STATUS, CONFLICT, AND WAR: THE MAJOR POWERS, 1820-1970
by Charles Samuel Gochman
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Political Science) in The University of Michigan 1975
Doctoral Committee:
Professor J. David Singer, Chairman Assistant Professor Stuart A. Bremer Professor Harold K. Jacobson Associate Professor Catherine M. Kelleher Professor John W. Shy ACKNOWLEDGEMENTS
Many individuals have contributed to the development and completion of this thesis. As with any research endeavor, it is not possible to acknowledge all of them. There are, however, two groups of people that loom very prominently in my mind. First, there are the members of my dissertation committee—Stuart Bremer, Harold Jacobson, Catherine Kelleher,
John Shy, and J. David Singer—to whom I wish to express my appreciation. Second, there are the members of the Correlates of War Project at the University of Michigan—J. David Singer,
Melvin Small, Stuart Bremer, John Stuckey, Hugh Wheeler, Larry
Arnold, and a number of younger colleagues—who have continually been a font of intellectual stimulation. To all these people,
I express my gratitude.
Two names—those of J. David Singer and Stuart Bremer— appear in both these groups, and merit special attention.
J. David Singer has undoubtedly had a greater influence than any other person on my academic development. He has been
—and continues to be—a creative teacher, a dedicated advisor, and, equally importantly, an ever-encouraging and always helpful friend. I am also fortunate to count among my friends,
Stuart Bremer. Over the past several years, he has been an indefatigable source of imaginative ideas, from whom I have learned much, and have much more to learn. To both these men, I owe a special debt of thanks.
v TABLE OF CONTENTS
DEDICATION iii
ACKNOWLEDGEMENTS iv
LIST OF TABLES ix
LIST OF FIGURES xiii
LIST OF APPENDICES xiv
CHAPTER
I. THE THEORETICAL ARGUMENT 1
Toward War: Dissatisfaction as a Source of Aggressive Behavior 5 The Frustration-Aggression Hypothesis in Psychology The Status Inconsistency Debate in Sociology The Relative Deprivation Hypothesis in Political Science A Nation-State Analogy Away from War: Constraints on Nation-State Behavior 16 The Limits of Capability Learning from Experience The Structure of the System Summarizing the Propositions and Positing a Model 28
II. THE RESEARCH DESIGN 34
The Referent World 34 Spatial Domain Temporal Domain Constructing the Indicators 38 The Predictor Variable: Status Inconsistency The Intervening Variables: Physical, Psychological, and Structural The Outcome Variable: Interstate Military Conflict Analyzing the Data 68
vi CHAPTER
III. FROM STATUS INCONSISTENCY TO MILITARY CONFRONTATION: THE BIVARIATE RELATIONSHIP .... 73
Assuming Homogeneity among the Major Powers . 73 Manipulating the Time Factor Altering the Level of Measurement Examining the Individual States 83 Summarizing the Findings 94
IV. FROM STATUS INCONSISTENCY TO MILITARY CONFRONTATION: INTRODUCING THE INTERVENING VARIABLES 95
The Nineteenth Century 104 Analyzing the Data Interpreting the Results Examining the "Residuals" Summarizing the Nineteenth-Century Findings The Twentieth Century 125 Analyzing the Data Interpreting the Results Examining the "Residuals" Summarizing the Twentieth-Century Findings
V. FROM MILITARY CONFRONTATION TO INTERSTATE WAR . . 152
The Nineteenth Century 157 The Indicators of Reachability The Indicator of War Experience The Indicators of Structural Relationships The Multivariate Relationship The Twentieth Century 168 The Indicators of Reachability The Indicator of War Experience The Indicators of Structural Relationships The Multivariate Relationship The Major Power/Major Power Conflict Dyads . 184 Summarizing the Results of the Second Stage . 189
vi i CHAPTER
VI. STATUS, CONFLICT, AND WAR 191
Status Inconsistency and Military Conflict . 191 Underrecognition and Confrontation Underrecognition and War The Intervening Variables and Military Conflict . 200 Physical Attributes The Psychological Factor Structural Relationships General Observations for Peace Research . . . 209
APPENDICES 215
BIBLIOGRAPHY 254
SPECIAL BIBLIOGRAPHY OF CONFLICT DATA SOURCES 271
vi i i LIST OF TABLES
Table Page
1. Status Inconsistency vs Involvement in Interstate Military Confrontations, 1820-1970 74
2. Q-scores from Contingency Table Analysis of Major Power Status Inconsistency vs. Involvement in Interstate Military Confrontations, 1820-1970 (One- to Five-year Time Lags) 76
3. Status Inconsistency vs. Involvement in Interstate Military Confrontations, by Century 77
4. Q-scores from Contingency Table Analysis of Major Power Status Inconsistency vs. Involvement in Interstate Military Confrontations, by Century (One- to Five-year Time Lags) 78
5. Observed and Expected Frequency of Major Power Participation in Interstate Military Confrontations at Different Ranks of Status Inconsistency, for 1820-1970 and by Century (One- to Three-year Time Lags) 81
6. Observed and Expected Frequency of Major Power Initiation of Interstate Military Confrontations at Different Ranks of Status Inconsistency, for 1820-1970 and by Century (One- to Three-year Time Lags) 81
7. Analyses of Variance and Biserial Correlations of Status Inconsistency vs. Interstate Military Confrontations, for 1820-1970 and by Century (One- to Three-year Time Lags) 82
8. American and German Status Inconsistency vs. Involvement in Interstate Military Confrontations . 84
i x Table Page
9. Q-scores from Contingency Table Analysis of Each Major Power's Status Inconsistency vs. Involvement in Interstate Military Confrontations, for 1820- 1970 Period and by Century (One- to Three-year Time Lags) 85
10. Analyses of Variance and Biserial Correlations of Each Major Power's Status Inconsistency vs. Interstate Military Confrontations, by Century (One- to Three-year Time Lags) 90
11. Biserial Correlations between Each Major Power's Involvement in Military Confrontations and the Three Intervening Variables, for the 19th Century (One- to Three-year Time Lags) 105
12. Standardized Probit Coefficients for the Three Intervening Variables when Predicting Each Major Power's Involvement in Military Confrontations, for the 19th Century (One- to Three-year Time Lags) 109
13. Point Biserial Correlations between Each Major Power's Status Inconsistency Scores and the Three Intervening Variables, for the 19th Century (One- to Three-year Time Lags) 113
14. Number of Conflict-years Involving Major Powers and the Proportion of these Conflict-years that Is Correctly Predicted by the Probit Equation, for the 19th Century (One- to Three-year Time Lags) 121
15. Biserial Correlations between Each Major Power's Involvement in Military Confrontations and the Four Intervening Variables, for the 20th Century (One- to Three-year Time Lags) 126
16. Standardized Probit Coefficients and the Inter- correlation Matrix for the Four Intervening Variables when Predicting Each Major Power's Participation in Military Confrontations, for the 20th Century (Subset of Underrecognized Cases, Three-year Time Lag) 132
x Table Page
17. Standardized Probit Coefficients for the Four Intervening Variables when Predicting Each Major Power's Involvement in Military Confrontations, for the 20th Century (One- to Three-year Time Lags) 135
18. Number of Conflict-years Involving Major Powers and the Proportion of these Conflict-years that Is Correctly Predicted by the Probit Equation, for the 20th Century (One- to Three-year Time Lags) 149
19. Cramer's Phi-square between Each Major Power's War Involvement and the Two Indicators of Reachability, for 19th Century Conflict Dyads ... 159
20. Biserial Correlations between Each Major Power's War Involvement and Prior War Experience, for 19th Century Conflict Dyads 162
21. Biserial Correlations between Each Major Power's War Involvement and System Polarity, for 19th Century Conflict Dyads 164
22. Correlation and Standardized Probit. Coefficients for the Four Intervening Variables when Predicting to Pooled Major Power War Involvements, for 19th Century Conflict Dyads 166
23. Cramer's Phi-square between Each Major Power's War Involvement and the Two Indicators of Reachability, for 20th Century Conflict Dyads . . . 169
24. Biserial Correlations between Each Major Power's War Involvement and Prior War Experience, for 20th Century Conflict Dyads 172
25. Biserial Correlations and Cramer Phi-squares between Each Major Power's War Involvement and Two Indicators of Structural Relationships, for 20th Century Conflict Dyads 175
26. Correlation and Standardized Probit Coefficients for the Five Intervening Variables when Predicting to Pooled Major Power War Involvements, for 20th Century Conflict Dyads 180
xi Table Page
27. Total Number of Major Power/Major Power and Major Power/Minor Power Conflict Dyads that Terminate in War and the Proportion of Each Type that Is Correctly Predicted by the Probit Equation, for the 20th Century 184
28. Correlation and Standardized Probit Coefficients for the Intervening Variables when Predicting to Pooled Major Power War Involvements, for All 19th and 20th Century Major/Major Conflict Dyads 186
29. General Summary of Findings from Chapters Three through Five 210
30. Number of Years as Major Power during the 1820-1899 and 1900-1970 Time Periods 252
31. National Involvements in Military Conflicts per Nation-Year 253
32. Proportion of National Involvements in Military Conflicts Resulting in War 253
xi i LIST OF FIGURES
Figure Page
1. Number of Military Conflicts per 10-Year Period . . 247
2. Number of Military Conflicts (at Various Levels of Violence) per 10-Year Period 248
3. Distribution of the Occurrence of Military Conflicts within 5-Year Periods 250
4. Distribution of the Occurrence of Military Conflicts (at Various Levels of Violence) within 5-Year Periods 251
x i i i LIST OF APPENDICES
Appendix Page
A. Data Sources 215
B. Interstate System Members 216
C. Identifying Interstate Conflicts 222
D. Summary Presentation of Military Conflict Data . . . 245
xi v CHAPTER I
THE THEORETICAL ARGUMENT
National policy makers . . . can always be relied upon ... to ignore their commitments to refrain from aggression whenever they believe that resort to force will serve their purposes. Schuman, 1968, p. 176
Social conflict can be defined as "a struggle over values or claims to status, power, and scarce resources, in which the aims of the conflicting parties are not only to gain the desired values
but also to neutralize, injure, or eliminate their rivals" (Coser,
1968, p. 232). A particularly brutal form of such conflict is the organized group violence known as war; it is one of the most destructive types of human behavior, yet it is also one of the most poorly understood. This dearth of knowledge about the causes of war has dire implications if our hope is for world peace, because an understanding of why wars occur is a prerequisite to
the development of effective measures for preventing them.
Despite the importance of such knowledge, the systematic
study of war is a rather recent phenomenon. Prior to 1914, there were only isolated examples of rigorous investigation, e.g.,
Bloch's The Future of War (1899). Carr (1964, p. 1) notes that
1 2
"war was still regarded mainly as the business of soldiers; and
the corollary of this was that international politics were the
business of diplomats." The First World War dissipated these
illusions. In the post-war years there were attempts at
collecting and analyzing data by such scholars as Richardson
(1960a, 1960b) and Sorokin (1937). And, of course, there was
Wright's massive volume, A Study of War (1942). However, it has only been in the last fifteen to twenty years that war has become a respectable research topic for significant numbers of social scientists (Pruitt and Snyder, 1969), and we have witnessed the rise of peace-research institutes and projects, and a multitude of new journals.1
Over the past two decades, the literature on war has become voluminous, encompassing theoretical essays, scientific 2 research, strategic analyses, and conflict-resolution proposals,
There are institutes located, for example, in Berlin, Boulder (Colorado), Hellerup (Denmark), Philadelphia, Clarkson (Ontario), Oslo, St. Louis, Stockholm, Tampere, and Tokyo. Major quantitative projects are McClelland's World Event Interaction Survey (1971), North's Studies in Conflict and Integration (forth• coming), Rummel's Dimensionality of Nations (Rummel, 1972; Hilton, 1973), and Singer's Correlates o_f War (Singer, 1 972; Singer and Small, 1972). Leading journals are Peace Science Society Papers (formerly, Peace Research Society Papers), Journal of Conflict Resolution, and Journal of Peace Research. 2 For representative works of the theorists, see Aron (1958, 1966), Boulding (1962), Knorr (1966), Rapoport (1960, 1964), Waltz (1959), and Wolfers (1962); of the strategists, Brodie (1958, 1959, 1966), Kahn (1961, 1962, 1965), Kissinger (1957), Schelling (1959, 1963, 1966), and Snyder (1959, 1961); and for resolution proposals, Melman (1962), Mitrany (1966), Osgood (1962), Russell (1959), Singer (1962), and Strachey (1962). 3
but aside from a consensus that unicausal explanations are
inadequate, there is little agreement on exactly why nations
become involved in war. Nevertheless, the explanations that
have been offered can be divided into two broad categories.
Some scholars emphasize the forces that drive states into conflict; others stress the constraints that bar the paths of war-prone states.
According to Organski (1968), the major driving force is d issatisfaction1 with the existing interstate order, and a primary cause of discontent is the frustration of being unable to reach valued goals. Analogies to this can be found in sister social sciences. Psychologists, for example, have shown that there
is good reason to expect that frustrated individuals will behave aggressively. And sociologists have found that in stratified social systems there are groups whose members feel relatively deprived, and they speculate that the members of these groups are prone to aggressive behavior.
It is quite evident, however, that all dissatisfied actors do not behave aggressively. Thus, if we are to account for aggressive behavior, we must look not only at driving forces, but also at those factors that may serve to constrain behavior.
1 Dissatisfaction is a psychological concept and, thus, it would be most accurate to say that "decision makers of the state" are dissatisfied with the benefits their state derives given the extant. interstate order. For reasons of style and clarity, however, I will, in the remainder of this paper, refer to the state's dissatisfaction, but anthropomorphism is not intended. 4
One such factor is prior experience. That is to say, the behavior of a person (or a state) is likely to be modified if prior experiences have been unrewarding. Another constraining factor is a lack of capability. Regardless of their frustration or experience, actors (whether they be persons, groups, or nation- states) cannot strike out unless they have sufficient resources to do so. Thus, Organski argues that it is not merely the dissatisfied states, but the dissatisfied and relatively powerful, that are likely to become involved in military conflicts.
In addition to these psychological and physical attributes, there are structural features that restrain behavior, the most important of which are the bonds that unite states and weave a network of commitments. We may again turn to our sister sciences for analogies. Psychologists believe that people who have competing commitments attempt to avoid open conflicts which might create cognitive dissonance. Sociologists hypothesize that people who have many competing affiliations feel cross-pressured, find that these cross-pressures serve to mitigate conflicts among these people. Thus, a complementary premise to the Organski argument is that states driven toward war may be constrained by the structural configuration of interstate relations.
It should be stressed that, in the set of ideas sketched in the paragraphs above, I have tried to capture only one of a number of implicit models that permeate the literature on war, but one that I and others find extremely plausible. It is my 5
belief that by making these models explicit and subjecting them
to systematic empirical analysis, we can significantly increase
our understanding of why nations become involved in military
confrontations and, ultimately, wars.
With these few preliminary thoughts outlined, let me
now:
(1) Present some literature to illuminate the preceding
discussion;
(2) Develop and operationalize several hypotheses on the
driving forces that lead to, and the constraining forces that
lead away from, war; and
(3) Test these hypotheses in a referent world.
Toward War: Dissatisfaction as a Source of Aggressive Behavior
I begin with an extremely brief review of some of the
hypotheses and research findings in the social science literature
concerned with individual and group responses to dissatisfaction,
and then discuss in more detail a few of the investigations that
have been undertaken in the area of peace research. Let us turn
first to work being done in the field of psychology.
The Frustration-Aggression Hypothesis in Psychology
Dollard et al. (1939) argue that frustrated people may
become aggressive and that their hostility will be directed
primarily toward the perceived source of their frustration. 6
Similarly, Feshbach (1964) posits that frustration threatens an
individual's self-esteem, and Pepitone (1964) suggests that a
real or imagined threat of loss of status or security may lead to
hostility and aggression. An outpouring of laboratory and field
experiments has, to a large extent, supported these propositions
(Allison and Hunt, 1 959; Berkowitz, 1960; Geen, 1968; Worchel ,
I960).
Researchers have found, however, that the intensity of
a person's frustration is not related to the aggressiveness of
his or her behavior (Buss, 1966; Cutter, 1963; Jegard and Walters,
I960). They also maintain that only particular types of
frustration breed hostility, and that an individual's reaction to annoying events depends upon how he or she interprets the situation (Maslow, 1943; Pastore, 1952; Rosenzweig, 1944). One
leading authority (Berkowitz, 1962) hypothesizes that frustration merely increases the potential for aggression; whether this potential becomes manifest depends upon several factors, among which are capability, learning, and the perception of societal constraints. The results of experiments by Berkowitz and Geen give credence to the importance of these intervening factors
(Berkowitz, 1965; Berkowitz and Geen, 1966; Geen and Berkowitz,
1966; Geen and O'Neal, 1969).
In short, psychologists have demonstrated that frustrated people may become aggressive. But frustration is not the only cause of aggressive behavior and, when people are frustrated, 7
their behavior may be inhibited by other factors.
The Status Inconsistency Debate in Sociology
Similarly, there is a concern in the sociological
literature with human responses to one type of social frustration, that associated with status inconsistency. Sociologists assume that stratification in complex societies is multi-dimensional and that an individual's rank in one status hierarchy does not necessarily coincide with his or her position in another (Sorokin,
1927; Weber, 1968). Lenski (1966) and others hypothesize that this status inconsistency is a source of stress, and support for this is found in Jackson (1962), who additionally argues that such inconsistency leads to frustration. The classic example from
American society is the confrontation between a black doctor and a white laborer. The former wishes to be treated in accordance with his or her professional standing, the latter insists on racial comparisons.
A number of researchers have examined the relationship between status inconsistency, on the one hand, and support for political and social reform on the other. The results have been ambiguous; some finding little or no support (Brandmeyer, 1965;
Kelly and Chambliss, 1966; Kenkel, 1956; Laumann and Segal, 1971;
Olsen and Tully, 1972), and others uncovering at least limited support (Goffman, 1957; Jackson and Burke, 1965; Lenski, 1954,
1956, 1967). It has been argued that the discrepancies in these 8
findings can, to a large extent, be attributed to the imprecision
of the definition and the incomparability of the dimensions and
scales used to test the status inconsistency concept (Hartman,
1974). Three general conclusions may, however, be drawn from
the sociological literature:
(1) Only people with certain types of status inconsistency
seek political change. The relationship exists primarily when
there are sharp discrepancies between individuals' ascribed and
their achieved statuses, i.e., between statuses to which people
are assigned on the basis of inalterable characteristics (e.g.,
race or national origin) and those which they attain on the basis
of achievement (e.g., education or income) (Jackson, 1962; Lenski,
1964; Segal and Knoke, 1968; Treiman, 1966). To explain this,
sociologists point out that achieved status can be raised (or
lowered) through individual effort, but changes in ascribed status
necessitate alterations in the culture and structure of the socio•
political system.
(2) A person's response to status inconsistency varies with the direction of the inconsistency (Jackson, 1962; Treiman,
1966). Individuals who have high ascribed but low achieved statuses
respond intra-punitively, often avoiding interpersonal interaction.
People with high achieved but low ascribed statuses respond in an aggressive, extra-punitive manner.
(3) People are likely to become aggressive only when their
low ascriptive status is socially visible (Box and Ford, 1969; 9
Malewski, 1963; Segal, 1969). Thus, sociologists suggest that the effects of status inconsistency are greatest in social systems where prestige considerations are very important and where the availability of stress-reducing mechanisms is minimal (Broom and
Jones, 1970; Jackson and Curtis, 1972).
The Relative Deprivation Hypothesis in Political Science
In studies on revolution, political scientists have drawn upon the findings of psychologists and sociologists, and have hypothesized that a vast discrepancy between expected achievement and existing conditions causes people to become discontent and motivates violent behavior. Davies (1962, p. 8) writes: "A revolutionary state of mind requires the . . . expectation of greater opportunity to satisfy basic needs, which may range from merely physical ... to the need for equal dignity and justice. But the necessary additional ingredient is a persistent, unrelenting threat to the satisfaction of these needs." Tanter and Midlarsky (1967) posit that the distance between a people's aspirations, based on long-term performance, and their expectations, based on immediate conditions, is a measure of the potential for violent revolution. And Gurr (1970, p. 13) states that "discontent arising from the perception of relative deprivation is the basic, instigating condition for participants in collective violence." 10
In sum, the relative deprivation hypothesis postulates
that, from experience or through example, people develop
expectations of what they believe to be their "fair share."
And, if they perceive that they are not accruing the benefits
to which they are "entitled," dissatisfaction is aroused and
a basis for violent conflict is established.
A Nation-State Analogy
These ideas, culled from literature on individual and
group behavior, can be analogized to account for nation-state
behavior. Following Galtung (1964), the interstate system can
be envisioned as a set of interacting units stratified in multi•
dimensional space. A state is "status inconsistent" when its
rank on one dimension does not correlate strongly with its
position on other dimensions. It is reasoned that differential
treatment, resulting from status inconsistency, leads to
frustration. "States which feel humiliated, hampered, and oppressed by the status quo seek ... to modify it" (Schuman,
1948, p. 378). If a state is high on power capability it may
possess the wherewithal to improve its low rank on other dimensions. In particular, a state with low "social status" in
the interstate system may try to raise its status by demonstrating
its physical prowess. Thus, Organski (1968, p. 371) postulates
that the likelihood of major war is greatest "when a dissatisfied challenger achieves an approximate balance of power with the dominant state." Drawing upon the sociological argument, 11
inconsistent states which rank higher on capability than on status
may be prone to extra-punitive, violent behavior, i.e., interstate
military confrontation. 1 On the other hand, states which rank
higher on status than on capability are likely to avoid
demonstrating their physical weakness, and will attempt to
increase capability through internal development or colonial
expansion.
System-Level Analyses
Several political scientists have, in one form or another,
investigated "status inconsistency leads to war" propositions.
East (1972) generates data for 130 states for the years 1946-1964 and constructs a three-dimensional stratification model of
prestige, economic position, and politico-military capability.
Many historical accounts have attributed involvement in interstate conflict to the perceived need of powerful states to maintain their national prestige. Taylor (1954, p. 433), discussing the First Moroccan Crisis, cites Bulow overruling Holstein: "All that matters is to get out of this muddle over Morocco so as to preserve our prestige in the world . . ." Albrecht-Carrie (1959, p. 148) accounts for the Tripolitan War by stating that "Tripoli had no economic value, but to have it fall to any other power would certainly have been a serious blow to Italian prestige." Taylor (1954, p. 494) relates that Franz Ferdinand told William II concerning the Balkan situation in 1913: "As soon as our prestige demands, we must intervene in Serbia with vigour." Wohlstetter (1962, p. 355) remarks on Japanese motives for the Pearl Harbor attack: "War with the United States was not chosen. The decision for war was rather forced by the desire to avoid the more terrible alternative of losing status or abandoning the national objectives." And again, a few pages later: "The risk of doing nothing about the United States, while attacking the British and Dutch, and, still more, the risk of not attacking the British and Dutch, seemed overwhelming and unthinkable — the acceptance of status as a tenth-rate power" (p. 357). 12
Using Pearson product-moment coefficients, he finds that the
amount of status inconsistency in the system is positively
related to the number of violent international conflicts in the
system, and that the association becomes stronger with time lags of one and two years.
Wallace (1973b) selects the states of a Euro-centric subsystem from 1825 to 1964 as his spatial-temporal domain and aggregates his measures at five-year intervals for the subsystem as a whole. He defines status inconsistency as the disparity between the rank order of states on the dimensions of power capability and diplomatic importance. Using simple bivariate correlations, stepwise multiple regression, and a form of dependence analysis, Wallace concludes that (1) inconsistency has a direct and statistically significant association with international war, (2) the relationship grows stronger with increased time lags, and (3) more variance can be accounted for when the 140-year temporal domain is broken into smaller sub-periods.
Both East and Wallace aggregate nation-state-level data in order to avoid a methodological difficulty that would arise if the derived status inconsistency measure is linearly dependent upon the measures for ascribed and achieved status (Blalock, 1966a,
1966b, 1967a, 1967b; Hyman, 1966; Mitchell, 1964). As a result, their findings permit no inferences to be drawn concerning the relationship between status inconsistency and war at the nation- state level (Ray, forthcoming; Singer, 1961). Consider, for example, 13
a system comprised of four states, each having an inconsistency
and a war involvement score. If we observe the system members at
three points in time, we may find their profiles to be as follows:
PEARSON INCONSISTENCY WAR INVOLVEMENT r
Tau1 Tau2 Tau3 Tau1 Tau2 Tau3
STATE A 20.00 40.00 20.00 35.00 15.00 35 .00 -i .00
STATE P. 5.00 30.00 55.00 20.00 15.00 10 .00 -1 .00
STATE C 20.00 15.00 10.00 5.00 30.00 55 .00 -1 .00
STATE D 35.00 15.00 35.00 20.00 40.00 20 .00. -1 .00 SYSTEM 80.00 100.00 120.00 80.00 100.00 120 .00 +1 .00 TOTAL
Correlating the inconsistency with the war involvement scores for each state, we uncover a perfect negative association, i.e., when
their inconsistency scores go up, their war scores go down, and vice versa. For the entire system, on the other hand, the relation
is positive. Thus, while East and Wallace find a relationship between status inconsistency and war involvement at the system and subsystem level, we cannot discern from their analyses whether it
is the most inconsistent states that are most war prone. Only when all states in the population have, concurrently, the same profile as one another can a system-level analysis result in precisely the same findings as a nation-state-level analysis.1
1 In the limited case, we could think of all states having the same scores as one another at each point in time; more generally, since total covariance is the sum of between- and within-group 14
Nation-State-Level Analyses
Midlarsky (1969) examines the status inconsistency hypothesis at the nation-state level for the years 1870-1945 and uncovers a statistically significant relationship. His measure of inconsistency is basically the mean difference between a state's score for national power capability and its score for diplomatic importance. Midlarsky's analytic technique is not longitudinal time series, but cross-sectional "time exposure."
Rather than measure the rank inconsistency of each state at different points within the temporal domain, he calculates the state's mean score for the entire 75-year period. An attempt to infer from his findings to the behavior of inconsistent actors raises time-specific difficulties that are somewhat analogous to those which result if one attempts to draw state-specific inferences from the East and Wallace analyses. Because Midlarsky's study is cross-sectional, it is impossible to ascertain whether states are most war prone at that point in time when they are, themselves, most inconsistent. To illustrate this, assume that we have observed the same interstate system as before, but at different points in time than we had earlier. We might discover the following: covariance, we are saying that between-group covariance is zero. An introduction to the problems associated with cross-level inference can be found in Alker (1965, pp. 101-06; 1969), Goodman (1953, 1959), Robinson (1950), and Shively (1969). 15
PEARSON INCONSISTENCY WAR INVOLVEMENT r Time tau1 tau2 tau3 tau1 tau2 tau3 Series STATE A 10.00 5.00 0.00 0.00 5.00 10.00 -1 .00
STATE Beta 15.00 10.00 5.00 5.00 10.00 15.00 -1 .00
STATE C 30.00 20.00 10.00 10.00 20.00 30.00 -1 .00
STATE D 40.00 30.00 20.00 20.00 30.00 40.00 -1.00
Time Mean Mean Exposure
STATE A 5.00 5.00
STATE B 10.00 10.00 + 1 .00 STATE C 20.00 20.00
STATE D 30.00 30.00
Correlating the inconsistency with the war involvement score for each state at each point in time, there is a perfect negative association; but for the mean score of all states over the entire time period, the relation is positive. Thus, we are left without time-specific information concerning the association of a state's status inconsistency and its war involvement.
Ray (1974) has also undertaken an examination of the status inconsistency issue at the nation-state level. He, however, employs a longitudinal design and, thereby, avoids the difficulties associated with "time exposure" analysis. Using data from 1816 to
1970 for ten major European states, Ray measures inconsistency as a function of the difference between the states' power capabilities and their diplomatic importance scores. Bivariate correlations, 16
multiple regression, and discriminant function analysis are
employed as well as controls for several intervening variables.
Ray uncovers no pattern of statistically significant relationship
between the status inconsistency of these states and the number,
magnitude, and severity of their international wars, nor does he
find any between inconsistency and the initiation of wars.
In light of Ray's findings, one further empirical
investigation should be mentioned. Von Riekhoff (1973) completed
a parallel study to Ray's, focusing on major powers from 1815 to
1965. He develops several alternative indicators of status
inconsistency, all based upon diplomatic importance and power
capability. Von Riekhoff uses regression and canonical analysis
to predict the number, magnitude, and severity of international
wars. He does not discover any relationships that are stronger
than those uncovered by Ray, but since they are generally in the
predicted direction, von Riekhoff concludes that some tentative
evidence exists for an association between status inconsistency
and war.
Away from War: Constraints on Nation-State Behavior
All the peace research that we have thus far reviewed
focuses on only a portion of the "status inconsistency leads to war" question. The researchers have looked, for the most part, at the forces that drive states into war. Yet, as the psychological and sociological literature suggests, there are also factors that 17
inhibit or, at least, place limits on aggressive behavior. It
may well be that status inconsistency inclines states toward
behaving aggressively, but whether that behavior becomes manifest
and subsequently escalates into war may depend on additional
factors. If this is so. our research needs to be two-stage.
We must first investigate whether status inconsistency is
associated with, let us say, military confrontations. Then
we must ask "under what conditions are these confrontations
likely to result in wars?" It is to the constraints on aggressive
behavior (both military confrontation and war) that we now turn.
The Limits of Capability
Berkowitz (1962) hypothesizes that frustrated individuals,
who are relatively powerless, feel vulnerable. If they are
unable to control or punish their tormentors, they become fearful
and avoid confrontation. Dollard et al. (1939) point out that
the greater the anticipated punishment, the less aggressively
people will behave. And Coser (1968, p. 233) notes that
"potential claimants for greater income, status, deference, or
power may be deterred from conflict because of fear of consequences."
Laboratory experiments support these contentions (Graham et al.,
1951; Hokanson, 1961).
A similar proposition may be made about nation-states, i.e., whether a dissatisfied state will resort to the use of military force depends, in part, upon its relative capability. The complete underdog, nearly incapable of acting, is not likely to initiate a 18
military confrontation; it is the discontented state which has
available resources that is most aggressive (Galtung, 1964).
Organski (1968, p. 371) comments: "World peace is guaranteed
when the nations satisfied with the existing international order
enjoy an unchallenged supremacy of power." And North (1968,
p. 231) presents precisely this argument when discussing the
viability of integrative relationships: "Reduced to simplest
terms, the durability of a given compact or other integrative
relationship will depend upon two main variables: (1) the
relative capability of each party, . . . and (2) the amount of
dissatisfaction evoked, or penalty demanded, by the relationship."
The association between relative capability and the use
of military force has been examined by Ferris and by Singer and
his colleagues. Their analyses have produced mixed results.
Ferris (1973) finds that the power disparity between states,
and changes in that disparity, are not good predictors of whether interstate disputes will involve military hostilities.
But he does uncover moderate support for the proposition that
these predictors are positively related to intense conflicts.
Singer and Small (1974) find that major powers experience war more often when their capability is below the mean for all major powers, and that major power wars are often initiated by states that are relatively inferior but gaining in capability. However, in a related study that included non-major states, Stuckey and
Singer (1973) show that the more powerful states (in terms of 19
demographic, industrial, and military strength) are involved in a greater number and more severe wars than the less powerful ones, and that powerful states are also more likely to initiate wars.
The findings of this latter study have been supported by subsequent analyses (Bremer, forthcoming).
Mindful of the literature review in the preceding section,
I hypothesize that status inconsistent 1 states are dissatisfied with the existing interstate order and are prone to involvement in interstate military confrontations. If their power capabilities are increasing, then confrontations become even more probable.
But whether these confrontations erupt into war depends upon additional factors, one of which is relative capability.
Following Organski (1968), if the opposing states are relatively equal in power capabilities, the likelihood of war is great; if unequal, the likelihood is smaller because the more powerful state is more likely to be able to obtain its ends without resorting to substantial military force, while the less powerful country is less likely to be able to defend its interests.
However, another factor needs to be considered—namely, that the capacity to employ armed force tends to diminish with distance from the target (Boulding, 1962). Thus, Gleditsch and
Singer (1975) find that while the average geographic distance between opponents in interstate wars increases between 1816 and
For the remainder of this study, "status inconsistent" will refer only to those situations in which a state is attributed less importance than would be expected, given its power capability. 20
1965, that distance is considerably less than the average inter-
capital distance between all countries in the interstate system.
Pearson (1974), looking at only post-WW II data, notes that there
are few hostile military interventions when the distance between
intervener and target country is great (> 1500 miles). Russett
(1967, p. 198) discovers that nearly two-thirds of the states
in military conflicts during the period 1946-1965 are
geographically proximate to one another. And Richardson (1960b,
p. 297) demonstrates that, from 1820 to 1945, sixty-five percent
of all deadly quarrels killing 317 or more people are between
neighboring opponents. None of these researchers would argue
that states become involved in military conflicts because they
are proximate. Rather, geographic nearness makes states salient
to one another and offers them the opportunity to effectively
utilize their military capabilities. In light of this evidence,
it is additionally hypothesized that wars are more likely to
occur if the parties to the confrontations are contiguous than
if they are distant.
Learning from Experience
Another intervening factor between status inconsistency and war involvement is learning. Experience serves as a source of expectation concerning the consequences of future behavior.
Berkowitz (1962) stresses that people's reactions to frustration can be altered by training, and that their interpretation of an event and their responses to it can be moderated or amplified. 21
Psychological experiments have shown that unrewarded aggressive
behavior as well as rewarded ami cable behavior produce non-
aggressive responses (Brown and Elliott, 1965; Horton, 1970).
In contrast, witnessing unpunished or rewarded aggressive
behavior can reduce people's inhibitions against overt violence
(Bandura, Ross, and Ross, 1961, 1963). Indeed, Bandura (1965)
finds that rewarded belligerence serves as an incentive for
future acts of violence, and other studies conclude that such
reinforcement not only increases the incidence, but also the
intensity of aggressive behavior (Geen and Pigg, 1970; Geen
and Stonner, 1971).
At the nation-state level, North (1968, p. 231) suggests
that "the durability of an integrative relationship will depend
. . . upon the precedents, that is, upon whether or not previous
agreements have worked to the satisfaction of the parties." And
Raser (1965, p. 225), in a speculative essay on the implication
for international relations of Mowrer's two factor learning theory, concludes that "behavior can be altered by the conscious manipulation of reward and punishment." But Singer and Small (1974) discover
that nation-states do not learn from the war experience of other states. For example, they find that punishing a war initiator by defeating it does not reduce the likelihood that another state will initiate a war within the same or subsequent year. It should however be noted that Singer and Small are not examining the question of whether a state learns from its own experience. 22
Their findings do not challenge the contention that a state
which wages a series of disastrous wars is not likely to soon
resume its aggressive behavior (Shy, 1971). Indeed, research
on major powers, currently underway at the Correlates of War
Project, shows that, since 1816, the greater the number of
battlefield fatalities sustained by a major power in war,
the longer the time interval before it becomes involved in
another war.
Every war is a punishing experience, some more costly
than others. I would surmise that decision makers consider
their losses in prior wars before plunging into another. And
I posit that the more costly these prior wars, the less likely
are decision makers to press for subsequent confrontations and
the smaller the likelihood that such confrontations will spill
over into war.
The Structure of the System
A final set of intervening variables can be found among
the structural properties of the interstate system. As every
beginning student in world politics learns, inter-state relations
differ fundamentally from intra-state relations in that the
former has — inter alia—no effective centralized government
(Herz, 1 959; Puchala, 1971; Ranney, 1966). Rousseau (circa 1756)
tells us that wars occur because there is nothing to prevent them.
Holsti (1967, p. 348) writes: "A more general reason for the use of violence in international relations is the absence of systemic 23
constraints on its use." And Waltz (1959, p. 234) concludes that if war results because state A has something that state B wants, "the efficient cause of war is the desire of state B [, but] the permissive cause is the fact that there is nothing to prevent state B from undertaking the risks of war."
However, anthropologists have found that viable and stable societies exist which lack both central government and specialized political roles. Colson (1953) describes one particular society in which the people's loyalties are divided between territorial and kinship groups. Each individual is integrated into a system of overlapping relationships. When a person acts to fulfill an obligation to one group, he or she is faced by counterclaims from other groups. "In a society of this type, it is impossible to have the development of the feud and the institutionalization of repeated acts of vengeance, for each act of vengeance, like each original incident, mobilizes different groups whose interests are concerned in the particular case and that alone" (p. 210).
Although the "impossibility of the feud" is perhaps over• emphasized, Colson's study accords well with the psychologists' findings that people who fear that their actions may incur social disapproval are often inhibited from behaving aggressively or are likely to displace their hostility onto substitute targets
(Rule and Percival, 1971; Worchel, 1966). Similarly, Colson's explanation finds support in the sociological literature. Indeed, 24
the explanation is equivalent to the "cross-cutting cleavages" proposition (Coser, 1956, 1968). Simply stated, in a society
in which there are many cross-cutting associations, cross- pressures on the individual along a plurality of fronts lessen the likelihood that any single conflict might become overly intense. (The term "cross-cutting" refers to the "physical" existence of competing commitments; the term "cross-pressure" refers to the "psychological" effects of these commitments.)
Galtung (1968, p. 490) applies this "cross-cutting cleavages" proposition to the behavior of nation-states: "If two nations are allies in one conflict (for instance, between East and
West, in the language of the cold war), they may nevertheless be antagonists in another conflict (for instance, between rich and poor nations), and this subjects them to cross pressures."
He reasons that cross-cutting associations prevent complete identification and involvement in any conflict, and that cross- pressured states tend to serve as mediators between states that are not cross-pressured.
There has been very little empirical research on the relation of cross-cutting to war and, to my knowledge, none at the nation-state level. The most prominent system-level analysis has been done by Wallace (1973a). He examines data for all members of the interstate system (Singer and Small, 1972) during the period 1815-1964, and uses smallest space analysis to cluster states, at five-year intervals, on military alliance, 25
international governmental organization, and diplomatic
representation dimensions. Although the explanatory power
of the variable is small, Wallace finds that a greater amount
of war in the system is associated with both very low and very
high levels of cross-cutting in the system, and relatively
less war with moderate cross-cutting. He speculates that,
at the one extreme, a heavily cross-cut interstate system
generates confusion as to the identity of allies and adversaries
and that this, in turn, leads to less predictable conflict
behavior; and that, at the other extreme, a system which is not
cross-cut lacks the countervailing links that serve to temper
behavior.
Although Wallace's findings at the system level may tempt
us to posit the same curvilinearity at the nation-state level,
I need only caution the reader about the dangers of cross-level
inference. The important question is whether Wallace's
interpretation of the relationship, i.e., that high levels of
cross-cutting in the system lead to confusion and increased war
in the system, suggests a parallel hypothesis concerning the
behavior of heavily "cross-pressured" states. I think that,
at the nation-state level, the curvilinear relationship is
somewhat less plausible than the simpler linear relationship
that accords with the anthropological and sociological literature.
Thus, I postulate that the more cross-cut a state's bonds, the more constrained it is and the less likely it is to become involved 26
in a military confrontation. And if such a confrontation does
occur, the probability that it will result in war will be reduced
if the opposing states have bonds with one another.
The "cross-cutting" literature, with its emphasis upon
the moderating influence of multiple affiliations, suggests to
me that the mere opportunity to have multiple affiliations may
have a similar effect. And this leads me to speculate about a
second structural variable, one that is concerned with the
opportunity for new alignments within the system. This second
variable is polarity. The literature ususally contrasts
bi-polarity, a situation in which two opposing camps leave little
room for re-alignment, with more flexible multi-polar systems.
There is, however, no consensus in the theoretical literature
on whether bi-polarity (Waltz, 1964, 1967), multi-polarity (Deutsch
and Singer, 1964), or some mixture of the two (Rosecrance, 1966)
is most conducive to peace, although the advocates of multi-
polarity are, perhaps, more numerous.
To date, empirical investigation has not served to increase
the likelihood of consensus. Singer and Small (1968) find positive
correlations between bi-polarity and the number, magnitude, and
severity of wars in the interstate system during the 19th century,
but mainly negative relationships in the 20th. Haas (1970) looks at twenty-one historical systems in Europe, Asia, and Hawaii for
the years 1649-1963 and concludes that the number of poles in the system is negatively associated with the number, magnitude, and 27
severity of wars in the system. And Wallace (1973a) examines the international system from 1815 to 1964 and finds a curvi• linear relationship in which the amount of war in the system is greatest when bi-polarity is very pronounced or not discernible and, conversely, is least in a moderately polarized system.
It should be noted that these latter empirical studies investigate the amount of war in the system. To my knowledge, there has been no systematic empirical research examining the effects of bi-polarity on national behavior. If, for example, we found that bi-polarity was associated with more war in the system, we still would not know whether all nations become more war prone, only particular types of nations (e.g., major powers), or only nations in particular positions (e.g., the most peripheral nations). In addition, the research to date has tended to focus on whether wars are likely to be longer or shorter, bloodier or less deadly, given that the system is bi-polar, rather than on whether or not a war will occur.1
While being sensitive to the possibility of a curvilinear relationship between bi-polarity and the incidence of war, I shall nevertheless hypothesize a linear relationship that complements
1One further point might be made. If one is investigating questions concerned with the amount of war rather than the occurrence of war, one's unit of analysis would more properly be the war than the year. That is, if one were interested in discovering what variables best account for the size of a war, it would be best to look at only those situations in which wars occur and then determine what variables account for the size of those wars' (Duvall, 1974; Zinnes, 1967). 28
that postulated for cross-cutting. I posit that the less bi-polar
the system, the greater the opportunity for multiple competing
affiliations and the less the likelihood of serious military
confrontations. If such confrontations do arise, the less bi-polar
the system, the smaller the probability that they will erupt, into war.
Summarizing the Propositions and Positing a Model
Before becoming more deeply committed to this research, we should confront one lingering doubt concerning its profitability.
The reader probably has noted that, in the empirical studies we have reviewed, there is often a lack of agreement from one set of results to the next. Depending on the level of analysis, the methodology, or the operationalization of the variables, some of the studies support the propositions under investigation while others do not. Wallace, East, Midlarsky, and von Riekhoff uncover a relationship between status inconsistency and war; Ray does not.
Stuckey and Singer find a relationship between capability and war;
Ferris, for the most part, does not. And so forth. Can it be profitable to, again, examine these same variables? I would say
"yes." In addition to the usual arguments that variables were not adequately operationalized and that better data is now available, there are more subtle and intriguing reasons. First, the analyses to date have rested mainly upon the search for continuous linear functions. Yet, the underlying association may be curvilinear or 29
show threshold properties which would not be captured by a linear
regression or a product-moment coefficient. Second, the absence
of strong evidence supporting a relationship, for example,
between status inconsistency or relative capability, on the one
hand, and war involvement on the other does not negate the
possibility that inconsistency and relative capability are
significantly associated with war. If we think about statistical
significance in terms of "necessary" and "sufficient" conditions,
it becomes clear that an insignificant bivariate relationship does not imply that a variable is without explanatory power when placed in a multivariate context. As an example, suppose that a dependent variable is associated with three predictor variables, that are distributed as follows:1
OBS # PRED 1 PRED 2 PRED 3 DEP VAR 1 25. 00 24. 00 23.00 4. 00
2 2 23. 00 22. 00 21 .00 5. 00
3 21. 00 20. 00 19.00 10. 00
4 19. 00 18. 00 17.00 15. 00
5 17. 00 16. 00 15.00 20. 00
6 15. 00 14. 00 13.00 24. 00
7 99. 00 98. 00 97.00 75. 00
8 99. 00 10. 00 9.00 75. 00
I am indebted to John Stuckey for suggesting a similar illustration of this principle. 30
OBS # PRED 1 PRED 2 PRED 3 DEP VAR
9 98.00 10.00 97. 00 76,.0 0
10 9.00 95.00 8. 00 77 .00
11 8.00 7.00 92. 00 78,.0 0
12 7.00 92.00 90. 00 79,.0 0
13 93.00 90.00 7. 00 80. .00
14 6.00 6.00 88. 00 85. ,00
15 87.00 5.00 6. 00 89. ,00
16 5.00 83.00 4. 00 91. ,00
17 4.00 79.00 3. 00 94. 00
18 3.00 2.00 76. 00 96. 00
19 75.00 1.00 2. 00 99. 00
INTERCORRELATION MATRIX
PRED 1 1.00
PRED 2 -.03 1.00
PRED 3 -.00 -.03 1. 00
DEP VAR .24 .26 • 24 1. 00
PRED 1 PRED 2 PRED 3 DEP VAR
If we examine the bivariate correlations, we would probably conclude that the relationships are not particularly important, since no single predictor variable accounts for more than seven percent of the variance in the dependent variable. However, a simple contingency table or a scatter plot of each predictor variable against the dependent variable, and a comparison of the 31
three tables or plots with one another would reveal a striking relationship: a high score (>50.) on any predictor variable appears to be a sufficient, and may be even a necessary, condition for a high score on the dependent variable. This can be shown statistically by combining the three predictor variables into a new variable that is given a score of "one" whenever any of the three predictors is high, and a score of "zero" otherwise. A one-way analysis of variance, with the dependent variable stratified by this new dichotomous predictor variable, is significant at the .00 level; and a regression, with the new dichotomous variable as the predictor, accounts for almost ninety-five percent of the variance in the dependent variable.
A simple bivariate analysis, that focused on the separate effects of the original predictor variables, would have failed to reveal this multi-variable relationship. Thus, the inclusion of several factors in an analysis may demonstrate an association where fewer variables prove insufficient. The number and the identity of variables to include, and the manner in which they are to be combined, of course, must derive from one's theory, model, or hypotheses.
Our theoretical argument is that states that are attributed less importance (i.e., have lower ascribed status) than would be expected on the basis of their capabilities are more prone to military conflict than states that have the opposite profile or are status-congruent. Whether these states 32
resort to aggressive behavior, however, depends upon additional
factors — some physical (capability and contiguity), some
psychological (prior experience), and some structural (cross-
cutting and polarity). We can portray this as a two-stage model
the first leading from status inconsistency to military
confrontation, the second from confrontation to war.
STAGE 1: NATIONAL STAGE 2: DYADIC
Intervening intervening variables variables 1) change in 1) difference power in power 2) prior war 2) contiguity experience 3) prior war experience 3) cross-cutting 4) reciprocal bonds bonds 4) polarity 5) polarity
status lnterstate interstate inconsistent military wars states confrontations
In the first stage, our unit of analysis is the year and we undertake a nation-state-level analysis. That is to say, we
first investigate whether, given the intervening variables
hypothesized above, status inconsistent nation-states are
unusually aggressive. Naturally, I do not expect all states engaged in military confrontations to be status inconsistent,
but rather that status inconsistent states will be involved in military confrontations much more frequently than would be expected 33
by chance. And, if it has not been evident from what I have said
previously, 1 would expect status inconsistent states to initiate a disproportionate number of military confrontations.
In the second stage, our unit of analysis becomes the conflict. We now will have a set of military confrontations and will have identified the dyads involved in each of them. We will determine whether the intervening variables specified above are sufficient to distinguish between those confrontations that result in war and those that do not. Unlike previous investigations that sought a direct link between status inconsistency and war involvement, no such connection is posited here. Status inconsistency is hypothesized to make states prone to military confrontation; but once such a confrontation occurs, a different set of dynamics is involved and the likelihood of war depends upon the physical, psychological, and structural relationships of the parties to the conflict.
In concluding this chapter, I again emphasize that the model presented in the preceding pages is only one of a number of contending explanations implicit in the literature. Having said this, let us turn to the research design with which I hope to test the model. CHAPTER II
THE RESEARCH DESIGN
The Referent World
Spatial Domain
To test the propositions posited in Chapter One, I have
selected the set of states that comprise the major power subsystem
from 1820 to 1970. Several factors have guided this choice.
There are those that might be labeled conceptual. "Status" is
likely to be salient to major powers, in that they have the
capacity to gather and process information that would make them aware of their position vis-a-vis other states and that they are
sufficiently near the top of the "pecking order" to have a
prominent benchmark against which to compare their gains and
losses. In addition, the major powers interact amongst themselves with sufficient regularity that we can consider them members of an international "system." Structural concepts such as "cross- cutting" and "polarity" are most likely to be meaningful under such conditions.
There is also a very practical reason for selecting the major power subsystem, namely, the availability of high quality
34 35
data. At least for the nineteenth century, the most reliable data that have been generated on national attributes and behavior exist for the major powers.
Finally, there is the question of relevance: the need to select the set of states that accounts for most of the behavior under investigation. As has been pointed out by many scholars, war is basically a great power activity. Indeed, major powers have actively participated in sixty percent of the interstate wars since the Congress of Vienna. These states have the ability to undertake independent action and, if status inconsistency is a source of dissatisfaction with existing arrangements, major powers are more likely than other states to believe that they are capable of altering the international order by means of armed conflict. To quote Model ski (1 972, p. 48): "War is the principle justifying and legitimizing the international status system at whose summit are the Great Powers; that status system in turn validates war as the means of preserving the status system." These conflagrations are a threat to the entire inter• state system and, to a large extent, world peace depends upon the elimination of major power involvement in international war.
I used two criteria to identify the test population.
First, the political entity had to be a member of the interstate system, i.e., it had to be a national political entity that independently controlled its own armed forces and received diplomatic recognition from any two states that fulfilled the 36
same requirements (cf. Singer and Small, 1966). A list of system members is presented in Appendix C. Second, there had to be historical consensus that the state was a major power. Whether a state meets the first criterion is relatively simple to determine; the second is more difficult. Even though we each have a "feel" for which states are major powers, there is no agreement on objective indicators. I therefore asked a number of diplomatic and military historians to complete a questionnaire aimed at identifying the major powers and the appropriate years for membership in this exclusive club. Of twenty-four American scholars polled, twenty responded. The concensus of these historians (defined as agreement among more than half of the respondents) follows. With the exception of Japan's "re-inclusion"
1960-1970, the consensus was nearly unanimous.
State Inclusive Years
Austria-Hungary 1820- 1918
Prussia/Germany 1820- 1918, 1 925-1945
Russia/USSR 1820- 1917, 1 922-1970
France 1820- 1940, 1 945-1970
United Kingdom 1820- 1970
Italy 1860- 1943
Japan 1895- 1 945, 1960- 1970
United States 1899- 1970
China 1950- 1970 37
For the remainder of this study, the term "major power" will refer to these states for the designated years. Parenthetically, it may be pointed out that the historians' judgment coincides
(excepting only Japan, 1960-1970) with the countries and dates suggested by Singer and Small (1972).
Temporal Domain
The temporal domain for this study begins in 1820, shortly after the close of the Napoleonic Wars and the conclusion of the Congress of Vienna. This was a time of important changes in international relations. The citizen army had been cast upon the stage, altering the means of military recruitment for future wars and increasing the possibility of massive international conflagrations. The major powers of Europe had joined together to defeat Napoleon and to restructure the political face of the continent. A so-called "century of peace" was constructed, shaken to its roots by the revolutions of 1848, jarred in 1853 by the Crimean War and in 1870 by the Franco-Prussian, and finally shattered in 1914. The turn of the twentieth century saw a new wave of social revolutions and the ascendancy of new major powers.
In the course of the twentieth century have come four of the five bloodiest interstate wars in recorded history and, in its wake, lie the remnants of several major powers and their empires. The century and a half since 1820 has been truly eventful, and an interpretation of today's conflicts may be informed by an understanding of these historical occurrences. 38
There are, however, some who suggest that historical events
and system transformations during the past 151 years have so
permanently altered relationships among political and social
variables that findings from the past can have no relevance for
the present. The identification of temporal discontinuities and
attendant changes in the inter-variable relationships is, of
course, an empirical question; and, since reliable data on the major powers are available, it is a question that we shall
investigate in this study.
Constructing the Indicators
The Predictor Variable: Status Inconsistency
Before we can test the hypotheses that are posited in
Chapter One, we will have to construct operational indicators for the key concepts contained in these hypotheses. A number of the concepts (e.g., status inconsistency and cross-cutting) have been borrowed from sister social sciences, in which they have been traditionally applied to individuals. It will be no easy feat to construct (for these concepts) indicators that can be appropriately applied to nation-states.
Fittingly, we begin with the most difficult concept: status inconsistency. The reader will recall from Chapter One that a nation is said to be status inconsistent when it fails to receive attributed importance commensurate with its power capabilities. Hence, status inconsistency has two components: 39
power capability and attributed importance. In the pages that follow, I will first construct an indicator of power capability,
then one of attributed importance, and, finally, I will combine
the two into a single index of status inconsistency.
Measuring Power Capability
Although political scientists have never concurred on how power capability should be measured, it is agreed that the concept is multidimensional. For the current investigation,
I tap three of these dimensions (cf. Small and Bennett, forthcoming). First, there is the demographic. The sheer number of people populating a country is one indicator of a nation's power because people are fuel for industrial fires and fodder for military cannons, and as such, they provide the basic "raw material" for national might. But while large numbers of people are an asset to the construction of a powerful nation, they are so only to the extent that they can be effectively mobilized. The industrial revolution brought people into the cities and these urban dwellers came to comprise the sector of society that was best educated and most familiar with technological advances. In short, these urban citizens became the most mobilizable sector of modern industrial society.
Therefore, I use two indicators of the demographic dimension of power: one is total population and the other is urban population. 40
A second dimension of national capabilities is economic.
Here too I use multiple indicators: fuel consumption 2 and 2
iron and steel production. These are probably the most
comparable cross-national indicators available to measure
the industrial base of states across the span of the nineteenth
and twentieth centuries. Without fuel and iron/steel, industry
cannot function and neither machines of war nor those of peace
can be produced.
Finally, no measure of national power in the "modern"
interstate system would be complete without a military component,
for military might serves as the ultimate arbiter. The basic
indicators of this dimension are the number of military personnel and the amount of money invested in military expenditure.
Having selected these six indicators, we are faced with questions of their comparability and their level of measurement.
Do the relationships among the indicators remain constant across
the century and a half under investigation and should each
indicator contribute equally in determining national power?
Is there an interval-scale isomorphism between the indicators and the concept, e.g., is the increase in power proportionate to increases on the indicators and do unit increases on the
1 Coal consumption is used prior to 1860; coal, oil, hydroelectric, solar, and nuclear energies are included in subsequent readings. 2 Pig-iron production is used prior to 1900; steel poduction is included for the twentieth century. 41
indicators correspond to unit increases in power regardless of the existing level of power?
The answer to the above questions is "probably not."
Certainly, we make a heroic assumption if we simply accept premises of inter-variable comparability and interval-scale isomorphism. We should be able to do better.
Let us make two "not very heroic" assumptions. First, we will posit that the six indicators do indeed tap the concept we wish to measure. Second, we will posit that the indicators are at least ordinally-scaled and have directionality; that, for example, spending more money on defense increases, ceteris paribus, a nation's power capability, although not necessarily proportionate to the increased cost. Since all of the indicators are supposed to tap some facet of "power," we could rescale the indicators so as to maximize their intercorrelation. If the original scores on the indicators were highly correlated, we would not expect the transformed scores to be very different; if the original scores were more weakly correlated, the transformed scores would be a better interval approximation. We may think of this procedure as regressing power (the outcome variable) on its six indicators (the predictor variables) so as to maximize the fit (R2).
I subjected all capability data to this transformation on an annual basis using Guttman-Lingoes' CM-III (Lingoes, 1972, 42
1973). This conjoint measurement algorithm rescales variables so that the average intercorrelation among them is maximized, subject to the restriction that rank order be preserved. In the process, mild non-linearities are minimized or eliminated, thereby producing transformed scores that can be more appropriately used with linear analytic techniques such as ordinary least- squares (OLS) regression.
The applicability of Guttman-Lingoes' CM-III to rescaling capability data was pointed out to me by Michael Mihalka. 2 The Guttman-Lingoes' CM-III algorithm permits variables to be "reflected," i.e., multiplied by -1, in order to maximize the average intercorrelation coefficient. While this is reasonable for some types of psychological tests (e.g., most preferable <—> least preferable) where directionality is an empirical phenomenon best determined by intercorrelations, it is not beneficial when directionality is known a priori. In 20 of the 139 years submitted to CM-III transformations, the military personnel vector was reflected (and, in five of these instances, total population was also reflected). Fifteen of these years occurred between the two world wars, when it seems unreasonable to assume that greater numbers of personnel detracted from a nation's capability. I, therefore, wrote a one-iteration algorithm analogous to GL/CM-III and resubmitted the original data for the twenty years that had had variables reflected. For sixteen of these years (1854, 1919-1933), no significant improvement over the intercorrelations produced by the original data scores was achieved by rescaling without reflecting, and so the standard scores of the original data values were used in subsequent analyses. In four years (1823, 1826, 1843, 1871), the transformed scores from my CM-III analogue were used for subsequent analyses. Due to the small number of states in my population, the data for five additional years had sufficiently peculiar rank profiles that GL/CM-III scores violated strict monotonicity (1947-1949) or produced an average intercorrelation coefficient significantly lower than that for the original data values (1868, 1935). In the former case, my one iteration CM-III scores (maintaining strict monotonicity) were used, and, in the latter, standard scores of the original values were used during subsequent analyses. 43
While the use of CM-III transformations addresses itself
directly to the level of measurement question, it only partly
answers the question of comparability among indicators. The
indicators are comparable to the extent that their average
intercorrelation is maximized, but CM-III does not assign
appropriate weights to each indicator. Although the output
from this algorithm does include a "single best score," this
is simply the mean transformed score for a given nation, i.e.,
equal weights are assigned to each variable. The problem of
weighting indicators will be addressed in subsequent sections.
Measuring Attributed Importance
Having thus selected and rescaled indicators of power
capability, we turn to the second component of status
inconsistency: attributed importance. We need to determine
each state's "social status" as assigned by other states in
the system. The indicator of status that I have selected is
diplomatic importance, based on the number of permanent missions
at the rank of diplomatic agent or higher received by each state
in my study. Before discussing the manner in which the indicator
is constructed, let me briefly offer some of the reasons that led
to this selection.
First, international diplomacy plays a central role in major power interaction. It is not simply a means of
communication; it is an arena for political power. Diplomacy 44
is the cornerstone of a global political network, the primary nodes of which are located in the capitals of the major powers.
Because the Powers sit astride these channels of communication, world diplomacy becomes an instrument subject to their manipulation.
Second, the sending of diplomatic missions may be thought of as a system-wide plebiscite on attributed importance
(Small and Singer, 1973). The creation and maintenance of such missions entail both economic and political costs and benefits, and as a result are far from universally distributed. Between
1817 and 1970, for example, the number of missions received by the average state was only one-third of the total possible.
Thus, in sending diplomatic missions, a state confers a degree of importance on the recipient and thereby enhances the recipient's stature. The degree to which the receipt of a mission enhances a state's stature depends, in turn, on the importance of the sender. The more central the sender's position in the diplomatic network, the greater is the import of its missions.
Finally, the elemental nature of this selection process has been recognized by a number of political scientists; every study of status inconsistency in the interstate system cited in
Chapter One uses as an indicator of attributed importance some variant of a diplomatic exchange index. But this cumulativeness also has a negative aspect. Neither I nor any of my colleagues 45
have been able to offer another objective measure of attributed
importance that economically permits regular (indeed, annual)
observations throughout the nineteenth and twentieth centuries.
To this extent, we are "stuck" with this indicator, for which
data is both available and reliable.
What we thus have is "choice data": information as to
which states select which other states because they are
diplomatically important to the selecting states. There is
a considerable body of sociometric and psychometric literature
that deals with just such data. 1 I will use a sociometric
technique (as opposed to a psychometric one) to compute
diplomatic importance scores for the major powers because the
sociometric technique produces a unique solution, it is
computationally less expensive, and its mathematical reasoning
more directly parallels that of the status argument. Diplomatic
importance scores will be derived by simply summing the diplomatic
missions (at the rank of diplomatic agent or higher) that each
major power receives, where each mission is weighted in
accordance with the "centrality" of the sending state. It is
assumed that the more central the sending state's position in
the diplomatic network, the greater is the import of its missions.
For an introduction to the sociometric literature see Coleman (1964), Holland and Steuer (1970), Moreno (1960), and the material cited below. For an introduction to the psychometric literature see Bloombaum (1970), Coombs (1964), Green and Carmone (1970), Guttman (1968), Kruskal (1964a, 1964b), Shepard (1962, 1974), and Shepard et al. (1972). 46
Thus, I first compute (on the basis of direct and
indirect diplomatic links) centrality scores for all states
in the interstate system. This is done by depicting the
(asymmetric) diplomatic bonds by "directed graphs"
(Harary, Norman, and Cartwight, 1965), which are, in turn,
displayed in matrix form, where the cell entries (zero or
one) of the resulting matrix indicate the direct links
between the states represented by the corresponding rows
and columns (Forsyth and Katz, 1946). The number of
indirect links (so-called higher-order chains) between any
two states is determined by raising the matrix to higher
powers (Festinger, 1949)."' The higher the power, the more
indirect are the links.
Let us take, as an example, the following directed
graph:
W --> X
which can be represented by the choice matrix C:
A review of matrix algebra can be found in Johnston (1972) and in Kemeny, Snell, and Thompson (1966). 47
Sender
Receiver W X Y Z
W 0 0 0 1
X 1 0 0 1 c
Y 0 10 0
Z 10 10
By multiplying the matrix C by itself, we obtain the squared 2 2 matix C .2 This matrix, C2, gives the number and location of
all 2-chains (i.e., 2-link chains) between states W, X, Y, and Z,
Sender
Receiver W X Y Z
W 10 10
X 10 11 c
Y 10 0 1
Z 0 10 1
From the matrix C2 (presented above), we see that there is one
2-chain leading from W to itself (W —> Z —> W), one 2-chain
from Y to W (Y —> Z —> W), and so forth. If there are n
2-chains between two states, then the corresponding cell entry
would contain the number n. Raising the matrix to the third
power, C 3, enables one to determine the number and location of
3-chains; in general, the matrix Ck has as elements the number
of k-chains between each pair of states. In order to prevent 48
self-choice, we need only place zeroes along the principal
diagonal.
This rather simple sociometric algorithm can be used
to measure the diplomatic centrality of a state, where centrality
is computed as an inverse function of each state's distance from
all other states. 1 In other words, if I is "closer" than J to
all other states, then I receives a higher centrality score
than J. To carry out these computations, all that need be found
is the shortest path between every pair of states. And we know,
from the discussion in the preceding two paragraphs, that the
shortest path between the states I and J is pii, the power to which the matrix C (the matrix of diplomatic choice data) must
be raised in order for cell cij (corresponding to states I and J)
to be non-zero. Since higher powers correspond to longer (less
direct) paths, an attenuation factor, a, is introduced so that
these longer paths contribute less to a state's centrality score
than do shorter paths. If a is a constant between 0 and 1, where the former corresponds to complete attenuation and the
latter to the absence of any attenuation, then the diplomatic n pii a , where n is the number j = l of states in the system. To briefly reiterate, the centrality of each state is inversely related to its distance from all
1 This measure of centrality is similar to indicators of status suggested by Arney (1973), Coleman (1964, pp. 444-55), and Katz (1953). 49
other states, so that the one that is most closely tied
diplomatically to all others is most central.
Centrality scores were computed (with the attenuation
factor set to .5) for the period 1817-1970 for all states
receiving two or more diplomatic missions in the given year.
Diplomatic importance scores were then derived for the major
powers by weighting each mission that they received by the
centrality of the sending state, and then summing these
weighted missions.1 The importance scores were normalized
as a proportion of the maximum possible score that could be
attained. Calculations were done approximately every fifth year, other years being interpolated. The variance in the quinquennial data was sufficiently low to assure that little distortion was introduced by interpolating the annual scores.
Multiple missions to major powers were removed from the matrix before computing diplomatic importance scores. If a single emissary was sent by one state to several different major powers, each major power was credited with 1/N portion of the mission, where N is the number of recipients. Conversely, if a joint mission from several states was sent to a single state, the recipient was credited with one mission from the most central sender rather than a mission from each of the joint senders. Emissaries from a contiguous minor power to its neighboring major power were also discounted. It was felt that such states had little choice in sending missions and thereby inflated the scores of the neighboring major power, while significantly deflating the scores of other major powers. For the period 1817-1970, minor powers sent missions to their bordering major powers ninety-two percent of the time—a considerable deviation from their behavior with other major powers. This would not have been a serious problem if each major power had an approximately equal number of small neighbors, but unfortunately this was not the case (witness Prussia as opposed to Britain). 50
Below are listed the normalized importance scores for the major powers for approximately every tenth year.
1817 1824 1833 1844 2854 1864 1874 _1_884
UK .34 .31 .2? .19 .25 .23 .43 .46
FRN .29 .30 .25 .22 .29 .27 .44 .48
GMY .25 .27 .22 .18 .19 .18 .35 .43
A-H .29 .31 .23 .21 .26 .22 .32 .33
ITA .17 .32 .36
USR .28 .28 .21 .13 .16 .16 .23 .25
1894 1904 1914 1925 1935 1950 1960 1970
USA .48 .54 .58 .62 .48 .51 .61
UK .47 .48 .52 .62 .62 .49 .49 .59
FRN .43 .45 .49 .58 .63 .45 .45 .55
GMY .43 .41 .41 .47 .49
A-H .28 .31 .40
ITA .35 .41 .48 .52 .58
USR .25 .27 .32 .18 .24 .25 .28 .49
CHN .05 .16 .20
JPN .26 .33 .36 .40 .40 .48
The question arises as to whether these "complex" diplomatic importance scores are any different than those we would obtain if we merely summed (without weighting) the number of missions received by each major power. In an earlier 51
paper, Small and Singer (1973) report that, for the interstate system as a whole, the mean rank order correlation between the number of missions each state receives and an index that weights these missions by the importance of the sending state is .94.
I computed a Kendall's tau-b between the number of missions received by the major powers and the quinquennial "weighted" scores derived from the sociometric matrix technique. The rank order correlation for this smaller subset was only .59.
Thus, to the extent that information concerning the "centrality" of the sending states contributes to the validity of the diplomatic importance scores for the major powers, the weighted indicator is worth its added complexity.
The Index of Status Inconsistency
We now have six interval-sealed indicators of power capability and a single indicator of diplomatic importance.
We seek to combine these indicators into an index of status inconsistency. I have argued that states expect to be attributed importance commensurate with their power capabilities and, to the extent that they are attributed less importance, they are status inconsistent. Nowhere in the status inconsistency literature, however, is the form of the functional relationship between capability and attributed importance specified. Early studies simply operationalized status inconsistency as the arithmetic difference between the achieved and ascribed status 52 indicators. This, however, produced perfect linear dependence and the impossibility of untangling the influence of inconsistency from the two elements that comprised it. Later studies (e.g.,
Ray, 1974) have operationalized status inconsistency as a non-linear, but "arbitrarily" specified, function.
Let us try to do better.1 We expect capability and attributed importance to be highly related to one another and that there will be a function to describe that relationship.
By status inconsistency we mean deviations from the scores predicted by that function. This immediately suggests a regression strategy where the residuals represent these deviations. Thus, we simply regress diplomatic importance on the six non-linearly transformed (CM-III) capability scores.
The resulting equation will give us the predicted "importance score" for a state, given its capability scores and the capability/importance relationship for all the states in the subsystem. A state's inconsistency is the difference (the residual) between the score we would predict and the one we observe for it.
A further issue needs to be resolved before we employ this regression strategy. To my knowledge, all previous studies of status inconsistency assumed that the relationship between importance and capability remained constant both across time and across space. While I accept the latter as inherent in the
1 I would like to thank Stuart Bremer for suggesting the following index construction procedure. 53
relative nature of the concept, I do not a priori accept the former supposition. That is, at a given point in time, a state's decision makers perceive that their country is status inconsistent because they expect it to be attributed a degree of importance commensurate with its capabilities and to be treated just as other states are; however, the contribution to power of the various capability indicators is likely to vary over time as some dimensions (e.g., total population) become less important and others (e.g., fuel consumption) more. Thus, I would not expect a constant weighting scheme to exist for the entire century and a half; or put another way, I would expect that a single regression equation for the entire period would produce large residuals.
Analysis of covariance was used to test whether a single weighting scheme was appropriate. Diplomatic importance was the outcome variable, the six capability scores were the covariates, and time identified the categories within which the regressions were performed. Since for any given year, the number of covariates might be larger than the number of observations
(nations), approximate ten-year time slices were used. This produced, on the average, sixty observations per period and afforded sufficient degrees of freedom. F-tests showed that, for any combination of sequential ten-year time slices, within category regressions were significantly different from an over• all category regression. This should be interpreted as saying 54
that the contribution of the six capability indicators varies
sufficiently across ten-year time slices to make any single
regression equation for the entire 151-year period inappropriate.
The mean within category multiple R was .9 (i.e., capability is
an excellent predictor of importance within each of the time
slices) and, as would be expected, labor-intensive indicators contributed less and capital-intensive indicators more as time
progressed. Ordinary least-squares (OLS) regression, within each of the time slices, proved appropriate, as the residuals were homogeneously distributed. Regressions were performed on standard scores, thus producing residuals that are comparable across time.
To again reiterate what we have done. We have regressed, within approximate ten-year time slices, the diplomatic
importance scores (that we derived in the preceding section) on the capability scores that we had earlier developed. The residuals from these regressions, i.e., the difference between the diplomatic importance scores (that we derived in the preceding section) and the scores that we now predict upon the basis of these regressions, are the status inconsistency scores for the states. Below are found the inconsistency scores for the major powers, approximately every tenth year. Negative values denote underrecognition, and positive values indicate ovorrecognition. 55
1824 lj 833 1844 1854 1864 lj 374 1884 1894
UK 0. 62 -0.38 0..0 3 -0. ,36 -0. ,68 -0 .29 -0.10 0.10
FRN -0. 69 0 .36 -0. .15 1 .2. 5 0. 60 0 .80 0.41 0.04
GMY -0. 33 -0 .02 0..1 5 0. 57 -0. 36 -0 .13 0.83 0.46
A-H 0. 50 -0 .13 0..1 9 0. 10 0. 76 0,. 18 0.17 -0.29
ITA 0. 18 0 .05 -0.68 -0.21
USR -0. 09 0 .18 -0. ,22 -1. 55 -0. 50 -0 .62 -0.63 -0.10
1904 1913 1925 1935 1950 I960 1970
USA 0. 17 0 .15 -0. 23 0. 10 -0. 08 -0, .06 0.24
UK -0. 03 -0 .31 -0. 06 0. 24 0. 52 0,.0 5 0.28
FRN -0. 08 0 .19 -0. 05 0. 31 -0. 51 0..0 7 -0.01
GMY 0. 17 -0, .17 0. 12 -0. 39
A-H -0. 10 0,.6 0
ITA -0. 00 0,.2 0 0. 34 0. 19
USR -0. 44 -0, .36 0. 15 0. 10 0. 37 -0. .30 0.38
CHN -0. 30 0..0 9 -0.57
JPN 0. 31 -0. .29 -0. 26 -0. 56 0..1 5 -0.32
Some patterns in the data are revealed by the status inconsistency scores. The United States, which is as often under- as overrecognized, is most underrecognized during her period of self-imposed isolationism following World War One;
Britain is more often than not underrecognized prior to 1935, but, as would be expected, is overrecognized following the
Second World War; and France, which tends to be overrecognized, 56
is just that preceding both world wars. As the historians tell
us, Prussia/Germany is underrecognized during her period of
unification (1864-1870) and preceding the two world wars; whereas both Austria-Hungary and Italy tend to be overrecognized, especially before the world wars. Russia/Soviet Union is primarily an underrecognized state, her periods of maximum overrecognition occurring after the Second World War; and although somewhat, concealed in the table above, China is underrecognized during only about half of her major power years.
Finally, as we would expect, Japan is by and large underrecognized, being most status inconsistent just prior to World War Two.
The Intervening Variables: Physical, Psychological, and Structural
Having constructed an index of status inconsistency and derived appropriate scores, we turn next to the intervening variables. In Chapter One I posited that several factors might serve to constrain status inconsistent states from becoming involved in military confrontations and, subsequently, wars.
These were contiguity, power capability, prior war experience, cross-cutting bonds, and polarity.
"Reachability"
The first two of these factors concern whether a dissatisfied state can "reach" another state militarily. The indicator of contiguity is the easier of the two to describe.
I have determined the contiguities for all states since 1816 57
and, for the purpose of this study, will say that two states are
contiguous to one another if their land frontiers intersect at
any point or they are separated by not more than six nautical
miles of water. Six nautical miles was selected because it
is the maximum distance between states that still permits the
intersection of territorial waters, given the three-mile limit
that (at least until very recently) has been generally accepted
in international law. Thus, the indicator of contiguity is a
simple dichotomy; states either share common land or water
frontiers, or they do not.
The second "reachability" factor is power capability.
The procedure for constructing an index for this variable is a
bit more complex than the one employed in the preceding
paragraph. The reader will recall that we have already derived
six transformed indicators of power, but have no a priori
weighting scheme for combining them. With no information,
the best single index of a state's power would be its mean on
the six indicators, i.e., equal weight for each. But as I have mentioned previously, I would suspect that the indicators do not
contribute equally to the formation of national power and that
the contribution from each indicator varies across time. This
suggests a factor analytic approach 1 to index construction
similar to Ferris' (1973).
1 For an introduction to factor analysis see Cooley and Lohnes (1962), Harman (1967), Overall and Klett (1972), and Rummel (1967, 1970). 58
If our six capability indicators all tap some facet of
power, then the degree to which they are intercorrelated
identifies a common latent structure. A one-iteration
principal-axis factor analysis (in this case, using the
correlation matrix of the indicators with 1.0 along the main
diagonal) produces a principal component solution. This
solution maximizes the amount of variance in the latent structure
accounted for by each independent factor, where each factor
accounts for less variance than the one that precedes it. Thus,
the first factor accounts for the most variance in the inter-
correlation (or variance-covariance) matrix of the capability
indicators. This first factor is, in a sense, what we mean
by "power" since it pulls from the correlation matrix the maximum variance that our indicators have in common.
It is not the more frequently reported factor loadings
(the squares of which are the amounts of variance in the variables
that are accounted for by the factors) that now interest us, but rather the factor scores. Each factor is a linear combination of the variables analyzed. Factor score coefficients are weights that represent the relative contribution of each variable to the construction of that factor. Factor scores are no more than the original data for the variables, weighted by the appropriate coefficients. The factor scores from the first factor of the principal component solution are the "best" linear combination of each state's six capability indicators. 59
1 subjected each state's CM-III transformed capability
indicators"' to a principal component analysis, employing the
same thirteen time slices used when computing status inconsistency.
The mean variance accounted for by the first factor was 64.1
percent. Factor score coefficients showed that, across time,
labor-intensive variables contributed less and capital-intensive
variables more to the composition of the national power index.
The factor scores correlated .97 (Pearson r) with the mean CM-III
transformed scores, despite the fact that factor score coefficients
were different for each variable and changed over time, indicating
that the major powers are highly ordered on a capability dimension.
Prior War Experience
A second category of intervening variables is prior war
experience. Here we are interested in whether participation in
a previous war may constrain a state's behavior in subsequent
conflict situations.
1 The effect of using the transformed scores is to produce a maximal non-metric principal component analysis for the original capability scores. 2 The regression coefficients from diplomatic importance on capability were not the same as the factor score coefficients. Indeed, a regression of diplomatic importance on the principal component factor scores did not produce a particularly high R . Thus, the linear combination of capability scores that maximizes the amount of total variance among these scores that can be accounted for by a single dimension is not the same as the combination that best predicts diplomatic importance. 60
Psychologists interested in learning behavior have
concentrated mainly on short-term memory processes (Kintsch, 1970;
McGeoch, 1952; Melton, 1964). What literature does exist on
long-term memory decay (Wickelgren, 1972) appears, in general,
to support Jost's (1897, p. 472) second law: "Given two
associations of the same strength, but of different ages, the older falls off less rapidly in a given length of time."
Hovland (1951), however, reports that there is evidence that
retention remains high for a short period before decaying.
This suggests, to me, the S-shaped logistic curve that has been found to accurately depict scores of biological, economic, sociological, and historical phenomena (Bailey, 1967, pp. 16-18;
Coleman, 1964, pp. 41-46; Hart, 1945; Lotka, 1956, pp. 64-76;
Taagepera, 1968). Lotka posits that these logistic regularities may be associated with processes in which the substance or structure itself acts as the nucleus for its own growth or decomposition, e.g., cell reproduction and decay. The decay of long-term memory would seem to be such a process.
There is little literature in political science or history to help us decide what length of time would be necessary for a people to "forget" a war experience, but most historians would suggest about ten to twenty years 1—the length of time associated
1 This estimate derives from several informative conversations with Melvin Small. He suggested that an indicator of the length of time necessary for forgetting may be the number of years that must pass before one's enemies in the last war can 61
with the rise of new "political generations." If, for our
purposes, we view "national forgetting" as the erosion of memory
concerned with the sacrifices of war, we can see that this
involves more than the fading of images held by older national
leaders; it also involves the evolution of a new "political
generation" which views the past wars as history, not personal
experience. Taking this into consideration, I have constructed
a fifteen-year decay function based on a simple logistic
forgetting model. I assume that the greater the battle deaths
proportionate to population suffered by nations at war, the more
punishing is the experience (cf. Richardson, 1960b, p. 298; Rosen,
1971, 1972). With the passing of time, battle deaths per million
population are discounted according to the (inverse) logistic
function pt= ^3(t 7.5) , where pt is punishment as perceived
t years after the war's end and max is the upper limit of the
logistic curve."' There is an inflection point at 7.5 years, and
the curve approaches zero after fifteen years.
The decay function is assumed to be the same for all wars,
all periods, and all nations. This is obviously an over•
simplification, but it is not unrealistic. Using a universal
be viewed as comic, rather than sinister, figures—the fourteen years before the production of the television program "Hogan's Heroes" perhaps being a case in point.
1 In order for punishment at time zero (p0) to equal battle deaths per million population at a war's end, max is set to ^ , -3.75) .
pn (1 + e 62
decay function for all wars does not mean that the punishment
brought about by large wars is the "same" as that caused by
smaller wars. It is quite possible (and, in fact, occurs on
numerous occasions) that the punishment score from a sizable war is greater, even after fifteen years of decaying, than
that for a small war before decay. Nor is it an unreasonable
strategy to use a universal decay function for all time periods, despite the possibility that the forgetting function might have been different when the means of communication were less developed and the dissemination of information less widespread, because weapons technology was also less sophisticated and major power wars generally less deadly. And for less deadly wars, the form of the decay function is not of as great importance, since the punishment scores are already nearer to zero. Finally, using a universal decay function for all nations is not untenable; the assumption of significant national differences in forgetting has no more support than the assumption of national similarities, and the latter has, at least, the merit of parsimony.
Inter-state Structure
The third set of intervening variables concerns the cross-pressures that result from the interrelationships among
the states. One variable that is purported to produce cross- pressure is labeled cross-cutting bonds. Here we determine whether a state is bonded to the same or to different states 63
on relevant dimensions. I have selected two important dimensions of national interdependence: the military and the economic. These two dimensions are highly visible, they lie at the heart of
international interactions, and they are major facets in the credibility of national commitments (Russett, 1963). For each major power, I construct an annual "trade x alliance" contingency table, representing its principal trade partnerships'' and formal military alliances with the other major powers,
principal trade partner
yes no
yes formal alliance partner no
and compute a Kendall's tau-b statistic. The resulting tau-b
Principal trade partners are those that account for a minimum of five percent of the total value of a state's imports and exports (cf. Wallensteen, 1973, p. 67). Trade data could not be obtained for the first sixty years of this study; the cross- cutting index is, thus, constructed for only the 1879-1970 period. And, because of the immensity and condition of the trade data set, the index was computed every fifth year, other years being interpolated. The relative stability of both alliance and trade partnerships, however, suggests that little information is lost when interpolating. 2 Kendall's tau-b, when applied to four-fold tables, is equivalent to a 0 or a Pearson r. It can be viewed as a measure of the independence of cells, reaching its maximum only under conditions of strong monotonicity. The statistic is undefined if a row or column of the 2x2 table is empty, that is, if a state does not have a major power ally or trade partner. In order 64
(with its sign reversed) is the measure of cross-cutting. A
completely "cross-cut" state would be one that is "bonded" to
all states in the subsystem, but is neither allied to any of
its principal trading partners nor trades principally with any
of its allies. Its "trade x alliance" matrix would have empty cells along the major diagonal, i.e., in the upper-left (a) and lower-right (d) corners. This would produce a score of +1.0.
A state that is not at all cross-cut would be one that is not allied to all subsystem members, but has a military alliance with all of its principal trade partners. Such a state would have empty cells along the minor diagonal (i.e., cells b and c) of its
"trade x alliance" matrix, producing a score of -1.0.
The second intervening structural variable is bi-polarity, for which I adopt a measure proposed by Singer and Small (1968).
The measure is computed solely on the basis of those military defense pacts among major powers that are aimed against other major powers. 1 The resulting index reflects the degree of freedom that major powers have to form new, logically-consistent alliances in a given year. Let us take an example. Assume that there are five states in a system, divided into two opposing dyads (A-B and to compute tau-b, 0.01 was added to each of the four cells of the matrix whenever such a situation arose. For the rationale behind the selection of "measures of association" for 2x2 contingency tables, see Weisberg (1974).
1 I have used Melvin Small's (Correlates of War Project) coding of the targets of alliances (Singer and Small, 1968). 65
C-D) and one unaligned state (E). How much freedom exists in this
system to form new, logically-consistent alliance dyads? D is
already allied to C and cannot logically ally with its enemies
A and B. The same reasoning holds for A, B, and C. Only E is
free to form alliances, and it can form the dyads A-E and B-E
or C-E and D-E, but no others. Hence, of the maximum number of
ten dyads ( N(N-1 )/2 dyads ) that might be formed in a system of
five states, only two alliance choices remain unmade. The system
is, thus, nearly bi-polar—8/10 of its possible dyads having been
used up—and we assign this configuration a score of 0.8. The
index ranges from 1.0 for perfect bi-polarity to 0.0 for total non-alignment in the system."'
The Outcome Variable: Interstate Military Conflict
We turn now to the outcome variable, major power interstate military conflict, defined as an explicit threat or use of military force by a major power, against a member of the interstate system.
Appendix C contains a detailed description of the data collection procedure, as well as a listing of the conflict cases; below, I shall merely present the categories into which the conflicts have been divided.
1 An alliance of all the major powers, i.e., uni-polarity (as occurs in 1820), results in a score of 0.0, since there are no major power targets for the alliance. Thus, the bi-polarity index equates an alliance of "all against none" with "no alliance," there being in both instances no opposing alignments of major powers. 66
In the current investigation, military conflicts are
classified under two broad headings: (1) interstate military
confrontations and (2) interstate wars. Interstatemilitary confrontations (or IMCs) encompass two levels of military
conflict:
interstate threat: explicit verbal statement, by a high official on behalf of a member state's government, declaring an intent to use military force against another member state for other than strictly defensive purposes; or, overt mobilization of armed forces by a member state, for other than strictly defensive purposes, during periods of dispute or high tension. In case of either verbal statement or mobilization, the target state must be clearly specified or easily identifiable. And,
interstate military action: the use of armed forces by a member state, directed against the territory and people of another member state; or, combat between armed forces, involving at least one member of the interstate system on each side. Military action taken by one state (e.g., the seizure of land or blockading of territory) may fail to provoke the target state into military action; if the target state remains passive, the confrontation is labeled "unreciprocated military action." Military action may, however, provoke the target state to engage the first actor in military combat; so long as the subsequent combat results in fewer than one thousand battle- connected deaths to the armed forces and/or lasts for less than twenty-four hours, it is labeled "hostilities."
Sometimes, however, hostilities escalate to higher levels of military violence. These sustained, more violent military conflicts
are classified as interstate wars, and defined as:
combat between armed forces, involving at least one member of the interstate system on each side, resulting in a total of one thousand or more battle-connected deaths to the armed forces, and lasting for more than twenty-four hours. 67
Thus, interstate threat is distinguished from interstate military
action in that the former does not involve the actual use of
military force. And interstate military action is, in turn,
distinguished from interstate war in that the latter, but not
the former, results in a total of one thousand or more battle
fatalities (while lasting more than twenty-four hours).
The outcome variable in the subsequent analyses will be
the incidence of either interstate military confrontation
(Chapters Three and Four) or interstate war (Chapter Five).
As has already been stated, status inconsistency is hypothesized
to make states prone to involvement in military confrontation;
but, once such a confrontation occurs, a different set of
dynamics is involved and the likelihood of war is hypothesized
to be associated with properties more immediate to the conflict
itself—properties that are most fruitfully sought in the
dyadic relationship of the specific protagonists to the conflict.
The investigations that follow focus upon only interstate
conflicts. Such extra-systemic combat as imperial or colonial
wars are omitted, as are internationalized civil conflicts and
purely internal civil wars. At times, some parties to the conflicts
under examination will be designated "initiators." This is not
meant to assess blame, but rather to identify the state that
first makes serious threats or first attacks in strength its opponent's armies or territory. 68
Analyzing the Data
The specification of the spatial-temporal domain, the
generation of data, and the construction of indicators are necessary elements of any research endeavor, but they are
hardly sufficient. The final and critical element is that of data analysis and interpretation. In the current investigation, we seek to discover whether underrecognized states, dissatisfied with their position in the extant interstate order, try to increase their attributed importance by bellicosely demonstrating their power capabilities. We also wish to determine the conditions under which the resulting interstate military confrontations (IMCs) escalate into war. The data analyses are divided into two sections.
In the first (Chapters Three and Four), nation-states are the units of analysis; in the second (Chapter Five), military confrontations. The research design will incorporate both cross- sectional and longitudinal aspects. For instance, we will be asking whether "in those years in which confrontations involving major powers begin, are the major powers that enter and/or initiate the confrontations more status inconsistent than those that do not?" Yet, at the same time, we will be explicitly lagging the status inconsistency variable in order to ascertain whether or not a period of time must elapse before status inconsistency is manifested as aggressive behavior.
The principal means of analyzing the data will be contingency table, biserial correlation, and probit analysis. 69
Contingency table analysis seeks to determine whether the distribution of data into categories defined by the intersection of two nominal- or ordinal-scaled variables is likely to have occurred by chance. Of the variety of coefficients available to measure the statistical relationship within a contingency table (Weisberg, 1974), we will rely heavily upon Yule's Q and Cramer's 0.
Biserial correlations (McNemar, 1969) are approximations of Pearson product moment coefficients, employed when we wish to ascertain the association between two variables, one of which is measured on a continuous scale and the other as a dichotomy.
The use of the biserial coefficient, rb, assumes that underlying the dichotomy is a normally-distributed continuous variable.
Thus, in the case of our analyses, we are positing that conflict is a matter of degree, and that the categorization (IMC/no IMC) of the outcome variable is a result of our definition or measurement procedure rather than being inherent in the concept.
Finally, probit analysis is one of a family of algorithms designed for regression on n-chotomous outcome variables.1 In the
1 Although probit analysis (Finney, 1971; Frank, 1971; McKelvey and Zavoina, forthcoming; Tobin, 1955; Zavoina and McKelvey, 1969) is the most widely used of these techniques, it has been shown, both empirically and mathematically (Mokken, 1971, pp. 105-11), that the simpler logistic probability distribution (logit) affords an excellent approximation of the cumulative normal distribution (probit) and is, for almost all purposes, indistinguishable. For a discussion of this and an introduction to logit, see Berkson (1944, 1946, 1949, 1953, 1955), DuMouchel (1974), Grizzle (1971), and Theil (1967, 70
normal regression situation, there is a continuous outcome
variable, Y, which is to be predicted by a given set of
variables, X1 . . . X n , where the underlying assumption is
that Y is approximately normally distributed in the sub- populations defined by the values of X1 . . . Xn . If, however, as in the present study, the outcome variable is dichotomous
(IMC/no IMC), then the least-squares assumption — that the error term be normally distributed about the regression line with mean zero and constant variance — is violated. But suppose, as was done in the preceding paragraph, that the dichotomous nature of the outcome variable results from measurement or definitional inadequacies, i.e., there is a continuous underlying conflict scale of which our categories are merely an imperfect reflection. What we wish to determine, for all values of X1 . . . Xn , are the probabilities of a particular case being in each of our two Y-categories.
Let us assume that there exists an index I = z3 Q+/41 ^1+/*2^2+
+ . . . ^>n^n that is the probability that a major power will engage in an interstate military confrontation. When I is small, the likelihood of IMC is low; when large, the likelihood is high.
Let us also assume that for each major power there exists a critical value of this index, denoted I . If the value of I for a given major power is less than the critical value I ,
1972). For a comparison of probit with regression and discriminant function analysis, see Aldrich and Cnudde (1975). 71
then that major power does not engage in interstate military
confrontation (Y = 0); if it is equal to or exceeds the critical
value, then the major power engages in IMC (Y = 1). That is,
0 if KI*
1 if I">I
Over the population of major powers, the I s are assumed to be
normally distributed with mean zero and standard deviation one.
Then, for a major power, the probability that, given I, Y = 0 is
/° 2
Pr(Y = o|l) = Pr(I -Mh2'2 dt (2) V2TT = 9(-I) (3) where 9(-l) is the cumulative standard normal distribution = 1 - 0(1) (4) = l - 9to0+/a1x1+/32x2+...-»anxn) (5) Conversely, the probability that Y = 1 is Pr(Y=l|l) - 1 - Pr(Y=0|I) = 9(1) (1) = 9(^0+/S1X1+/i2X2+...+/2nXn) (2) 72 The similarity to the multiple regression equation is apparent. As in ordinary regression, we wish to estimate the parameters ^O'^l'^' "* '^rf ^n iterative maximum-likelihood procedure A ^ A A is used to select those parameter estimators /3g ,/3, ... ^ that make the probabi1i ty of Y=0 large for cases in which Y = 0 and the probability of Y=1 large for cases in which Y = 1. These coefficients are related to the probability that unit changes in the predictor variables will alter the category assignment of the outcome variable, and have a "slope" interpretation analogous to regression coefficients. Having now completed the tedious, but necessary, process of specifying the spatial-temporal domain, describing the data set and the resulting indicators, and identifying the modes of analysis, let us put the model to the test. CHAPTER III FROM STATUS INCONSISTENCY TO MILITARY CONFRONTATION: THE BIVARIATE RELATIONSHIP We begin with an examination of the first stage of the model. In this chapter we explore only the bivariate relationship between status inconsistency and conflict, introducing the intervening variables in the next chapter. The reader will recall that, in formulating the model, I hypothesized that underrecognized (i.e., status inconsistent) countries would be involved in interstate military confrontations more frequently than would be expected by chance. Diagrammatically, status interstate inconsistent + military states confrontations In the pages that follow we will examine the extent to which this proposition is supported by empirical evidence. Assuming Homogeneity among the Major Powers The most direct test of the posited association is to examine the entire 1820-1970 period without differentiating among 73 74 the major powers. As a first cut, we simply dichotomize the countries into two groups: those that are underrecognized in a given year and those that are not. Two contingency tables are constructed. The first table, labeled "participate," includes all major power involvements in interstate military confrontations; the second table, labeled "initiate," includes only those confrontations that are initiated by a major power, i.e., only those in which it is a major power that first makes serious threats or first attacks in strength its opponent's armies or territory. Since every war begins as a military confrontation — it is coded as a "war" only after it reaches one thousand battle fatalities — the IMC observations in the tables include threats, military actions, and "pre-war" combat. TABLE 1 STATUS INCONSISTENCY VS. INVOLVEMENT IN INTERSTATE MILITARY CONFRONTATIONS, 1820-1970 Participate Initiate no IMC IMC no IMC IMC over• 79.9%: 20.1% over• 81.9% 18.1% recognized (318) ( 80) 398 recognized (326) ( 72) under• 77.1% 22.9% under• 79.2% 20.8% recognized (326) ( 97) 423 recognized (335) ( 88) 644 177 621 160 Q = +.08 Q = +.09 75 We can see (from Table 1) that there is little association between being status inconsistent and participating in, or initiating, an interstate military confrontation. In any given year, those major powers that are underrecognized have only a slightly greater likelihood (22.9%) of participating in a confrontation than those major powers that are overrecognized (20.1%). Similarly, underrecognized states are only slightly more likely (20.8%' vs. 18.1%) to initiate a confrontation. The Yule's Q, a measure of the independence of cell frequencies, confirms this point. The Q can range from +1.00 (if no- over- recognized, or all underrecognized, states participate in or initiate IMCs) to -1.00 (if all overrecognized, or no under- recognized, states participate in or initiate them). A Q- coefficient having a value near zero, like the ones we obtain, shows that there is little relationship between status inconsistency and involvement in interstate confrontations during the 1820-1970 period when we do not (1) differentiate among the major powers, or (2) allow for time lags. Manipulating the Time Factor Let us maintain, for the moment, the assumption that all major powers respond similarly when they experience status inconsistency, and explore the possibility that time lags need to be introduced, i.e., that it requires a period of time before status inconsistency is manifested as aggressive behavior. The 76 reader will recall that the theoretical argument set out in Chapter One posits that aggressive behavior is an expression of dissatisfaction aimed at rectifying an existing arrangement. Because we are concerned with existing arrangements and because most of the military activities upon which we are focusing (e.g., threats and small-scale uses of force) require only a minimum of preparation, we need only explore rather brief time lags. In order to test for the possibility that some minimal period of time is necessary, I introduce one- to five-year lags. TABLE 2 Q-SCORES FROM CONTINGENCY TABLE ANALYSIS OF MAJOR POWER STATUS INCONSISTENCY VS. INVOLVEMENT IN INTERSTATE MILITARY CONFRONTATIONS, 1820-1970 (0NE- TO FIVE-YEAR TIME LAGS) 1*3. Participate Initiate 0 + .08 + .09 -1 + .06 + .05 -2 + .05 + .06 -3 + .01 + .03 -4 -.09 -.06 -5 -.11 -.05 As might be expected, Table 2 demonstrates that the already weak association between status inconsistency and interstate military confrontation diminishes with the passage of time, actually becoming negative as the lag is extended to four years. 77 Continuing to manipulate the time element, we explore the possibility of inter-century differences—a phenomenon that has recurred in several studies of interstate war (cf. Singer, 1972). If the association between status inconsistency and participation in, or initiation of, interstate confrontations is different in the nineteenth than in the twentieth century, we may be obscuring this important finding by simultaneously analyzing the entire 1820-1970 period. TABLE 3 STATUS INCONSISTENCY VS. INVOLVEMENT IN INTERSTATE MILITARY CONFRONTATIONS, BY CENTURY Parti ci pate Initiate (19th century) (19th century) no IMC IMC no IMC IMC over• 82.3% 17.7% over• 84.2% 15.8% recognized (172) ( 37) 209 recognized (176) ( 33) under• 78.9% 21.1% under• 80.6% 19.4% recognized (187) ( 50) 237 recognized (191) ( 46) 359 87 367 79 Participate Initiate (20th century) (20th century) no IMC IMC no IMC IMC over• 77.2% 22.8% over• 79.4% 20.6% recognized (146) ( 43) 189 recognized (150) ( 39) under• 74.7% 25.3% under- 77.4% 22.6% recognized (139) ( 47) 186 recognized (144) ( 42) 285 90 294 81 78 TABLE 4 Q-SCORES FROM CONTINGENCY TABLE ANALYSIS OF MAJOR POWER STATUS INCONSISTENCY VS. INVOLVEMENT IN INTERSTATE MILITARY CONFRONTATIONS, BY CENTURY (ONE- TO FIVE-YEAR TIME LAGS) Lag Participate Initiate total 19th 20th total 19th 20th period century century period century century 0 + .08 + .11 + .07 + .09 + .12 + .06 -1 + .06 + .00 + .14 + .05 + .01 + .11 -2 + .05 -.10 + .20 + .06 -.10 + .22 -3 + .01 -.05 + .09 + .03 -.05 + .11 -4 -.09 -.15 -.02 -.06 -.18 + .07 -5 -.11 -.05 -.15 -.05 + .02 -.12 The data presented in Table 3, and the corresponding Q-coefficients for these data (reported in the first row of Table 4), fail to support the supposition that we are masking inter-century differences. That is to say, dividing the cases by century (i.e., 1820-1899 and 1900-1970) does not substantially alter the relationship between status inconsistency and involvement in military confrontations. In both the nineteenth and twentieth centuries the status-conflict relationship is weak, but in the predicted direction. Introducing time lags for each century (Table 4) produces, as it did for the entire 1820-1970 period, a general pattern of diminishing association, although there is some meager evidence supporting a relationship 79 between status inconsistency and involvement in military confrontations when a two-year lag is introduced for the twentieth century. Altering the Level of Measurement To this point, we have merely manipulated the time element and have found little to suggest that, a significant bivariate relationship exists between status inconsistency and interstate military confrontation. But the apparent absence of such a relationship may well be a result of my decision to dichotomize the predictor variable. Another way that we might examine the data at hand is to alter the level of measurement of the predictor variable. For example, let us rank the major powers each year on status inconsistency, and then ask whether the highest ranking nations (regardless of their identities) are more often involved in confrontations than those nations that have lower ranks. The principal difference between this procedure and the one used in the preceding section is that status inconsistency is now being treated as an ordered variable, rather than a dichotomous one. Depending upon the time lags introduced in our data set, there are from 803 to 821 major power nation-years (where a nation-year equals one nation classified as a major power for one year) and from 177 to 188 conflict-years in which major powers become involved in interstate military confrontations. This gives 80 us, for instance, an expected frequency of .22 conflict-years per nation-year (i.e., 177/821) when no time lags are used, or approximately one conflict-year in every fifth nation-year. Thus, if there is no relationship between a nation's rank on status inconsistency and the frequency with which it becomes involved in interstate military confrontations, we would expect to observe at all ranks that states become involved in IMCs in approximately one of every five years that the rank is occupied by a major power. On the other hand, if status inconsistency is positively related to involvement in military confrontations, we should expect that the relative frequency of IMC involvement should be greatest for the highest ranking states, and decline as we move toward lower ranks. Looking at Tables 5 and 6, we can see that the status inconsistency argument does rather poorly in the individual centuries as well as in the overall period. Indeed, with longer lags there is a slight tendency for the more underrecognized states to be among the 1 east conflict prone countries. Only during the twentieth century with no lag or a lag of one year, do the number one-ranked states display the highest frequency of conflict involvement and, even then, the actual number of years of involvement turns out to be only one standard deviation above the mean for all the nations. Not only do Tables 5 and 6 fail to produce the monotonically decreasing frequency pattern that would characterize a perfect fit to the "underrecognition leads to conflict" hypothesis, but they clearly fail to even approximate this fit. 81 TABLE 5 OBSERVED AND EXPECTED FREQUENCY OF MAJOR POWER PARTICIPATION IN INTERSTATE MILITARY CONFRONTATIONS AT DIFFERENT RANKS OF STATUS INCONSISTENCY, FOR 1820-1970 AND BY CENTURY (ONE- TO THREE-YEAR TIME LAGS) Rank Total Period 19th Century 20th Century 0 -1 -2 -3 0 -1 -2 -3 0 -1 -2 -3 1 .22 .24 .22 .22 .18 .18 .17 .19 .29 .32 .29 .25 2 .22 .21 .22 .21 .23 .18 .19 .18 .22 .25 .25 .25 3 .19 .24 .22 .26 .19 .23 .18 .19 .20 .25 .27 .36 4 .21 .27 .26 .23 .18 .25 .24 .19 .25 .29 .29 .27 5 .21 .21 .25 .24 .21 .15 .17 .22 .20 .29 .36 .27 6 .23 .23 .26 .23 Exp't .22 .23 .23 .23 .20 .20 .20 .20 .24 .27 .28 .27 Freq. TABLE 6 OBSERVED AND EXPECTED FREQUENCY OF MAJOR POWER INITIATION OF INTERSTATE MILITARY CONFRONTATIONS AT DIFFERENT RANKS OF STATUS INCONSISTENCY, FOR 1820-1970 AND BY CENTURY (ONE- TO THREE-YEAR TIME LAGS) Rank Total Period 19th Century 20th Century 0 -1 -2 -3 0 -1 -2 -3 0 -1 -2 -3 1 .21 .22 .18 .18 .16 .16 .15 .17 .27 .29 .22 .19 2 .19 .17 .19 .19 .20 .15 .15 .17 .19 .20 .24 .22 3 .18 .22 .19 .22 .19 .23 .18 .18 .17 .20 .20 .27 4 .18 .22 .25 .19 .15 .22 .23 .17 .22 .24 .27 .22 5 .18 .19 .20 .22 .18 .14 .14 .21 .18 .25 .29 .25 6 .22 .19 .24 .21 Exp't .19 .20 .20 .20 .18 .18 .18 .18 .22 .23 .23 .22 Freq. 82 Moving to a still higher level of measurement, i.e., treating status inconsistency as a continuous variable, still does not alter the pattern of our findings. To see this, we need only view the results of analyses of variance and biserial correlations presented in Table 7. TABLE 7 ANALYSES OF VARIANCE AND BISERIAL CORRELATIONS OF STATUS INCONSISTENCY VS. INTERSTATE MILITARY CONFRONTATIONS, FOR 1820-1970 AND BY CENTURY (ONE- TO THREE-YEAR TIME LAGS) Participate Lag Total Period 19th Century 20th Century r2 b r b N eta N eta2 N r eta2 rb rb rb 0 821 + .00 .00 446 + .00 .00 375 + .00 .00 -1 815 + .00 .00 438 + .00 .00 377 + .00 .00 -2 809 + .00 .00 431 - .00 .00 378 + .01 .00 -3 802 + .00 .00 424 - .00 .00 378 + .00 .00 Initiate Lag Total Period 19th Century 20th Century 2 f 2 2 2 . 2 N r, eta N eta N eta r rb r r b b b b 0 821 + .00 .00 446 + .00 .00 375 + .00 .00 -1 815 + .00 .00 438 + .00 .00 377 + .00 .00 -2 809 + .00 .00 431 - .00 .00 378 .01 .00 -3 802 + .00 .00 424 - .00 .00 378 .00 .00 NOTE: Status inconsistency scores were multiplied by -1.0 so that positive rbs denote that inconsistency is associated with more IMC involvement. 83 The weakness of the bivariate relationship between status inconsistency and involvement in military confrontations (both participation and initiation), during the entire period and for the individual centuries, is amply demonstrated. While we do find that underrecognition generally covaries (rb) positively with IMC involvement, the squares of both the biserial correlation coefficients (interpreted as approximations of the amount of variance in IMC involvement accounted for by the variance in the status inconsistency scores) and the etas (interpreted as the degree to which the status inconsistency scores for states that become involved in confrontations are distinct from the scores for states that do not become involved) are, for all practical purposes, zero. Examining the Individual States The major conclusion that can be drawn from all the foregoing analyses is that for the major powers as a whole —regardless of whether we look at the entire period or the individual centuries, and irrespective of time lags, levels of measurement, or analytic technique—only a tenuous bivariate relationship exists between status inconsistency and participation in, or initiation of, interstate military confrontations. This conclusion is predicated upon an original assumption that all major powers react similarly to being status inconsistent—and if this assumption is correct, we might better expend our energy examining other variables. It may be, however, that by "lumping" 84 together the data for all states we are masking substantial national differences. And, indeed, this turns out to be the case. For example, if we dichotomize the predictor variable and look at individual countries, we find that the United States is considerably more likely to become engaged in military confrontations during those twentieth century years in which it is underrecognized, while Germany in the nineteenth century is somewhat more inclined to initiate confrontations when it is overrecognized. TABLE 8 AMERICAN AND GERMAN STATUS INCONSISTENCY VS. INVOLVEMENT IN INTERSTATE MILITARY CONFRONTATIONS Participate Initiate (USA 20th Century) (GMY 19th Century) no IMC IMC no IMC IMC over• 97.1% 2.9% over• 87.5% 12.5% recognized ( 34) (1) 35 recogni zed ( 28) ( 4) under- 70.8% 29.2% under• 91.7% 8.3% recognized ( 17) ( 7) 24 recognized ( 44) ( 4) 51 8 72 8 Q = +.87 Q = --.22 In Table 9, I present the Q-coefficients for all nine major powers, examining the individual centuries as well as the entire 1820-1970 period, and introducing one- to three- year time lags. 85 TABLE 9 Q-SCORES FROM CONTINGENCY TABLE ANALYSIS OF EACH MAJOR POWER'S STATUS INCONSISTENCY VS. INVOLVEMENT IN INTERSTATE MILITARY CONFRONTATIONS, FOR 1820-1970 PERIOD AND BY CENTURY (ONE- TO THREE-YEAR TIME LAGS) Nation Lag Participate Initiate total 19th 20th total 19th 20th period century century period century century USA N=60 N=59 N=60 N = 59 *** ** 0 + .86 + .87 + .83 + .84 *• • -1 + .53 + .46 + .67 + .62 -2 + .16 + .18 + .31 + .33 -3 + .01 + .04 + .30 + .32 UK N = 139 N=80 N = 59 N = 139 N = 80 N = 59 0 + .08 + .20 -.04 + .18 + .22 + .16 -1 + .15 -.08 + .55 -.08 -.22 + .21 ** ** -2 + .21 -.07 + .54 + .24 -.07 + .56 • -3 + .06 -.09 + .44 + .03 -.08 + .34 FRN N=139 N=80 N = 59 N = 139 N = 80 N = 59 0 + .06 + .30 -.16 + .10 + .40* -.27 -1 -.08 + .20 -.45 + .11 + .30 -.12 -2 + .01 + .09 -.03 + .04 + .05 + .18 -3 + .05 + .12 + .10 + .08 + .08 + .40 86 TABLE 9---Continued Nation Lag Participate Initiate total 19th 20th total 19th 20th period century century period century century GMY N-108 N = 80 N = 28 N = 108 N=80 N=28 0 -.07 .00 -.31 -.21 -.22 -.31 -1 + .05 -.02 + .39 -.07 -.24 + .39 -2 -.09 -.26 + .39 -.22 -.50 + .39 -3 -.09 -.06 + .25 -.22 -.28 + .25 A-H N = 94 N--80 N = 14 N = 94 N=80 N = 14 0 -.07 -.08 -.30 -.25 -.18 -.64 -1 + .03 + .11 -.30 + .03 + .25 -.64 -2 + .03 -.06 -.30 + .03 + .07 -.64 -3 + .03 -.26 + .30 + .03 -.14 .00 ITA N=74 N = 40 N-34 N = 74 N = 40 N-34 0 -.03 + .63 -.20 -.03 + .63 -.20 -1 -.15 + .02 + .47 -.15 + .02 + .47 -2 -.42 -.45 + .33 -.37 -.45 + .40 ** -3 + .28 + .51 + .82* + .34 + .51 + .85 87 TABLE 9---Continued Nation Lag Parti ci pate Initiate total 19th 20th total 19th 20th period century century period century century USR N=136 N=80 N=56 N=136 N = 80 N = 56 ** 0 +.35 +.34 + .58 + .41 + .34 + .60 k k *** ** •k-kk -1 +.50 4.35 + .74 + .56 + .35 + .75 •kk u •kk •kk ** -2 +.49 +1.00 + .63 + .55 +1.00* + .62 -3 -.11 +.33 -.02 -.03 + .33 -.04 CHN N-21 N=21 N = 21 N = 21 0 + .43 + .43 + .43 + .43 -1 -.50 -.50 -.50 -.50 -2 + .02 + .02 + .02 + .02 -3 -.33 -.33 -.33 -.33 JPN N-50 N = 45 N = 50 N=45 0 -.35 -.41 -.35 -.41 -1 -.15 +1.00* -.24 +1.00* -2 -.05 +1.00^ -.13 + i .oo?i -3 + .43 +1.00* + .36 +1.00* NOTE : The N's for each country are those for zero time lag. # The Q i s equal to +1. 00 because the country is never involved in an IMC when it is overrecognized. However, Russia is overrecognized for only ten years during the nineteenth century and Japan for only four years during the twentieth. Fisher exact test: *** < .OK ** <" .05< * <".10 88 No clear pattern emerges from Table 9, although the status inconsistency argument appears to most applicable to the twentieth century (in general, with a one- or two-year lag) and to best fit the two superpowers — the United States and the Soviet Union. There does not seem to be much difference between the effect of status inconsistency on conflict participation and its effect on conflict initiation, but this is not surprising since major powers are coded as the initiators in eighty-six percent of their conflict involvements. Looking at the individual states: the United States' pattern has the strongest fit with the status inconsistency hypothesis, the relationship between inconsistency and military confrontation diminishing as time lags are introduced. The United Kingdom's experience does not support the hypothesis in the nineteenth century, but is representative of it when lags are introduced in the twentieth. There is a weak association between status inconsistency and military confrontation in the nineteenth century for France, but even that relationship does not hold in the twentieth century. Germany, somewhat like Britain, does not support the hypothesis in the nineteenth, but, with the introduction of time lags, produces a weak association in the twentieth. Austria-Hungary appears not to support the hypothesis in either century; and Italy curiously may support it in the nineteenth when there is no time lag, and also in the twentieth when lags are introduced. Russia supports the hypothesis 89 in both centuries, though more strongly in the twentieth; and China and Japan produce very unstable coefficients due to the small number of observations in the former case and the infrequent incidence of "overrecognition" in the latter. In short, the bivariate relationship between inconsistency and military confrontation is not overwhelming, but there does appear to be some—and at times, a sizable—association for selected states. Other than the fact that the weakest fits are associated with the traditional powers of central Europe (France, Germany, and Austria-Hungary) and the strongest associations occur in the twentieth century, there does not seem to be any prominent rationale for the disparity in the findings . Interestingly, these country-specific findings are greatly diminished if status inconsistency is treated as a continuous variable. In Table 10, we can see that the associations for the United States, Britain, Italy, and the Soviet Union largely vanish, making it quite visible that the "interval-level" correlation coefficients reflect significantly less powerful relationships between status inconsistency and involvement in confrontations than were uncovered in the contingency table analysis. Fully cognizant of the fact that Q-scores tend to be larger than other contingency table coefficients, this finding nevertheless suggests an intriguing inference—simply stated, the association between underrecognition TABLE 10 ANALYSES OF VARIANCE AND BISERIAL CORRELATIONS OF EACH MAJOR POWER'S STATUS INCONSISTENCY VS INTERSTATE MILITARY CONFRONTATIONS, BY CENTURY (ONE- TO THREE-YEAR TIME LAGS) Nation Lag Participate Initiate 19th century 20th century 19th century 20th century 2 2 n 2 + 2 r, 2,2 2 2 N r, r, eta N r, r, eta N r^ r^ eta b b rb rb e b b USA 0 X X 59 + .13 .05* X X 59 + .13 .05* -1 X X 59 + X X 59 + .03 .01 -2 X X 58 + X X 58 + .03 .01 -3 X X 57 - .01 X X 57 + .02 .01 UK 0 80 + .02 .01 59 + .01 80 + .02 .01 59 + .01 .01 -1 79 + 59 + .03 .01 79 + 59 + -2 78 + 59 + .04 .02 78 - 59 + .02 .01 -3 77 + 59 + .12 .07** 77 + 59 + .05 .02 FRN 0 80 + 59 - 80 + .01 59 - -1 79 + .03 .02 59 - 79 + .05 .03 59 + .06 .03 -2 78 + .01 .01 59 - 78 + .01 59 + -3 77 + .01 .01 58 - 77 + .01 58 + .04 .01 GMY 0 80 + 28 - .12 .07 80 - .02 .01 28 - .12 .07 -1 79 + 29 - .01 .01 79 - .03 .01 29 - .01 .01 -2 78 - .03 .01 30 - .05 .03 73 - .10 .94* 30 - .05 .03 -3 77 - .01 31 - .03 .02 77 - .05 .02 31 - .03 .02 TABLE 10---Continued Nation Lag Participate Initiate 19th century 20th century 19th century 20th century 2 2 2 2 2 2 N r, r, 2 eta^ N r, r, eta N r^ eta N r, r, eta b b b b b b A-H 0 80 - .01 14 - 80 - .02 .01 14 - .06 .04 -1 79 - 15 - .04 .03 79 + .01 15 - .18 .11 -2 78 - 16 + .06 .04 78 + 16 + .01 -3 77 - 17 + .06 .04 77 + 17 + ITA 0 40 + .13 .06 34 + 40 + .13 .06 34 + -1 39 - 35 + .01 39 - 35 + .01 -2 38 - .02 .01 36 + .08 .05 38 - .02 .01 36 + .07 .05 -3 37 + .03 .01 37 + .09 .06 37 + .03 .01 37 + .19 .11** USR 0 80 - 56 + .06 .03 80 - 56 + .04 .02 -1 79 - 56 + .10 .06* 79 - 56 + .06 .04 -2 78 + .02 .01 56 + .03 .02 78 + .02 .01 56 + .02 .01 -3 77 + 56 - .01 .01 77 + 56 - .02 .01 CHN 0 X X 21 + .03 .02 X X 21 + .03 .02 -1 X X 20 - .01 .01 X X 20 - .01 .01 -2 X X 19 + .01 .01 X X 19 + .01 .01 -3 X X 18 - .08 .05 X X 18 - .08 .05 TABLE 10---Continued Nation Lag Parti ci pate Initiate ...... T 19th century 20th century 19th century j 3th century 2 2 2 2 • + 2 N r, r, eta N r, rj~ eta^ eta | N rb eta b b b b rb JPN 0 X X 45 - .02 .01 X X 45 _ .02 .01 -1 X X 45 + .12 .06 X X 45 + .15 .06* -2 X X 45 + .02 .01 X X 45 + .03 .01 -3 X X 45 + .01 .01 X X 45 + .03 .01 NOTE: In this table, a "blank" designates a coefficient with a value of .00, while an "x" designates that a country is not a major power for a sufficient number of years during the nineteenth century for coefficients to be meaningful. In addition, status inconsistency scores have been multiplied by -1.0 so that positive rbs denote that inconsistency is associated with more IMC involvement. F-test from analysis of variance (anova): ** < .05< *<.10 significance level 93 and involvement in military confrontations may better be described as a threshold than as a continuous linear relationship. 1 ln the literature on status inconsistency among individuals, some sociologists (e.g., Treiman, 1966; Wesolowski, 1966) have speculated that, over time, individuals may come to accept their status discrepant positions. This suggests a parallel hypothesis that, over time, status inconsistent states may come to accept their situation. Put another way, the average level of status inconsistency may be internalized and serve as a benchmark against which to measure current position. The question then becomes whether a state is more likely to participate in, or initiate, a confrontation (not when it is underrecognized, but) when it is more status inconsistent than usual . In order to examine this question, I computed a ten-year moving average on status inconsistency for each major power, and then constructed a dichotomous indicator that had a value of "one" if a state's status inconsistency score for a given year was above its moving average for that year, and "zero" if it was below. By means of contingency tables, 1 compared this dichotomous "moving average" indicator with the dichotomous "absolute" measure of status inconsistency that has been used in all the preceding analyses. The Q-scores and Fisher exact tests comparing these two indicators with one another are reported below: Nation 19th Century 20th Century N Q-score Fisher N Q- score Fisher Exact Exact USA 33 + .96 .00 UK 71 + .96 .00 41 + .97 .00 FRN 71 + .69 .00 41 .77 .01 GMY 71 + .50 .03 19 + .71 .18 A-H 71 + .90 .00 14 + 1.0 0 .00 ITA 31 + .34 .28 25 + .73 .22 USR 71 + .92 .00 38 + .95 .00 CHN 12 4 .96 .01 JPN 21 + .79 .17 Surprisingly, the moving average indicator proved to be quite highly correlated with the absolute measure of status inconsistency. And, as a consequence, when the analyses (that have been reported in this chapter) were re-run, using the moving average indicator, the relationship between inconsistency and military confrontation did not materially alter. In subsequent chapters, therefore, only the absolute measure of status inconsistency is used. 94 Summarizing the Findings To summarize, we have found that the bivariate relationship between status inconsistency and major power involvement in military confrontations is, for all practical purposes, non• existent when we fail to differentiate among the countries in our spatial domain. Neither the introduction of time lags nor the division of the data set into separate centuries alters this result. However, by examining the relationship for the individual states, we discover that some major powers — in particular, the United States and the Soviet Union—are decidedly more prone to involvement in confrontations when under- rather than overrecognized. Several other states also exhibit this tendency, but not very strongly. By and large, the association between status inconsistency and participation in, or initiation of, interstate military confrontations is greater in the twentieth century than in the nineteenth. And finally, it appears that the relationship between underrecognition and military confrontation is most evident when the status inconsistency variable is treated dichotomously. CHAPTER IV FROM STATUS INCONSISTENCY TO MILITARY CONFRONTATION: INTRODUCING THE INTERVENING VARIABLES In an attempt to uncover a strong bivariate association between status inconsistency and interstate military confrontation (IMC), we introduced time lags and inter-century differences, altered the level of measurement of the predictor variable, and discarded the assumption of homogeneous behavior. However, the findings were less than dramatic. We did uncover a number of sizable relationships in the twentieth century, but even those states that exhibit the strongest association do not generally become involved in military confrontations when they are underrecognized. We may take the United States as an example (Table 8). Seven of the eight military confrontations in our data set in which the United States becomes involved during the twentieth century occur while she is status inconsistent. Yet, seventy percent of the time that she is underrecognized, the US is not involved in IMCs. This suggests that, at best, the posited bivariate association between underrecognition and military confrontation does not constitute a fully-specified model, i.e., 95 96 status inconsistency alone is inadequate to account for major power conflict involvement. This should not surprise us, for in the theoretical chapter I hypothesized that certain physical, psychological, and structural variables intervene between status inconsistency and aggressive behavior. Let us refresh our memories as to the nature of these variables and the predicted direction of their effect on the status-conflict relationship. I hypothesized that if a state is predisposed to involvement in military confrontations (i.e., it is underrecognized), an increase in its power capabilities will increase the likelihood that it will manifest aggressive behavior. On the other hand, I hypothesized that the more deadly and more recent a state's prior war experience, the lower the probability that it will desire to engage in a new altercation. Finally, I posited that a highly polarized major power system will decrease the opportunities for multiple competing affiliations and thereby increase the likelihood of confrontation; whereas, the more cross-cut a state's bonds, the more cross-pressured it will be and the less likely it will engage in aggressive behavior. Schematically, 97 status interstate inconsistent military states confrontations used only in 20th century runs (not measured prior to 1879) + = increases likelihood of IMC - = decreases likelihood of IMC Since we already have evidence to suggest the existence of inter-century differences, I shall examine the effects of the intervening variables on the status-conflict relationship separately for each of the centuries in our temporal domain. Biserial correlation and probit analysis will be used in this investigation. Before turning to the results, several caveats need to be made. First, although the model presented in Chapter One purports to "explain" only the behavior of underrecognized states, I nevertheless believe it inadequate to examine the effects of the intervening variables on merely that subset of cases. For example, if we were to discover that our model has high predictive power for the subset of underrecognized states, we would not know whether the goodness-of-fit (1) was associated with the fact that underrecognized states are prone to involvement in military confrontations or (2) was a general finding applicable to the major powers regardless of status inconsistency. If the latter 98 were true, i.e., the finding was not associated with status inconsistency, we could view the subset of underrecognized states simply as a random sample from a known population. A hypothesis congruent with the latter finding would be that major powers reside in an anarchistic world in which states are at all times prone to confrontations, subject to contextual constraints. In order to distinguish between the "status inconsistency" and "anarchy" hypotheses, we need to examine the effects of the intervening variables on both the subset of underrecognized states and on the entire population of cases, and determine whether the resulting coefficients for the former could have occurred by chance, given the descriptive parameters for the latter, i.e., could we have randomly selected a sample of cases with coefficients that are this different from the population parameters? A second caveat, concerning the relevance of significance levels and confidence intervals, follows from this first, point. Strictly speaking, neither tests of significance nor confidence intervals are appropriate when analyzing non-sampled data. In particular, the coefficients from analyses on a population of cases (e.g., all major power IMCs) are descriptive parameters and it is not meaningful to ask whether these coefficients could have occurred by chance. However, I report standard errors and significance levels for two reasons. One, whether the subset of underrecognized states is a population or a random sample is a 99 question that we hope to answer; and if it is the latter, then sample statistics are appropriate. Two, some readers may take the unorthodox view that, even for a population of cases, levels of significance are an aid for judging the stability of an estimate, especially if there is the possibility that one may subsequently sample from that population (Winch and Campbell, 1970). Both these reasons are given advisedly; ultimately, the reader must decide upon their soundness. A third caveat concerns the interpretation of probit coefficients. The coefficients represent the amount of change in the outcome variable (as "measured" on its hypothesized underlying scale) brought about by unit changes in the predictor variables or, analogously, the increment in the probability of the observed outcome variable being in a different response category, given unit changes in the predictor variables (McKelvey and Zavoina, forthcoming). The parallel to the interpretation of regression coefficients is evident. However, since regression coefficients represent the amount of change in the observed outcome variable brought about by unit changes in the predictor variables, the coefficients from probit and unstandardized regression are not directly comparable. Fourth, we know that regression coefficients may be transformed into beta weights by normalizing the coefficients by a ratio of standard deviations (sx/sy , where x is the predictor and y the outcome variable). In probit analysis, however, the 100 variance of the outcome variable on its underlying interval scale is not known. This is unimportant if we are merely interested in (1) predicting the probability of a particular outcome (in which case we would use the raw coefficients), or (2) determining the relative importance of predictors within a single equation (in which case we need only normalize for the variance in the predictor variables). However, if we wish to make comparisons across equations, we need to control for the possibility that the outcome variables will have different variances. I have used the square root of the "estimated total sum of squares divided by the degrees of freedom," i.e., y TSS/df , as an approximation of the standard deviation of the outcome variable (Iversen, 1971; McKelvey and Zavoina, forthcoming). The degree to which this may bias the estimate of b is not known. Nor is much known about the small sample properties of multivariate probit analysis. My own work suggests that, with dichotomous outcome variables, the probit coefficients are often considerably larger than regression coefficients estimated on the same data. The standard errors of the probit coefficients also tend to be larger. Comparisons of regression and probit on known distributions confirm these points and show that the probit coefficients more closely approximate the true parameters that generate the underlying distribution of the observed limited (non-continuous) outcome variable (Zechman, 1974). Finally, the reported R2 from probit analysis is only an estimate of the proportion of the variance in the hypothesized 101 underlying outcome variable accounted for by the predictors. Since probit uses a maximum likelihood rather than a least squares technique, the probit coefficients are those that maximize the probability of obtaining the values of the outcome variable, rather than minimizing squared deviations between predicted and observed values. Thus, the R2 must be estimated rather than directly derived and may tend to be slightly larger than the "true" value (McKelvey and Zavoina, forthcoming; Zechman, 1974). On the other hand, since regression tends to underestimate—and, at times, may greatly under• estimate— the true parameters of the predictor variables when the outcome variable is limited (non-continuous), the R2 from probit is likely to more accurately reflect the predictors' goodness-of-fit. Nevertheless, since the distribution of R2 is unknown, we should be careful not to over-interpret the statistic and perhaps may want to develop another measure based upon the predicted probabilities of the outcome variable derived from the raw probit coefficients. The simplest such measure, the percent of correct predictions, is unsatisfactory for our purposes. The reason is rather straightforward. If the model proves to be a poor prognosticator of interstate military conflicts, then the probit algorithm will continually predict approximately the mean of the outcome variable; and since military conflicts are relatively rare events, it will, in effect, repeatedly forecast "no conflict." 102 But "no conflict" will in all likelihood be a "correct" prediction, given the distribution of the outcome variable. Thus, the percent of correct predictions might well prove to be a highly-inflated summary measure. Rather than use such a statistic, I introduce a different, and rather conservative, measure of the probit equation's power to make point predictions. It is, of course, predicated upon the assumption that interstate conflict is a rare event and, that at any given point in time, one would be more often right to predict that a state would not become involved in a conflict than that it would. The new measure, the point predictive power of the equation, reflects the degree to which the model improves our predictions as against the null hypothesis that a state will not become involved in conflict.1 The rationale upon which the statistic rests is simple. When attempting to predict a state's involvement in conflict, one can make two types of errors. First, one can fail to predict conflict involvements that occur; and, second, one can predict conflict involvements that do not occur. We would say that we had a powerful predictor if we had a model that could minimize both types of errors. For example, if the model predicts eight of a nation's ten conflict involvements, we would say that it increases our predictive power by eighty 1 It is important to recognize that I am postulating a null hypothesis of no conflict involvement (rather than the known proportion of conflict involvements) and, hence, this is not a measure of the probability of correct prediction vis-a-vis actual involvements. 103 percent if and only if these eight occasions are the only times it predicts conflict. If, on the other hand, the model had predicted conflict involvement on forty occasions — only eight of which had been correct—the predictive power of the model would be only one-fifth as good as we originally had thought. Thus, instead of eighty percent predictive power, it would have sixteen percent predictive power. Point predictive power, P, is defined to be the proportion of conflict involvements correctly predicted, multiplied by the proportion of "conflict involvement" predictions that are correct. The statistic ranges from zero (no improvement over the null hypothesis) to one (perfect prediction). In calculating P, I have considered that if the predicted probability of the outcome variable derived from the raw probit coefficients is equal to or greater than .5, then this is a prediction that the state will become involved in conflict; if less than .5, it is a prediction that the state will not become involved. 1 1 If we used the actual proportion of conflict involvements, pa, as our HQ (rather than the hypothesis of no conflict involve• ment), we would want to subtract from P a factor, Pa, that reflects how many conflict involvements we would get if we randomly selected pa cases from our population. For example, it there are one hundred cases in our population—of which ten are conflict involvements — then we would expect that one-tenth of the cases drawn at random would be conflict involvements. Now, if we had an urn with one hundred balls (ninety white and ten red) and we selected randomly with replacement ten balls, we would expect to draw nine white balls and one red ball. Our correction factor Pa—to be subtracted from our predictive power P—would be .01; that is, the proportion of conflict involvements correctly predicted (one red ball of ten red balls in the urn) multiplied by the proportion of "conflict 104 The Nineteenth Century Analyzing the Data Let us now return to the findings. Table 11 displays the biserial correlations between each of the intervening variables and involvement in military confrontations during the nineteenth century. For each major power there are five rows of correlation coefficients. The first row corresponds to the "anarchy" hypothesis; it contains the direction of the associations (positive or negative) and the squares of the correlation coefficients (approximations of the amount of variance in a nation's IMC involvements accounted for by variance in each of the intervening variables) for all annual observations of a major power during the nineteenth century. For example, the first row of correlations in Table 11 tells us that Britain is in our data set for eighty years in the nineteenth century. During that time, increases in her capabilities (i.e., positivedelta power) , the severity of her prior war experience, and the polarity of the major power system all covary positively with her participation in interstate military confrontations, although war experience covaries negatively (though very weakly) with her initiation of confrontations. The only sizable associations are those of systemic polarity with IMC participation (.30) and IMC initiation (.23), though all but one involvement" predictions that are correct (one red ball out of ten balls drawn from the urn). P - Pa could attain negative values and would never reach 1.0 as long as there was one conflict involvement. TABLE 11 BISERIAL CORRELATIONS BETWEEN EACH MAJOR POWER'S INVOLVEMENT IN MILITARY CONFRONTATIONS AND THE THREE INTERVENING VARIABLES, FOR THE 19th CENTURY (ONE- TO THREE-YEAR TIME LAGS) Nation Lag N Parti ci pate Initiate Power War Exp. Polarity A Power War Exp. Polarity 2 2 2 2 2 2 Predicted ---> + + + - + rb - rb rb rb rb rb *** UK 0 80 + .01 + 00 + .30 + .03 - .00 + .23 0 42 + .04 - 00 + •<* + .07 - .02 + .10 under• -1 42 .06 .13 + + 01* + + .13 - .00 + .05 recognized -2 41 + .06 16* + .17** + .08 + .06 + .-3 41 + .05 + 02 + .29 + .09 - .00 + .23 FRN 0 80 + .00 _ 00 + .00 + .00 _ .01 + .00 ' 0 28 + •00* + 01 + .01 + •00* + .01 + .01 under• -1 27 + .22 + .00 + .01 + .22 + .00 + .01 recognized -2 26 + .11 + 14 + .16 + .12 + .01 + .21* .-3 25 + .27** + 13 + .03 + .36** + .00 + .05 GMY 0 80 - .00 - 00 + .14 - .01 - .01 + .31 0 48 - .07 - 01 + .28** - .19* - .02 + .73*** under• -1 48 - .06 - .00 + .15* - .12 - .00 + .43*** recognized -2 48 - .05 + 00 + .01 - .13 + .00 + .12 -3 48 - .05 - 00 + .08 - .13 - .00 + .29** TABLE 11-—Continued Nation Lag N Participate Initiate Delta Power War Exp. Polarity A Power War Exp. Polarity 2 2 2 2 2 2 Predicted »- bob + rb " rb + rb A-H 0 30 + .00 + .01 + .08* - .00 - .00 + .10* 0 25 + .00 - .09 + .04 - .00 - .07 + .19 under• -1 25 + .01 - .03 - .00 + .01 - .03 - .00 recognized -2 24 - .01 - .00 - .00 - .01 - .00 - .00 k-3 23 - .02 - .00 + .47 - .02 - .00 + .47** ITA 0 40 + .03 - .00 + .02 + .03 - .00 + .02 0 23 + .00 + .03 - .01 + .00 + .03 - .01 under• -1 23 + .07 + .00 + .04 + .07 + .00 + .04 recognized .-3 22 + .04 - .00 + .09 + .04 - .00 + .09 USR 0 80 + .03 - .05 + .04 + .03 - .05 + .04 0 70 + .03 - .04^ + .04 + .03 - .04 + .04 under• -1 69 + .07 - .12 + .01 + .07 - .12** + .01 recognized -2 68 + .02 - .05 + .03 + .02 - .05 + .03 -3 67 + .02 - .04 + .06 + .02 - .04 + .06 NOTE: Italy (with a lag of two years) engages in confrontation too infrequently when underrecognized to compute stable estimates. F-test from analysis of variance: *** < .01 < ** < .05 < * < .10 significance level 107 of her correlations (and that imperceptible) are in the direction predicted by the model. The next four rows correspond to the "status inconsistency" model; here we look only at the subset of cases in which the major power is underrecognized. The latter three rows (lags one, two, and three) examine the covariation between the intervening variables and involvement in interstate military confrontations one, two, and three years after the state is underrecognized. In other words, the constaining factors are always correlated with the IMC observation for the same year; what is lagged is the putative motivating force (i.e., status inconsistency). Thus, in the third row of correlations in Table 11 (UK, one-year lag), if Britain is underrecognized in 1822, 1827, etc., we wish to know whether the intervening variables are such as to permit or constrain confrontations one year later, i.e., 1823, 1828, etc. It appears from Table 11 that the intervening variables (in particular, polarity) are most efficacious for Britain and Germany. For these two states, polarity covaries positively and strongly with both participation and initiation of confrontations; particularly strong is the association with IMC initiation when Germany is underrecognized (no time lag). At the other extreme, Italy displays no sizable associations and Russia has only two moderate biserial correlations (war experience with IMC participa• tion, one-year lag, and IMC initiation, one-year lag). The associations for Austria-Hungary and France lie between those 108 for the first two states and those for the latter two states. For Austria-Hungary, polarity (three-year lag) covaries strongly with IMC involvement and, for France, there are several "respectable" biserial correlations (mostly involving increases in power) when lags are introduced. It may be noted that almost all sizable correlations are in the direction predicted by the model . Looking at the probit coefficients and measures of goodness-of-fit in Table 12, we find precisely the pattern of associations displayed in Table 11. There are very few sign reversals and those associations that previously produced strong biserials now show good "coefficient to error" ratios. This consistent pattern in the beta weights (which are partial coefficients "controlling" for the other intervening variables) is simply a reflection of the moderately low level of rnulti- collinearity in the nineteenth century among the physical, psychological, and structural variables used in the analysis. For the United Kingdom and Germany, for France with lags of one, two, and three years, and for Austria-Hungary with a three-year lag, the estimated multiple correlation coefficients (R ) and measures of point predictive power (P) are not inconsiderable. TABLE 12 STANDARDIZED PROBIT COEFFICIENTS FOR THE THREE INTERVENING VARIABLES WHEN PREDICTING EACH MAJOR POWER'S INVOLVEMENT IN MILITARY CONFRONTATIONS, FOR THE 19th CENTURY (ONE- TO THREE-YEAR TIME LAGS) Nation Lag N Participate Initiate *2 Delta Power War Exp. Polarity R2 P A Power War Exp. Polarity ___ R P Predicted »~ + - + + - + UK 0 80 11(15) 07(14) 49(14) 25 27 16(16) 00(15) 42(14) 20** 17 0 42 13(21) 03(20) 36(20) 16 25 24(22) -14(22) 23(21) 14 09 under- -1 42 25(22) 13(19) 33(21) 21 05 37(24) -03(20) 20(22) 17 13 recog. -2 41 28(24) 33(18) 34(20) 3C 23 32(26) 22(18) 28(21) 23 22 -3 41 26(21) 12(19) 51(21) 30 50 29(22) -01(20) 46(21) 27* 22 FRN 0 30 04(15) -01(15) 04(15) 00 00 05(16) -11(16) 03(15) 01 00 0 28 06(25) 10(24) 07(25) 02 00 06(25) 10(24) 07(25) 02 00 under- -1 27 62(30) -12(24) 28(24) 35 40 62(30) -12(24) 28(24) 35 40 recog. -2 26 40(28) 24(21) ^8(26) 39* 40 53(29) -03(23) 53(27) 42 43 _-3 25 66(32) 18(20) 36(26) 53 23 82(33) -16(25) 41(28) 61 46 GMY 0 80 -04(22) -04(19) 34(17) 12 00 -13(29) -09(21) 49(18) 27** 00 0 48 -34(47) -28(28) 38(21) 31* 22 -82(46) -30(25) 23(11) 88 56 under- -1 48 -47(48) -20(26) 32(20) '34 17 -85(43) -24(18) 31(13) 88*** 50 recog. -2 48 -51(49) 06(26) 10(23) 28 00. -88(46) -20(19) 22(13) 86* 33 -3 48 -47(42) -13(25) 23(19) 30 00 • -83(39) -23(19) 29(13) 85*** 50 TABLE 12 —Continued Nation Lag N Participate Initiate K2 i A2 A Power War Exp. Polarity R P A Power War Exp. Polarity R P Predicted *- + - + + - + i A-H . 0 80 03(17) 13(16) 27(16) 08 00 -02(19) 01(19) 23(16) 08 00 0 25 28(32) -93(117) -02(18) 76 00 14(49) -40(118) 36(39) 40 00 under- -1 25 22(37) -41(51) -18(31) 15 00 22(37) -41(51) -18(31) 15 00 recog. -2 24 -11(36) -03(36) -05(31) 01 00 -11(36) -03(36) -05(31) 01 00 _-3 23 03(27) 27(28) 98(46) 90 44 03(27) 27(28) 98(46) 90** 44 ITA 0 40 14(28) 16(34) 24(37) 06 00 14(28) 16(34) 24(37) 06 00 0 23 -01(31) 28(43) 14(44) 04 00 -01(31) 28(43) 14(44) 04 00 under- -1 23 19(32) 20(39) 36(49) 14 00 19(32) 20(39) 36(49) 14 00 recog. -3 22 -00(41) 19(33) 57(57) 23 00 -00(41) 19(33) 57(57) 23 00 USR 0 80 15(17) -24(17) 19(15) 13 00 15(17) -24(17) 19(15) 13 00 " 0 70 17(18) -19(18) 19(17) 11 00 17(18) -19(18) 19(17) 11 00 under- -1 69 28(17) -42(21) 09(15) 28** 00 28(17) -42(21) 09(15) 28** 00 recog. -2 68 14(18) -22(18) 17(17) 11 00 14(18) -22(18) 17(17) 11 00 -3 67 11(18) -19(19) 23(17) 11 00 11(18) -19(19) 23(17) 11 00 NOTE: All coefficients have been multiplied by one hundred so as to eliminate decimal points. Numbers within parentheses are standard errors. X* from probit {yj with 3 df): *** < .01 < ** < .05 < * < .10 significance level Ill Interpreting the Results These latest findings contrast rather sharply with the findings for the nineteenth century presented in Table 9. The reader will recall that the bivariate relationships between status inconsistency and military confrontations in the nineteenth century were not at all strong, and that Britain and Germany — the countries that now produce the strongest associations — earlier displayed the poorest fits to the "underrecognition leads to conflict" hypothesis. In light of our current results, two questions come readily to mind: (1) Is it possible that some significant bivariate relationships between status inconsistency and military confrontation were "masked" in our earlier analysis (Table 9)? And, (2) Do the associations that we now uncover, having introduced the intervening variables, apply to the total population of cases for a given state or do they hold only for the subset of underrecognized cases? If the former is true, then it is the intervening variables alone, and not status inconsistency, that are important. As for the first question, one manner by which the bivariate relationships might have been masked is that our intervening variables — changes in power, prior war experience, and polarity—may be correlated with status inconsistency such that the constraining effects of these variables occur 112 coterminously with underrecognition. That is to say, if it is often the case that, when a particular state is underrecognized, its capabilities are declining, its war experiences are devastating, and system polarity is low—i.e., status inconsistency is negatively correlated withDelta power, positively correlated with prior war experience, and negatively correlated with polarity—then we would not expect to find a strong positive relationship between status inconsistency and military confrontation. Point biserial correlations (McNemar, 1969) can be used to examine the relationship between each continuously- measured intervening variable and the dichotomized status inconsistency indicator. The use of point biserial correlations, rather than biserial, is appropriate if we wish to maintain the assumption (stated at the end of the preceding chapter) that the association between underrecognition and involvement in military confrontation may better be described as a threshold than as a continuous linear relationship, and is thus consonant with the strategy of grouping the underrecognized cases as distinct from the overrecognized ones—a strategy employed throughout the current chapter. We see from the point biserial correlations reported in Table 13 that for only one state—Germany—is the masking phenomenon likely. For nineteenth century Germany, both war experience and polarity are such as might constrain conflictive behavior when she is underrecognized (i.e., war experience is 113 TABLE 13 POINT BISERIAL CORRELATIONS BETWEEN EACH MAJOR POWER'S STATUS INCONSISTENCY SCORES AND THE THREE INTERVENING VARIABLES, FOR THE 19th CENTURY (ONE- TO THREE-YEAR TIME LAGS) Nation Lay N Status Inconsistency & Power War Exp. Polarity pb pb pb UK 0 80 +.16 +.08 +.02 -1 79 +.07 +.01 +.14 -2 78 -.22** +.03 +.16 -3 77 +.06 +.04 +.22* FRN 0 80 +.13 -.38*** -.14 -1 79 -.04 -.32 -.27** -2 78 -.01 -.32*** -.39*** -3 77 +.07 -.31*** -.45*** *** *** GMY 0 80 -•02 +.30,,, -.51,,, -1 79 -2 78 +.20 +.35 -.51 -3 77 +.03 +.36*** -.41*** A-H 0 80 +.13 -.01 +.22** -1 79 +.io -.04 +.27::, -2 78 -.03 -.08 +.37^ -3 77 +.05 -.12 +.47 ITA 0 40 +.25 -.14 +.16 -1 39 +.33** -.12 +.31** -2 38 +.14 -.11 +.31, -3 37 +.06 -.05 +.30 USR 0 80 -.04 -.09 +.25„ -1 79 -. 1 3 -.06 +-25„ -2 78 +.07 -.03 +.26, -3 77 +.03 +.00 +.19 F-test from analysis of variance: *** < .01 < ** < .05 < * < .10 significance level 114 positively, and polarity is negatively, correlated with status inconsistency). Given the absence of any sizable relationship between war experience and German involvement in military confrontations (Table 11), however, it is the polarity variable that is important. For one other major power—France — there is the possibility that the status-conflict relationship might be masked. Hypothetically, system polarity may constrain France when she is underrecognized, but there is little evidence in Table 11 that this intervening variable appreciably affects her conflict behavior. For the remaining major powers — Britain, Austria-Hungary, Italy, and Russia — the intervening variables do not introduce confounding effects, and, for them, the finding that there are no significant bivariate relationships remains unaltered. This then brings us to the second question—the crux of the matter: do the associations that we uncover, having introduced the intervening variables, apply to the total population or just the subset of underrecognized cases, i.e., does status inconsistency make a difference in the nineteenth century? The evidence that we have compiled is not amenable to a simple, unambiguous interpretation. There would appear to be three crude categories in which to classify the nineteenth century major powers. First there is the situation in which status inconsistency is not important, but the intervening variables are. Next is the category in which status inconsistency is important, 115 but the intervening variables are not. Finally there is the classification in which the intervening variables are important when a state is status inconsistent. The United Kingdom falls into the first category in that the evidence fails to support the contention that status inconsistency is an important factor in Britain's involvement in military confrontations during the nineteenth century. In Table 9 we were unable to uncover for her a significant status- conflict relationship and we subsequently demonstrated (Table 13) that the relationship is not being masked by any of the intervening variables. We now find (Table 12) that the overall fit of the intervening variables and the point predictive power of the probit equations when Britain is underrecognized are not AO appreciably different from the R and P for Britain during the entire nineteenth century, and virtually all the standardized probit coefficients for the intervening variables (regardless of time lag) are within one standard error of the coefficients for the entire century. This does not mean that our intervening variables have no predictive power, for they account for approximately twenty to twenty-five percent of the "variance" in Britain's involvement in military confrontations, with polarity being by far the most consistent and powerful influence. We have simply found that the intervening variables—or, at least, polarity—are applicable to the entire population of cases and 116 that stratifying the population according to status inconsistency is no more than a means of "random" sampling. The plights of Italy and Russia during the nineteenth century are somewhat opposite that of Britain; they fall into the second category—the category that represents the unmediated association between our predictor and outcome variables. Both Italy and Russia are more likely to participate in, and initiate, military confrontations when they are under- rather than over- recognized (Table 9). However, neither country is likely to become involved in an IMC more than twenty-two percent of the time that it is status inconsistent. Thus, if we were asked to predict, for a particular year, whether or not either of these countries would become involved in a military confrontation, given that it was underrecognized, we would do best to say "no." Having additional information concerning the state's power capabilities, prior war experience, and the polarity of the major power system will not, for the most part, improve our predictive power. Finally, France and Germany, and perhaps Austria-Hungary, fall into the third category—the category to which our posited model applies. States in this third category are conflict-prone when underrecognized, but are constrained from involvement in confrontations by intervening factors. Taking France as a case in point, for the eighty-year period during the nineteenth century when she is a major power, none of the hypothesized intervening 117 variables are assoicated with her participation in, or initiation of, interstate military confrontations. However, during the nearly thirty years in which France is underrecognized (with a lag of one, two, or especially three years), the intervening variables give us rather sizable fits and predictive power. Increases in French power capabilities would appear to be most important, although the polarity of the major power system may play some role; prior war experience contributes little to our ability to predict French involvement in military confrontations. The coefficients for changes in power (when time lags are introduced) are usually two to three standard errors removed from the parameters for the entire eighty-year period, and the coefficients for polarity are generally one to two standard errors removed. It is noteworthy that the effects of both these intervening variables are in the direction predicted by the model. For Germany, interpreting the results is a bit more difficult. For the subset of cases in which Germany is under- recognized, we find that, with no time lag and a lag of one year, AO both the R and P associated with IMC participation (i.e., all IMCs in which Germany is involved) are considerably larger than the corresponding statistics for the entire eighty-year period (Table 12). However, the standard errors of the probit coefficients are of such magnitude as to make it problematic to argue that, for IMC participation, the probit coefficients for the underrecognized subset are significantly different than the 118 coefficients for the entire period. On the other hand, when looking at German conflict initiation (i.e., only those IMCs that Germany initiates), such doubts are less justified. The coefficients for changes in power and polarity for the underrecognized subset are quite distinct (about two standard errors removed) from the coefficients for the entire period, *2 and the R~s and Ps are also quite different and respectably robust. It should be noted, however, that while the effect of polarity is in the predicted direction, that for changes in power is not. Finally, Austria-Hungary presents the most difficult case to interpret. There appears not to be any bivariate relationship between status inconsistency and military confrontation (Table 9), nor do the intervening variables seem to have much predictive power—with one exception. For the subset of underrecognized cases with a three-year lag, *2 we uncover a sizable R and respectable P. From Tables 11 and 12 it is evident that this relationship is accounted for almost entirely by the large positive association between system polarity and IMC involvement. Since for Austria-Hungary this is the single instance of any association among the variables and is based upon only three conflict involvements, I am skeptical about placing much confidence in this relationship. It is not clear to me what distinguishes the major powers in this last category from those in the other two categories. It 119 is not their scores on status inconsistency; for example, Germany during the nineteenth century is, on the average, slightly more status inconsistent than Britain, but France is less. Nor is the classification based upon national power capabilities; France, on the average, being slightly more powerful than Britain, Germany less powerful. Neither do I see any reason to suggest a geopolitical interpretation that would emphasize the central locale of Germany, France, and Austria-Hungary in the nineteenth century major power system, and the peripheral location of Britain, Russia, and Italy. What may account for Germany's and France's adherence to the status inconsistency model is their position in the military-political structure of nineteenth century Europe. Germany's years of status inconsistency are concentrated in the 1820 to mid-1870s period; France's, prior to 1860. This is the time of Germany's (Prussia's) spectacular rise and of the rapid, if tumultuous, rebuilding of post-Napoleonic France. The histories of Prussia and France during this period are ones of careful scrutiny of European alignments accompanied by opportunism. At the same time, we find Britain aloof from continental affairs, Russia expanding eastward against non-major power entities, and Austria-Hungary, under Metternich and until the Seven Weeks War (1866), trying to maintain the major power status quo. It may thus be speculated that German and French adherence to the model is associated with their carefully-orchestrated climbs to prominence, during which neither is sufficiently powerful to 120 heedlessly vent her frustration, but both are willing to act when the opportunity presents itself. (In keeping with this scenario, the absence of any relationship between status inconsistency and conflict involvement for Britain is associated with her political isolation from the "European" major powers.) This argument, however, does not account for Austria-Hungary's fit to the model —one that is largely dependent upon her concentration of status inconsistent years during the 1880s and 1890s; but, as noted above, it may well be misleading to draw inferences from an association based upon only three conflict involvements. Examining the "Residuals" Before moving on to the twentieth century, we should briefly examine one further item. Does our model more accurately predict certain levels or types of conflict and are our rather modest findings the result of our failure to differentiate among these levels and types? Table 14 offers a means of systematically examining this question. I have divided each major power's conflicts in two different ways. First, I have divided the conflicts by level of violence, that is, as to whether they are wars, military actions short of war, or merely threats to use force."' Second, I have distinguished conflicts by type, in this particular case, between those conflicts that confront one major ^See Chapter Two and Appendix C for the definitions of war, military action, and threat. TABLE 14 NUMBER OF CONFLICT-YEARS INVOLVING MAJOR POWERS AND THE PROPORTION OF THESE CONFLICT-YEARS THAT IS CORRECTLY PREDICTED BY THE PROBIT EQUATION, FOR THE 19th CENTURY (ONE- TO THREE-YEAR TIME LAGS) Nation Lag Part i ci pate Initiate Level of Conflict Type of Conflict Level of Conflict Type of Conflict N War N Mil. N Thr't N Maj/ N Maj/ N War N Mil. N Thr't N Maj/ N Maj/ Act. Maj. Mi n. Act. Maj. Min. UK 0 2 .50 12 .33 6 .33 5 .60 15 .27 1 1 .00 11 .09 6 17 5 .40 13 .08 1" 0 1 1.00 7 .00 4 .50 3 .67 9 .11 1 1 .00 6 .00 4 00 5 .20 12 .00 under- -1 2 .50 6 .00 2 .00 3 .33 7 .00 1 1 .00 5 .00 1 00 3 .33 5 .00 recog. -2 1 1 .00 6 .33 3 .00 3 .33 7 .29 1 1 .00 5 .20 3 00 3 .33 6 .17 L-3 1 1.00 4 .50 5 .40 3 .67 7 .43 1 1 .00 3 .00 5 20 3 .67 6 .00 FRN 0 7 .00 11 .00 4 .00 7 .00 15 .00 6 .00 10 .00 4 00 6 .00 14 .00 ' 0 1 .00 6 .00 3 .00 3 .00 7 .00 1 .00 6 .00 3 00 3 .00 7 .00 under- -1 1 1.00 6 .50 2 .50 2 .50 7 .57 1 1 .00 6 .50 2 50 2 .50 7 .57 recog. -2 2 .50 4 .50 2 .50 2 .50 6 .50 1 .00 4 .50 2 50 1 .00 6 .50 .-3 3 .33 3 .33 2 .50 4 .50 4 .25 2 1 .00 3 .33 2 50 3 .67 4 .50 GMY 0 4 .00 2 .00 4 .00 4 . 00 6 .00 3 .00 2 .00 3 00 2 .00 6 .00 0 4 .25 0 2 .50 3 .00 3 .67 1 .00 1 1.00 2 1'. 00 2 .50 2 1.00 under- -1 4 .25 0 2 .00 3 .00 3 .33 3 .67 0 1 00 1 1.00 .33 recog. -2 3 .00 0 2 .00 3 .00 2 .00 2 .50 0 1 00 1 1 .00 2 .00 .-3 3 .00 0 3 .00 3 .00 3 .00 2 .50 0 2 50 1 1.00 3 .33 TABLE 14---Continued Nation Lag Participate Initiate Level of Conflict Type of Conflict Level of Conflict Typ e of Conflict N War N Mil. N Thr't N Maj/ N Maj/ N War N Mil. N Thr't N Maj/ N Maj/ Act. Ma j. Min. Act. Maj. Min. A-H 0 5 .00 3 .00 6 00 5 .00 9 .00 3 .00 3 .00 6 00 4 .00 8 .00 r 0 1 .00 1 .00 2 00 0 4 .00 0 1 .00 2 00 0 3 .00 under- -1 • 1 .00 2 .00 2 00 0 5 .00 1 .00 2 .00 2 00 0 5 .00 recog. -2 0 1 .00 3 00 2 .00 2 .00 0 1 .00 3 00 2 .00 2 .00 L-3 0 1 .00 2 1 00 2 1.00 1 .00 0 1 .00 2 1 00 2 1 .00 1 .00 ITA r o 2 .00 2 .00 2 00 1 .00 5 .00 2 .00 2 .00 2 00 1 .00 5 .00 0 2 .00 1 .00 1 00 1 .00 3 .00 2 .00 1 .00 1 00 1 .00 3 .00 under- -1 1 .00 1 .00 1 00 1 .00 2 .00 1 .00 1 .00 1 00 1 .00 2 .00 recog. -3 1 .00 2 .00 1 00 1 .00 3 .00 1 .00 2 .00 1 00 1 .00 3 .00 USR 0 3 .00 4 .00 7 00 2 .00 12 .00 3 .00 4 .00 7 00 2 .00 12 .00 0 3 .00 4 .00 6 .00 2 .00 11 .00 3 .00 4 .00 6 00 2 .00 11 .00 under- -I 3 .00 4 .00 6 00 2 .00 11 .00 3 .00 4 .00 6 00 2 .00 11 .00 o recog. -2 3 .00 4 .00 6 00 L .00 11 .00 3 .00 4 .00 6 00 2 .00 11 .00 1-3 2 .00 4 .00 6 .00 1 .00 11 .00 2 .00 4 .00 6 .00 1 .00 11 .00 123 power with another major power and those that pit a major power against only non-major powers. Since, during the first stage of the research design, the unit of analysis is the year, we can assign only one level or type of conflict to a given year. Believing that the model should be more capable of predicting the severer levels and types, I have so assigned conflicts when more than one of them occur in a given year. Thus, if a war, military action, and threat all occur in the same year, the country is "credited" with a war involvement. If there is no war, then the country receives a "military action" for that year, etc. Similarly, if a country is involved in a conflict with a major power and one with a minor power in the same year, that year is assigned a major power conflict. The proportions in the table represent the fraction of times that the model correctly predicts the year in which a particular level or type of conflict occurs. Thus, in the first row of Table 14 we see that for the total period during which Britain is a nineteenth century major power, there are twenty years in which she participates in military conflicts. There are two war-years, of which the model correctly predicts one (.50); twelve military action-years, of which we predict four (.33); and six years during which her activities are limited to threats, of which we predict two (.33). These twenty conflict-years can also be divided into five years during which Britain participates in at least one conflict against another major power—three (.60) 124 of which wo correctly predict; and fifteen years in which her conflicts are with non-majors — four (.27) accurately predicted. The right-hand side of the table repeats the same information, only this time it is for only those conflicts that a country initiates. As is our usual procedure, rows two through five for each nation represent the years in which it is underrecognized, the last three rows encompassing time lags. The number of IMCs may vary from row to row depending on the time slices. From Table 14 it is fairly obvious that the model does not predict war-years more accurately than military action- or threat-years. Nor does it do appreciably better in forecasting major power/major power rather than major power/minor power conflicts. Put another way, the modest findings for the nineteenth century are not the result of any obvious systematic bias in the levels or types of conflict to which the model predicts. Summarizing the Nineteenth-Century Findings Thus, to summarize our results for the nineteenth century: the "underrecognition leads to conflict" model as postulated with its mediating factors in Chapter One is not universally supported by the evidence—either for conflicts as a whole or for various levels and types of conflict. We find that, for one major power (Britain), status inconsistency plays no role; and, for two others (Italy and Russia), the putative mediating factors have negligible effects. But we do uncover some evidence for three states (France, Germany, and Austria-Hungary) in support of the model. And we 125 suggest that the factor that distinguishes the behavior of some states from that of others may be their position (e.g., challenger, maintainer of the status quo, outsider) within the military- political structure of the major power system. We discover that, in general, the model best fits the data when a lag of three years is introduced and when we are predicting the initiation of military confrontations. But we also find that, in the nineteenth century, only two of three mediating factors appear operative, those being polarity and changes in power. The effects of polarity are consistently in the predicted direction, i.e., the more polarized the major power system, the more likely a country will engage in conflictive behavior. On the other hand, for the two countries for which changes in power are relevant, one (France) is more conflictive when its power capabilities increase and the other (Germany) when its capabilities decrease. The Twentieth Century Analyzing the Data The findings for the twentieth century are both more powerful and more complex than those for the preceding century. An overview of the biserial correlations between the intervening variables and involvement in military confrontations (Table 15) offers us an introduction to the complexity. By and large, there are many more sizable associations in the later than in TABLE 15 BISERIAL CORRELATIONS BETWEEN EACH MAJOR POWER'S INVOLVEMENT IN MILITARY CONFRONTATIONS AND THE FOUR INTERVENING VARIABLES, FOR THE 20th CENTURY (ONE- TO THREE-YEAR TIME LAGS) Nation Lag N Parti ci pate Initiate A Power War Exp. Polarity X-Cutting Delta Power War Exp. Polarity X-Cutting 2 2 2 2 Predicted --> " rb • ^ " rb " rb " rb USA 0 59 - .00 + .05 - .05 - .00 + .01 + .08** - .01 0 24 - .01 + .11 : > - .09 - .10 - .00 + .28 - .00 un.der- -1 25 - .00 + .02 + .29 - .03 - .00 + .02 + .29** - .03 recog. -2 25 - .21 + .22 - .22 - .21 + 20 .-3 25 - .12 + .44 + .35** %%** - .12 + .44 + .35 " '11** - .39 - .39 ** ** UK 0 59 + .00 + .08* - .01 '+ .11 + .01 + .14 - .05 + .30 0 18 - .10 + .06 - .00 + .00 - .10 + .06 - .00 + .00 under- -1 16 - .14 + .00 + .03 - .03 - .09 + .05 - .03 + .19 recog. -2 15 - .00 - .12 + .16 + .06 - .00 - .12 + .16 + .06 .-3 13 + .11 + .09 - .09 + .08 + .11 + .09 - .09 + .08 *** FRN 0 59 - .09 + .22 - .00 + .11 - .07 + 29 - .01 + .09 - .04 - .01 + .09 0 36 - .09 + + .00\ * under- -1 36 + 'l\***.60 + .33 - .10 - .16* + .33^ - .10 - .16* + .20 -2 36 + .37 + .12 - .08 - .07 + .17 recog. - .io* - .02 + .60 .-3 36 + .12 + .09 - .13 - .00 + .14 - .16 + .00 + .50 + .18* GMY 0 28 - .01 - .12 + .31 + .04 - .01 - .12 + .31 + .04 A-H 0 14 + .41 - .08 - .01 + .34 - .03 - .04 TABLE 15— Continued Nation Lag N Participate Initiate Delta Power War Exp. Polarity X- Cutting A Power War Exp. Polarity X- Cutting 2 2 2 2 2 2 2 — Predicted — + 4- r 4- r r 4- r r rtT - rb rb - b b b b b ITA 0 34 4- .06 4- 09 4- .00 - .04 4- .06 4- .09 4- .00 - .04 USR 0 56 _ .05 _ 04 4- .14** + .00 _ .05 _ .01 4- .16** _ .02 * " 0 31 - .00 - 07 4- .03 4- .00 - .00 - .03 4- .05 - .04 under- -1 30 - .02 - 08 4- .01 4- .03 - .03 - .03 + .03 - .01 recog. -2 29 - .02 - 04 + .09 4- .04 - .03 - .01 4- .15* - .01 .-3 28 - .04 - 10 4- .11 4- .00 - .05 - .07 4- .11 - .01 CHN 0 21 _ .05 4- 01 _ .00 _ .00 _ .05 4- .01 _ .00 _ .00 under- [ o 9 + .07 4- 31 4- .21 - .17 4- .07 4- .31 + .21 - .17 JPN 0 45 + .19* _ 08 _ .01 4- .07 + .19* _ .08 _ .01 4- .07 0 41 4- .04 - .06 - .03 4- .17* 4- .04 - .06 - .03 4- .17* under- -1 38 4- .34 - 06 - .02 - .01 4- - .06 - .02 - .01 recog. -2 35 4- 07 .02 .01 4- .33, .07 .02 .01 •33* ------.-3 32 4- .25* - 09 - .02 - .00 4- .25 - .09 - .02 - .00 NOTE: Germany, Austria-Hungary, and Italy are underrecognized on too few occasions and/or engage in confrontation too infrequently when underrecognized to compute stable estimates. In addition, China is too infrequently underrecognized to obtain stable estimates when time lags are introduced. F-test from analysis of variance: ***<.01< **< .05*^ * .10 significance level 128 the earlier century. But, unlike the nineteenth, some of the mediating effects of the intervening variables in the current century appear to be quite different from those that were hypothesized in Chapter One. In particular, prior war experience would seem to be positively related to involvement in subsequent military confrontations for the United States, Britain, France, and, perhaps, Italy and China, although not for Germany, the Soviet Union, and Japan. And cross-cutting bonds, less important because of the smallness of the biserial correlations, also appear to deviate from the direction hypothesized in the model—at least for France and, perhaps, Britain and Japan, with only the United States clearly exhibiting the negative relationship posited in the model. The effects of the two remaining intervening variables, polarity and changes in power, are, however, generally in the hypothesized direction when sizable; yet, again, France may be an exception. Before attempting to explain why these coefficients deviate from the expected, we would like to know if the directions of the bivariate biserials in Table 15 remain unaltered when we "control" for the effects of the other variables. For example, is prior war experience still positively associated with involvement in subsequent confrontations after we have allowed changes in capability, level of system polarity, and cross-cutting to account for all the "variance" that they can? The probit coefficients would normally help us to answer this question since the "variance" 129 accounted for by the other variables is "partialled out." But, as we shall see below, there are conditions under which these estimators are imprecise. When we examine the standardized probit coefficients in Table 17 (p. 135), we find, in a number of instances, that there are sign reversals and that the relative size of the betas are a good deal different than we might expect based upon the biserials. What we are witnessing, however, may not be the effects of partialling out "variance," but rather the suppressor effects that can result from multicolinearity among the intervening variables. That is to say, if two or more of the intervening variables are highly correlated with one another and simultaneously employed in an equation, the importance (i.e., the coefficient for) one or more of these variables may be inflated while that for others is, correspondingly, deflated. Under these conditions it is not possible to have much faith in the direction or magnitude of the probit coefficients. It is possible, however, to partially disentangle the effects of the intercorrelated intervening variables (i.e., to specify which variables are most powerful in predicting conflict involvement). The capability to do so is based upon the fact that the multiple correlation coefficients are not affected by multi- collinearity. A simple way to view this is that the probit coefficients for some of the collinear variables are being deflated to compensate for the inflation of other coefficients, but the 130 overall strength of association is unaltered. Based upon this fact, we can re-run the probit analysis, alternately removing one intervening variable at a time. Great care should be taken when interpreting the results of this procedure. If the correctly-specified model necessitates the inclusion of all four intervening variables, of which two or more happen to be significantly correlated, then the estimated parameters will be inefficient (i.e., have large error terms) but they will not be biased (i.e., the expected value will be the true population parameter). The more highly collinear the intervening variables, the more inefficient the estimators. However, when we omit highly correlated—but necessary—variables, the resulting estimates will be biased (although they will also be more efficient). That is to say, if and X^ are two correlated variables that are necessary to account for an outcome variable Y, then by omitting X-j from the analysis, we credit X^ with the "explanatory power" unique to X^ and that shared jointly with . Thus, to the extent that X-j and X^ are correlated, the coefficient for X^ is biased. However, we do gain some information. If K2 Ry-x X 1S *^i the multiple correlation coefficient resulting from 12 A p the prediction of Y from X-j and X^, and Ry.^ is that from Y on A2 A2 2 X,, alone, then Ry^ ^ - Ry^ is that portion of Y that is uniquely accounted for by X-j. In this manner we can identify the most powerful variables — the ones that contribute most to 131 the decline in the strength of relationship measures—even though we cannot get an unbiased estimate of the parameters. This is an expensive and time-consuming process and, for exploratory purposes, I decided to select only one set of cases for each major power. To maximize comparability, I chose (when possible) the same cases for each state: the subset of underrecognized cases with a three-year lag. This subset of cases produced the strongest fits during the nineteenth century and, as we shall see below in Table 17, it generally contains the best fits in the twentieth century as well. For one major power—China—a solution for the underrecognized subset could only be obtained when there was no time lag. Three other powers —Germany, Austria-Hungary, and Italy—were too infrequently involved in confrontations when underrecognized to permit the probit algorithm to converge and, as a result, were not examined. By and large we find that the probit estimates obtained from the "completely-specified" model (the first row for each country in Table 16) are upheld when we alternately exclude intervening variables (rows two through five for each country). Of course, the inordinately inflated beta weights (>1.0) from I he "completely-specified" model are reduced when collinear variables are excluded (as seen for France and China), but the directions of the coefficients remain quite stable, and the omission of the variables with the larger beta weights in row one i'. associated with the most precipitous decline in the R s in TABLE 16 STANDARDIZED PROBIT COEFFICIENTS AND THE INTERCORRELATION MATRIX FOR THE FOUR INTERVENING VARIABLES WHEN PREDICTING EACH MAJOR POWER'S PARTICIPATION IN MILITARY CONFRONTATIONS, FOR THE 20th CENTURY (SUBSET OF UNDERRECOGNIZED CASES, THREE-YEAR TIME LAG) Nation Lag N A Power War Exp. Polarity X-Cut. R2 Predicted + - + A Power War Exp. Polarity X-Cut USA -3 24 -62(48) 26(57) 52(60) 15(51) 69* 27(62) 65(69) 09(54) 54* War Exp. -17 -54(43) 68(62) -04(20) 76** Polarity 06 43 -74(51) 40(44) 09(42) 72* X-Cut. 21 -87 -59(47) 10(26) 58(73) 72** IMC -34 66 -62 UK -3 12 35(28) 02(34) -55(69) 62(76) 80 18(55) -20(66) 52(59) 37 War Exp. -23^ 35(27) -57(52) 62(72) 80 Polarity 34 -81 37(35) 42(35) 41(48) 46 X-Cut. 30 -11 51(40) 10(55) -35(60) 30 IMC 33 31 29 -k-r-k FRN -3 33 -13(14) 100(45) 153(73) 58(26) ?Kk* 101(50) 161(30) 60(28) 94 War Exp. 02 -43(39) 30(32) 49(33) Polarity -06 -66 34** -66(34) 28(19) 06(22) 52 X-Cut. 07 42 -53(27) 71(35) 64(45) 61** IMC -39 35 31 TABLE 16---Continued -2 Delta Power War Exp. Polarity X-Cut. Nation Lag N R^ Predicted + - + A Power War Exp. Polarity X-Cut USR -3 25 -13(18) -77(97) 69(45) -06(28) -85(112) 64(43) -06(29) 75 War Exp. -11(19) 79(57) 27(23) 54 Polarity -21(25) -74(64) -29(30) 39 X-Cut. -13(18) -67(57) 74(45) 69 IMC CHN^ 0 9 35(41) 154(148) 157(246) 235(297) 65 198(133) 185(215) 318(258) 68 War Exp. -46 57(41) 155(330) 74(283) 64 Polarity -49 89 49(42) 129(139) 53(126) 60 X-Cut. 54 -94 -97 59(41) 80(96) 00(90) 56 IMC 26 55 45 -42 JPN -3 32 74(36) -27(29) -29(22) 13(19) 88 -66(57) 02(26) -29(31) 29 War Exp. -18 82(41) -41(28) 21(22) 83** Polarity 11 52 63(35) -45(39) 06(26) 73* X-Cut. -05 -56 -18 71(37) -35(32) -28(23) 86 IMC 50 -29 -13 06 NOTE: All coefficients have been multiplied by one hundred so as to eliminate decimal points. Numbers within parentheses are standard errors. (^) The coefficients for China are estimated on the subset of underrecognized cases with no time lag. k O > XL from probit (% with 3/4 df): *** < .01 < ** < .05 < * <_.10 significance level 134 rows two through five. Thus we see that changes in power account for a large proportion of unique "variance" in American, British, and Japanese participation in military confrontations; war experience is salient for French and Soviet participation; polarity has its greatest unique effect on British, French, and Soviet conflict behavior; and cross-cutting is important for British and French behavior. Due to the very high degree of multicollinearity among the indicators for China, the exclusion of any particular variable does little to reduce K2 her R (although cross-cutting does the most). Emboldened by the results reported in Table 16, I will move on to a description of the relationships uncovered by the probit runs as depicted in Table 17. It should be kept clearly in mind, however, that such description and any subsequent interpretation of the effects of the individual intervening variables are highly tenuous, although the overall fits of the probit equations are not. We can see from Table 17 that the first intervening variable—changes in power—is not, in general, very powerful. For the two states for which it is most important—Austria- Hungary and Japan—and for a third state—Italy—it is positively associated with involvement in military confrontations, 1 Since R is merely as estimate of the "true" multiple correlation coefficient, the exclusion of a variable may be accompanied, from time to time, by a slight increase in the statistic. TABLE 17 STANDARDIZED PROBIT COEFFICIENTS FOR THE FOUR INTERVENING VARIABLES WHEN PREDICTING EACH MAJOR POWER'S INVOLVEMENT IN MILITARY CONFRONTATIONS, FOR THE 20th CENTURY (ONE- TO THREE-YEAR TIME LAGS) Nation Lag N Parti ci pate Initiate A2 Power War Exp. Polarity X-Cut. R P Power War Exp. Polarity X-Cut. A A R P Predi cted »- + - + - + - + USA 0 59 -03(20) 21(35) 34(24) 09(37) 14 00 -05(21) 08(38) 32(25) 08(38) 10 00 * 0 24 -55(47) 77(55) 90(40) 101(66) 63** 26 -72(52) 39(58) 86(42) 37(71) 70 30 under- -1 24 02(23) 53(67) 88(51) 67(74) 50 27 02(23) 53(67) 88(51) 67(74) 50 27 recog. -2 24 -63(46) 18(60) 54(43) 21(65) 56, 13 -63(46) 18(60) 54(43) 21(65) 56, 13 -3 24 -62(48) 26(57) 52(60) 15(51) 69 33 -62(48) 26(57) 52(60) 15(51) 69 33 •k -k UK 0 59 -05(17) 34(21) 27(23) 33(17) 18 11 02(17) 26(20) 13(23) 48(17) 29 11 " 0 18 -51(43) 46(50) 30(50) -21(35) 34 10 -51(48) 46(50) 30(50) -21(35) 34 10 under- -1 16 -43(36) 69(52) 73(53) -07(31) 35 46 -37(33) 10(26) -41(60) 94(117) 92* 36 recog. -2 14 04(33) -03(64) 32(60) 17(31) 16 44 04(33) -03(64) 32(60) 17(31) 16 44 .-3 12 35(28) 02(34) -55(61) 62(76) 80 33 35(23) 02(34) -55(61) 62(76) 80 33 FRN 0 59 -22(14) 91(39) 117(59) 44(22) 77*** 16 -22(16) 88(38) 107(57) 47(24) 69*** 27 ' 0 36 -10(11) 111(65) 156(99) 50(31) •k k -10(17) 104(66) 135(97) 49(35) 70* 27 37 17 under- -1 35 -26(15) 141(107) 143(141) 36(42) -26(15) 141(107) 143(141) 36(42) 89*** 64 89*** 64 recog. -2 34 -19(10) 122(59) 162(83) 43(22) -26(17) 125(87) 145(124) 40(36) 90*** 44 98*** 60 *** -3 33 -18(14) 100(45) 158(73) '58(26) -22(19) 99(64) 149(104) 61(37) 96*** 46 88 38 •k k go,*** 22 GMY 0 28 00(00) -101(84) -02(02) 00(00) 99 22 00(00) -101(84) -02(02) 00(00) A-H 0 14 74(47) -41(35) -19(29) 71* 36 64(45) -32(36) -24(31) 54 50 TABLE 17---Continued Nation Lag N Parti ci pate Initiate A2 A Power War EXD. Polarity X-Cut. P/ P A Power War Exp. Polarity X-Cut. R P Predicted + - + - + - + - ITA 0 34 20(21) 57(27) 22(31) -32(26) 30 25 20(21) 57(27) 22(31) -32(26) 30 25 USR 0 56 -14(16) -30(23) 48(21) 01(21) 31 09 -16(17) -31(23) 43(23) -14(22) 29* 13 r 0 31 -04(23) -39(31) 16(26) -14(30) 14 21 -04(23) -46(30) 09(26) -44(31) 19 25 under- -1 29 -03(26) -30(30) 19(27) 04(32) 11 42 -20(26) -35(30) 10(27) -35(33) 14 18 recog. -2 27 -10(23) -15(26) 55(35) 31(29) 32 41 -11(21) -20(25) 63(46) 03(29) 39 36 .-3 25 -13(18) -77(97) 69(45) -06(28) 72* 26 -21(20) -86(107) 58(46) -27(31) 70* 30 CHN 0 21 -21(30) 50(62) -05(71) 41(87) 10 20 -21(30) 50(62) -05(71) 41(87) 10 20 under- [ 0 9 35(41) 154(148) 157(246) 235(297) 65 64 35(41) 154(148) 157(246) 235(297) 65 64 JPN 0 45 71(28) -21(28) -33(20) 39(18) 87 17 71(28) -21(28) -33(20) 39(18) 87*** 17 0 41 65(34) -19(32) -35(21) 41(19) 84 07 65(34) -19(32) -35(21) 41(19) •kk 84 07 under- -1 38 87(40) 11(45) -44(30) 20(20) 83** 27 87(40) 11(45) -44(30) 20(20) 83** 27 recog. -2 35 77(35) -34(39) -43(29) 27(26) k k 77(35) -34(39) -43(29) 27(26) 76 20 76** 20 .-3 32 74(36) -27(29) -29(22) 13(19) 74(36) -27(29) -29(22) 13(19) •kk 88** 20 88 20 NOTE: All coefficients have been multiplied by one hundred so as to eliminate decimal points. Numbers within parentheses are standard errors. from probit (7. with 4 df): *** < .01 < ** <• .05< * ^ .10 significance level 137 as hypothesized in Chapter One. However, for four other states —most noticeably the United States and Britain, but also France and the Soviet Union—the relationship may be negative, although the large error terms make it difficult to say. The effects of the second intervening variable—prior war experience—are the same as we found with the biserial correlations. Contrary to what is hypothesized in Chapter One, this variable is generally positively associated with participation in, and initiation of, interstate military confrontations. Again, Germany, the Soviet Union, and perhaps Japan are exceptions. For almost all major powers, the third intervening variable — polarity — is positively related to involvement in confrontations as posited in the model. The two anomalies are Austria-Hungary and Japan, for neither of which is polarity a particularly strong intervening force. And finally, the coefficients for the fourth intervening variable —cross-cutting—do not support the hypothesis posited in the model. By and large we find that the more cross-cut a state's bonds, the greater the probability of military confrontation. While the Soviet Union, Italy, and Austria-Hungary generally display the posited negative relationship, the magnitude of the standard errors suggests that these estimates may not be stable. Despite our seeming inability to correctly predict the direction of the associations between the intervening variables and involvement in military confrontation for many states, the probit equations produce very good fits to the data. The multiple 138 correlation coefficients and measures of point predictive power are quite sizable for all the major powers, particularly for France and Japan. And as we found when we examined the bivariate relationship between status inconsistency and IMC involvement in Chapter Three, the strengths of associations in the twentieth century are decidedly greater than those for the nineteenth century. Interpreting the Results The findings presented above raise two basic issues: *2 (1) While the R s and Ps from the probit equations demonstrate that we have identified some very potent influences on major power conflict behavior, we may nevertheless have incorrectly specified the direction of the association for a number of countries. We, therefore, need to ask whether there are plausible alternative explanations for these associations. And, (2) While we have obtained much stronger fits to the data in the twentieth century than we had in the nineteenth, we need to ascertain whether knowing that a major power is status inconsistent significantly enhances our ability to predict its conflict behavior in the current century. Let us turn to the first issue concerning alternative explanations for the sign reversals in the probit. coefficients. Given the small N and the degree of multicollinearity among the intervening variables, the simplest explanation for these reversals 139 is that the coefficients are unstable. As we can see, the standard errors of the probit coefficients are generally quite large and it is not difficult to imagine what would happen if a few data points were different. Indeed, if our R s had turned out to be small, this section of the chapter would have been quite brief, as I would have probably interpreted the sign reversals to be randomly-generated "noise." However, the consistently high R's necessitate that I at least speculate about historical, if not theoretical, reasons that might account for the numerous discrepancies between our latest findings and the relationships that I postulated in Chapter One. Again, I wish to make it clear that I am assuming in the explication below that the variables with the largest probit coefficients generally account for the most unique "variance" in the outcome variable and have signs that accurately reflect the direction of their associations (see Table 16), but the reader is cautioned about the tenuous nature of this assumption. The first intervening variable—change in power—is hypothesized to be positively associated with involvement in military confrontations. Although we find that this hypothesized relationship is generally supported by the biserial correlations, we discover that the partial (probit) coefficients for the United States, Britain, France, and, to a lesser extent, the Soviet Union are negative, i.e., controlling for the other intervening variables, these states are more conflict-prone when 140 their power capabilities are decreasing. This does not appear to be associated with any systematic difference between the national power scores for these states and the scores for the other major powers. What, then, do the four aforementioned states have in common that might account for the sign reversals? I would contend that, for most of the years during the twentieth century in which they are in our data set, the US, UK, France, and, to a lesser extent, Russia/USSR are status quo powers. By this I mean that they have fairly clearly defined spheres of influence and are more or less satisfied with the existing demarcations of these boundaries. I would postulate that status quo powers perceive their preferential positions to be threatened when their relative capabilities decline and, under such circumstances, bellicose behavior becomes a mechanism for warning adversaries and underlings about the dangers of adventurous policies. It is interesting to note that, in the twentieth century, the three states—Austria-Hungary, Japan, and, to a lesser extent, Italy—that exhibit the posited positive association are all "dissatisfied" powers. Both Japan and Italy openly pursue expansionist policies in defiance of the status quo powers; and Austria-Hungary, which is in our data set for only a very few years, can accurately be described during these years 1 Singer and Small (1974) find, when looking at wars between major powers, that the United States is declining in power prior to each of its involvements. This finding, however, is based upon only three war experiences (WW I, WW II, and the Korean). 141 as dissatisfied with existing spheres of influence and intent on reasserting her control over the Balkans. In short, the effect of changes in power on conflict behavior may well depend on whether or not a nation is a status quo power. The second intervening variable—prior war experience— was originally hypothesized to be negatively associated with involvement in military confrontations, but most of the biserial correlations and probit coefficients for the twentieth century are positive. This leads me to surmise that for these states (in particular France and China, but also the United States, Britain, and Italy) war losses are not sufficiently severe to outweigh perceived gains from military confrontation. In fact, it is to be argued that, for these states, wars actually serve to enhance the belief that military force is a useful means of obtaining one's ends. What we appear to have isolated are the victors (or, at least, the undefeated protagonists) of large wars. All these states also become involved in the use of military force to preserve order, or to enforce claims, shortly after the cessation of major wars. On the other hand, the three states —Germany, the Soviet Union, and Japan—that display the posited 1 The attentive reader may have boggled at the difference between the zero-order biserial correlation (Table 15) and the partial coefficient (Table 17) for prior German war experience when predicting IMC involvement. The figures below should demonstrate the importance of the war experience variable, as well as the extent to which the coefficient for the highly collinear polarity variable is increased when war experience is omitted from a run. On the right is presented the intercorrelation 142 negative association between war experience and involvement in subsequent confrontations are all countries that are denied tangible gains and/or suffer catastrophic losses as a result of participation in large-scale wars. Concomitantly, foreign occupation (Germany after WW I), civil war (USSR after WW I), and military and economic exhaustion (Japan after Russo-Japanese) often make it impossible for these states to engage in renewed hostilities. It is, therefore, not too unlikely that these two sets of states have very different conceptions about the usefulness of military force—the former perceiving that the matrix and, on the left, the standardized probit coefficients for the four intervening variables when predicting German involvement in military confrontations (no time lag) during the twentieth century. Just as in Table 16, the probit coefficients for the "completely-specified" model (see Germany, Table 17) are given in the first row, while, in each subsequent row, one intervening variable is alternately omitted. When (in row three) war experience is omitted, the R^ declines precipitously, yet, at the same time, the coefficient for the highly correlated polarity indicator dramatically increases. The decline in the R^ illustrates the importance of the war experience variable. The increase in the polarity coefficient is due to the fact that, in row three, the coefficient reflects both the "explanatory power" unique to polarity and that shared jointly with the omitted war experience variable. A0 A Power War Exp. Polar. X-Cut. A War Po- X- 3ower Exp 00(00) -101(84) -02(02) 00(00) 99 -101(84) -02(02) 00(00) 99 War Exp. -lT^ -06(22) 62(34) 13(22) 42 Polar. -02 -62 -00(01) -100(142) 01(01) 99 X-Cut. -11 20 -00(00) -101(113) -02(02) 99' IMC -11 -35 (NOTE: All coefficients have been multiplied by one hundred so as to eliminate decimal points. Two asterisks (**) denote "significant at the .05 level.") 143 fruits of combat may well outweigh possible losses, the latter having much lower expectations concerning the utility of force. The third intervening variable—polarity — is posited to be positively related to involvement in confrontations, and this hypothesized relationship is strongly supported by the data. There are some sign reversals with the biserials, but the partial coefficients clearly indicate a positive relationship for most major powers. There are, however, two exceptions to this finding —Austria-Hungary and Japan—although for neither of these states is polarity a very important intervening variable. The sign reversal for Austria-Hungary is somewhat misleading, being accounted for by a shift in polarity of only four percent during the fourteen years in which she is a major power; that for Japan is more complex. I would argue that, unlike the other major powers, Japan perceives herself to be excluded from the alliance network during most of her years as a major power. When the system is highly polarized, Japan's interaction opportunities are not, as posited for other states, severely reduced; indeed, Japan's cooperation is actively pursued by the competing alliances. When the system is less polarized, Japan has somewhat more numerous interaction opportunities, but, at the same time, her support is less vigorously sought. It may be hypothesized that, under such circumstances, Japan employs demonstrations of force to "prop up" her sagging prestige, an action not needed when she is being "courted" in the bi-polar world. 144 Finally, the coefficients for the fourth intervening variable—cross-cutting—do not, for the most part, support the hypothesized negative association posited in the model. Even the United States, which exhibits negative biserial correlations, has positive partial coefficients. (The three major powers—Austria-Hungary, Italy, and the Soviet Union — that display the posited negative partials also have large standard errors, suggesting that these coefficients are probably not stable.) The repeated evidence supporting a positive relationship between the cross-cutting indicator and the outcome variable suggests two possible interpretations. One is that major powers are simply not cross-pressured. As explained in Chapter Two, the cross-cutting indicator ranges from -1.0 (no cross-cutting bonds) to +1.0 (completely cross-cut bonds). However, descriptive statistics reveal that the mean cross- cutting score for a major power, regardless of time lag, is nearly always negative—the highest mean score being +.03. What we seem to be tapping in the twentieth century is the proportion of military allies with which a major power trades. That is, the states with the highest cross-cutting scores turn out to be those that do not trade (heavily) with all their major power allies, rather than those that have distinctly different sets of trade and alliance partners. Thus, it may be argued that these states (having the highest cross-cutting scores) are not more cross-pressured, but simply less reinforced and, consequently, 145 the cross-cutting hypothesis may not be relevant. The other interpretation is that our measure of cross-cutting is quite satisfactory, but that our hypothesis is incorrect. That is to say, while states that have more cross-cut bonds are more cross-pressured, cross-pressures may not mitigate conflicts. It may be argued that cross-pressures produce tension and uncertainty, and thereby exacerbate potentially conflictful situations. Having suggested interpretations for the unexpected effects of some of our intervening variables, we are brought to the second of the two issues raised at the beginning of this section — the one concerning the importance of status inconsistency in the twentieth century. In particular, to what extent do the results from the probit runs on the subset of underrecognized cases differ from the runs on all twentieth century cases for a given major power? Since, as we have noted (and not withstanding the preceding interpretations), the standardized probit coefficients for the twentieth century may be unreliable guides to significant changes, i.e., they may be inefficient estimators, we will rely upon the R s and Ps to address this question. Somewhat overstating our answer, I would say that the evidence for the twentieth century demonstrates that we can more accurately predict the years in which a major power will engage in military confrontations if we know that it is underrecognized 146 than we can if we did not know. Put another way, the mediating effects of the intervening variables are greater for most major powers when they are underrecognized and, by extension from our model, are greater when they are prone to confrontation. More specifically, there are two major powers — the United States and A? China — that display dramatic increases in R s and Ps when we stratify by status inconsistency. The United States, which was earlier shown to have eighty-seven percent of its conflicts while underrecognized (Table 8), now appears to be affected by the intervening variables only when it is status inconsistent. Similarly, China, a major power for which we were unable to identify a consistent bivariate association between underrecognition and military confrontation, also appears to have quite predictable conflict behavior when status inconsistent. For three other states —Britain, France, and the Soviet Union—we appear to have more predictable behavior when they are status inconsistent, but perhaps not significantly so. Britain and the Soviet Union both exhibit moderate R s for their total years in the twentieth century major power subsystem. When lags are introduced for underrecognized A2 Britain, we get considerably improved R~s and Ps; and for the Soviet Union, with a lag of three years, we also get hefty increases. For France, we have a rather sizable fit for all fifty-nine major power years, yet obtain still greater predictive power when we examine only her status inconsistent years. However, for all three countries, the improvements in R s and Ps are probably not 147 sufficiently large to warrant a statement that the subsets of underrecognized cases are, with certainty, different from the set of all years—our inability to determine whether the differences are significant being associated with the fact that the distribution of the R statistic is unknown. The findings for Japan contradict the statement that we can more accurately predict the years in which a major power will engage in military confrontation if we know that it is status inconsistent, for we can predict Japanese conflict behavior equally well for all years in which she is in our data set. This, however, should be expected since Japan is status inconsistent in forty-one of the forty-five years during which she is a major power. We are, therefore, really only examining underrecogni zed Japan—and, for underrecognized Japan, our model produces sizable fits to the data. Finally, there are three states—Germany, Austria-Hungary, and Italy—that are too infrequently involved in military confrontation when status inconsistent for the probit algorithm to converge on a solution. All three have weak, negative bivariate relationships between status inconsistency and confrontation when there is no time lag (Table 9), although Germany and Italy have positive associations when lags are introduced. Since we have too few cases to make the comparisons between underrecognized years and all years for these states (and, hence, should probably say that the difference is unimportant), I shall only comment that the 148 intervening variables produce significant R s for Germany and Austria-Hungary, but are less powerful for Italy. Examining the "Residuals" Before summarizing the results for the twentieth century, let us again briefly examine the "residuals"—as we did for the nineteenth century—to discover whether our model is predicting differentially for various levels or types of conflicts. We discover, when we look at Table 18, that it is not. We do not systematically predict better for war-years than for military action- or threat-years. Nor do we predict major power/ major power conflicts more accurately than major power/ minor power conflicts. Although there are some differences from country to country, there is nothing that I would be willing to call a pattern. Summarizing the Twentieth-Century Findings We have found it rather difficult to interpret the findings for the twentieth century. For although we discovered that each of the intervening variables accounts for some "unique variance" in the involvement of major powers in military confrontations and that the entire set of variables, in conjunction with our measure of status inconsistency, provides us with considerable predictive power, the directions of the associations between the mediating and outcome variables are often different than those posited in Chapter One. Consequently, I have suggested TABLE 18 NUMBER OF CONFLICT-YEARS INVOLVING MAJOR POWERS AND THE PROPORTION OF THESE CONFLICT-YEARS THAT IS CORRECTLY PREDICTED BY THE PROBIT EQUATION, FOR THE 20th CENTURY (ONE- TO THREE-YEAR TIME LAGS) Nation Lag Parti ci pate Initiate Level of Conflict Type of Conf1ict Level of Conflict Type of Conflict N War N Mil. N Thr't N Maj/ N Maj/ N War N Mil. IN Thr't N Maj/ N Maj/ Act. Maj. Min. Act. Maj. Min. USA 0 2 .00 2 .00 4 .00 5 .00 3 .00 1 .00 2 .00 4 .00 4 .00 3 .00 0 2 .00 2 .00 3 1.00 5 .60 2 .00 1 .00 2 .00 3 1 .00 4 .75 2 .00 under- -1 1 .00 1 , .00 3 .67 4 .50 1 .00 1 .00 1 .00 3 .67 4 .50 1 .00 recog. -2 1 .00 1 .00 2 .50 3 .33 1 .00 1 .00 1 .00 2 .50 3 .33 1 .00 .-3 1 .00 0 3 .67 3 .67 1 .00 1 .00 0 3 .67 3 .67 1 .00 UK 0 1 .00 11 .18 5 .20 1 .00 16 .19 1 .00 8 .25 5 .20 1 .00 13 .23 0 1 .00 2 .50 2 .00 1 .00 4 .25 1 .00 2 .50 2 .00 1 .00 4 .25 under- -1 0 5 .80 2 .00 1 .00 6 .67 0 3 .67 2 .50 1 .00 4 .75 recog. -2 0 3 .67 3 .67 1 1.00 5 .60 0 3 .67 3 .67 1 1 .00 5 .60 .-3 0 1 .00 3 .67 0 4 .50 0 1 .00 3 .67 0 4 .50 FRN 0 ! .00 9 .33 1 .00 0 11 .27 ] .00 8 .38 1 1 .00 0 10 .40 0 1 .00 5 .40 0 0 6 .33 1 .00 4 .50 0 0 5 .40 under- -1 1 .00 3 1 .00 1 1.00 0 5 .80 1 .00 3 1 .00 1 1.00 0 5 .80 recog. -2 1 .00 5 .80 1 1.00 0 7 .71 1 .00 4 .75 1 1.00 0 6 .67 .-3 .00 5 .60 1 1.00 0 7 .57 1 .00 4 .50 1 1.00 0 6 .50 GMY 0 4 .50 2 .00 1 .00 5 .40 0 4 .50 2 .00 1 .00 5 .40 TABLE 18---Continued Nation Lag Parti ci pate Initiate Level of Conflict Type of Conflict Level of Conflict Type of Conflict N War N Mil . N Thr't N Maj/ N Maj/ N War N Mil . N Thr't N Maj/ In Maj/ Act. Maj. Mi n. Act. Maj. i Mi n. i A-H 0 0 3 .67 2 .50 0 5 .60 0 3 .67 1 .00 0 .50 ITA 0 2 .50 7 .29 2 1 .00 0 11 .45 2 .50 6 .33 3 .67 0 hi .45 i USR 0 2 .50 9 .11 6 .17 7 .29 10 .10 1 1 .00 7 .14 6 .17 4 .50 1 10 .10 r o 2 .50 5 .20 6 .50 6 .33 7 .43 1 1 .00 4 .00 6 .67 4 .75 7 .29 under- -1 2 1 .00 5 .40 6 .83 6 .50 7 .86 1 1 .00 4 .25 6 33 4 .50 7 .29 recog. -2 2 1 .00 4 .50 6 .67 6 .50 6 .83 1 1 .00 3 .33 6 50 4 .50 6 .50 .-3 1 .00 1 1 .00 5 .40 3 .33 4 .50 0 1 1 .00 5 40 2 .50 4 .50 CHN 0 2 .50 6 .33 1 .00 2 .50 7 .29 2 .50 6 .33 1 .00 2 .50 7 .29 under- [ o 1 1 .00 3 .67 1 1.00 2 .50 3 1.00 1 1 .00 3 .67 1 1 00 2 .50 3 1.00 JPN 0 3 .67 3 .00 0 2 .50 4 .25 3 .67 3 .00 0 2 .50 4 .25 0 2 .00 3 .33 0 1 .00 4 .25 2 .00 3 .33 0 1 .00 4 .25 under- -1 3 .67 2 .00 0 2 .50 3 .33 3 .67 2 .00 0 2 .50 3 .33 recog. -2 3 .67 2 .00 0 2 .50 3 .33 3 .67 2 .00 0 2 .50 3 .33 .-3 3 .67 2 .00 0 2 .50 3 .33 3 .67 2 .00 0 2 .50 3 .33 151 several factors — including status quo foreign policies, victories in major wars, and perceived exclusion from international alignments — that might "explain" these discrepancies. We have discovered that the strongest evidence linking underrecognition, in the presence of certain contextual variables, to involvement in military confrontations is found for the United States and China; this connection also tends to exist for Britain, France, the Soviet Union, and Japan. And furthermore, the relationship is best supported when a three-year time lag is introduced. Finally, there does not appear to be much difference between our ability to predict participation in confrontations and our ability to forecast the initiation of confrontations; nor do we more accurately forecast war-years than military action- or threat-years, or confrontations with major powers more accurately than those with minor powers. CHAPTER V FROM MILITARY CONFRONTATION TO INTERSTATE WAR Having examined the first stage of the model—and having discovered a large number of discrepancies 'between what the literature suggests and the data reveal—we turn our attention to the second stage. Here the unit of analysis becomes the conflict, rather than the year, and we focus upon dyadic relationships. We have a population of confrontations; some eventuating in war, and others not. We are interested in knowing whether we can accurately predict which interstate military confrontations (IMCs) will escalate into these more severe and sustained conflagrations. In Chapter One it is argued that the same intervening variables that account for changes in the probability of a confrontation occurring also largely account for the escalation of military confrontations into full-scale wars. However, rather than viewing these variables as attributes of nations, it is necessary to now see them as properties of conflicting dyads. Thus, it is hypothesized that the greater the discrepancy between the national power of opposing parties, the Jess the likelihood 152 153 that the confrontation will erupt into war. Concomitantly, since the opportunity to employ large-scale military force is greatest when countries are contiguous to one another, non- neighboring opponents are less likely to become involved in war, while contiguous states are more likely. A third factor in the posited conflict spiral is past experience, and it is hypothesized in Chapter One that the greater the joint suffering of the parties in prior war encounters (regardless of the identity of their opponents), the less probable it will be that they will push current confrontation to open warfare. Finally, the structural interrelationships among the component units are important.. The greater the bi-polarity of the major power subsystem, the more likely the occurrence of war; but, if the opponents are major trade partners or military allies, the probability of war is reduced. Schematically, the second stage appears as below: TRADE* & ALLIANCES interstate interstate military wars confrontations *used only in 20th century runs (not measured prior to 1879) + = increases likelihood of IMC - = decreases likelihood of INC 154 The procedures for the basic index construction of the intervening variables are detailed in Chapter Two and it would be redundant to reiterate them here. It shall suffice for me to outline the manner by which these measures of national attributes are converted into indicators of dyadic relationships. First, the indicator of differences in national power capabilities between opposing states (PWRDIF) is formulated as a simple dichotomy. This is necessary because we used data for only the major powers when we computed power capability scores in Chapter Two. As a result, we do not have scores for non-major powers. We can, however, make a simplifying assumption that major powers are more or less equal to one another in terms of power capabilities, but that they are considerably more powerful than other states. Thus, if a military confrontation is between two major powers, PWRDIF is scored "zero"; if between a major and minor power, it is scored "one." It should be recognized, however, that while this dichotomous indicator reflects differences in power, it also taps a host of other differences between major and non-major powers, e.g., the greater number and diversity of major power interactions, the systematically different treatment of major powers by other states, the different perceptions of decision makers concerning the role and ability of major powers, the use of major power languages as international languages and major power currencies as international currencies, the large concentration of home offices of multinational corporations in major powers, and the 155 preferential voting structure in most universal international governmental organizations that accords greater weight to major power votes. While differences in national capability are probably the most fundamental distinction between major and non-major powers, and while I will continue to label the indicator "PWRDIF," the reader would do well to keep in mind the assortment of differences housed under this rubric. The indicator of the second variable—contiguity— is also dichotomously scored and, as outlined in Chapter Two, reflects common land boundaries and overlapping territorial waters. The indicator of the third variable—prior war experience---is simply the sum of the battle deaths per million population (modified by the inverse logistic decay function) of the opposing states. The indicator of system bi-polarity is the same as that used in the preceding chapters. And finally, trade and alliances are dichotomous indicators, measured as detailed in Chapter Two. In the current chapter, we are interested in whether the states in conflict are major trade partners or have mutual defense pacts with one another. The primary problem associated with these rules for indicator construction is that they assume that all confrontations are strictly dyadic. But, since thirty percent of the interstate military confrontations (IMCs) in the current data set involve more than one state on a given side, a rule had to be adopted In order to include these cases in the analyses. I decided to 156 treat these "n-nation" confrontations as a set of dyadic encounters between each major power involved in the IMC and the state on the opposing side that would maximize the effect of a particular variable. This coding rule reflects my hunch that national decision makers, like political researchers, do not have an adequate calculus for aggregating the attributes of their opponents. They are therefore likely to make "worst case" estimates on a dyadic basis. That is, if any of their opponents are contiguous, the most likely site for an attack is from or against the contiguous adversary; if any opponent is more powerful, then that adversary is most feared; if any opponent is an ally, then the greatest threat to the alliance is a conflict with that adversary; and so forth. In addition, the decision to maximize the effects of the intervening variables means that each major power is included only once in each n-nation IMC and, therefore, the importance of n-nation confrontations is not artificially inflated as it might be if a strategy was adopted such as including all possible conflict dyads that contain at least one major power member. Thus, according to the "maximizing" decision rule, a major power is coded as participating in a major/major confrontation if any state in the opposing coalition is also a major power. Similarly, if any state in the opposing coalition is contiguous to a participating major power, then the major power is reported to be in a confrontation with a contiguous country. The war experience score is computed as the sum of the war experience 157 of a given major power and the state in the opposing coalition having the most severe war experience. And a major power is said to be in a confrontation with a trade partner or ally if it has this relationship with any state in the opposing coalition. The bi-polarity indicator is, of course, unaffected by n-nation confrontations. The decision to treat these coalition IMCs as a set of dyadic disputes does no violence to the analytic results for the nineteenth century; a re-analysis of the data, omitting the n-nation cases, does not produce any significant changes. In the twentieth century, however, omitting n-nation confrontations necessitates that we exclude the hostilities that escalate into the two world wars. These two conflagrations incorporate more than half of all twentieth century war dyads in our data set and an even larger proportion of the major power/major power war dyads. Their omission eliminates all German and all Austro- Hungarian war dyads, and reduces by a sizable amount the "variance" in the outcome variable for all other major powers. In short, since the world wars exert such an appreciable influence on the twentieth century relationships reported below, the decision to treat n-nation confrontations as a set of dyadic encounters has more import for the later than for the earlier century. The Nineteenth Century The object of the following data analyses is to ascertain, for all conflict dyads that contain at least one major power member, 158 whether or not the intervening variables — as they exist at the time of the confrontation—serve to enhance or constrain the occurrence of war. For this reason no time lags are introduced. Parallel analyses are undertaken on overlapping sets of data. One data set includes the entire population of conflict dyads; the other, only status inconsistent dyads, i.e., dyads in which at least one major power member is status inconsistent. In this manner, we shall be able to determine (1) whether a particular intervening variable appreciably enhances our ability to account for the occurrence of war and (2) whether knowing that the conflict dyad contains a status inconsistent state increases our ability to correctly predict the outbreak of war. It should be remembered that, in the model postulated in Chapter One, it is posited that (1) a relationship does exist between inconsistency and confrontation, but that (2) no direct association exists between inconsistency and war. We shall continue to differentiate between nineteenth and twentieth century relationships and to examine the effects of the intervening variables separately for each major power. Correlational and probit techniques are employed as the primary modes of data analysis. Since there are, in general, too few observations for each nation to carry out multivariate analysis, we begin by examining bivariate associations. 159 The Indicators of Reachability Let us look first at the two dichotomously-measured indicators of reachability (difference in power and presence of contiguity) to see whether or not they help us to account for the outcome variable (no war/war). As a measure of the strength of association between the reachability indicators and the outcome variable, I use the contingency table statistic Cramer's 0. Phi is a chi-square-based measure that, in the 2x2 table, is equivalent to the Pearson product moment coefficient r (Hays, 1963). TABLE 19 CRAMER'S PHI-SQUARE BETWEEN EACH MAJOR POWER'S WAR INVOLVEMENT AND THE TWO INDICATORS OF REACHABILITY, FOR 19th CENTURY CONFLICT DYADS All Dyads Stat. Inc. Dyads PwrDif Con tig PwrDif Contig 2 + 2 0 + 2 Predicted —>- N - 0 0 N 0 - r UK 27 - .01 19 - .12 FRN 29 - .07 - .00 17 - .06 + .00 GMY 11 - .05 + .15 6 .00 .00 A-H 14 - .00 - .00 8 .00 + .11 ITA 7 - .22 + .53 6 - .20 + .50 USR 16 - .09 - .00 15 - .09 - .00 Fisher exact test: *** < .01 < ** < .05 < * < .10 significance lev 160 As can be seen from the 0 s reported in Table 19, the directions of the relationships between the two reachability indicators and war involvement are generally as predicted; that is, difference in power is for the most part negatively correlated, and contiguity positively associated, with war involvement. However, neither reachability indicator is particularly powerful. Indeed, it is only for the small subset of Italian cases that we find sizable 0^s. Having found that the associations reported in Table 19 are not very powerful, we nevertheless ask whether the relationships between the reachability indicators and war involvement are different for the subset of status inconsistent dyads than for the set of all conflict dyads. Chi-square tests are used to investigate this question. The logic underlying the tests is as follows: if status inconsistency is unassociated with war involvement, then we would expect that the proportion of status inconsistent dyads that results in war would be the same as the proportion of all dyads that results in war. Thus, for each major power, the distribution of cases for status inconsistent dyads (the observed frequency) is compared with the distribution of cases for all dyads (the expected frequency). Due to the small number of cases in this particular investigation, the observed frequency is adjusted by 0.5 units, so as to reduce the difference between the expected and observed frequencies in each cell of the table (cf. Blalock, 1960). Not a single X proves to be 161 significant at the .10 level, demonstrating that the 0 s for the underrecognized dyads are not appreciably different from those for all conflict dyads. On the basis of the preceding paragraphs, I conclude that, for major powers during the nineteenth century—whether or not they are in status inconsistent dyads — there is a weak negative zero-order relationship between difference in power and war involvement, and a weak positive relationship between contiguity and involvement. Thus, a confrontation between two major powers is slightly more likely to erupt into war than one between a major and a minor power; indeed, thirty percent of major-major as compared to eighteen percent of major-minor confrontations during the nineteenth century result in war. And, similarly, a confrontation between neighboring states (at least one of which is a major power) is more volatile than one between noncontiguous states — thirty percent of the former as compared to seventeen percent of the latter ending in war. The Indicator of War Experience Turning to the indicator of war experience, not much needs to be said. As we found when examining the first stage of the model, there is very little relationship during the nineteenth century between prior war experience and subsequent conflict (in this instance, war) involvement. 162 TABLE 20 BISERIAL CORRELATIONS BETWEEN EACH MAJOR POWER'S WAR INVOLVEMENT AND PRIOR WAR EXPERIENCE, FOR 19th CENTURY CONFLICT DYADS All Dyads Stat. Inc. Dyads War Exp. War Exp. 2 2 Predicted —>- N r " N r, " b__ b_ UK 27 - .01 19 - .08 FRN 29 - .00 1 7 - .01 GMY 11 - .00 6 + .23 A-H 14 + .01 8 - .01 ITA 7 + .00 6 .02 USR 16 - .08 15 - .09 F-test from anova: *** < .01 < ** < .05 *^ * £ .10 significance level Biserial correlations between the continuously-measured war experience indicator and the dichotomously-measured outcome variable (no war/war) are presented in Table 20. We can see that, for the set of all conflict dyads, there is no relationship very different from zero. For the subset of conflict dyads having at least one status inconsistent major power member, only Germany has a squared biserial correlation of any size, but the magnitude of the sampling error of the biserial r suggests that this is probably not significantly different from the .00 correlation for all German conflict dyads. Indeed, none of the biserial 163 correlations for the status inconsistent conflict dyads is significantly different from the rbs for all conflict dyads. Thus, whether we examine all conflict dyads or only status inconsistent conflict dyads, there is, for the nineteenth century major powers, no meaningful bivariate relationship between prior war experience and the escalation of subsequent confrontations into wars. The Indicators of Structural Relationships Finally, we look at the indicators of structural relationships — polarity and reciprocal interstate bonds. From the biserial correlations in Table 21, we see that major power polarity appears to be—as it was in the first stage of the model—the most powerful intervening variable in the nineteenth century. However, unlike our earlier findings, polarity is now consistently negatively associated with war involvement, and the magnitude of the sampling error of the biserial r suggests that this relationship exists regardless of whether or not status inconsistent major powers are involved in the conflict dyads. Other researchers have also uncovered this negative association between major power bi-polarity and, in their investigation, the magnitude and severity, as well as frequency of war in the nineteenth century interstate system (Singer and Small, 1968). These findings of a negative relationship between bi-polarity and war—in conjunction with the positive relationship 164 between bi-polarity and military confrontation, unveiled in the preceding chapter—offer empirical support during the nineteenth century for Waltz' (1964, 1967) contention that bi-polarity is associated with recurrent crises and confrontations, but that these conflicts remain limited and do not evolve into wars. TABLE 21 BISERIAL CORRELATIONS BETWEEN EACH MAJOR POWER'S WAR INVOLVEMENT AND SYSTEM POLARITY, FOR 19th CENTURY CONFLICT DYADS All Dyads Stat. , Inc. Dyads Polarity Polarity Predicted —N + r, 2 N b • ^ UK 27 - .00 19 - .37 FRN 29 - .17 17 - .22 GMY 11 - .33 6 - .04 A-H 14 - .17 8 - .38 ITA 7 - .77** 6 - .83* USR 16 - .26 15 - .34 from anova: *** < .01 < ** "5 .Ob"^ * 5 10 significance As for the measures of reciprocal interstate bonds, it has already been mentioned that reliable trade data are not available for the nineteenth century major powers, and consequently we cannot examine the relationship between trade partnerships and 165 war involvement. The alliance data, however, do produce an interesting result. There are only four instances (Convention of Olmutz, 1850; Seven Weeks War, 1866; Montenegrin Troubles, 1880; and China Concessions to Russia, 1898) during this earlier century when major powers threaten to use or actually employ military force against a state with which they have a mutual defense pact, one of these confrontations resulting in war. Thus, while the number of observations is obviously too small for us to confidently describe the relationship between alliances and war involvement, we can say that military confrontations between nineteenth century alliance partners are quite rare. This should increase our confidence that the relationship between bi-polarity and war involvement is not being distorted by military confrontations within the poles. The Multivariate Relationship Although we have too few observations to examine the "completely-specified" second stage of the model for the individual major powers, we have the fortunate situation that the directions of the zero-order relationships are generally consistent across states. Thus, it is possible to pool our cases and to cautiously interpret the results as applicable to all nineteenth century conflict dyads containing major powers. TABLE 22 CORRELATION AND STANDARDIZED PROBIT COEFFICIENTS FOR THE FOUR INTERVENING VARIABLES WHEN PREDICTING TO POOLED MAJOR POWER WAR 1INVOLVEMENTS , FOR 19th CENTURY CONFLICT DYADS All Dyads K2 PwrDif Contig War Exp. Polarity P R Predicted - 4- - + 0 or r^ 98 -.11 + .14 -.06 _ 38*** 98 -.17 + .07 -.15 -.36 .18** .00 Stat. I nc. Dyads 0 or r, 67 -.11 + .20 -. 0 5 (. 1 8) -,50(.15)*** b b* 67 -.22(.17) +.21(.16) -.27(.25) -.50(.18) .36*** .17 NOTE: Numbers within parentheses are standard errors. Fisher exact test (for 0), f-test from anova (for r, ), and ^ from probit {yJ- with 4 df) *** < .01 < ** < .05 < * < .10 significance level. 167 When we do so (Table 22), there are no real surprises. The zero-order correlations (0 and r^) are in the same direction and are approximately of the same magnitude as the partial probit coefficients (b ). This is a reflection of the low level of multicollinearity among the four intervening variables. The only coefficient that differs for the pooled data from that for the individual states is war experience, and it will be recalled that this variable is basically uncorrelated with war involvement except for a positive association with Germany in underrecognized conflict dyads. Since, however, that positive correlation is based upon only six cases, it is not surprising that it exerts little influence on the pooled data. With the exception of bi-polarity, the effects of the intervening variables are in the predicted direction, but account for little "variance" in major power war involvement; the bi-polarity variable produces a moderately strong correlation, but one opposite to that which we hypothesized in Chapter One. Although the measures of both strength of association and predictive power increase when we examine only those conflict dyads containing status inconsistent major powers, "X2--tests on the 0s, the error of the sampling distribution of the r^s, and the standard errors of the I) s, all suggest that the reported correlations and probit coefficients for the status inconsistent dyads are not significantly different from those for all nineteenth century conflict dyads. 168 And finally, from the probit "residuals" it appears that we come nearest to predicting wars between major powers (as opposed to ones between major and minor powers). But, given the small number of war involvements that we can correctly predict in the nineteenth century, it seems somewhat trivial to linger on this point. The Twentieth Century There are, again, in the twentieth century, too few observations for the individual major powers to permit multi• variate analysis. Thus, we once more turn to the examination of bivariate associations. The Indicators of Reachability We find for the reachability indicators (Table 23) considerable convergence between the nineteenth and twentieth century results. As in the nineteenth century, the directions of the relationships between the intervening and outcome variables are, by and large, those that are posited in the model . Differences in power are inversely related to war involvement; that is, confrontations between major powers are more likely to result in war than are confrontations between major and minor powers. Approximately half of the major-major confrontations end in these more severe conflagrations, whereas only slightly more than ten percent of the major-minor 169 TABLE 23 CRAMER'S PHI-SQUARE BETWEEN EACH MAJOR POWER'S WAR INVOLVEMENT AND THE TWO INDICATORS OF REACHABILITY, FOR 20th CENTURY CONFLICT DYADS All Dyads Stat. Inc. Dyads PwrDif Contig PwrDif Contig Predicted —N - 02 + N - i + 02 USA 14 - .12 - .22 10 .00 - .25 UK 22 - .35** 10 - .17 FRN 15 - .34* + .20 9 - .25 + .25 GMY 11 - .59** + .13 8 - .56 + .05 A-H 7 - 1.00 + .03 3 - 1.00 ITA 15 - .20 + .42* 4 - .33 + 1 .00 USR 32 - .17** + .00 29 - .15** + .00 CHN 11 - .15 - .45 6 - .40 - 1 .00 JPN 13 - .01 + .00 13 - .01 + .00 Fisher exact test: *** < .01 < ** < .05 < • *L .10 significance le< 170 confrontations escalate into war. The negative relationship between difference in power and war involvement appears to contradict Ferris' (1973) finding that, for the 1850-1965 period, there is some evidence to suggest a positive association between power disparity and involvement in intense military conflict. However, since Ferris is working with a different population of cases, a different indicator of power, and, most importantly, is not controlling for the average power disparity in conflict, dyads, the discrepancy may be more apparent than real. In addition, it should be remembered that the dichotomous PWRDIF indicator used in the current study is tapping not only differences in power, but also the host of other attributes that distinguish major powers from minor powers. The second reachability variable — contiguity—is not strongly associated with war involvement. Almost all the coefficients are positive, but weak; the few sizable coefficients are generally based upon a very small number of cases. Two countries — the United States and China — actually have negative correlations. The negative findings for the United States 1 If the two world wars were coded to reflect, all possible conflict dyads, rather than one conflict dyad for each major power participant, then the proportion of major-minor confrontations ending in war would increase to approximately one in three. This, however, would introduce a number of dyads that were only peripherally engaged in combat, as well as some that occurred simply because minor powers were in the path of onrushing armies. In addition, the inclusion of all possible conflict dyads would create a situation in which the two world wars would totally dominate the twentieth century statistical results. 171 legitimately reflect the fact that all American war involvements in the twentieth century (WW I, WW II, Korea, and Vietnam) take place abroad, while the negative correlations for China are somewhat misleading. China is involved in eleven military conflicts — including two wars — since becoming a major power; only one of these conflicts (the Korean War) is fought against noncontiguous adversaries. And in that war, direct Chinese intervention occurs only after a contiguous country (People's Republic of Korea) has been invaded. Thus, for most major powers during the twentieth century, contiguity is positively related to war involvement, but the association is generally weak. Indeed, if we combine the data for all major powers, we discover that the same proportion (twenty-four percent) of military confrontations between noncontiguous countries, as between neighboring countries, escalates into wars. 2 In the twentieth century, as in the nineteenth, -tests demonstrate that, for both reachability indicators, the correlations for conflict dyads containing status inconsistent major power members are not significantly (at the .10 level) different from those for all conflict dyads. On the whole, then, the results for the two centuries are quite similar—although, during the current century, the correlations between difference in power and war involvement are stronger than those for the preceding eighty years, while the association between contiguity and involvement is probably somewhat weaker. 172 The Indicator of War Experience The third intervening variable in our model—prior war experience—turns out to be important for only a very few major powers in the twentieth century. The greatest surprises in Table 24 are the exceedingly large positive associations between German, Austro-Hungarian, and Chinese war experience and subsequent war involvement. TABLE 24 BISERIAL CORRELATIONS BETWEEN EACH MAJOR POWER'S WAR INVOLVEMENT AND PRIOR WAR EXPERIENCE, FOR 20th CENTURY CONFLICT DYADS All Dyads Stat . Inc. Dyads War Exp. War Exp. 2 2 Predicted — N N - rb " rb USA 14 - .03 10 - .34 UK 22 - .10 10 - .20 FRN 15 - .17 9 - .22 GMY 11 + .99*** 8 + .99** A-H 7 - .05 3 + .99 ITA 15 - .20 4 - .37 USR 32 + .08 29 + .07 CHN 11 + .25 6 + .99** JPN 13 - .04 13 - .04 from anova: *** <. 01 < ** < .05 < • < .10 significance level 173 The correlations for Germany and Austria-Hungary are not only surprising, they are also somewhat deceiving. For example, during the twentieth century, Germany is involved in two wars (WW I and WW II) out of eleven instances of military confrontation. In 1914, the German war experience score is zero and, in 1939, it is also extremely low. Similarly, Germany's opponents in 1914 (Russia) and 1939 (France) have relatively low war experience scores. Yet, despite the very small absolute values of the dyads' joint war experiences, the Russo-German (1914) and Franco-German (1 939) dyads have relatively large sums when compared with the other nine war experience scores involving German conflict dyads — indeed, these are the highest war experience scores of any German conflict dyads. Hence, very small absolute values 2 nevertheless produce very large r^'s. Identically, the Austro- Hungarian correlation for status inconsistent conflict dyads (based on only three observations) also reflects the fact that a very small war experience score (Austria-Hungary/Russia, 1914) can nevertheless be the largest score for a subset of dyads. On the other hand, the correlation for Chinese status inconsistent dyads accurately reflects that her one war involvement while underrecognized (Korean War, 1950) occurs not only for the Chinese dyad having the largest war experience value, but also when both she and her opponent (the United States) have considerable national war experience scores. Thus, despite the somewhat varied direction of the war experience/war involvement relationships 174 for the individual major powers, it is only the Chinese that appear to be appreciably more war prone under conditions of high battle losses in prior conflagrations. Finally, the magnitude of the errors of the sampling distribution suggests that there are no significant differences in the r^s for status inconsistent dyads as compared to all conflict dyads, with the exception of those for Austria- Hungary and China. However, due to the small number of observations for these latter two states (three and six respectively), I am indeed skeptical about the "significance" of these differences. The Indicators of Structural Relationships The set of structural intervening variables produces some interesting findings (Table 25). First, polarity appears to be positively related to war involvement in the twentieth century (the only significant difference between the coefficients for status inconsistent dyads and those for all conflict dyads being for the four Italian observations). The correlations are not generally very powerful, but they are dramatically different from the findings for the nineteenth century. Thus, whereas in the earlier century we uncovered support for Waltz1 (1964, 1967) argument that bi-polarity may increase the probability of confrontation but will reduce the probability of war, we see that the twentieth century better approximates the Deutsch and Singer 175 TABLE 25 BISERIAL CORRELATIONS AND CRAMER PHI-SQUARES BETWEEN EACH MAJOR POWER'S WAR INVOLVEMENT AND TWO INDICATORS OF STRUCTURAL RELATIONSHIPS, FOR 20th CENTURY CONFLICT DYADS All Dyads Stat. Inc. Dyads Polarity Trade Polarity Trade 2 2 Predicted — N + - N - rb rb i USA 14 .00 + . 49** 10 - .14 + .43* UK 22 + .15 + .09 10 + .25 + .03 FRN 15 + .20 + .34* 9 + .12 + .25 GMY 11 + .09 + .59** 8 + .17 + .56 A-H 7 - .07 + 1.00 3 - .31 + 1.00 ITA 15 + .36* + .10 4 + mgg*** + .33 USR 32 + .03 - .00 29 + .02 + .00 CHN 11 - .00 6 + .18 JPN 13 _ .01 + .15 13 _ .01 + .15 F-test from anova (for r, ) and Fisher exact test (for 0 ): *** <; mQi < ** < .05 < * < .10 significance test 176 (1964) contention that bi-polarity increases the probability of both conflict and war. Second, we find that, contrary to expectation, trade consistently correlates positively with war involvement. That is to say, if the opposing countries in a conflict dyad are major trading partners, then that confrontation is more likely to erupt into war than one between non-trade partners. Indeed, approximately half of the confrontations involving trade partners, as compared to fifteen percent of those involving non-trade partners, result in war. (And, again, there are no significant differences between the coefficients for the status inconsistent dyads and those for all conflict dyads.) This association between trade and war may not be as counterintuitive as if first appears. Wallensteen (1973) reports that, during 1920-1965, nearly all military conflicts between topdogs and underdogs occur in areas in which the intervening topdog has sizable trading interests (although this relationship does not hold for topdog-topdog confrontations). And Russett (1967) finds that for pairs of countries involved in conflicts resulting in over one hundred battle-related fatalities between 1946 and 1965, those that belong to the same trade group are more than twice as likely to fight one another than are nations that belong to different groups or to no group. One plausible explanation for this positive association between trade and war involvement is that economic exchange 177 makes trade partners more salient to one another and increases the opportunity for differences on economic issues to place strains on the bilateral relationship. It may be that common economic interests often serve to lessen these strains; however, once a conflict between trade partners reaches the level of military confrontation (the level on which our analysis focuses), the conflict may have already exceeded that point at which these mutual economic interests might mitigate national behavior. A second plausible explanation—partially incongruent with the first—is that, while major trade partners are likely to be salient to (and interact often with) one another and to thereby become more attentive to incompatibilities, trade represents such a small fraction of a nation's gross national product that it is unlikely to foster that degree of economic interdependence that might mitigate conflict at any level. While the first two explanations focus upon the increased opportunities for incompatibilities to arise between major trade partners, a third possible explanation is that we might simply be drawing a spurious inference from the positive correlations between trade and war involvement. Linnemann (1966), in a cross-national study of international trade patterns in 1959, finds that the amount of trade between countries is positively related to the size of their gross national products and negatively related to the geographic distance between them. Thus, Linnemann's findings would suggest that major powers are 178 most likely to trade with other major powers and with countries that are geographically proximate. If this is so, the positive correlations between trade and war involvement may reflect no more than that both these variables are associated with the relative capabilities of, and geographic distance between, states. When looking simply at bivariate relationships (as we are presently doing), we are unable to eliminate these possibly confounding effects. We shall, however, reconsider this problem below when we examine the multivariate relationships. Finally, let us look at the third indicator of structural relationships—military alliances. As in the previous century, there are too few instances in the twentieth century when a major power becomes involved in a military confrontation with a defense pact ally to say anything definitive. Of the eight occasions on which states having mutual defense pacts oppose one another, two (Italy-Germany, 1915; the Soviet Union-Hungary, 1956) result in war, the remaining six do not. This is approximately the same proportion of confrontations/wars as found for the entire population of cases. Thus, once a confrontation occurs, the fact that the opposing states have a mutual defense pact with one another does not appear to alter the likelihood that the confrontation will escalate into war. However, the fact that there are only eight confrontations between allies strongly suggests that countries that have mutual defense pacts rarely become involved in military confrontations with their cosignatories. 179 The Multivariate Relationship Since we once more find a great deal of similarity across major power conflict dyads, we shall again pool our observations and proceed with multivariate analysis. Caution is, of course, appropriate when interpreting the resulting coefficients. We see in Table 26 that the zero-order correlations (0 and r^) are in the same direction and are of approximately the same magnitude as the partial probit coefficients (b ). In addition, the coefficients for the status inconsistent dyads are not significantly different from the coefficients for all twentieth century conflict dyads. There are no real surprises in the direction or magnitude of the coefficients from the pooled data. As would be expected from the analyses on the "by nation" conflict dyads, differences in power and trade are the most powerful intervening variables. We see that major power/major power confrontations are more likely to evolve into war than are major power/minor power confrontations and, similarly, confrontations between trade partners are more volatile than those between non-trading states. The question that comes immediately to mind is, of course, how best to interpret the positive association between trade and war involvement. The reader will recall that it was suggested above that the trade variable may simply be tapping power capability awl geographic proximity, and that it is these latter two factors, rather than trade per se, that are associated with war involvement. CORRELATION AND STANDARDIZED PROBIT COEFFICIENTS FOR THE FIVE INTERVENING VARIABLES WHEN PREDICTING TO POOLED MAJOR POWER WAR INVOLVEMENTS, FOR 20th CENTURY CONFLICT DYADS All Dyads *2 PwrDif Contig War Exp. Polarity Trade R Predicted —>- N + - + or r, 130 -.45*** +.01 -.13 +.17 +.38*** D b* 130 -.36 -.03 -.10 +.17 +.30 .35*** .32 §j Stat. Inc. Dyads 0 or rb 82 -.40*** -.10 -.09(.15) +.!!(.15) +.37*** b* 82 -.33(.13) -.06(.13) -.03(.14) +.20(.15) +.36(.14) .35*** .34 NOTE: Numbers within parentheses are standard errors. Fisher exact test (for 0), 2 f-test from anova (for rb), and ^* from probit (X with 5 df): *** < .01 < **<.05< *< .10 significance level. 181 To rule out the possibility of spurious inference, we would have to "control" for power and proximity. The probit algorithm does just that; the b*s reported in Table 26 are partial coefficients, i.e., the probit coefficient for each variable reflects the association between that variable and war involvement, after the effects of the other variables in the equation have been removed. Thus, the positive probit coefficients for trade (Table 26) depict the relationship between trade and war involvement when differences in power and geographic contiguity are controlled. To this extent, the inference that trade is positively related to war involvement is not spurious. However, it should be recognized that this is not a fully satisfactory control for the factors that Linnemann (1966) claims to be important for international trade flows. This is because the measures of capability and proximity that I use are both dichotomously scaled; continuously-scaled measures would offer stronger evidence as to whether spuriousness may be ruled out. However, the fact that we have, at least in a crude manner, controlled for both potentially confounding variables and still uncovered a positive association between trade and war involvement should reduce the possibility that we might draw incorrect inferences from the statistical results. Returning to Table 26, we see that polarity is the third most powerful intervening variable—the more bi-polar the major power subsystem, the more probable the outbreak of war. Contiguity, 182 on the other hand, is basically unrelated to war involvement. The near zero coefficient is, in small part, due to the differential effects that variable has for different nations in conflict dyads; but even in the "by nation" analyses, contiguity did not prove to be a particularly powerful discriminator. This is a substantively interesting finding. Nearly twenty-eight percent of the dyads involved in confrontations during the nineteenth century and more than forty-two percent of the dyads in the twentieth century contain contiguous nation- states, phenomenally high percentages given the much smaller proportion of states in the interstate system that are contiguous to major powers. In the nineteenth century, fifteen percent of all possible dyads that contain at least one major power are comprised of geographically contiguous states. During the twentieth century, this figure declines to eight percent. Standard scores (i.e., z-scores) can be computed to determine the likelihood that we could obtain as high a proportion of contiguous dyads involved in confrontations (.28 in the nineteenth century and .42 in the twentieth) as we do, given the known proportion of contiguous dyads (.15 in the nineteenth century and .08 in the twentieth). The standard score for the nineteenth century (N=98) is 3.48; for the twentieth century (N=130), 14.43. Thus, the proportion of contiguous conflict dyads in the nineteenth century is about three and one-half standard deviations larger than would be expected—a situation that would arise by chance about 183 five times in ten thousand. The proportion of contiguous conflict dyads in the twentieth century is approximately fourteen and one-half standard deviations larger than would be expected—a situation that approaches statistical impossibility if geographic contiguity is unrelated to involvement in military confrontations. It can only be concluded, then, that contiguity offers a very large number of opportunities for military confrontation. However, the findings in the current chapter demonstrate that, once a confrontation has been initiated, major powers are no more likely to go to war with neighboring adversaries than with more distant opponents — suggesting that, for major powers, geographic distance does not place very great constraints on the usability of large-scale military force. Turning to the fifth of our intervening variables — prior war experience—the near zero coefficients are expected. As has already been noted, the only sizable correlations in the "by nation" analyses are for China (reflecting a single high score) and for Germany and Austria-Hungary (having exceedingly small absolute scores). Thus, when these observations are pooled with those for other major power dyads, the Chinese case has little influence and the German and Austro-Hungarian ones appear relatively small (actually increasing the association between low scores and war involvement). Finally, the question arises as to whether we are better able to predict that war will erupt given certain types of 184 conflict dyads; in our particular analyses, whether we can predict better for major power/major power conflicts (i.e., PWRDIF = 0) than for those between major and minor powers (i.e., PWRDIF = 1). For an answer, we look at the "residuals" from the twentieth century probit analyses. TABLE 27 TOTAL NUMBER OF MAJOR POWER/MAJOR POWER AND MAJOR POWER/MINOR POWER CONFLICT DYADS THAT TERMINATE IN WAR AND THE PROPORTION OF EACH TYPE THAT IS CORRECTLY PREDICTED BY THE PROBIT EQUATION, FOR THE 20th CENTURY All Dyads Stat. Inc. Dyads N Proportion N Proportion major/major dyads 18 .67 18 .67 major/minor dyads 10 .00 7 .00 It is quite evident from Table 27 that we are not able to predict the occurrence of major power/minor power wars. On the other hand, we can accurately forecast two-thirds of major power/ major power wars. Given this stark finding, I look at the data for the second stage of the model one final time, omitting all cases of major/minor confrontation. The Major Power/Major Power Conflict Dyads In re-analyzing the data, using only the major power/ major power conflicts, we confront the most severe small-N problem 185 to date. The maximum number of conflict dyads involving any particular major power during the nineteenth century is eight (for France), and the number is as small as one (for Italy). With these few observations it makes little sense to examine the data by nation, and we are forced to pool the observations for all major/major conflict dyads. Eliminating duplicate cases, we obtain the results reported in the top half of Table 28. The top half of Table 28 reveals that, in general, polarity and difference in power are strongly and inversely related to the eruption of war in nineteenth century major power/ major power conflict dyads; while contiguity and prior war experience are more weakly and positively related. The effect of the war experience factor is opposite that which we found when examining all nineteenth century conflict dyads (Table 22), suggesting that involvement in major/major dyads may override A2 past experience. The R for the total probit equation is a phenomenal .98, the measure of point predictive power a strong .44. In effect, we correctly predict four of six major/major war involvements, missing only the British/Russian conflict in Crimea and the Austro-Hungarian/Prussian conflagration in the Seven Weeks *2 War. The high R reflects the fact that we barely fail to forecast (">.50) these latter two war involvements; the predicted probability for Britain/Russia being .46 and that for Austria- Hungary/Prussia .47. Since we lose but two observations (France/ Austria-Hungary in the War of Italian Unification, 1859, and TABLE 28 CORRELATION AND STANDARDIZED PROBIT COEFFICIENTS FOR THE INTERVENING VARIABLES WHEN PREDICTING TO POOLED MAJOR POWER WAR INVOLVEMENTS, FOR ALL 19th AND 20th CENTURY MAJOR/MAJOR CONFLICT DYADS 19th Century—All Major/Major Dyads A2 PwrDif Contig War Exp. Polarity Trade R P Predicted —*- _N_ - + - + - 0 or rb 20 -.33 +.09 +.30 -.73*** b* 20 -.69 +.21 +.23 -.67 .98*** .44 20th Century—All Major/Major Dyads 0 or r. 34 +.17 +.19 -.05 +.02 +.42** 34 +.15 +.22 +.07 +.17 +.54 .34 .59 Fisher exact test (for 0), f-test from anova (for r. ), and \ from probit (7- with 4/5 df) *** < .01 < ** < .05 < * < .10 significance level 187 Germany/Japan in the Triple Intervention, 18.95) when we examine only status inconsistent dyads, the coefficients for this subset of cases prove to be nearly identical to those recorded in Table 28 and are, thus, not reported. What this does tell us, however, is that almost every major power/major power conflict dyad during the nineteenth century contains a status inconsistent member. In the twentieth century we also have the small-N problem that confronted us in the earlier century. Major power confrontations with other major powers range from a maximum of eleven (for the Soviet Union) to a minimum of one (for Austria- Hungary). Thus, we once more pool the observations for conflict dyads (bottom half of Table 28). We find that, in the twentieth century, only the trade variable has a significant impact on war involvement, again suggesting that the trade indicator may be more strongly reflecting interstate salience than economic interdependence. The effects of both difference in power and contiguity deviate from what we uncovered for all twentieth century conflict dyads (Table 26). Contiguity is, as originally hypothesized, positively associated with war involvement (although the association is weak). But PWRDIF is also, though unexpectedly, positively correlated. The PWRDIF indicator being used in the analysis of major power/ major power dyads is not the same as the one used in previous analyses; since we are now examining only major (lowers, 188 interval-scale scores (as opposed to dichotomous measures) of power capability are being analyzed. It might be argued that these interval-scale scores offer a stronger test of the "difference in power" hypothesis. As we have already noted, the nineteenth century PWRDIF coefficients, presented in the top half of Table 28, strongly support the hypothesis that war is more likely when opposing states are relatively equal in power. The twentieth century coefficients, presented in the bottom half of the table, do not. Indeed, for major power/ major power conflict dyads during the twentieth century, the greater the disparity in power capabilities, the greater the likelihood of war. The relationship is, of course, a very weak one — the square of the biserial correlation coefficient demonstrates that "difference in power" accounts for only three percent of the "variance" in the outcome variable. The coefficients for the remaining two variables — bi-polarity and prior war experience—are also very weak. Bi-polarity is, as we found earlier, positively related to war involvement in the twentieth century; prior war experience is, for all practical purposes, unrelated to subsequent war involvement. A2 The R from the probit analysis is not as sizable as we might have expected, reflecting the fact that a large number of predictions lie around the .50 probability mark. The measure of point predictive power is quite robust, as we correctly forecast thirteen of eighteen war involvements. We do, however, fail to 189 predict some rather crucial conflagrations, e.g., the Russo- Japanese War (1904), the German invasion of the Soviet Union (1941), and the Korean fighting between China and the United States (1950). Finally, it should be noted that all major power/major power conflict dyads in the twentieth century contain at least one status inconsistent member and, therefore, the coefficients for that subset of cases are identical to those reported in the bottom half of Table 28. Summarizing the Results of the Second Stage The analyses of dyadic relationships in the current chapter produce much more consistent results across states than did the national-level analyses in Chapters Three and Four. During both the nineteenth and the twentieth centuries we find, as predicted, that (1) confrontations between major powers are more likely to evolve into wars than are confrontations between major and minor powers, (2) the more severe the previous war experiences of the parties to the confrontation, the smaller the probability of war (although this relationship is extremely weak and has several exceptions), and (3) countries linked by mutual defense pacts rarely become involved in military conflict with one another. In the earlier century we discover, as hypothesized, that confrontations between contiguous states are more likely to erupt into war than those between non-neighboring countries; although no consistent relationship for this variable is uncovered in the 190 later century. We find a negative relationship between bi-polarity and war in the nineteenth century; a positive association in the twentieth. And contrary to expectation, trade is shown to be positively related to war involvement in the present century. It is demonstrated that the effects of the intervening variables are the same for conflict dyads containing status inconsistent major powers as they are for the entire population of conflict dyads. And finally, we discover that although the posited model does not help us to predict whether or not major power/mi nor power conflict dyads will eventuate in war, rather good prediction is obtained for major power/major power dyads. CHAPTER VI STATUS, CONFLICT, AND WAR We have completed our investigation of the relationships posited in Chapter One. It is now time to "take stock"—to recapitulate and integrate the findings, and to ascertain what implications may be drawn from the preceding analyses. I shall first proceed systematically through the hypotheses that comprise the model tested in this thesis, restating the expected relationships and reiterating the empirical associations that have been uncovered. I shall then conclude with a few observations for peace research in general. Status Inconsistency and Military Conflict I originally hypothesized that status inconsistent states are prone to involvement in interstate military confrontations, but argued that no direct link exists between underrecognition (i.e., status inconsistency) and war involvement. Subsequent analyses lead to the following general conclusions: (1) Status inconsistency is not universally associated with conflict proneness; although underrecognition has some import 191 192 in the nineteenth century, its effect on conflict behavior is largely a twentieth century phenomenon. And, (2) While we find that status inconsistency is associated with major power military confrontations (at least during the twentieth century), it has no additional effect on war involvement. Underrecognition and Confrontation There is a considerable amount of empirical evidence that, if status inconsistency is associated with military confrontation, it is mainly a twentieth century relationship. To begin with, bivariate analyses reveal, at best, only a few moderate associations between status inconsistency and involvement in military confrontations during the nineteenth century. And multivariate analyses demonstrate that knowing which states are underrecognized decidedly improves our ability to predict the occurrence of interstate confrontations for only two (France and Germany) of six major powers. In addition to this, we find that eighteen of twenty major power/major power conflict dyads in the nineteenth century contain at least one underrecognized state and only one-fourth of the twenty dyads actually contain two status inconsistent members. According to the binomial probability distribution, the likelihood that either of these could occur by chance is large—the former would be expected to occur one in three times and, the latter, nearly two in three times. To picture this, imagine that we have an urn containing red (status 193 inconsistent) and white (non-status inconsistent) balls, where there is one ball for each major power and the distribution of colors is proportionate to the distribution of status inconsistent states. If there is a total of T balls, W of which are white and R red, then the probability P-j of simultaneously drawing from the urn two balls, at least one of which is red (i.e., not drawing two white), is (T(T-l)/2) - (W(W-l)/2) W(W-l) = i > T(T-l)/2 T(T-l) and the probability P^ of selecting two balls, both of which are red, i s R(R-l)/2 R(R-l) T(T-l)/2 T(T-l) The classic binomial problem would be to determine the likelihood of drawing K or more pairs containing at least one red ball in N trials, given the expected probability P-j (or, similarly, the likelihood of drawing K or more pairs containing two red balls, given the probability P0). To place this example back into context, we are asking "what is the likelihood of selecting, at random, eighteen or more conflict dyads containing at. 1 east one status inconsistent member from a group of twenty conflict dyads, given that the probability of selecting such a dyad is P-j?" Or, similarly, "what is the likelihood of selecting five or more 194 conflict dyads in which both members are status inconsistent, given that the probability of selecting such a dyad is P0?" With the data that we have, however, we need to make a minor adjustment. Since the number of major powers and the proportion of underrecognized states vary from one year to the next, we really have N urns, each with its own expected probability. The question becomes, "what is the likelihood that from these N urns we could draw K or more pairs having at least one red ball (or K or more pairs having two red balls), given the mean expected probability P-j (or P^) for the urns?" The likelihood is expressed by the cumulative binomial formula N k=K where Q = 1 - P. In the nineteenth century, the probability (P-j) of selecting a conflict dyad containing at least one status inconsistent member is .833, resulting in a likelihood of .32 that we could draw by chance eighteen or more dyads with at least one underrecognized member in twenty attempts; and the probability (P^) of selecting a conflict dyad in which both members are status inconsistent is .264, giving a likelihood of .64 that we could randomly select five or more dyads in which both members are underrecognized. Thus, "all the empirical evidence—whether it be bivariate or multivariate, national level or dyadic— 195 points to the fact that there is no consistent, association between underrecognition and military confrontation for major powers in this earlier century. On the other hand, there is evidence supporting a positive association between underrecognition and military confrontation in the twentieth century. The United States, United Kingdom, and Soviet Union all display statistically significant bivariate relationships in this later century, and most other major powers (with the exception of Austria- Hungary, which is dismembered in 1918) also show positive coefficients. Similarly, in the multivariate analyses, knowing which states are underrecognized increases our ability to forecast the occurrence of interstate confrontations across all major powers for which sufficient data observations are available.^ Finally, we discover that all thirty-four major power/major power conflict dyads in the twentieth century contain at least one status inconsistent member, and twenty-two of these dyads have underrecognized countries on both sides. During the twentieth century, the expected probability that a major power/major power conflict dyad will contain at least one status inconsistent member is .833; the expected probability that it will contain two such members is .265. From the binomial distribution, we can ascertain that the likelihood of selecting 1 There are three major powers — Germany, Austria-Hungary, and Italy—that are too infrequently involved in confrontation when status inconsistent to permit stable parameter estimates. 196 by chance thirty-four dyads with at least one status inconsistent member is approximately two in a thousand, and the likelihood of selecting twenty-two or more dyads in which both parties are underrecognized is less than four in a million. The reader may feel that these probabilities underestimate the "real" likelihood that major power conflict dyads will contain status inconsistent members, and that they are simply an artifact of the "maximizing" decision rule applied to n-nation confrontations. Under the "maximizing" rule, if any major power in a coalition is underrecognized, then states in the opposing coalition are considered to be in a confrontation with a status inconsistent state. A much more conservative manner of dealing with n-nation confrontations is to select all possible pairs of opposing states. Then, for example, if there are three states on either side, there are nine conflict dyads. Using this decision rule for the twentieth century, we find that forty-one of forty-six major power/ major power conflict dyads contain at least one status inconsistent major power, and twenty-two of forty-six contain two. With the expected probabilities being respectively .834 and .264, the likelihoods derived from the binomial distribution are .20 and .001. Thus, while we could expect to randomly select forty-one or more major power/major power dyads with at least one status inconsistent member one-fifth of the time, the likelihood of randomly selecting twenty-two or more dyads in which both parties are underrecognized is only one in a thousand. 10/ In short, there appears to bo a consistent relationship between status inconsistency and major power involvement in military confrontation during the twentieth century. This relationship is particularly strong for major power/major power confrontations, suggesting that underrecognized major powers demonstrate their dissatisfaction by confronting other major powers. To the extent that this is true, policies of exclusion and diplomatic isolationism like those directed by the western Powers against the Soviet Union and, later, the People's Republic of China are counter-productive in that, ceteris paribus, they increase the likelihood of military confrontation. On the other hand, policies explicitly aimed at fostering diplomatic rapprochement are no guarantee for more peaceful behavior. That is to say, while the effects of status inconsistency are pervasive in the twentieth century, underrecognition is not a sufficient cause of military confrontation nor a necessary cause of major power/mi nor power confrontation (though it may be a necessary factor in major power/major power involvement). Underrecognition and War The second conclusion from the analyses on status inconsistency is that it. has no effect on major power war involvement beyond increasing (at least during the twentieth century) the likelihood of military confrontation. For one thing, knowing that a conflict dyad contains one or more status 198 inconsistent states does not improve our ability to predict whether or not a war will erupt. This is true during both centuries and for all major powers. A second piece of evidence is found for major power/ major power confrontations. In the nineteenth century there are six major power/major power war dyads, four of which contain one status inconsistent member and a fifth contains two status inconsistent states. The expected probability of selecting a dyad having at least one underrecognized member is .850; and of selecting a dyad with two such members, .250. The likelihood of obtaining by chance five or more dyads, each containing at least one underrecognized major power is .78; of obtaining one or more dyads with two underrecognized major powers, .82. Thus, the actual distribution of status inconsistent states in nineteenth century major power/major power war dyads is highly likely. The most convincing evidence, however, is found amongst the twentieth century results. Using the "maximizing" decision rule for including n-nation conflicts, there are eighteen major power/major power war dyads. Seven of these contain one status inconsistent state, and the other eleven contain states that are both underrecognized. Given the distribution of status inconsistent states in the years in which these wars occur, the expected probability of selecting a war dyad having at least one underrecognized member is .834, and the probability of 199 selecting a dyad with two such members is .263. The likelihoods of obtaining by chance as many or more dyads in each category as we actually do are, respectively, .04 and .002. That is, given the expected probabilities, the war dyads are unlikely to be random choices. However, once we control for the distribution of status inconsistent states in conflict dyads (as opposed to the distribution within the population of major powers in war years), we receive a very different picture. Since all major power/major power conflict dyads in the twentieth century have at least one underrecognized member, the probability is unity that all war dyads (which are a subset of conflict dyads) will also contain a status inconsistent member. Similarly, the proportion of conflict dyads having two underrecognized major powers is .647 and, as a result, the likelihood of randomly selecting eleven or more of eighteen war dyads with this property is .73. If we use a decision rule for n-nation wars that includes all possible major power/major power war pairs, the effect is the same. To summarize, then, once we have controlled for the distribution of underrecognized major powers in conflict dyads, we find that the likelihood is quite large that we will obtain —simply by chance—as many major power/major power war dyads containing status inconsistent members as we do. Thus, the overwhelming conclusion is that status inconsistency does not alter the likelihood of major power war . beyond its effect in increasing the probability of interstate 200 military confrontations. The implication of this finding would appear to be that once countries engage in confrontation, a set of dynamics quite distinct from the original precipitating factors may well determine the likelihood of war. As a result, amelioratory procedures, designed to salve the sources of the precipitating injury or prevent the development of the motivating factors, may be ineffective after a confrontation has already begun. Such procedures need to be enacted before the use of military force is threatened or actually employed. The Intervening Variables and Military Conflict It was originally hypothesized that there are environmental factors — some physical, some psychological, and some structural — that, while not in themselves causes of military conflict, nevertheless serve to alter the probability that confrontations and wars will erupt. What have we discovered about these intervening variables? Physical Attributes Three hypotheses concerning "reachability" were posited: (1) Given that a state is prone to conflict, the probability that it will become involved in a military confrontation increases if its power capabilities are increasing and decreases if its capabilities are decreasing. (2) Given that states are engaged in military confrontation, the likelihood of war is great if the opposing states are relatively 201 equal in power capabilities and the likelihood is smaller if the states are very unequal in capabilities. And, (3) Given that states are engaged in military confrontation, wars are more likely to occur if the parties to the confrontation are contiguous than if they are distant. We find that the first hypothesis is only partially supported by the empirical evidence. For nineteenth century major powers, the relationship between change in power and involvement in military confrontation is generally quite weak, but in the predicted direction (with only Prussia/Germany displaying a sizable negative association). In the twentieth century, change in power is again weakly associated with involvement in confrontation for most major powers, but nearly every significant coefficient is in the predicted positive direction. The states displaying these positive coefficients appear to espouse generally expansionist foreign policies, while there is a noticeable tendency for the status quo major powers to have smaller, but negative coefficients. Thus, those policy makers who fear the growth of power capabilities in expansionist states have grounds for concern. On the other hand, at least for the twentieth century, we should be wary about the destabilizing effects of any precipitous decline in the capabilities of the status quo major powers. Once a major power becomes engaged in military confrontation, there is a very consistent relationship between the relative 202 capabilities of the parties involved and the likelihood of war. The findings from the dyadic analyses demonstrate that confrontations between major powers tend toward war, while confrontations between major and minor powers are more likely to avoid this outcome. The relationship between relative equality in capabilities and war involvement is particularly pronounced in the twentieth century. When we focus exclusively on confrontations among major powers, we find that the posited relationship between inequality in power and the decreased probability of war is quite strongly supported in the earlier century, although there is a weak relationship between inequality and increased likelihood of major power/major power war in the current century. Overall, however, the implication of the findings from the second hypothesis on capabilities seems clear, if ominous: given the catastrophic effects of twentieth century major power wars, military confrontation between the major' powers —even the threat to use force—must be avoided. Finally, we find only scanty support for the third reachability hypothesis, that concerning geographic propinquity and war. In the nineteenth century the relationship is positive as predicted, but very weak; in the twentieth, it is generally nonexistent. Focusing exclusively on major power/major power conflict dyads, contiguity has a weak positive association with war involvement in both centuries. Since the data reveal, especially for the twentieth century, that a large number of the 203 military confrontations in which major powers engage involve neighboring states, the absence of any appreciable association between contiguity and war involvement is of considerable interest. In essence, the data analyses support the propositions that bordering states are salient to one another, and that the large number of interaction opportunities available to contiguous states afford considerable latitude for military confrontation. But; controlling for this latter fact, major powers are nearly as likely to wage war against more distant opponents as against bordering countries. In short, major powers are capable of (and do) reach out with substantial military force beyond their immediate neighbors, raining death and destruction almost as readily on the more distant as on the contiguous. Indeed, it is this very ability to reach out that largely identifies that class of states that we designate "major powers." The Psychological Factor In addition to the preceding physical attributes, a psychological factor was posited to comprise a portion of a state's decision-making milieu. It was hypothesized that: (1) The more costly previous wars (operationalized in terms of battle deaths), the less likely is a state to become involved in, or to initiate, subsequent military confrontations. And, (2) Given that such a confrontation does nevertheless occur, the greater the losses suffered by the parties to the 204 confrontation in their previous war encounters (regardless of the identity of their prior opponents), the smaller the probability that the confrontation will erupt into war. The data for the nineteenth century offer weak to negligible support for the first proposition that war losses reduce the probability of subsequent conflict involvements. Analyses on the twentieth century data yield more sizable coefficients. However, the coefficients for most twentieth century major powers are positive, suggesting a revanchist syndrome. The only states revealing the predicted negative coefficients are those that, during the current century, suffer catastrophic military and associated economic losses without tangible gain (i.e., Japan in the Russo-Japanese War; Germany and Russia in WW I). This raises the question of what, if anything, nations "learn" from prior war experiences. It appears that the twentieth century major powers have discovered that military adventurism can be a profitable tactic for obtaining national ends; only the most ravaging war experiences seem to deter these countries from subsequent involvements in military confrontations. Given the gloomy implication that war losses do not deter subsequent conflict involvement, do they nevertheless dissuade states from escalating confrontation into war? The analyses reveal that, for the most part, battle losses are, as predicted, inversely related to subsequent war involvement. However, it is 205 an extremely weak relationship, and the signs become positive when we look only at nineteenth century major power/major power conflict dyads. By and large then, while increased battle losses do have a tendency to reduce the probability of subsequent war involvement, the effect is negligible. Structural Relationships The last set of intervening variables are concerned with structural relationships. It was hypothesized that: (1) The less bi-polar the major power subsystem, the smaller the probability of both military confrontation and war. (2) The more cross-cut a state's bonds, the less likely it is to become involved in military confrontation. And, (3) Given that such a confrontation does occur, the probability that it will result in war will be reduced if the opposing states have bonds with one another. The first hypothesis, associating bi-polarity with military confrontation and war, is only partially corroborated in the nineteenth century, but finds more consistent support in the twentieth. During the nineteenth century, bi-polarity is regularly (if only moderately) associated with greater involvement in military confrontation. At the same time, however, bi-polarity is strongly associated with less war involvement. Thus, in the nineteenth century, bi-polarity leads to probing and sparring, but not war. In the twentieth century, bi-polarity is nearly 206 always associated with both a higher likelihood of military confrontation and a greater probability of war. This implies, for the current century, that policies that lead to the loosening of bloc ties serve to mitigate the possibility of overt military conflict. It should be made clear, however, that the loosening of these ties will not, in itself, prevent conflict; rather, the demise of rigid alignments simply eliminates a cleavage that tends to focus and exacerbate existing international tensions. As for the second hypothesis—positing a negative association between cross-cutting and involvement in military confrontation — it is disconfirmed. The relationship is only examined for the twentieth century, but during this century the majority of the cross-cutting coefficients are positive, though generally weak. As explained in Chapter Four, one interpretation of the positive (if weak) association between cross-cutting and military confrontation is that, during the twentieth century, major powers simply do not have enough cross- cutting bonds to produce cross-pressures and, therefore, the cross-cutting hypothesis is just not applicable to international interactions in the current century. A judgment as to the validity of this interpretation must await a more precise specification of what does, and what does not, constitute cross-cutting. However, an intellectually attractive counter-interpretation of the positive association between cross-cutting and military confrontation is that the states that have the most cross-cut bonds 207 are (as hypothesized) the most cross-pressured, but that (contrary to expectation) cross-pressures produce tension and uncertainty, and thereby exacerbate potentially conflictful situations. At the present time, it is impossible to choose between these alternative interpretations. This, however, is not a major problem, since it appears that cross-cutting is only peripherally related to major power involvement in military confrontations. Finally, the third structural hypothesis posits that interstate bonds serve to lessen the likelihood of war. Looking first at the effects of military alliances, we find that, during both centuries, military confrontations and wars rarely involve states having mutual defense pacts with one another. While this implies that alliances make conflicts among alliance members less likely, we have seen that during the twentieth century they increase the probability of wars between alliance blocs. Thus, if alliances are to be used as a mechanism for preventing wars, the evidence suggests that a "grand alliance" of all against none would be maximal. The post-Napoleonic Concert of Europe may well be an example of this. However, current political realities would appear to preclude such a "solution." It should be remembered that, between the two world wars, "collective security" proved to be a dismal failure because governments were unwilling to place common national or human interests above their own nation-specific interests. 208 Today, there is little evidence to suggest that that situation has changed. Looking at a second type of interstate bond, i.e., trade partnership, we uncover quite a different association from what we find for military alliances. During the twentieth century (the only period for which we have data), trade proves to be positively and strongly related to war involvement. Thus, if major trading partners become involved in a military confrontation with one another, then the probability of war sharply increases. This implies that trade is by no means a route to more peaceful interaction among nations. Trade appears to make nations more salient to one another—perhaps amplifying bilateral tensions— while not creating sufficient economic interdependence to mitigate conflict behavior. This finding, in conjunction with the finding reported in the preceding paragraph, strongly suggests that (contrary to our original cross-cutting hypothesis) having a major trade partnership, but not a military alliance, with another country is a potentially volatile relationship. It may be that this relationship arises most often between countries that have few common interests beyond purely economic ones, or between countries that have a tradition of military animosity, yet possess the raw materials, manufactured goods, or technological expertise to make trade economically attractive. Whatever the reason for the relationship, having a major trade partnership, but not a military alliance, with another nation appears to offer an 209 environment that is conducive to the escalation of military confrontations into interstate wars. This completes the review of the hypotheses that we examined in this thesis and the empirical associations that were uncovered. As a summary of this chapter, I present Table 29 (p. 210). The table is devoid of all nuances and exceptions; it contains only the most general trends in our data. More detailed results can, of course, be found in Chapters Three through Five. General Observations for Peace Research I conclude this chapter, and the thesis, by highlighting some implications that our findings hold for research on war in general. In this light, I see four pertinent points. First, it cannot be assumed (but rather should be explicitly examined) that on any dimension the major powers, or any other group of states, comprise a homogeneous set of actors. This suggests that there may be serious problems in drawing nation- specific inferences from analyses that have employed data pooled across states. This is true for system-level studies in which scores are aggregated over all countries and from which nation- state-level inferences are subsequently drawn, and for "modal- nation" analyses in which, after pooling and simultaneously analyzing scores from a number of different countries, nation- specific inferences are drawn from what is really an examination TABLE 29 GENERAL SUMMARY OF FINDINGS FROM CHAPTERS THREE THROUGH FIVE Stage One: Predicting to Military Confrontation Stat. Inc. A Power War Exp. Polarity X-Cutting Predicted + + - + 19th C + 20th C +/- +/- o Stage Two: Predicting to Interstate War Stat. Inc. PwrDif Contiguity War Exp. Polarity Ally:Trade Predicted 0 - + - + 19th C 20th C + = increases probability of military conflict - = decreases probability of military conflict 0 = does not alter probability of military conflict 211 of a composite "average" nation. In a similar vein, this lack of homogeneity presents difficulties for probability modeling on data pooled from a number of states. When probability distributions are compared to actual distributions of events in order to determine the presence or absence of such properties as contagion or independence, an assumption is made that actors display similar patterns of behavior on whatever dimension is being examined. If this assumption is violated, no inferences can be drawn. A second finding of general importance is the existence of inter-century differences. Whether or not the nineteenth/ twentieth century dichotomy is the optimal break-point cannot be ascertained from what we have done; to locate such a point would necessitate a much more thorough search procedure. It is, however, quite evident that the relationships among some of the key variables in this study differ from century to century. The reason why there are inter-century differences is, presently, a matter of speculation. My suspicion is that it is associated with a fundamental reorientation of international politics. Politics in the nineteenth century was dominated by a limited number of Euro-centered aristocratic regimes, having both familial ties and, equally importantly, vast areas of unclaimed territory into which to expand. International politics in the twentieth century is dominated by bourgeois-nationalist governments, operating in a world beset by a proliferating number of "independent" actors, 212 the concomitant disappearance of unclaimed territory for national expansion, and the development of rather rigid ideological schisms —all tending toward making politics assume the aspects of a "zero-sum game." This, of course, is only speculation. But whatever the cause, the fact that there exists this nineteenth/ twentieth century dichotomy should give pause to anyone who would indiscriminately select a time slice for analysis. For what we have discovered is an absence of temporal homogeneity that can produce the same types of inferential fallacies as are generated by a lack of spatial homogeneity. It is only fair, however, to insert a caveat: the more sub-periods into which we divide a time series, the fewer observations within each sub-period, and the more likely we are to obtain different relationships merely by chance. Complicating the situation is the fact that there is no certain rule that informs us that we have too few data points. Third, this study suggests that the conflict process is divisible into fairly distinct stages. I have employed a simple two-stage model; perhaps a multi-stage typology, such as the one used to categorize the military conflict data presented in Appendix C, would be more adequate (cf. Barringer, 1972; Bloomfield and Beattie, 1971a, 1971b; Bloomfield and Leiss, 1969; and Wright, 1965a). Using such a "stage" conception of the process, we should be able to determine whether the same variables that "explain" the occurrence of confrontations also "explain" the occurrence of wars and, similarly, whether the same variables 213 account for both the occurrence (yes/no) and the amount (magnitude, severity, and intensity) of military conflict. Moreover, the findings from the present study suggest that, while nations may become involved in confrontations for a variety of reasons, the behavior of conflict dyads is likely to be highly predictable. Thus, field theoretic approaches focusing on dyadic interactions (e.g., Rummel, 1969, 1971) may be very powerful techniques for understanding how wars evolve. Finally, as is clearly stated in the opening chapter, it has been my intent to combine into a single model some of the more frequently-posited "explanations" of why countries become involved in military conflict and to empirically examine the model's ability to account for nation-state behavior. I have done this. The construct tested in this thesis, however, offers only one of a number of plausible interpretations. In the literature on war we find many factors—among others, power, need for resources, self-preservation, and the desire to regain lost territory—that are alleged to contribute to the existence of military hostilities. What needs to be done is to concisely specify and test competing models of conflict involvement in order to identify those countries, time periods, and/or mixes of models that best account for and, ultimately, explain conflictive behavior among states. To reiterate the point with which I began this thesis, an understanding of why wars occur is a prerequisite to the development of effective measures for 214 preventing them. It is my profound hope that the preceding chapters have to some extent increased this understanding. Yet, even if they have, we are unfortunately still far removed from the day when they shall beat their swords into plowshares, and their spears into pruninghooks: nation shall not lift up sword against nation, neither shall they learn war any more. Isaiah 2:4 APPENDIX A DATA SOURCES All the data used in this thesis—with the single exception of the interstate military conflict list—have been acquired from the Correlates of War Project at the University of Michigan. As an associate of the project, I have, myself, spent many months compiling and re-constructing data sets of interstate contiguities and military alliances. And, for the purpose of this thesis, I devoted an additional year to compiling the list of interstate military conflicts that appears in Appendix C. I, therefore, recognize and appreciate the effort that has been expended by my colleagues—both past and present— in data generation, and I would like to thank the project's director, J. David Singer, for making project data sets available to me. 215 APPENDIX B INTERSTATE SYSTEM MEMBERS On the following pages are listed those countries that qualify as interstate system members and the inclusive years during which they are members. To qualify, a state must have independent control over its own armed forces and receive diplomatic recognition from any two states that fulfill the same requirements (cf. Singer and Small, 1972). The inclusive dates of membership for some states differ from those found in Singer and Small, and reflect the extension of the temporal domain to include 1970, as well as some revised estimates of when countries fulfilled, or failed to fulfill, the qualifications for system membership. 216 217 INCLUSIVE YEARS IN INTERSTATE ABB. NATION SYSTEM AFG Afghanistan 1921-1970 ALB Albania 1913-1914 1920-1939 1944-1970 ALG Algeria 1962-1970 ARG Argentina 1825-1970 AUL Australia 1920-1970 AUS Austria 1919-1938 1955-1970 A-H Austria-Hungary 1816-1918 BAD Baden 1816-1871 BAR Barbados 1966-1970 BAV Bavaria 1816-1871 BEL Belgium 1831-1940 1944-1970 BOL Bolivia 1839-1970 BOT Botswana 1966-1970 BRA Brazil 1824-1970 BUL Bulgaria 1878-1970 BUR Burma 1948-1970 BUI Burundi 1962-1970 CAO Cameroun 1960-1970 CAN Canada 1920-1970 CEN Central African Republic 1960-1970 CHA Chad 1960-1970 CHL Chile 1827-1970 CHN China 1843-1970 COL Colombia 1825-1970 CON Congo 1960-1970 COS Costa Rica 1849-1970 CUB Cuba 1902-1906 1909-1970 CYP Cyprus 1960-1970 CZE Czechoslovakia 1919-1939 1945-1970 DAH Dahomey 1960-1970 DEN Denmark 1816-1943 1945-1970 DOM Dominican Republic 1882-1888 1892-1916 1925-1970 ECU Ecuador 1837-1970 EGY Egypt/U.A.R. 1855-1882 1922-1970 218 INCLUSIVE YEARS IN INTERSTATE ABB. NATION SYSTEM EQG Equatorial Guinea 1968-1 970 EST Estonia 1920-1 940 ETH Ethiopia 1897-1 936 1941-1 970 FIJ Fiji 1970-1 970 FIN Finland 1918-1 970 FRN France 1816-1 942 1944-1 970 GAB Gabon 1960-1 970 GAM Gambia 1965-1 970 GDR German Democratic Republic 1954-1 970 GFR German Federal Republic 1955-1 970 GMY Germany/Prussia 1816-1 945 GHA Ghana 1957-1 970 GRC Greece 1831-1 941 1944-1 970 GUA Guatemala 1840-1 970 GUI Guinea 1958-1 970 GUY Guyana 1966-1 970 HA I Haiti 1861-1 915 1934-1 970 HAN Hanover 1838-1 866 HSE Hesse Electoral 1816-1 866 HSG Hesse Grand Ducal 1816-1 871 HON Honduras 1854-1 970 HUN Hungary 1919-1 970 ICE Iceland 1942-1 970 IND India 1947-1 970 INS Indonesia 1949-1 970 IRN Iran (Persia) 1826-1 970 IRQ Iraq 1932-1 970 IRE Ireland 1923-1 970 ISR Israel 1949-1 970 ITA Italy/Sardinia 1816-1 970 IVO Ivory Coast 1960-1 970 JAM Jamaica 1962-1 970 JPN Japan 1859-1 945 1952-1 970 JOR Jordan 1947-1 970 KEN Kenya 1963-1 970 KHM Khmer Republic (Cambodia) 1954-1 970 KOR Korea 1883-1 905 PRK Korea, Dem. People's Rep. 1949-1 970 ROK Korea, Republic of 1949-1 970 219 INCLUSIVE YEARS IN INTERSTATE ABB. NATION SYSTEM KUW Kuwait 1961-1970 LAO Laos 1954-1970 LAT Latvia 1920-1940 LEB Lebanon 1945-1970 LES Lesotho 1966-1970 LBR Liberia 1920-1970 LIB Li by a 1952-1970 LIT Lithuania 1921-1940 LUX Luxemburg 1891-1914 1918-1940 1944-1970 MAG Malagasy 1960-1970 MAW Malawi 1964-1970 MAL Malaysia 1957-1970 MAD Maldive Islands 1965-1970 ML I Mali 1960-1970 MLT Malta 1964-1970 MAU Mauritania 1960-1970 MAS Mauritius 1968-1970 MEC Mecklenburg Schwerin 1816-1867 MEX Mexico 1823-1970 MOD Modena 1824-1860 MON Mongolia 1929-1943 1949-1970 MNT Montenegro 1877-1919 MOR Morocco 1839-1911 1956-1970 NEP Nepal 1948-1970 NTH Netherlands 1816-1940 1945-1970 NEW New Zealand 1920-1970 NIC Nicaragua 1854-1970 NIR Niger 1960-1970 NIG Nigeria 1960-1970 NOR Norway 1905-1940 1945-1970 PAK Pakistan 1947-1970 PAN Panama 1904-1970 PAP Papal States 1816-1860 PAR Paraguay 1846-1870 1876-1970 PMA Parma 1818-1860 PER Peru 1826-1880 1883-1970 220 INCLUSIVE YEARS IN INTERSTATE ABB. NATION SYSTEM PHI Philippines 1947-1970 POL Poland 1919-1939 1945-1970 POR Portugal 1816-1970 RUM Rumania 1861-1970 RWA Rwanda 1962-1970 SAL Salvador 1854-1970 SAU Saudi Arabia 1930-1970 SAX Saxony 1816-1867 SEN Senegal 1960-1970 SIE Sierra Leone 1961-1970 SIN Singapore 1965-1970 SOM Somalia 1960-1970 SAF South Africa 1920-1970 SPN Spain 1816-1970 SRI Sri Lanka (Ceylon) 1948-1970 SUD Sudan 1956-1970 SWA Swaziland 1968-1970 SWD Sweden 1816-1970 SWZ Switzerland 1816-1970 SYR Syria 1946-1958 1961-1970 TAW Taiwan 1949-1970 TAZ Tanzania/Tanganyika 1961-1970 THI Thai land 1881-1970 TOG Togo 1960-1970 TRN Transvaal 1889-1899 TRI Trinidad 1962-1970 TUN Tunisia 1825-1881 1956-1970 TUR Turkey/Ottoman Empire 1816-1970 TUS Tuscany 1816-1860 SIC Two Sicilies 1816-1860 USR U.S.S.R. (Russia) 1816-1970 UGA Uganda 1962-1970 UK United Kingdom 1816-1970 USA United States of America 1816-1970 UPP Upper Volta 1960-1970 URU Uruguay 1843-1970 VEN Venezuela 1835-1970 DRV Vietnam, Democratic Rep. 1954-1970 RVN Vietnam, Republic of 1954-1970 WRT Wuerttemburg 1816-1871 221 INCLUSIVE YEARS IN INTERSTATE NATION SYSTEM Yemen Arab Republic 1947-1970 Yemen People's Republic 1967-1970 Yugoslavia/Serbia 1868-1941 1944-1970 Zaire (Congo, Kinshasa) 1960-1970 Zambia 1964-1970 Zanzibar 1963-1964 APPENDIX C IDENTIFYING INTERSTATE CONFLICTS To test the model posited in this thesis it was necessary to identify the serious interstate conflicts and disputes in which major powers have become engaged since 1820. The world politics literature suggests that the characteristic that is common to all such events is the threat or use of military force. Being unable to locate any satisfactory compilation of interstate military conflicts that encompassed the appropriate spatial and temporal domains, I expended the greater part of one year constructing such a data set. Criteria for Inclusion For a case to qualify for inclusion in this data set, it had to first satisfy three criteria. One, the military conflict had to be between 1 members of the interstate system, i.e., it had "4 have collected one type of conflict (i.e., internation• alized civil conflict) that is better described as "cross-state" rather than "inter-state" since it involves intervention within, rather than between, system members. Although these internation• alized civil conflicts are not included in the preceding analyses, they are nevertheless listed in this appendix. 222 223 to be between national political entities that had independent control over their own armed forces and received diplomatic recognition from any two members of the interstate system. (Appendix B lists the members of the system and the years in which they hold membership.) It was not necessary that the conflict take place within or along the borders of a member state; it was only necessary that regular military forces (or irregular forces under the direct command) of a member state be used. The second criterion was that one of the states using, or threatening to use, military force during the conflict had to be a major power. Major powers and the years during which they held that status were identified through a mail questionnaire sent to twenty-four diplomatic and military historians. (A list of the major powers can be found in Chapter Two.) The third criterion was that the conflict be government- directed, non-accidental, and non-routine. I was searching for cases in which governments were making demands, backed by the threat or use of combat forces, on other governments. If hostile action was clearly unrelated to government policy but was rather the result of some accident (e.g., the inadvertent sinking of a ship) or the activities of groups over which the government had no effective control (e.g., "independent" tribesmen, bandits, or terrorists), the case was excluded. Similarly, if national forces were contributed to an international governmental organization's 224 peace-keeping operation and were thereby effectively removed from the direct control of the supplying government, their participation in military activities was not included. Finally, there were routine interactions among both friendly and hostile states that would introduce much "noise" if included, e.g., the supplying of advisors and noncombat support to allies, the sporadic (but non- sustained) border clash by small numbers of troops along a hostile frontier, or the downing of a single aircraft that had violated national airspace. Gathering the Data Several hundred books and articles were used in collecting the cases that comprise the major power interstate conflict data set. I initially combined eighteen lists 1 covering various spatial and temporal domains, selecting cases that satisfied my criteria. I then greatly expanded my collection by thoroughly searching Langer's voluminous and highly-respected Encyclopedia of World Hi story (1972 ). After twice reading Langer, I next garnered all appropriate cases from Dupuy and Dupuy's extensive survey, Encyclopedia of Military History (1970). Having constructed a candidate list, I proceeded to research each case using annual ] Cady (1968), Carroll (unpublished), Deitchman (1964), Emerson (1972, 1973), Goldmann (1971), Greaves (1962), Holsti (1966), Kellog (unpublished), Kende (1968), Leiss and Bloomfield (1967), North (1969), Richardson (1960), Sabrosky and Morton (unpublished), Singer and Small (1972), Sorokin (1937), Wainhouse (1966), Wood (1968), and Wright (1965). 225 registers, official documents, and newspaper reports, as well as national, regional, and general histories. I compared my pruned list to two others that were being simultaneously compiled, Leng's unpublished collection of interstate military confrontations (1816-1945) and Jones and Bennett's unpublished list of civil wars (1816-1970), and found no case in either of these that fulfilled my three criteria and, yet, did not appear in my collection. Finally, I asked a diplomatic historian to review the conflict cases in order to judge the merit of their inclusion and to suggest possible cases that may have escaped my research net. The validation process is still continuing at this time. The Behavioral Correlates of War Project, under the direction of Russell Leng, is in the midst of collecting a conflict data set for all members of the interstate system since 1816. The Leng team is systematically combing a selected set of national histories, written in several languages. When their undertaking is completed, my data set will be compared with the BCOW compilation in order to determine the relative merits of the two different search procedures. CIassifying the Cases I have sorted the military conflicts that I collected into one of four categories. Only the first three of these categories are strictly interstate, i.e., conflict between members of the interstate system. The fourth category is really "cross-state" since it contains cases in which a member state intervenes in a 226 civil conflict within another member state. The categories are as follows: (1) interstate war: combat between armed forces, involving at least one member of the interstate system on each side, resulting in a total of one thousand or more battle-connected deaths to the armed forces, and lasting for more than twenty-four hours. (2) interstate military action: combat between armed forces, involving at least one member of the interstate system on each side; or, the use of armed forces by a member state, directed against the territory and people of another member state. Military action taken by one state may provoke the target state to engage the first actor in military combat; so long as the subsequent combat results in fewer than one thousand battle- connected deaths to the armed forces and/or lasts for less than twenty-four hours, it is labeled "hostilities." Military action taken by one state (e.g., the seizure of land or blockading of territory) may, however, fail to provoke the target state into military action; if the target state remains passive, the conflict is labeled "unreciprocated military action. (3) interstate threat: explicit verbal statement, by a high official on behalf of a member state's government, declaring an intent to use military force against another member state for other than strictly defensive purposes; or, overt mobilization of armed forces by a member state, directed against another member state for other than strictly defensive purposes, during periods of dispute or high tension.2 In case of either verbal statement or mobilization, the target state must be clearly specified or easily identifiable. (4) internationalized civil conflict: physical intervention (with or without combat) by the armed forces of a member state in a civil conflict within the territory Whether a military action is reciprocated usually depends upon the target state's willingness (or unwillingness) to submit nonviolently rather than upon any fundamental difference in the initiator's actions. 2 Shows-of-force, by themselves, are not sufficiently explicit in terms of intent to use armed force to be included. 227 of another member state for the purpose of supporting one side to the conflict or suppressing the conflict entirely. Intervention that is for the purpose of protecting the lives and property of foreign nationals and does not significantly interfere in the ongoing civil conflict is excluded. The civil conflict remains internationalized, and does not strictly become inter• state, so long as the combat remains confined to the territory of the national political unit in which it has erupted and so long as the major opposing factions in the civil conflict do not join in common cause to resist the forces of the intervening state. 1 Presenting the Cases My purpose in collecting the conflict data set was to identify serious interstate disputes involving major powers, operationalized in terms of the threat or use of military force by a major power. My confidence in the inclusiveness of the compilation is bolstered by the facts that (1) violent interstate behavior generally leaves substantial traces and (2) the military activities of the major powers are particularly likely to be well documented. I have kept the threshold of violence realistically high so as to maximize the probability that the event would be reported. Naturally, as the level of violence diminishes, my confidence in the inclusiveness of the data set also declines, i.e., I am more certain that I have collected the population of cases that, according to my coding rules, qualifies as interstate wars than I am that I have found all those cases Intervention in a civil conflict, as distinct from "inter• state military action," is characterized by the prior existence of factional military confrontation within the target state, other than that created by the intervening state as a pretext for interfering. 228 that qualify as interstate threats. Nevertheless, having labored one year on this data set, I must express my satisfaction with its overall quality. In a separate volume, to be deposited with the Department of Political Science at the University of Michigan, I have explicated and cited sources for each case in the conflict data set—in general, the more obscure the case, the longer the explication and the more numerous the citations. Below can by found an enumeration of those cases that are included in the interstate military conflict data set and also those cases that I label internationalized civil conflict. For each case, I list the date during which major powers were involved, the name I have given to the conflict, the major power parti ci pants and their opponents, as well as the level of violence that the conflict attained. If no major power was involved during a portion of a conflict, then an inclusive date (enclosed in parentheses) is given for the entire conflict following the conflict name. In listing participants, the purported initiator is given first, the "defender" is given after the "vs." The coding of initiator/ defender was somewhat poetic; it was an attempt to identify the actors that were responsible for beginning the military confrontation, but when this was impossible to establish, e.g., both sides exchanging minor fire, I identified as the initiator the side that escalated the conflict to a more serious phase, e.g., escalated the exchange of small arms fire to a major attack. In column four, following the identification of some conflicts as 229 either "threats" or "unreciprocated military actions," the reader will find an asterisk (*). This is used only for major power/ major power conflicts, and denotes that the threat or use of military force was unreciprocated, i.e., the target of the threat or action did not respond (militarily) against the initiator. The asterisk notation is employed only for major power/major power conflicts because the list is primarily designed to identify major power conflict behavior. (By and large, it can be assumed that threats by major powers against non-major powers are not reciprocated.) The following are the cases that satisfy the criteria for major power interstate military conflicts and internationalized civil conflicts. The abbreviation "M.Act.(u)" represents "unreciprocated military action"; the abbreviation "M.Act.(r)" denotes "reciprocated military action," i.e., hostilities. All other notation should be self-explanatory. DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1820- 21 Neapolitan Revolution A-H intervention Civil 1821 Piedmont Uprising A-H intervention Civil 1821 Morea Insurrection USR vs TUR Threat 1823 Spanish Revolution (1820-23) FRN intervention Civil 1826 Danubian Principalities Insurrections USR vs TUR Threat 1827-•28 Portuguese Constitution Rebellion UK intervention Civil 1827 Battle of Navarino Bay UK,FRN,USR vs TUR,Egyptians M.Act.(r) 1828- 29 Russo-Turkish War USR vs TUR War 1828- 29 Russo-Turkish War FRN vs TUR,Egyptians M.Act.(r) 1831 Miguelite Wars (1828-34) UK intervention Civil 1831 Miguelite Wars (1828-34) FRN intervention Civil 1831- 32 Italian Uprisings A-H intervention Civil 1832 Italian Uprisings FRN intervention Civil 1831 Belgian Independence Revolution (1830-33) FRN intervention Civil 1832 Belgian Independence Revolution (1830-33) FRN vs NTH M.Act.(r) 1832- 33 Belgian Independence Revolution (1830-33) FRN,UK vs NTH M.Act.(u) DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1835- 39 Carlist War (1834-39) FRN intervention Civil 1836- 39 Carlist War (1834-39) UK intervention Civil 1838-•39 Occupation of Vera Cruz FRN vs MEX M. Act.(r) 1838- 40 Argentine Affair FRN vs ARG M. Act.(r) 1838- 40 Argentine Affair FRN intervention Civil 1840 Second Syrian Crisis (1839-40) FRN vs UK,GMY Threat* 1845- 48 Wars of la Plata (1843-52) FRN,UK(1845-47) vs ARG M.Act.(r) 1845-•49 Wars of la Plata (1843-52) FRN,UK intervention Civil 1847 Occupation of Ferrara A-H vs PAP M.Act.(u) 1848-•49 Austro-Sardinian War ITA,Italian States vs A-H War 1848-•49 Sicilian Insurrection UK,FRN intervention Civil 1848-•49 1st Schleswig-Holstein War GMY vs DEN War 1849 Hungarian Insurrection (1848-49) USR intervention Civil 1849 Insurrection in Tuscany (1848-49) A-H intervention Civil 1849 War of the Roman Republic FRN,A-H,SIC vs PAP War 1849 German States Insurrections GMY intervention Civil DATE CONFLICT MAJOR 1POWE R PARTICIPANTS LEVEL 1850 Convention of Olmutz Crisis A-H ,BAV,GMY intervention Civil 1850 Convention of Olmutz Crisis A-H vs GMY Threat 1850 Don Pacifico Affair UK vs GRC M.Act.(u) 1853 Leiningen Mission A-H vs TUR Threat 1853- 56 Crimean War (1853-56) USR vs TUR,FRN(1854-56), War UK(1854-56),ITA(1855-56) 1854 Crimean War (1853-56) A-H vs USR Threat* 1855 Crimean War (1853-56) A-H vs USR Threat* 1854- 57 Occupation of Piraeus UK,FRN vs GRC M.Act.(u) 1856- 57 Neuchatel Affair GMY vs SWZ Threat 1856- 57 Anglo-Persian War IRN vs UK War 1856-•60 2nd Opium "War" UK,FRN(1857-60) vs CHN M.Act.(r) 1859 War of Italian Unification A-H vs ITA,FRN War 1860 Italo-Roman War ITA vs PAP War 1860 Italo-Roman War FRN vs ITA Threat* 1860-•61 Italo-Sicilian War ITA vs SIC War 1860-•61 Italo-Sicilian War FRN vs ITA M.Act.(u)* DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1860-•64 Taiping Rebellion (1851-64) UK,FRN intervention Civil 1861 Occupation of Tsushima USR vs JPN M.Act.(u) 1861 Trent Affair UK vs USA Threat 1861- 62 Mexican Expedition FRN,UK,SPN vs MEX M.Act.(u) 1862- 67 Franco-Mexican War FRN vs MEX War 1863- 65 Anti-foreign Movement JPN vs USA,FRN,NTH,UK M.Act.(r) 1864 2nd Schleswig-Holstein War A-H,GMY vs DEN War 1866 Seven Weeks War GMY,ITA vs A-H,German Allies War 1870 Tientsin Massacre FRN,UK,USA vs CHN Threat 1870-•71 Franco-Prussian War FRN vs GMY,German Allies War 1876 Russo-Turkish War (1877-78) USR vs TUR Threat 1877-•78 Russo-Turkish War (1877-78) USR vs TUR War 1878 Russo-Turkish War (1877-78) UK vs USR Threat* 1880 Hi Valley Dispute USR vs CHN Threat 1880 Montenegrin Troubles UK,FRN,USR,GMY,A-H,ITA vs TUR Threat 1880 Montenegrin Troubles UK vs TUR Threat DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL C O O C Tunisian Adventure FRN vs TUN M.Act.(r) Tunisian Adventure TUR vs FRN Threat 1882 Alexandria Affair •UK,FRN intervention Civil 1882 Alexandria Affair UK vs EGY M.Act.(r) 1884-•85 Si no-French War FRN vs CHN War 1885-•86 Anglo-Russian Afghan Crisis UK vs USR Threat* 1885-•87 Anglo-Russian Afghan Crisis UK vs KOR M.Act.(u) 1885 Khevenhuller Mission A-H vs BUL Threat 1886 Greco-Turkish Dispute UK,A-H,GMY,USR,ITA vs GRC M.Act.(u) 1886-•87 Bulgarian Crisis USR vs BUL Threat 1890 British-Portuguese Colonial Dispute UK vs POR Threat 1893 Siamese Conflict FRN vs THI M.Act.(r) 1894 Mosquito Controversy UK vs NIC M.Act.(u) 1895 Delegoa Bay Railway Ultimatum UK vs TRN Threat 1895 The Triple Intervention USR,GMY,FRN vs JPN Threat* DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1897 Cretan Insurrection USR,A-H,GMY,ITA,FRN,UK vs GRC M.Act.(u) 1397-•98 China Concessions GMY vs CHN M.Act.(u) 1898 China Concessions USR vs CHN Threat 1898 China Concessions UK vs CHN Threat 1898 China Concessions FRN vs CHN M.Act.(u) 1897 Chemulpo Naval Demonstration UK,JPN vs USR,KOR Threat* 1898 Nigerian Dispute (1897-98) UK vs FRN Threat 1898 Evacuation of Crete UK,FRN,USR,ITA vs TUR Threat 1898 Fashoda Crisis UK vs FRN Threat* 1899-•02 Boer War TRN vs UK War 1899-•00 Masampo Naval Base Concession USR vs KOR Threat 1900 Masampo Naval Base Concession JPN vs USR Threat* 1900-•01 Boxer Rebellion (1900-01) USR,GMY,JPN,FRN,UK,USA,ITA,A-H vs CHN M.Act.(r) 1900-•04 Boxer Rebellion (1900-01) USR vs CHN M.Act.(r) 1900 Dominican Debt Crisis FRN vs DOM Threat 1902-•03 Venezuelan Debt Blockade UK,GMY,ITA vs VEN M.Act.(u) 1903 Dominican Debt Demonstration GMY vs DOM Threat DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1903 Panama Canal Dispute USA intervention Civil 1904- 05 Russo-Japanese War JPN vs USR War 1905 Macedonian Question A-H,USR,UK,FRN,ITA vs TUR M.Act.(u) 1906 Sinai Ultimatum UK vs TUR Threat 1906- 09 Cuban Intervention USA intervention Civil 1907- 10 French-Moroccan Conflict FRN vs MOR M.Act.(r) 1908- 09 Bosnian Crisis YUG,MNT vs A-H Threat 1908 Persian Indemnity Retribution USR vs IRN M.Act.(u) 1909 Persian Civil War (1905-09) USR intervention Civil 1910 Anti-Zelaya Revolution (1909-10) USA intervention Civil 1911-•12 2nd Moroccan Crisis FRN intervention Civil 1911 2nd Moroccan Crisis GMY vs FRN Threat* 1911 2nd Moroccan Crisis UK vs GMY Threat* 1911-•12 Persian Invasion USR vs IRN M.Act.(u) 1911-•12 Italo-Turkish War ITA vs TUR War DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1912 Nicaraguan Intervention USA intervention Civil 1912 1st Balkan War (1912-13) USR vs BUL Threat 1912-13 1st Balkan War (1912-13) A-H vs YUG Threat 1913 1st Balkan War (1912-13) USR vs TUR Threat 1913 1st Balkan War (1912-13) A-H,ITA,UK,FRN,GMY vs MNT M.Act. (u) 1913 1st Balkan War (1912-13) A-H vs MNT Threat 1913 Albanian Confrontation A-H vs YUG Threat 1914 Occupation of Vera Cruz USA vs MEX M.Act.(r) 1914-16 Dominican Republic Intervention USA intervention Civil 1916-24 Dominican Republic Intervention USA vs DOM M.Act.(u) 1914- 18 World War I A-H, GMY, Allies War vs UK,FRN,USR(1914-17),JPN, ITA(1915-18),USA( 1917-18),Allies 1915- 34 Haitian Customs Dispute USA intervention Civil 1916- 17 Pershing Mexican Expedition USA vs MEX M.Act.(r) 1917 Cuban Revolt USA intervention Civil 1917- 22 Russian Civil War (1917-22) JPN intervention Civil 1918 Russian Civil War (1917-22) GMY intervention Civil 1918- 19 Russian Civil War (1917-22) FRN,UK,USA(1918-20) intervention Civil DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1918- 19 Estonian Independence Fighting USR vs Estonians,UK M.Act. (r) 1919- 21 Turkish Nationalist Movement (1919-23) ITA vs TUR M.Act. (u) 1920- 21 Turkish Nationalist Movement (1919-23)' FRN vs TUR M.Act. (r) 1920- 23 Turkish Nationalist Movement (1919-23) UK,FRN,ITA vs TUR M.Act. (u) 1922 Turkish Nationalist Movement (1919-23) UK,FRN,ITA vs GRC Threat 1920 Spartacist Uprising FRN intervention Civil 1921 Panama-Costa Rica Boundary Dispute USA vs PAN Threat 1921 London Reparation Ultimatums FRN,UK,BEL,ITA vs GMY Threat 1921- 25 London Reparation Ultimatums FRN,UK(1921),BEL vs GMY M.Act. (u) 1923- 25 Ruhr Occupation FRN,BEL vs GMY M.Act. (u) 1923 Corfu Incident ITA vs GRC M.Act. (u) 1924 Sudan Ultimatum UK vs EGY M.Act. (u) 1926- 33 Nicaraguan Insurrection (1925-33) USA intervention Civil 1927 Egyptian Army Ultimatum UK vs EGY Threat 1928-•29 Chinese Civil Wars (1920-49) JPN intervention Civil 1928 Freedom of Assembly Ultimatum UK vs EGY Threat 1929 Chinese Eastern Railway Dispute USR vs CHN M.Act. (r) DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1931- 33 Manchurian War JPN vs CHN War 1934 Dollfuss Affair ITA vs GMY Threat* 1934 Durazzo Demonstration ITA vs ALB Threat 1934 Italo-Ethiopian War (1935-36) ETH vs ITA M.Act.(r) 1935- 36 Italo-Ethiopian War (1935-36) ITA vs ETH War 1934- 37 Japanese Expansion in North China JPN vs CHN M.Act.(u) 1934 Sinkiang Uprisings (1931-37) USR intervention Civil 1937 Sinkiang Uprisings (1931-37) USR intervention Civil 1935- 39 Spanish Civil War ITA,GMY intervention Civil 1937- 41 Sino-Japanese War JPN vs CHN War 1938 Russo-Japanese Border Conflict JPN vs USR M.Act.(r) 1938-•40 Tientsin Crisis JPN vs UK,FRN M.Act.(u)* 1938 Anschluss GMY vs AUS M.Act.(u) 1938 Annexation of the Sudetenland GMY vs CZE M.Act.(u) 1939 Russo-Japanese War JPN vs USR.MON War 1939 Destruction of Czechoslovakia GMY vs CZE M.Act.(u) DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1939 Annexation of Memel GMY vs LIT M.Act.(u) 1939 Conquest of Albania ITA vs ALB M.Act.(r) 1939-45 World War II (Europe/Africa) ITA (1940-43), Allies War vs UK,FRN(1939-40,1944-45), USR(1941-45),USA(1941-45), ITA(1943-45),Allies 1941-45 World War II (Pacific) JPN vs USA,UK,CHN,USR(1945),Allies War 1939 Russian Invasion of Poland USR vs POL M.Act.(r) 1939 Baltic Bases Demand USR vs EST Threat 1939 Baltic Bases Demand USR vs LAT Threat 1939 Baltic Bases Demand USR vs. LIT Threat 1940 Baltic Bases Demand USR vs EST M.Act.(u) 1940 Baltic Bases Demand USR vs LAT M.Act.(u) 1940 Baltic Bases Demand USR vs LIT M.Act.(u) 1939- 40 Russo-Finnish War USR vs FIN War 1940 Russo-Finnish War FRN,UK vs USR Threat* 1940 Closing of the Burma Road JPN vs UK Threat* 1940- 41 Japanese Seizure of Indochina JPN vs FRN M.Act.(r) 1940 Rumanian Acquiescence USR vs RUM M.Act.(u) DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1 9444-5 Greek Insurrection UK intervention Civil 1945- 46 Azerbaijan Revolt USR intervention Civil 1943- 49 Berlin Blockade USR vs USA,UK,FRN M.Act. (u)* 1950 Peking-Taipeh Conflict CHN vs TAW M.Act.(r) 1950-•53 Korean War PRK, CHN vs ROK,USA,UN War 1951-•52 Anglo-Egyptian Clashes UK vs EGY M.Act.(u)** 1954-•55 Bombardment of Offshore Islands CHN vs TAW M.Act.(r) 1955 Anglo-French Intervention in Sinai War UK,FRN vs EGY War 1956 Anglo-French Intervention in Sinai War USR vs UK,FRN Threat* 1956 Burma Border Dispute CHN vs BUR M.Act.(u) 1956 Russo-Hungarian War USR vs HUN War 1956 Polish October Crisis USR vs POL Threat 1957 Yemen-Adenese Border Conflict YAR vs UK M.Act.(r) 1957 Syrian Crisis USR vs TUR Threat 1957 Syrian Crisis USA vs USR Threat* 1953 Tunisian Border Conflict FRN vs TUN M.Act. (r) DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1958 Lebanese Crisis USA intervention Civil 1953 Jordanian Civil Conflict UK intervention Civil 1958-•59 Quemoy Crisis CHN vs TAW M.Act.(r) 1958 Quemoy Crisis USA. vs CHN Threat* 1959 Sino-Indian Border Clashes CHN vs IND M.Act.(r) 1960 U-2 Incident (& RB-47 Incident) USR vs TUR,PAK,NOR Threat 1960-•63 Cameroun Rebellion FRN intervention Civil 1961- 64 Vietnam War (1956-75) USA intervention Civil 1964-•73 Vietnam War (1956-75) RVN,USA,Allies vs NLF,DRV War 1961 Bizerta Crisis TUN vs FRN M.Act.(r) 1961 Berlin Wall Crisis USA vs USR Threat*** 1962 Quemoy Conflict CHN vs TAW M.Act.(r) 1962 Sino-Indian War CHN vs IND War 1962 Cuban Missile Crisis USR vs USA Threat* 1962 Cuban Missile Crisis USA,OAS vs USR M.Act.(u)* 1962 Cuban Missile Crisis USA vs CUB Threat 1-963- 65 "Crush Malaysia" Campaign INS vs MAL,UK,AUL,NEW M.Act.(r) DATE CONFLICT MAJOR POWER PARTICIPANTS LEVEL 1 963•6- 4 Cypriot Civil Conflict UK intervention Civil 1964 Yemeni-Adenese Border Attack UK vs YAR M.Act.(r) 1964 East African Insurrections UK intervention Civil 1964 Gabon Coup FRN intervention Civil 1964-•73 2nd Laotian Civil War (1963- ) USA intervention Civil 1965 Sikkam Border Dispute CHN vs IND Threat 1 965-66 Dominican Republic Civil War USA(1965),OAS intervention Civil 1967 Sikkam Border Conflict CHN vs IND M.Act.(r) 1968 Invasion of Czechoslovakia USR,Warsaw Pact Allies vs CZE M.Act.(u) 1969- 71 Chad Intervention FRN intervention Civil 1969 Sino-Soviet Border Clashes CHN vs USR M.Act.(r) 1 970-73 Cambodian Civil War (1967-75) USA intervention Civil NOTE: Three conflicts listed as "wars" by Singer and Small (1972) are coded differently above. First, the French-Spanish encounter in 1823 is coded as a French intervention in an ongoing Spanish civil conflict rather than as a war between France and Spain. Second, the Battle of Navarino Bay (1827)—a single naval engagement that lasts less than a day—is coded as a "reciprocated military action" rather than as a "war." And, finally, the Boer War is coded as an interstate war (rather than an extra-systemic war, as found in Singer and Small) because a more recent assessment of the membership of the interstate system suggests that the Transvaal is a full-fledged member of the system in 1899, the year in which the war begins. In the table above, one asterisk (*) denotes that a threat or military action initiated by a major power, and directed against another major power, is not reciprocated. Two asterisks (**) denote that neither party to a conflict is coded as an initiator. This occurs in the Anglo-Egyptian Clashes (1951-52) when British forces respond to attacks initiated by Wafd-supported guerrilla forces that are not under the direct control of the Egyptian government. Three asterisks (***) denote simultaneous initiation. This occurs in the Berlin Wall Crisis of 1961—both the United States and the Soviet Union are coded as initiators in this conflict. APPENDIX D SUMMARY PRESENTATION OF MILITARY CONFLICT DATA The figures and tables on the following pages offer a pictorial representation of the military conflict data presented in Appendix C. Figure One depicts the number of military conflicts involving major powers that occurs during each decade from 1820 to 1 970. The solid line represents the number of interstate conflicts (wars, military actions, and threats), and the broken line gives the same information for a 11 major power military conflicts (wars, military actions, threats, arid internationalized civil conflicts). Figure Two presents the number of military conflicts at various levels of violence that occurs during each decade. When depicting data on "military action," both the number of reciprocated actions and the number of unreciprocated actions are given, and then the sum of the two is displayed as "all military action." Figure Three contains bar graphs that indicate how many of the thirty five-year periods in the 1820-19/0 temporal domain contain no military conflicts, one conflict, two conflicts, three conflicts, and so forth. The height of the bar represents the number of periods in which 0, 1, 2, 3, etc. conflicts occur. 245 246 Once again, this information is presented for all major power military conflicts and for interstate conflicts. Figure Four offers the same information for the different levels of conflict. On the last two pages of the appendix, 1 present, the total number of major power nation-years during each century in the 1820-1970 temporal domain (Table 30), and also, for each level of military violence, the proportion of national involvements in military conflicts per nation-year (Table 31). Finally, at. the bottom of the last page, I note the proportions of national involvements in military conflicts that result in war (Table 32). 247 FIGURE 1 NUMBER OF MILITARY CONFLICTS PER 10-YEAR PERIOD NOTE: For this and the following graphs, each conflict is coded only once. Since any conflict may span several levels of violence, only the highest level (war > military action > threat) is selected. For example, if a conflict contains a threat, military action, and war, only "war" is coded. Similarly, if an internationalized civil conflict evolves into an interstate conflict, the conflict is coded as being interstate. 248 FIGURE 2 NUMBER OF MILITARY CONFLICTS (AT VARIOUS LEVELS OF VIOLENCE) PER 10-YEAR PERIOD 249 FIGURE 2---Continued Number Interstate Threat 1 9 3 4 5 6 7 8 9 I 2 5 6 Year 00000000 0 0 0 0 ssssssss s s Number Internationalized Civil Conflict 8 3 4 5 6 7 8 1 2 4 5 Year 0 0 00000000 0 0 ssssssss s s 250 FIGURE 3 DISTRIBUTION OF THE OCCURRENCE OF MILITARY CONFLICTS WITHIN 5-YEAR PERIODS frequency of occurrence All Military Conflicts 5 | 1 4 . 1 3 , 1 | r 2 1 | 1 1 0 I ' 1 1 1 ' 1 1 ' ' ' 0 1 2 3 4 5 6 7 8 9 10 I'M number of conflicts per 5-year period frequency of occurrence Interstate Military Conflicts 1 2 3 4 5 6 7 8 9 10 1M number of conflicts per 5-year period 251 FIGURE 4 DISTRIBUTION OF THE OCCURRENCE OF MILITARY CONFLICTS (AT VARIOUS LEVELS OF VIOLENCE) WITHIN 5-YEAR PERIODS 12 10 8 Recip- Unrecip- frequency of 6 rocated rocated occurrence 4 12 2 11 1 I 1 10 2 3 4 0 1 2 3 4 5+ 9 8 Interstate Interstate 7 War Military 6 Action 5 4 3 2 1 0 1 2 0 1 2 4 5 number of conflicts per number of conflicts per 5-year period 5-year period frequency of occurrence 11 10 9 Interstate 8 Internationalized Threat 7 Civil 6 Conflict 5 4 3 2 1 0 1 2 4+ 0 1 2 3 4 5 number of conflicts per number of conflicts per 5-year period 5-year period 252 TABLE 30 NUMBER OF YEARS AS MAJOR POWER DURING THE 1820-1899 AND 1900-1970 TIME PERIODS 1820- 1900- Major Powers 1 899 1970 USA '1899-1970) 1 71 UK 1820-1970) 80 71 FRN 4 820-1940, 1945- 1970) 80 67 GMY [1820-1918, 1925- 1 945) 80 40 A-H [1820-1918) 80 19 ITA (1860-1943) 40 44 USR [1820-1917, 1 922-1 970) 80 67 CHN [1950-1970) -- 21 JPN [1895-1945, 1960- 1 970) 5 57 Total Nation-Years 446 457 253 TABLE 31 NATIONAL INVOLVEMENTS IN MILITARY CONFLICTS PER NATION-YEAR Military Years Wars Actions Threats Civil 1820-99 .05 (23) .08 (35) .07 (29) .03 (14) 1900-70 .07 (32) .13 (61) .05 (25) .05 (24) Interstate All Years Military Conflicts Military Conflicts 1820-99 .20 (87) .23 (101) 1900-70 .26 (118) .31 (142) NOTE: The number of conflicts at each level of violence during the 1820-99 and 1900-70 time periods is given in parentheses TABLE 32 PROPORTION OF NATIONAL INVOLVEMENTS IN MILITARY CONFLICTS RESULTING IN WAR Interstate All Years Military Conflicts Military Conflicts 1820-99 .26 .23 1900-70 .27 .23 NOTE: For the purpose of presentation in the tables above, only one level of conflict is coded for any nation in a given year. 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