,∗∗ p < .05,∗∗∗ p < .01

Standard errors are robust to two-way country clustering.

Table 10: IV Results

(1) (2) (3) (4)war war war war

marr exist -0.360∗∗ -0.140(0.171) (0.143)

conn -0.0647∗ -0.101(0.0336) (0.113)

3 Gen. Blood 0.0397∗∗∗ 0.0531∗∗

(0.00707) (0.0211)

Adjacent 0.0419∗∗∗ 0.0432∗∗∗

(0.0127) (0.0124)

Capital Distance 0.00214 0.00252(0.00327) (0.00343)

Same Religion 0.00578 0.0229(0.00838) (0.0218)

Country FE X X50 year FE X XN 53975 53975 53975 53975∗p < .10,∗∗ p < .05,∗∗∗ p < .01

Standard errors are robust to two-way country clustering.

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7 Centrality and Conflict

Up until now we have been focusing on bilateral measures of connectednessbetween ruler pairs. However, the global structure of the network may well beimport. Thus, we turn our attention to the relationship between a country’snetwork centrality and it’s conflict outcomes.

Table 11: Centrality and Conflict

(1) (2) (3) (4)war R. Ally war R. Ally

Higher Centrality -0.00601∗∗∗ -0.0101∗∗∗ -0.00311∗∗∗ -0.00713∗∗∗

(0.00112) (0.00357) (0.00106) (0.00250)

Lower Centrality 0.00407 0.0145∗∗∗ 0.00468∗∗∗ -0.00483(0.00257) (0.00552) (0.00162) (0.00439)

Adjacent 0.0384∗∗∗ 0.0537∗∗∗

(0.0126) (0.0147)

3 Gen. Blood 0.0372∗∗∗ 0.0349(0.00962) (0.0222)

Same Religion 0.000823 -0.0197(0.00782) (0.0186)

Capital Distance 0.00388 0.0126(0.00256) (0.00860)

Constant 0.0393∗∗∗ 0.101∗∗∗ -0.0166 -0.0725(0.00773) (0.0155) (0.0177) (0.0544)

Country FE X X50 year FE X XN 78613 28028 78613 28028∗p < .10,∗∗ p < .05,∗∗∗ p < .01

Standard errors are robust to two-way country clustering.

Table 11 provides suggestive evidence that two states are more likely to allywhen the centrality of the more connected state is low. If high centrality iscorrelated with hegemony, this may correspond to balancing behavior against adominant country.

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8 Conclusion

Our paper constructs a dataset which links European noble genealogy, a list ofsovereign European monarchies and reigns, interstate wars, and several covari-ates. It documents a dramatic increase in kinship connections between Europeanmonarchs over time. It then uses exogenous variation in network structure toprovide evidence of a causal relationship between living kinship ties and conflictoutcomes.

Specifically, we show exogenous increases in kinship network distance leadto an increased likelihood of war. While our framework predicts we should alsosee a decreased likelihood of fighting as allies, evidence on this is inconclusive.We conclude that the rise in kinship connections is an important factor in thewell-known long-run decrease in the frequency of war.

We tentatively estimate that increased kinship ties explain 30 percent ofthe decrease in European warfare. Suppose, conservatively, that the presenceof a kinship connection reduces the chance of war between a pair of states by.67 percent. This number is a lower bound on the effect of disconnecting on-path deaths in the latter part of our sample. Relative to pre-1600, the share ofmonarchs connected after 1800 increased by 53 percentage points. Preventingall these new ties from being formed would therefore be expected to increase theshare of dyads at war after 1800 from 1.17% to 1.53%. This is approximately30 percent of the decrease in war over the same period.

Our data provides a rich environment to study the influence of interpersonalrelationships on long-run macroeconomic, political and institutional outcomes.In this paper, we have focused on the relationship between kinship and conflict.However, the same data and network tools might well be applied to more tra-ditional economic questions. We think future work investigating the long-runimplications of kinship networks for trade, growth, and development will befruitful.

Another interesting path for future study is explicitly modeling the networkformation in this environment. Strategic marriages played a major role in in-ternational relations during our period of interest. Building a structural modelof strategic marriage and fertility decisions is also an interesting direction forfuture research.

9 Appendix A: Data Construction

Creating the data set was a complex undertaking. We strive to be maximallytransparent in how we assembled our data.

9.1 Genealogical Data

Our genealogical data is taken from the 2014 update of Tompsett’s Royal Ge-nealogy collection. Tompsett’s data covers the “genealogy of almost every rulinghouse in the western world” with the most detailed bibliography of any com-parable source. Professor Tompsett is an Englishman whose initial interest inthe project arose from a passion for British geneology, and this is reflected inan oversampling of the British peerage. A substantial portion of Tompsett’sdata comes from “The Complete Peerage or a History of the House of Lords

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and all its Members from the Earliest Times” (Cokayne, 1953) and “Europais-che Stammtafeln” (Loringhoven, 1964) in their various series and editions. Aconsultation with New England Historic Genealogical Society revealed these tobe well-regarded and reliable.

9.2 State List

We restrict our analysis to (i) sovereign (ii) hereditary or elective monarchiesin (iii) Christian Europe. Our reasons for these restrictions is discussed in thetext.

Of these criteria, the hardest to operationalize is sovereignty. We used thefollowing procedure in determining whether a state is independent. Any politywe find reference to in Europe over our time period in any listing is given thepresumption of sovereignty; this includes the super-national organizations of theHoly Roman Empire, German Confederation, and the short-lived North GermanConfederation.

We judge polities to be lacking sovereignty for several reasons. A primaryreason for being excluded is via being a province, dependent, or vassal of anotherstate. For example, the Balkan states for most of our time period are vassals ofthe Ottoman Empire and are thus excluded from the dataset.

De-facto sovereignty is often ambiguous for the members of the three super-national organizations to which we attribute sovereignty. For states completelywithin the Holy Roman Empire, we attribute sovereignty only to Electors. Formembers of the German Confederation, we retain the 11 states with a vote inthe Federal Assembly. For the short-lived North German Confederation, weretain the two member Kingdoms and five member Grand Duchies.

A related reason for exclusion is due to being a puppet state set up by aforeign occupier. When a period of foreign occupation or domination lasts 5or fewer years, and the previous dynasty subsequently regains the throne, wehave the two countries in existence and at war throughout. An exception isthat a foreign occupation of less than 5 years will be coded as an interregnum ifthe incumbent ruler formally and substantively abdicates (even if the dynastyeventually regains the throne). For example, in the Great Northern War theSaxon King Augustus II abdicated his claim to the Polish throne for a shortperiod. This is coded as an interregnum. During these interregnums, as withany period a ruler is not coded, the country drops out of the data.

When a period of foreign domination lasts for more than 5 years, but theprevious dynasty eventually regains the crown, we have the dominated stateas not existing during the occupation. This means, for example, that muchof the German and Italian speaking world is not sovereign during the heightof Napoleon’s power. For cases like Spain during the Peninsular War (wherea government attempted to organize resistance from the sieged city of Cadiz)where there is at least partially organized resistance with loyalty with the pre-vious ruler, we err on the side of keeping the previous ruler installed and at warthroughout.

When a foreign installation is permanent, we have the installed leader run-ning the country at the end of hostilities. For states where a de jure countryhas two rulers simultaneously in different parts for a period of decades (i.e.longer than a civil war), we consider it two separate countries. For Hungary,

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this means we record a Christian ruler during the century when the better partof the country was ruled by an Ottoman puppet.

Finally, we list only the more powerful member of a pair of states underpermanent personal union. Personal unions were a common occurrence in ourperiod in which a single individual would rule two or more countries. Unsur-prisingly, two states ruled by the same individual will have aligned (though,surprisingly often, not perfectly aligned) foreign policies. When we see a pair ofstates entering and leaving personal unions (such as due to divergent inheritancelaws, as in the case of Hanover and the United Kingdom) in our time period,we list them as separate sovereign states throughout. However, if a personalunion lasts continuously until one of the states is abolished (or until the end ofour sample period) then we combine the two states into one sovereign entity.We consolidate the two states not at the beginning of the personal union, butrather when the first person to inherit both states simultaneously comes intopower.

Of these criteria, the most imperfect is the decision to only list electorsof the HRE. Many states of the HRE had a great degree of autonomy, andeven fought wars against Austria and the Holy Roman Emperor. Importantexamples are the Thirty Years War and the wars of the Austro-Prussian Rivalry.Further, the ‘Golden Bull of 1356’, which fixed the constitutional structure of theHRE, established electoral dignity as non-transferable and conferring a degreeof sovereignty higher than normal HRE membership. There is also the factthat by being electors, they had influence over who was elected Emperor, andtherefore influence over wars fought by the Empire.

The list of electors is very stable over time. When, in the course of the ThirtyYears War, the Emperor transferred the treasonous Electoral Palatinate’s voteto Bavaria this led to a major constitutional crisis. Eventually a vote for thePalatinate was restored, but Bavaria retained the Palatinate’s electoral status.Along with the creation of the Electorate of Hanover in 1692 (thereby creatingan odd number of electors again), these are the only changes in the course ofour sample. So we feel comfortable attributing sovereignty to all the electors.

However, there are a handful of states, mostly northern Italian, which werenon-Elector members of the HRE that acted with a degree of autonomy, espe-cially in the early part of our sample. These include Savoy, Milan, Modena,Parma, and Florence. Bavaria before getting its electoral status also partic-ipated in wars independently of the HRE. Some of these would be otherwiseeliminated for much of their histories for being republics.

On the one hand, it would be impossible to include in our analysis theliterally hundreds of members of the HRE, each of which had different degreesof sovereignty. On the other, to include members of the HRE only if theyhad revealed autonomy by fighting wars that were separate from the largerHRE would be to introduce an unacceptable selection bias into our data. Thislegalistic approach was determined to be the safest one.

The following are the member states of the Holy Roman Empire, Ger-man Confederation, and North-German Confederation to which we attributesovereignty.24

1495-1705: Kingdom of Bohemia25

24While the Archduchy of Austria never had electoral dignity, its lands were never fullywithin the Holy Roman Empire, and is therefore attributed sovereignty.

25We drop Bohemia as an independent state at this point due to our rules concerning

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1495-1871: Margraviate of Brandenburg (later, Kingdom of Prussia)1495-1806: Duchy of Saxony1495-1623: County Palatinate of the Rhine (later, Electoral Palatinate)26

1623-1806: Duchy of Bavaria1648-1803: Electoral Palatinate1692-1806: Electorate of Brunswick-Luneburg (later, Electorate of Hanover)Napoleon disbands the Holy Roman Empire in 1806, and replaces it with a

puppet organization named the Confederation of the Rhine. During this period,many of the above states lost sovereignty.

After the defeat of Napoleon, the German Confederation is founded. Formembers of the German Confederation, we attribute sovereignty to the 11 stateswith a vote in the inner session of the Federal Assembly.

1815-1866: Duchy of Holstein, Kingdom of Hanover, King of Bavaria, King-dom of Saxony, Kingdom of Wuttemberg, Electorate of Hesse, Grand Duchy ofBaden, Grand Duchy of Hesse

1815-1839: Grand Duchy of Luxembourg1839-1866: Duchy of LimburgAfter the dissolution of the German Confederation, several states are tem-

porarily independent, before being subsumed into the German Empire. Otherstates become sovereign members of the North German Confederation.

1866-1871: Saxony, Hesse, Mecklenburg-Schwerin, Mecklenburg-Strelitz, Old-enburg, Saxe-Weimar-Eisenach

1866-1871: Independent Kingdom of Bavaria, Independent Kingdom of Wurt-temburg, Independent Baden

1866-1918: Independent Principality of Lichtenstein1839 - 1918: Independent Grand Duchy of Luxembourg

9.3 Ruler Data

For every year of a sovereign state’s existence we attempt to match a ruler fromour genealogical data. Our ruler data is primarily derived from Bertold Spuler’s‘Rulers and Governments of the World Volume 2: 1492-1925’. When multipledates of rule change are listed, such as the date of the death of a parent andsubsequent coronation of their child, the earliest date is used. In elective monar-chies, where the next ruler is clear but formal election is delayed this is usedto prevent interregnums. For the few occasions in which two simultaneous andcooperative rulers are listed by this or another source, we choose the individualwho seemed to be the dominant decision maker. As all of these situations aremarriages or involve closely related individuals, this subjectivity will not mat-ter much to our results. For the most of the state years where Spuler does notlist rulers, we use “Monarchs, Rulers, Dynasties and Kingdoms of the World”(Tapsell, 1984). A handful of imputations from outside these sources are notedin the data.

Finally, there is the special case of the Netherlands Stadtholder. While theSeven United Netherlands might in some ways be described as a republic (orconfederation of republics), in times of foreign conflict, they were represented bya general Stadtholder. Traditionally, this Stadtholder was the Duke of Orange.

permanent personal unions26The electoral dignity of The Palatinate is temporarily transferred to Bavaria during the

Thirty Years War. Bavaria comes to control an additional electoral vote.

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Foreign countries would maintain royal missions with the Duke, with the under-standing that he represented the Netherlands internationally. Therefore, in theyears before the Netherlands are actually a monarchy, we record the Stadtholderor (failing the existence of a general Stadtholder) the Duke of Orange as theSeven United Netherlands’ monarch.

In situations where Spuler is ambiguous (i.e. he lists both an incumbent rulerand a claimant) we attempt to keep rulerless periods as brief as possible. Duringcivil wars and succession crises, we have the incumbent ruling throughout thewar; if the pretender wins we have him ruling subsequently. When incumbencyis seriously in doubt, we err on the side of having the eventual winner rulingthroughout.

States completely lacking in executive leadership are listed without a ruler,dropping out of our sample. This can sometimes occur as a result of a successioncrisis. We attempt to keep such interregnums to a minimum.

9.4 War Data

The most difficult portion of this data to assemble is the set of war dyads. Awar dyad is a pair of states which are at war in a given year. Our goal is tohave a list of every year in which there is a conflict involving at least two stateswith matched rulers. Prior to 1816, there is no standard list of interstate wars.This arises from ambiguity about what a state is, what a war is, and when warsbegin or end. Our list is based on “A Study of War” (Wright, 1942), which usesa primarily legalistic definition of war

A supplements, use was also made of “Encyclopedia of Wars” (Phillips andAxelrod, 2005), “An Encyclopedia of World History: Fifth Edition” (Langer,1972), Brecke’s “Conflict Catalog” (2012), “War in the Modern Great PowerSystem: 1495-1975” (Levy, 1983), and version 4.0 of “Correlates of War” (Sar-kees and Wayman, 2010). These last two lists use a definition of war primarilybased on the number of battle fatalities.

Before 1816, the best comprehensive list of wars with a clear criteria for in-clusion is Wright’s. Wright attempts to document every war from 1480-1940. Hedefines war as “all hostilities involving members of the family of nations, whetherinternational, civil, colonial, or imperial, which were recognized as states of warin the legal sense or which involved over 50,000 troops. Some other incidentsare included in which hostilities of considerable but lesser magnitude, not rec-ognized at the time as legal states of war, led to important legal results. . . ”(Wright, p. 636). Wright defines war in the legal sense as “the legal conditionwhich equally permits two or more hostile groups to carry on a conflict by armedforce” (Wright, p. 8).

In the post-1816 period, the gold standard in war records is the Correlatesof War (CoW) database. CoW defines interstate wars as “those in which aterritorial state that qualifies as a member of the interstate system is engagedin a war with another system member. An inter-state war must have: sustainedcombat involving regular armed forces on both sides and 1,000 battle-relatedfatalities among all of the system members involved. Any individual memberstate qualified as a war participant through either of two alternative criteria: aminimum of 100 fatalities or a minimum of 1,000 armed personnel engaged inactive combat.”

.

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9.5 Procedure for Generating Primary War List

Because Wright is the best list of wars that covers our entire period of interest, itforms the basis of our primary war dyad data. However, a number of difficultiesprevent us from relying solely on Wright’s text.

First, Wright has clear typos. To list a few examples: in his coding of TheSecond Northern War (table 35), Hanover is not listed as a participant, despitea footnote noting the year they signed a peace treaty ending their involvement;France is listed as entering the 1st Opium war after the war’s conclusion (thisentry seems to be correctly attributed to the war a row below); and he fails torecord Hungarian Participation in Ottoman War of 1537-1542, in which Hungarywas the principle theater, and the Austrian Archduke (listed at war with theOttomans) was in personal union with the Royal Hungarian Crown.27

Second, Wright does not report information on war sides and war exit that isimportant for our purposes. For example, in the Thirty Years War and FrenchRev and Napoleonic Wars there are several shifts in the sides of the conflict.However, Wright does not comprehensively record these side flips.

Third, because we are primarily concerned with which leaders are fightingwars, we need to understand whether our coded rulers correspond to Wright’sunderstanding of state leadership. For example, Wright records a Polish Russianconflict in which a Russian Tsar intervened to help a Polish King put down anti-Russian insurgents. However, because the Polish King and Russian Tsar werealigned in this incident, it does not make sense to call it an international warfor our purposes.

Finally, our list of states does not perfectly match Wright’s (excepting non-European states, theocracies, states in permanent personal unions, states facinginterregnums, republics, and a few non-Elector HRE members, our list is a su-perset of Wright’s). Therefore, it is important for us to search for wars includingstates not in his list.

For every war, Wright lists five pieces of information. These are: The yearevery state in his list entered the war; the side each state primarily fought on;which of these sides was the war’s aggressor; when the war ended; and the war’stype (civil, imperial, or balance of power). Occasionally, he also lists the monthof the war’s inception or close, as well as the number of important battles fought.

To reconcile Wright’s schema with our own, we needed the following addi-tional pieces of information.

• Did any states which we code as sovereign but Wright does not participatein any of Wright’s wars? Were there any wars between our states involvingone or fewer of Wright’s?

• In wars involving more than 2 states, did any state leave the conflict early?

• Did any war participants switch sides over the course of the conflict?

• If the HRE (or German Confederation) is participating, to what extentwas it acting collectively?

• Did the conflict involve a succession crisis or civil war, which might leadus to have a different understanding of war ‘sides’ than Wright?

27Wright is particularly unclear in his criteria for Hungarian war participation in the 16thCentury.

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• Check whether Wright made any unambiguous typos or errors

For every war listed in Wright (1942), we began by searching for a corre-sponding account of the war in question in Phillips and Axelrod (2005) andLanger (1972). Usually these are sufficient to answer the above questions, butadditional sources were consulted when these left the situation unclear. Forthe Thirty Years War, a supplementary source was “The Thirty Years War”(Parker, 1997). For the Schmalkaldic War a supplementary source was “TheAge of Reformation” (Smith, 1920). Any deviation from a naıve Wright codingwas noted and cited in our data.

For wars involving only one or fewer members of Wright’s system, we searchedPhillips and Axelrod (2005) and Brecke (2012). Any interstate war appearingin these lists and matching this criterion was included (that is, it is a war whichdoes not include more than one Wright state – we assume that Wright’s list iscomprehensive for such cases), are considered potential additions to Wright’s listof wars. When necessary an additional source is cited to confirm the war meetsone of Wright’s war criteria. Levy (1983) and Sarkees and Wayman (2010) wereused primarily as sanity checks.

A few notes follow on how we proceeded in ambiguous cases.For states in personal unions (like Denmark with Norway and the various

crowns held simultaneously by the Spanish rulers), if one we code one stateat war we assume the other is as well, unless we find evidence otherwise. Forthe Austrian Habsburgs, we extend this principle to Bohemia and Hungary(when they are ruled by close relatives of the Austrian Archduke rather than theArchduke or Holy Roman Emperor himself).28 By construction, we never havea ruler of two nations at war with himself. However, we do have observations ofHabsburg brothers at war (during the Habsburg Brother’s War between RudolfII and his cousin Matthias who became King of Hungary in 1608).

Only extremely rarely does this principle come into conflict with our extremedeference to Wright’s list of wars. This is because he rarely lists two countriesin personal union seperately in his lists of states potentially in conflict. The onesignificant exception is Hungary (a state for which Wright has several codinganomalies, such as stating they do not participate in several Habsburg-Ottomanwars taking place in Hungarian lands). In these cases, we stick to the principlethat states in personal unions are typically united in their efforts, and look forevidence beyond Wright for coding them otherwise. Levy (1983) and Sarkeesand Wayman (2010) were sometimes helpful in these cases.

Wright lists some wars as being fought by the ‘German Empire’. For thebulk of our period this is the HRE. Later in the sample, this is the NGC, GC, orGE. When the HRE fights conflicts we look at evidence from our sources on thisconflict and other concurrent conflicts whether the HRE acting at that momentin a united or divided manner. If there is no evidence of disunion, it is assumedthat the ‘German Empire’ fighting entails the Habsburg crowns and all electorsfighting alongside the Holy Roman Emperor. Otherwise, we mark the HRE asbeing divided and cite individually which of its members participated.

Some records are maintained of war covariate information from Wright, Levy,and the Correlates of War. In future work, this may be used in analysis or to

28For more information on the unusual unity of the Austrian Habsburgs, and its legal andnormative foundations, see ‘The Rise and Fall of the Habsburg Empire’ Tapie 1971, especiallyp. 38-39

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form an alternate war dyad list that uses a casualty based definition.

9.6 Alliance Data

Data on formal alliances is derived from “International Military Alliances 1648-2008” (Gibler, 2009) and “Hegemonic Threats and Great Power Balancing inEurope, 1495-1999” (Levy and Thompson, 2005). Gibler accumulates a com-prehensive database of every formal alliance in his time period. Gibler’s list ofstates follows the CoW criteria for system membership and also almost fullyencompasses our list. Levy and Thompson’s list, while going back to 1495 onlycontains alliance behavior for the ‘great powers’.

Levy gives alliance targets explicitly, but Gibler’s targets were determinedfrom Gibler’s summary. If the target of an alliance involves a succession crisiswe leave out the target.

9.7 Covariates Data

Covariates are assembled from a variety of publicly available sources. Religioncorresponds to the religion a ruler publicly professed. When no explicit referenceto a ruler’s religion can be found, the state or dominant domestic religion isused. During the early phases of the Protestant Reformation, it is often unclearwhen a ruler fully converts to a protestant sect. For example, a ruler mightbe privately sympathetic to a Lutheranism, and aid the spread of those ideas,but not publicly convert himself. We always erred on the side of caution insuch cases, and consider a ruler as converting to Protestantism only we foundevidence they publicly did so. Outside this period, coding the religion of rulerswas straightforward.

Coding capital locations was usually straightforward as well. In the rarecases a state had multiple political centers of power, we selected the dominantone. The one instance in which this selection was not straightforward was forthe capital of the Holy Roman Empire. This entity had multiple legislative,executive, and judicial centers. Until 1532 we selected Frankfurt as the capital,due to its role as the traditional location for the selection and coronation ofEmperors. After 1532 we selected Regensburg, which held periodic ImperialDiets through 1663, and a permanent one after that date. These locations havethe added benefit of being near the HRE’s center of mass.

Landlocked-ness and country adjacency were derived from the CentenniaHistorical Atlas Map Series, version 3.10 (2016). This map series gives thepolitical borders for Europe and the Middle East at five week periods from the11th century to the present. Generally, coding is straightforward. For countriesthat are briefly occupied, the occupied and occupying country are consideredadjacent so long as the two countries were adjacent beforehand. Countriesthat gain control of new regions according to Centennia gain the adjacency ofthe regions they subsume. However, in the case of a non-permanent personalunion, the adjacencies of the countries are considered seperately. For example,during the Kalmar Union the King of Denmark ruled Norway in a personalunion. However, because for reasons discussed above, Norway is listed as anindependent state, Denmark is not coded as adjacent to Russia. Conversely,because Croatia is in a permanent personal union with Hungary throughout

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our sample, Croatia is not listed as an independent state, and Hungary inheritsCroatia’s adjacency.

9.8 Generating The Main Dataset

The above data were all collected in excel spreadsheets. Insofar as was possiblethe data was collected at the monthly level. However, spotty monthly data,especially in the first half of our data, makes a monthly analysis impractical.Other data collected but not used include information on formal alliances, re-gencies, ruler dynasty name, ruler cause of death, and several war covariatesfrom Wright, Levy and Correlates of War. We cannot testify to these data be-ing comprehensive, but think they might be of use to future researchers, if onlyas a base to work from. Therefore, they remain in the data.

When reading the data from the excel data into Stata, we only upload coun-try years in which there is a ruler correctly matched to the Tompsett data.Only twice do we identify a ruler who was not matchable to Tompsett. Theone notable example is Stefan II of the Principality of Zeta, whose absence,unfortunately, prevents the Principality from entering into our final sample.

Rulers are read in as beginning their reign at the start of the year in whichtheir reign commences. Similarly, if a country covariate changes during thecourse of the year, it is read as having changed at the beginning of the year. Ifthe covariate shifts several times during a year, only the last value is read intothe final data. War are read in as starting at the beginning of the year theycommenced, and ending at the end of the year they ended.

10 Appendix B: Robustness Analysis

In this section we address possible threats to our identification strategy. In thefirst subsection we discuss some additional statistics. In the second subsection,we examine the robustness of these results to a war list constructed accordingto alternate rules.

10.1 Threats to Identification

Our analysis relies on deaths being unrelated to the political situation in acountry. For example, our identification strategy would not be appropriate ifwars led to an increase in deaths. This would lead to concerns about reversecausality.

To a certain extent, this concern is addressed by our placebo death shock,discussed above. However, for whatever reason, war (or a political situationconducive to war) between a pair of countries might disproportionately lead tothe deaths of people on the shortest path between the pair of rulers.

To address this concern, we examine the deaths of individuals on shortestpaths whose deaths seem to be causing (or lengthening) wars. There are 47individuals on shortest paths that die at the start of, during, or at the end of awar. Figure 7 shows that we can find the cause of death for 81 percent of theseindividuals. Only one death is from battle29, and none of the deaths are dueto assassination or execution. Dropping this one death from the data does not

29Duke Frederick IV, who was hit by a cannonball in the Great Northern War

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Figure 7: Causes of death individuals who die on the shortest path between a pair ofrulers at war in the same year. ??? indicates those for whom a cause of death couldnot be found. Old unremarkable are those with an encyclopedia entry, who die afterage 65, but have no listed cause of death.

significantly change any of our results. Figure 9 displays the age distribution ofthese deaths.

The causes of deaths for those who drive the contemporaneous results aresimilar to the causes for all on-path deaths. There are 263 of these individuals.These are shown in figure 8. In the larger list there are a handful of individualswho were assassinated (although all by domestic extremists) or executed. Alarger Dropping these deaths from the data leaves our results unchanged.

A second possible concern is our selection of shortest path as the key networkmeasure. It is possible that it is not the shortest path between rulers whichmatters, but rather all network paths. It is also possible that on shortest-pathdeath shocks matter, but through a mechanism other than through changingnetwork distance.

Table 12 examines the impact of any death along any path between rulers(not only the shortest path). This is done by using resistance distance, whichis an all-paths distance measure. After controlling for factors that increase theprobability of such a death, these deaths are shown to have no effect. Restrictingthis to the subset of deaths that increase resistance distance but leave shortestpath length unchanged (e.g. shortest path deaths when there are multiple equallength shortest paths) gives the same (null) result.

11 Appendix C: Additional Tables

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Figure 8: Causes of death individuals who die on a shortest path. ??? indicates thosefor whom a cause of death could not be found. Old unremarkable are those with anencyclopedia entry, who die after age 65, but have no listed cause of death.

Figure 9: Age of death for the 47 individuals with on-path deaths who die during acontemporaneous war.

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Table 12: War: Alternate Placebo

(1) (2) (3) (4)war war war war

Resistance Increasing -0.0318∗∗ -0.00386 -0.00325 -0.00233Death (0.0125) (0.00356) (0.00363) (0.00377)

Shortest Path Length -0.00109 -0.00184 -0.00121(0.000868) (0.00126) (0.00117)

Resistance 0.00181 0.00176(0.00185) (0.00195)

Adjacent 0.0293∗∗∗

(0.00913)

3 Gen. Blood 0.0180∗∗∗

(0.00674)

Same Religion 0.0000399(0.00292)

Neither Landlocked -0.0100(0.0101)

Capital Distance 0.00718∗

(0.00401)

Constant 0.0510∗∗∗ -0.192∗∗∗ 0.0359 -0.0606(0.0171) (0.0495) (0.0425) (0.0533)

Country FE X X X50 year FE X X XN 30418 30418 30418 30418∗p < .10,∗∗ p < .05,∗∗∗ p < .01

Standard errors are robust to two-way country clustering.

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Table 13: Revealed Alliance and Connections

(1) (2) (3) (4)R. Ally R. Ally R. Ally R. Ally

Connected 0.123∗∗ -0.00292 0.102∗ -0.00261(0.0413) (0.0180) (0.0399) (0.0217)

Shortest Path Length -0.0110∗ 0.00198(0.00488) (0.00258)

Resistance Distance -0.0164 0.00386(0.00959) (0.00656)

3 Gen. Blood 0.0961∗∗∗ 0.0960∗∗∗

(0.0240) (0.0238)

Adjacent 0.0559∗ 0.0557∗

(0.0250) (0.0250)

Same Religion 0.00670 0.00651(0.0233) (0.0231)

Neither Landlocked 0.146∗∗ 0.146∗∗

(0.0546) (0.0546)

Capital Distance -0.00792 -0.00787(0.0144) (0.0144)

Observations 36212 36212 36212 36212

Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Regression of dyadic revealed alliance on contemporaneous network characteristics followingequation 2. The sample size is reduced because for a revealed alliance to be possible at leastone of the pair must be at war. Revealed alliance is when both nations are at war with thesame state and not at war with each other. Connected indicates whether a pair of countriesare connected via kinship. Shortest path length and resistance distance are measures ofkinship distance multiplied by a dummy for whether a connection exists. 3 Gen. Bloodindicates whether the rulers share a common ancestor within 3 generations (i.e. a greatgrandparent or closer). Capital distance is in log kilometers.

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