Of Time and Power: Leadership Duration in the Modern World 9781503623439

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Of Time and Power

Written under the auspices of The Center of International Studies, Princeton University

Of Time and Power Leadership Duration in the Modern World

HENRY BIEN EN and NICOLAS VAN DE WALLE

STANFORD UNIVERSITY PRESS STANFORD, CALIFORNIA

Stanford University Press Stanford, California © 1991 by the Board of Trustees of the Leland Stanford Junior University Printed in the United States of America CIP data appear at the end of the book

Epigraph from Poems of Dylan Thomas, © 1943. Reprinted by permission of New Directions Publishing Corp.

To Joyce Carol Oates and Raymond Smith and to Etienne and Francine van de Walle

Acknowledgments We owe more than the customary thanks to many people and institutions. Henry Bienen received financial support from the Leon Lowenstein Foundation and from the Ford Foundation that enabled him to take one semester's leave and also provided funds for summer research. Princeton University's generous leave policy provided for an additional semester's leave. Ford Foundation funds also provided some support for Nicolas van de Walle. The Center of International Studies provided funds for computer time and programming and also for research assistance. The collection of the sample was facilitated by the work of Peter Lewis on Africa, Leslie Bienen on Africa and the Middle East, Pedro Madero on Latin America, and Ashok Subramanian on Asia. A number of colleagues read and commented on our work. They provided insights on substantive issues involving leadership change in various regions of the world and on our model and methodologies. We want to thank the Princeton demographers Charles Westoff, Anne Pebley, Jane Mencken, and James Trussell. Professor Trussell gave us assistance beyond the call of duty in thinking through the techniques we should use and guiding us to new sources for methodologies. Robert Tignor of Princeton's History Department commented on parts of the manuscript. A number of Princeton political scientists were helpful; these include Stanley Kelley, Jr., Atul Kohli, and Ezra Suleiman. John Waterbury and Lynn White helped us with the intricacies of Middle Eastern and East Asian leadership change, as did Nancy Bermea for Spain and Portugal. Peter Johnson, Latin American bibliographer at Princeton, provided similar assistance for Latin America. David Card, Angus

viii

Acknowledgments

Deaton, Barry Nalebuff, and Mark Gersovitz, economist colleagues, assisted on methodological issues. Professor Gersovitz, as always, was generous with his time and his thoughts. Detailed readings of the entire manuscript were done by our colleagues David Bachman, Forrest Colburn, and Jeffrey Herbst and by Professor John Londregan of Carnegie Mellon University. To them, thanks. We want to acknowledge the special help of Professor Jeremiah Ostriker, chairman of Princeton's Department of Astrophysical Sciences, who applied his wide-ranging curiosity and skills in helping us to clarify our thoughts. Neil Weiner of the University of Pennsylvania's Criminology Center was of great help in these regards also. Professor Paul Allison of the Department of Sociology at the University of Pennsylvania has been a pioneer in elaborating hazard model analysis, and we benefited from his comments. Doug Mills of Princeton's Computer Center helped us too. Professor James Caporaso of the University of Washington shared his views and his work. We owe thanks to anonymous readers for the American Political Science Review and to Bruce Bueno de Mesquita, who read for Stanford University Press. Jerri Kavanagh and Hongqiu Yang typed many drafts. We are grateful to them, and to Nan Nash and Sylvia Klun for their patience and assistance. We deeply appreciate the work of Julia Johnson Zafferano, Associate Editor at Stanford University Press. H. B. N.v.d.W.

Contents

1.

Introduction

1

2. The Sample, the Variables, and the Coding

15

3· Modeling Leader Longevity

36

4· Analyses of the Sample

53

5· Analyses of the Subsamples 6. Conclusions

So

Appendix: The Complete Sample of Leaders and Their Individual Characteristics Notes Index

98 109 195 211

Tables and Figures

Table 1. The Subsamples, by Region and by Civilian and Military Leaders Table 2. The Subsamples, by Period of Time Table 3· The Frequency Distribution for Years in Power Table 4· Total Number of Leaders Left Over After X Years, and as a Percentage of the Total Table 5· Sample Proportions and Means of the Covariates Across Regions Table 6. Full Sample Proportional-Hazard Models Table 7· The Correlation Between a Leader's Time in Power and His Predecessor's Table 8. Full Sample Time-Dependent Hazard Model Table 9· Sample of Multiple Entries Table 10. The Correlation Between a Leader's Time in Power and His Time in Power in Previous Tenure Table 11. Gompertz Hazard Functions for Different Subsamples Table 12. Proportions and Means of the Covariates for Different Subsamples Table 13. Proportional-Hazard Models: Preferred Models for Subsamples Table 14. The Frequency Distribution for Years in Power: Leaders After 1945

16 25 54 56 6o 61 70 73 75

77 82 86 87 89

Tables and Figures Table 15. Long-Lasting Leaders: Leaders in Power At Least 25 Years Figure 1. Hazard Models Figure 2. Distribution of Time in Power (Full Sample) Figure 3· The Risk of Losing Power: The Entire Sample of Leaders Figure 4· The Risk of Losing Power: Gompertz Distributions for the Different Regional Samples Figure 5· The Risk of Losing Power: The Subsamples Figure 6. Post-1945 Leaders, by Type of Exit

xi 91 48 55 57

83 84 90

Of Time and Power Time is bearing another son. Kill Time! She turns in her pain! The oak is felled in the acorn and the hawk in the egg kills the wren. -Dylan Thomas, The Ballad of the Long-Legged Bait

CHAPTER 1

Introduction Why do some leaders stay in power for long periods and others fall quickly? How risky is it to lead a country? Are some countries or regions of the world riskier than others? Are military leaders more durable than civilian ones? These questions and many others surrounding length of stay in power are addressed in this study of time and power.

The Problem A brief story from the Republic of Guinea will illustrate a puzzle that motivates the effort undertaken. Sekou Toure came to the United States for heart surgery and died on March 26, 1984. He had been president of Guinea from its independence in 1958 until his death, and he was Africa's longest serving chief executive. Toure's prime minister, Lansana Beavogui, immediately assumed office as acting president, but a little more than a week later a group of junior military officers carried out a coup, deposed Beavogui, and announced that Colonel Lansana Conte was president. Power struggles continued within the military and between Conte and his prime minister, Colonel Diarra Traore. When Conte was out of the country in July 1985, Traore led an army group against Conte. The coup was put down by loyalist forces, and the coup leaders were executed. Guinea's experience was hardly unique in Africa. And, many countries outside Africa-Haiti, for example, post-Duvaliershare this pattern: A long-standing chief executive dies or is removed, and new leaders find it hard to consolidate power. Aperiod of struggle ensues, and a new leader falls quickly. Whether

2

Introduction

his successor can entrench himself remains to be seen. Of course, long-serving chief executives also face challenges to their rule; Toure himself had challenges over the more than 25 years he was in power. A different pattern also has existed in many nations. After years of rapid turnover, one man is able to consolidate power and remain in office a relatively long time. In Syria, for example, eighteen leaders had come to power but none of them had remained for more than four years prior to Hafiz a! Assad's assumption of power in 1970; Assad has now ruled for over two decades. Not only do different patterns appear from country to country, but the same country can also go through what seems to be different leadership cycles over time. Periods of rapid leadership change are followed by lengthy tenures or medium tenures of around eight or ten years. The different examples prompt the questions: What are the patterns of leadership longevity across time and place? And how can they be explained? Why do some leaders stay in power for relatively long periods whereas others fall from power after only days or weeks at the pinnacle? Answers to these questions have focused on the characteristics of individual leaders, their knowledge, skill, ability, ruthlessness, and "fit" with the particular country over which they rule. Answers have also been given that stress the characteristics of particular countries. The Economist, for example, asked: Why the recent longevity of leaders in the European Economic Community? Its answer was a simple one: wealth. It attributed the ability of Fran~ois Mitterand, Margaret Thatcher, Helmut Kohl, Felipe Gonzalez, and even Ronald Reagan to maintain themselves in office to the sustained growth in the Western economies and to a new consensus over economic policy.' Others have pointed to the governability of specific countries and have examined ethnic heterogeneity or rates of economic growth to account for the stability of political systems or the maintenance in power of particular leaders. Models of political stability have often been quite complex, focusing on interactions between variables such as political participation and rates of growth, political demands, and the ability of institutions to handle or process these demands.'

Introduction

3

This study tries to map and to explain durability of leaders in power. It is important to describe the patterns of leadership duration that have existed in all countries since their independence, because many overall impressions of leaders' longevity in power are derived from anecdotal evidence. 3 Few studies try to map out, much less explain, duration of leaders for large samples. The work of Jean Blonde! is an exception. 4 Blonde! described leadership duration, comparing regions of the world, types of leadership structure, leadership styles, and types of political systems, and he tried to account for duration in power of leadership. Blonde! employed regional variables-that is, country of origin and the region to which the country belonged-as well as variables that pertained to the leader himself to try to explain length of tenure in power. Blonde! was also interested in examining whether or not regime type was an important explanatory variable. Does it matter for leaders' duration in power if leaders operate in one-party systems, multi-party systems, or no-party systems such as military regimes or traditional monarchies? Blondel's study, though useful, was largely descriptive. We employ very different modes of analysis in this volume. The aim of this study is not, however, to update Blondel's work. Rather, this work explores the probability of a leader's loss of power over different intervals of time. We start our leadership study at the independence of countries, with important exceptions discussed in Chapter 2. We end our formal analysis at the end of 1987, although we take account, descriptively, of recent events. This study is not about regime stability or instability. Indeed, there often is a confusion between regime instability and the pattern of leadership turnover that exists. An individual may stay in power for an extended period together with a great deal of regime instability. Mobutu Sese Seko has remained at the helm in Zaire for 25 years while insurgencies, invasions, ethnic strife, and political conflict have waxed and waned. Nor was Ethiopia under Haile Selassie a stable country; however, he reigned for 44 years. A leader may stay in power by virtue of reducing the political or territorial space over which he rules. Thus, some Latin American civilian leaders like Jose Sarnay in Brazil and Raul Alfonsfn in

4

Introduction

Argentina yielded a good deal of latitude on military affairs to military leaders. Periodically, Burmese leaders have tried to subdue ethnic autonomy among Karens and Shans, but control of Burma's territory from Rangoon is incomplete. Leadership longevity certainly is not the same thing as power enhancement or regime stability. Indeed, there are important relationships to be explored between leadership turnover and broader regime instability. 5 Leaders' duration in office may affect a country's civil liberties or its economic performance." It has been argued that leaders become more authoritarian the longer they stay in power and that a consequence of long-lived leaders is that political decision-making ossifies. Arguments exist to the effect that rapid turnover in power makes it difficult, if not impossible, to implement policies for structural change or even to have coherent decision-making. Rapid turnover of leaders is seen as both a cause and a consequence of instability. But until leadership turnover is systematically mapped and explored, perhaps it is premature to test its consequences. 7 The present study deals with a topic complicated in its own right: the analysis of leaders' risks of falling from power. To pursue the study it is necessary to define below the terms used: leaders, risks, power. It also will be necessary to describe in detail the particular modes of analysis employed, because the techniques of survival analysis used here are perhaps more familiar to demographers and to econometricians than to political analysts interested in leadership and the maintenance of power. Chapter 2, "The Sample, the Variables, and the Coding," explains in detail the variables that are employed in this analysis, why they were chosen, and how they are defined. Chapter 3, "Modeling Leader Longevity," explains the techniques used. Here, a brief preview is given of those discussions and a major analytical problem is stated.

Characteristics of Leaders and the Problem of Heterogeneity Most studies of political leaders have examined their importance for critical outcomes of social, economic, and political change. The backgrounds of leaders have been examined to see how their personalities, careers, socioeconomic backgrounds,

Introduction

5

and political styles have developed. Then, analysis is undertaken of how leaders' styles, traits, characters, and backgrounds effect the organizations they have led and the countries they have ruled. There is a long pedigree for these concerns. Some would argue that modern political science begins with Machiavelli's The Prince and his interest in relating the Prince's skills to outcomes of political conflict. 8 Machiavelli, like Aristotle before him, thought it useful to know the social origins of leaders and to relate principles of rule to leadership qualities. Marx, and many of those who called themselves Marxists or explicitly argued against Marx, also addressed the importance of individual rulers. A huge historiography has developed around the role of the great man in history. An important part of the literature on the Russian Revolution and on Lenin has taken up the role of the "hero in history." It has not been uncommon for Marxists and their opponents to spend much effort analyzing the importance of Lenin and Stalin to the success of the revolution and its transformation." A similar literature has tried to assess the importance of Mao for China and of Fidel Castro for the evolution of the Cuban Revolution. 10 The concept of totalitarianism was developed with the role of the individual leader at its core, although different thinkers had conflicting points of view of the centrality of leadership in their definitions. 11 The study of new states gave added impetus to analyzing the importance of individual leaders who seemed to loom so large in both the anticolonial struggles and the formation of the independent nation. 12 Not all scholars of political development engaged in psychobiography, but few ignored totally the insights gleaned from the evolving work of psychobiographers, especially the work of Erik Erikson. It has been a difficult but important undertaking to isolate characteristics of leaders and then to assess their impact on societies. It has been perhaps even harder to isolate leaders' skills and to determine how important these have been for a leader's ability to maintain himself in power. 13 Many illustrations can be given for the difficulty of making assessments of leaders' attributes that are independent from the maintenance of power itself.

6

Introduction

Before Jomo Kenyatta died in 1978, many observers of Kenya's politics considered his vice president, Daniel arap Moi, to be unintelligent. "Moi" jokes made the rounds of Kenya's political circles. Yet Moi has been in power more than a decade and is now considered a canny political leader. Similarly, as the Soviet system opens up, reports surface that Leonid Brezhnev on his assumption of power was considered to be a man who was not very smart, an interim figure after Nikita Khrushchev, yet Brezhnev had a long, if not distinguished, reign. Perhaps the most telling example is also from the Soviet Union. Prior to Lenin's death, Stalin was thought by his peers to be crude and boorish and certainly not the intellectual equal of the revolutionaries he outsmarted, outmaneuvred, and ultimately repressed. Historians accord him greatness even if they consider him to be a paranoid despot. Examples abound of leaders who were considered "smart" or "skilled" but who did not hold power very long, such as Kofi Busia in Ghana and Sylvanus Olympio in Togo. In these cases, and in many others, were contemporary observers wrong in their initial judgment or did leaders grow (or decline) in office? Or is it that long-lived leaders were not especially clever but fortuitous, or that "the times" were right for them? Or is it that the structural characteristics of their societies were much more important than leaders' personal characteristics? These questions motivate this study. In Chapter 2, certain characteristics are described that can be isolated for leaders. These do not include personal traits and abilities precisely because such characteristics are often understood ex post facto and are derived from the very fact of long or short tenures. Thus, theories that ascribe leadership durability to the skills and abilities of leaders are hard to test empirically. This is the so-called sample heterogeneity problem faced by many analysts who try to ascertain the importance for outcomes of the variable nature of the sample that interests them, whether that sample is workers who are unemployed or leaders who are in power. It is not likely that all leaders have the same abilities, although individuals who get to the top politically can be assumed to have skills that distinguish them from most people. But how leaders' abilities and skills can be measured, compared, and related to their stays in power is not clear. 14

Introduction

7

A similar problem arises in measuring leaders' resources for contending and then holding power. Leaders came to political struggles with social and political backgrounds that entail ties to factions, classes, ethnic groups, and institutions. They both represent and redefine the interests of groups and institutions. Leaders have ideological commitments that may be net assets or liabilities. Ideological commitments or even basic social goals are resources that are not necessarily transferable across societies or across time within a society. This is also true for skills. The particular configuration of skills and circumstances that sustained Emperor Selassie in power for almost half a century would not necessarily prevail in another country, or for that matter even in contemporary Ethiopia. We can think of political leaders having skills, resources, and goals and formulating strategies under conditions of constraint and possibilities that they must assess. Political leaders then try to build support through policy formulation and implementation and to isolate their opponents. Skills, styles, resources, policies, constituencies, luck, and external constraints no doubt interact in complicated ways.'' However, it is hard to isolate and to measure these variables. Studies tend to focus either on leaders' characteristics or on policy outputs and coalition building. Few studies integrate the personal characteristics of leaders, their policy preferences, policy outputs, and constituency support, and then link them to a leader's learning process and subsequent personality development. This would be no small task to undertake. We could, in principle, try to measure political resources and leadership skills as well as leadership styles in order to explain length of stays in power, but such skills and resources have to be measured independently, not deduced from longevity per se. Similarly, personal legitimacy and "charisma" must be distinguished analytically from power and control and measured independently, if possible, if these concepts are to add anything to our analyses. Indeed, if we had completely independent measures of leadership skills, not ex post facto measures, we could test the hypothesis that in weakly institutionalized political systems, or in systems where political legitimacy is low, a leader's length in power depends on how well he wields patron-client

8

Introduction

networks and how well he uses carrots and sticks to reward followers and penalize opponents. 16 We could test whether the more time a leader has in power, the more knowledge he acquires about his own political system. ' 7 We could examine how important skill and knowledge are for building on marginal advantages by using incumbency and networks of information and control to create stronger political positions. Put differently, we could test whether or not the longevity in power of leaders is really a selection process over time among leaders of varying skills. So far, the discussion has dealt largely with the importance of the characteristics of the leaders themselves and with problems entailed by explaining and then using heterogeneity in a sample of leaders to explain their duration in power. In the study that unfolds in the following chapters, the focus is on a few rather easily measured characteristics of leaders, not on difficult-tomeasure behaviors of leaders. Attention is also paid to structural factors that pertain to the countries over which leaders rule. Societies vary with respect to a host of variables: size, wealth, social and ethnic homogeneity, and arguably such elusive features as "national character." The last would not be accepted by all analysts, but none would deny that national histories differ or that countries have variable resource bases or are more or less vulnerable to exogenous shocks or have more or less tractable neighbors. Thus analysts make implicit judgments that a country is particularly difficult or easy to rule, if not always, at least at specific junctures in its history. Germany during the Weimar Republic might be thought to have been a harder country to rule than a defeated postwar Germany eager to rebuild. Lebanon would be considered a heterogeneous country with many historic conflicts, but it was perhaps made harder to rule after the Arab-Israeli wars starting in 1948. Judgments made about "governability" are rarely formed or explored any more systematically than are judgments made about leadership skills. 18 And, surely, there has been little contemporary work of a systematic nature that relates the emergence of specific leadership attributes or skills to societies at differing points in their histories. There are problems in operationalizing many of the country variables that are hypothesized to be relevant for explaining

Introduction

9

leadership duration. Problems of data collection and of specification of country variables are noted at some length in Chapter 2. They pose difficulties that social scientists who deal with aggregate data know well. Thus we can move towards an early and very brief discussion of the results of the study. These are presented next in order to illustrate the goals of analysis and the ways of thinking about risks of falling from power.

Thinking About Risks The data will show that the risk for leaders of losing power declines the longer that they have been in power, although the decline is not perfectly monotonic. This is an interesting finding, and it is not one that is intuitively evident or that can be gleaned from knowing that some leaders stay in power a much longer time than others. It is not a tautology to state the proposition that the longer leaders have been in power the longer they will remain in power. This statement is not necessarily true. It might be, even though it is not, that after twenty years in power all leaders fall, or that after some extended period of time in power, risks of losing power start rising again or that risks are constant no matter how long a leader stays in power. 1" One could have a theory in advance of the analysis of the data that leaders take a "random walk" through history. A hypothesis that leaders face constant risks of falling from power could be put forward. Perhaps leaders stand at the edge of a precipice, which is loss of power. They must initially take a step to the right or the left. The step could be expressed as policy or personnel choices. If they go the wrong way, they topple. But if, by chance, their moves take them three steps away from the edge of the cliff, then they can survive an exogenous shock, say falling commodity prices, which pushes them only one step back towards the cliff. Leaders are eliminated randomly over time, but a few survive for long periods through no particular merit of their own. This is not a completely implausible theory of leadership survival. It will be shown, however, that the risks of falling from power are not constant but they decline as leaders remain longer in power. In advance of analyzing the data, we thought that the risks of

10

Introduction

falling from power would be highest in the first few years and then would decrease sharply, level off, and start to rise again. To put it somewhat more formally, we hypothesized that there is an observable pattern of leaders' conditional risk of losing power over time, with leaders facing the highest risk in the first years of their rule. We thought that the risk of losing power would decrease over time. We assumed that there would be some threshold, say two or three years, after which risk decreased only to rise again later, thus producing a pattern that could be graphed as aU-shaped curve. This idea was based in part on the postulation of a "senility" effect. First, leaders long in power could be assumed to be quite old. Their health might falter, they would have less energy, and their attention spans would weaken. Of course, this would depend on the age at which they assumed power. We knew that many African leaders became the first leaders of independent countries at relatively young ages. Post-World War II Asian leaders were on average older than African ones when they took over. With a large enough sample, we could take the age variable into account as affecting risks over time. There were other reasons to expect a priori that risk would rise again after extended periods in power. Leaders might grow stale; they might no longer be able to process information. Others would cut them off from information as second- and third-level leaders created their own fiefdoms. Populations might be expected to grow tired of the old leader. Ambitious followers would grow impatient. We had, however, no precise expectation as to when threshold effects might appear, again increasing risks after lengthy tenures. We very much had in mind the example of Selassie, where the aging Emperor of Ethiopia faltered in the 197o's, failing to manipulate the new institutions and social groups that had evolved in the 196o's. His own attention and skills atrophied, and he was overthrown at age 82. zo The first hypothesis that risks would decrease sharply after a few years in power turns out to be substantiated, although with some qualifications. The second hypothesis that risk would increase again after some lengthy period of time was not substantiated. One aim of this study is to describe the pattern of risks that leaders face. Another goal is to explain this pattern.

Introduction

11

The hypothesis can be put forward that the population of leaders is made up of the skillful and the unskillful. We would expect a high turnover rate in the first several years because the less skillful are ousted from office. By ten years' duration in office, perhaps only the skillful would remain. It may be true that it is equally difficult for leaders to stay in power regardless of how long they have been in power, but by the time they are past the first few years, all of the unskillful leaders simply have been ousted. This is the type of process that demographers describe concerning conception: there may be an equal probability of conception every month, but we observe a drop in the probability of conception for nonpregnant women as time goes on because those most likely to conceive do so right away and are selected out of the population. 21 Another hypothesis is that every leader faces a threshold effect in staying in power. We could imagine that the first years in power are the hardest for leaders. They may be hard because it takes time in power to accumulate resources, build political networks, construct instruments of repression, acquire followers through giving out awards, and neutralize opponents. Initial success may allow leaders to build support. This is what Arthur Stinchcombe has called the "liability of newness" in another context. 22 It is also possible that leaders' knowledge about their political systems and their general skills may increase over time. Again, this proposition returns us to the problem of heterogeneity in the sample, now stated dynamically rather than as an initial starting point for the sample as a whole. One could argue that there are special difficulties associated with early years in office when leaders develop and hone skills for maintaining power. They may start off with somewhat different skills or relatively the same amount of intelligence and skill, but over time some become better and some worse at ruling; some learn a lot, others learn little. Survival would be a combination of skill and luck. The problem is that we cannot confront the hypothesis about learning over time and becoming more skillful any more than we can confront the initial hypothesis about a sample of leaders who are skillful and unskillful at the start, because, as noted, we are skeptical that it is possible to make the necessary judgments about large samples of leaders independently of their duration

12

Introduction

in power. In individual cases, we may have good ideas about the pluses and minuses over time of knowledge, skill, network development, support, and, for that matter, the role of external actors. 23 But we cannot easily weigh these even in individual cases, and in the aggregate we cannot account for them directly. 24 Nonetheless, we can and do test to see if certain leaders, say ones with a military background, or ones who attain power at a certain age, have a greater or lesser propensity to survive in power. Tests are carried out to see whether leaders in countries characterized by particular levels of economic development or rates of growth, or other factors, tend to survive in power longer than leaders in countries that exhibit different patterns. The core of the analyses is in Chapters 4 and 5, where the global sample of 2,256 leaders from 167 different countries is analyzed and then broken down by regions of the world.

Time The passage of chronological time is at the center of our analysis of leadership risk. We explore the probability of a leader's loss of power over different intervals of time. The survival analyses that we employ allow us to analyze both the probability of leaders' loss of power and the timing of the event that occurs. When we say that a leader's risk decreases over time, we are referring to some unmeasured relationships that are occurring or changing over time. Leaders may be learning or becoming more skilled so that over time they become less vulnerable. Or, as we have already noted, unskillful leaders may be winnowed out over time. Or, populations may become more accustomed to leaders with whom they get more acclimated, and thus populations may become more apathetic about leadership change. We can proliferate hypotheses as to why the probability of removing leaders from power decreases with the length of time that they hold power. But not all these hypotheses can be tested properly. Arguments have been made to the effect that to state the proposition that something is a function of time is to say that there is a covariance with a set of causes as yet undiscovered. 25 As Nancy Brandon Tuma and Michael T. Hannan argue, time dependence may well reflect faulty specification of explanatory

Introduction

13

variables affecting a process. However, they also argue that, in situations where key causal variables are difficult to measure, a strategy can be employed that attaches substantive interpretation to the "effects" of various measures of time. 2" They state, and we can do no better than to quote them, that: Still, true behavioral (or structural) time dependence seems meaningful in certain classes of situations. These situations often involve causal variables that are either functions of time or closely related to time: age, duration, cohort, and experience. Although it may be argued that any of these variables actually serves as a proxy for other variables that are hard to measure directly, it is often useful to parameterize the change process in terms of them. Such efforts provide clues to the underlying structure and direct attention to those situations in which an intensive effort to measure more subtle latent factors is well advised. 27

Our study will show that the length of time a leader has been in power is the best predictor of how long he will continue to hold power out of all the variables we employ to try to account for leaders' longevity. We will show that the length of future time in power is positively related to the past time in power. Levels of risk do not decline monotonically, but the hazard functions do not turn up. Of course, death comes in the end. All survivors in power must exit from natural causes if not through coups or political maneuvering. Thus, we had to construct a method for taking account of leaders who do die of natural causes and for leaders who were still in power as of our cut-off date in 1987. We discuss this issue of "censored data" and other issues concerning our sample in Chapters 2 and 3· Indeed, by no means have all critical analytical problems been elaborated so far. Chapter 2 takes up what is meant by executive authority and thus who is in the sample. This chapter also deals with the not-sosimple matter of exits from power. The chapter provides justification for the sample coding and the variables chosen. The analytical techniques of survival analysis, sometimes called event-history analysis, and specifically of hazard models, have not been discussed as yet. Their discussion is somewhat technical and is reserved for Chapter 3· Both simple life-table analysis and more complex hazard models are employed. The latter have been used by demographers who want to explain life ex-

14

Introduction

pectancy and by economists who are interested in explaining bankruptcies or unemployment rates. However, these modes of analysis have not been in the usual tool kit of political scientists much less those interested in exploring political leadership. 28 Thus Chapter 3 is devoted to explaining methods in detail. We now continue an exploration of the analytical issues that concerned us as we framed the study and developed the sample.

CHAPTER 2

The Sample, the Variables, and the Coding A sample had to be constructed in order to explore why some leaders stay in power for long periods and others survive less than one year. Critical decisions involved: Who should be in the sample and what years and countries should the sample cover? Individual leaders were the foci for analysis, not regimes or collectivities of leaders such as cabinets, juntas, or politburos. 1 The sample is a global one. It consists of 2,256 leaders from 167 different countries in all regions of the world. Table 1 gives the breakdown of the sample by region and by military and civilian background. It also gives the share of censored observations. 2 The Appendix lists all leaders individually and provides information concerning age, date of entry, years in power, whether a leader entered constitutionally or not, whether the leader was military or civilian, and whether he was in power more than once. The Appendix also gives the sources for information presented.

Defining Executive Leaders The aim was to ascertain the risks of loss of power over time for the person who held executive power in a given country. Of course, leadership is shared in varying degrees within regime types. Measuring centralization of power across political systems and across time is a dangerous endeavor. It is difficult enough to decide who is primus inter pares in some systems without trying to analyze the relationship between degree of power

16

The Sample, the Variables, and the Coding TABLE 1

The Subsamples, by Region and by Civilian and Military Leaders --

Region

Middle East Africa Asia Latin America N. America, Europe, and Australasia Total

Civilian

Military

- - - -

Total (censored)

80 91 168 580

35 60 63 439

115 151 231 1,019

(34) (54) (48) (92)

693 1,612

47 644

740 2,256

(299)

(71)

held and durability in power. In many countries, a head of state is not the person in power. Fidel Castro has been first secretary of the Communist Party in Cuba for many years, not president of Cuba, yet no one would deny that he is the leader of Cuba. Deng Xiaoping did not hold the top constitutional position in either the state hierarchy or the Chinese Communist Party after his return to power in the late 197o's, yet he has been the dominant leader in China for the past decade. Ne Win resigned as president of Burma in 1982 and San Yu became president, though Ne Win retained chairmanship of the ruling party until he resigned during the turmoil of 1988. Some analysts of Burma argued that he was still the power behind the scene after his resignation; indeed, in the shadowy politics of Burma, it was not perhaps easy to say who ruled that country from 1982 to 1988. Current accounts of the Mongolian People's Republic place Khorloin Choibolsan at the center of events for the 192o's, but one expert places his leadership much later.' Where no clear leader emerges, we leave a period blank as we did for Mongolia between 1928 and 1934. These interregnum periods are listed in the Appendix. 4 Strongmen are not always clearly identifiable when militaries rule. Both military and civilian governments can have collective executives. Sometimes juntas are truly collective, and we have occasionally coded them as such. For Yugoslavia after Tito's death in 1980, leaders were not coded since the operative constitutional rule was for a collective leadership with rotating chairs. Similarly, the Swiss Federal Council was not coded either. In Eastern Europe under communist rule, it was not easy al-

The Sample, the Variables, and the Coding

17

ways to find the first among equals. Authority has sometimes been shared, or a collective leadership has operated alongside an aging leader, as occurred in Todor Zhikov's Bulgaria when he became quite old. In Western Europe, too, there are difficulties in specifying who wields the most power when there are power-sharing arrangements. In Finland, power has been shared, but prime ministers have come and gone with some frequency while presidents have had relatively long stays in power. Finnish presidents were chosen for our sample because they seemed dominant. In Portugal and Spain in different periods, monarchs often picked new prime ministers and there was some sharing of executive authority between king and prime minister, but prime ministers had the most power; 5 prime ministers were thus chosen for our sample. In Third Republic France, prime ministers were the rulers. After Charles de Gaulle came to power in 1958, French presidents were coded as leaders. However, in 1986 President Mitterand shared power with Prime Minister Jacques Chirac, and Chirac was coded from 1986 until the end of the sample frame in 1987. 6 No doubt, country experts can dispute these judgments. Overall analytical results, however, would not be changed by relatively few coding alterations. Another set of choices involves provisional leaders. Usually provisional leaders were not coded as coming to power. There are different kinds of provisional leaders. A leader may be appointed by a ruling group as a referee but never wield power on his own. Or, a judicial figure may sometimes be appointed to be head of state while legislatures are choosing a new executive. This occurred with some frequency in Latin America in the nineteenth century, and such leaders were not included in the sample. Where a leader did control territory and made viable claims to be a chief executive, as in China between 1916 and 1925 or in Zaire between 1961 and 1964, we coded him. Interim leaders may try to retain power. If they did so, we coded them into our sample. A leader may come to power but may state that he has no intention of retaining power. We tried to examine accounts that would allow us to make judgments about the sincerity of this claim. For example, the first coming to power of Jerry Rawlings in Ghana in 1979 was not coded, nor

18

The Sample, the Variables, and the Coding

was the incumbency of Siwar Dahab in Sudan in 1985. Both established provisional governments and left power. Of course, many military leaders claim they will hand over power and have no such intention; again, judgments cannot be avoided. David Lansana lasted less than a week in Sierra Leone after a military coup, but he hoped to maintain office. He was included in the sample.

Defining Exit Entry, then, poses some problems for coding leaders, though not insurmountable ones. The other end of leadership duration is exit from office. It, too, poses problems for analysis of leadership longevity. We are interested in the retention and loss of power. Leaders cease to hold power and new ones enter in a number of ways. Power struggles are one mode of replacing a leader. Death from natural causes leads to leadership change. Assassination also removes leaders, but the cause of assassination may or may not be embedded in a power struggle. Assassination can be a random event and nonpolitical. For example, it has been asserted that Prime Minister Hendrik Verwoerd was assassinated in 1966 in South Africa by a deranged person who did not have political motives. The assassination of King Faisal in Saudi Arabia may or may not have been a political act. In India, Prime Minister Indira Gandhi's assassination clearly was related to her repression of the Sikh movement in the Punjab region. Although it is often hard to know the truth about an assassin's motives, all assassinations were coded as political, and, as discussed below, we coded the assassinated leader as leaving pow!:'r, not as censored data. It was not always possible to be certain that a leader died from an accident. There is dispute over whether President Samora Machel in Mozambique and President Zia ul-Haq in Pakistan died from accidents or whether their aircraft were sabotaged. Nor is there always certainty about "death from natural causes." Did Poland's Boleslaw Beirut die naturally in 1948? Did the Bulgarian leader Georgi Dimitrov die naturally in Moscow in 1947? These "natural" deaths came at a time when East European leaders were being purged. Nonetheless many observers do attribute these two deaths to natural causes.

The Sample, the Variables, and the Coding

19

Death from "natural cause" may be hastened by the responsibilities of power or by the efforts to remain in power. Indeed, since part of what informed our study initially was the view that leaders might well become more vulnerable in old age, it can be argued that death from natural causes ought not be treated differently from removal from power through someone's will. On the other hand, there are good reasons to treat death from illness differently from other kinds of exits precisely because such an event, while often correlated with age, is different in kind from removal by an opponent. Another complication comes from death through suicide, which may or may not stem from personal problems unrelated to power maintenance. The number of suicides in our sample is quite small. They are coded as natural deaths, which means they are censored observations that fall outside the analyzed exits. Our aim has been to estimate the risk of a loss of power occurring during different time intervals. We also examine the effects of specific factors on that risk. We want to measure loss of power through political struggle. Thus, further complications arise when a leader is constrained by a constitutional rule that limits terms. Examples are the Twenty-Second Amendment to the U.S. Constitution, or Mexico's rule of one six-year term for each president since Lazaro Cirdenas in 1934. In principle, a leader can try to overcome a limiting rule or change it in any system. President Ferdinand Marcos changed the constitution in the Philippines to allow him to remain in office, as have many Latin American leaders. Constitutional rules also exist, of course, which specify that a leader should finish a term in office. These rules are also honored in the breach. African "life presidents" may lose their presidencies (and their lives) before natural causes produce a constitutional change. Elected presidents have had a hard time finishing their terms in Argentina in the twentieth century. Countries were not excluded from our sample because of constitutional rules either mandating terms of office or restricting tenures. Countries go in and out of constitutional governments, and leaders change constitutions. 7 We did not try to divide countries as democracies and nondemocracies, or electoral and nonelectoral regimes. Many countries have moved in and out of using elections to select rulers.

20

The Sample, the Variables, and the Coding

Elections have counted sometimes in Argentina, Peru, or Ecuador, to name just a few Latin American countries. Similarly, sometimes elections have determined leaders in Nigeria and the Sudan, but very infrequently have elections determined who will lead in the overwhelming majority of African states after the first "independence" elections.' Elections have been important in Lebanon, and elections have determined leaders in Israel since the founding of the Israeli state in 1948, but elsewhere in the Middle East elections have not been the dominant mode for selecting leaders. Elections have been the means for selecting rulers in most West European countries since World War II, although it was only in the 197o's that Portugal, Spain, and Greece became sustained systems after periodic dictatorships. Another exit event that is different from loss of power through struggle and that also raises complications is voluntary retirement. It may be difficult to know how voluntary a particular act of retirement is. A leader may think that he has a small chance of continuing in office, and so he leaves. Reports from Khrushchev's son's diary tells us that the Soviet leader, when confronted with knowledge of an insider's putsch against him, felt that he could not go against the majority of the Presidium. He accepted his defeat and went into retirement. This was not voluntary, but he ceased to fight. Others have fought to the bitter end. But a leader may simply want to retire to home and hearth. Indeed, some of these voluntary retirements create problems for knowing whether the chief executive has really departed, as in the case of Ne Win in Burma. President Julius Nyerere retired from office in Tanzania but held on to the chairmanship of the ruling party. In that case, a new leader, Ali Hassan Mwinyi, was coded as coming to power in Tanzania in 1985. In Cameroon, President Ahmadu Ahidjo stepped aside in 1982 but tried, unsuccessfully, to return to power a few years later. Voluntary retirements are, indeed, very difficult to code in nondemocratic systems. One way to try to deal with death from natural causes, retirements, and rules that limit terms in office is to look at nonconstitutional as compared to constitutional exits from power. Losing power can be distinguished from giving it away, albeit with the difficult judgments sometimes required. Then risks of loss of power can be analyzed for those who have constitutional 4

The Sample, the Variables, and the Coding

21

and nonconstitutional exits. However, one problem with focusing on exits from office is that, in the constitutional case, a leader may want to stay on and may contest and lose an election or may retire because he thinks that his skills, resources, or networks may not be up to the task of staying in office. Because the voluntary/involuntary exit distinction is more often ambiguous than the constitutional/nonconstitutional exit distinction, we have chosen to code for constitutional exits and entrances, but not for voluntary/involuntary ones. Nonetheless, in developing countries there are often serious difficulties in assigning constitutionality to leadership succession. For example, Isabel Peron's presidency in Argentina was terminated by a coup in March 1976. A military junta designated General Jorge Rafael Videla as president. We coded this as an unconstitutional ascendance to power. Subsequently, Generals Roberto Viola and Leopolda Galtieri came to power in 1981 and General Reynaldo Bignone in 1982. We coded these as constitutional changes. They were done within the constitutional framework that the military had established, and they were done in an orderly fashion. Of course, many Argentines saw any military succession as unconstitutional and the military leaders as therefore illegitimate. In nineteenth- and twentieth-century Latin America, legislatures elected presidents under great pressure from military strongmen. Presidents picked their successors and had their decisions ratified. These changes were thus "constitutional." A formalistic approach to constitutionality was taken in our sample. We followed, where possible, official encyclopedia and biographical dictionaries, which gave accounts of constitutional versus unconstitutional leadership change for many Latin American countries. Elsewhere, coding was done from historical accounts. This is not a fool-proof way of coding for constitutional succession. Obviously, there is overlap between military leaders and those who use extraconstitutional means to achieve power, but not all military leaders are unconstitutional ones. The coding of "constitutional" is also problematic. Where the consolidation of a communist regime was through elections, as in Czechoslovakia, the first communist leader was coded as coming to power constitutionally. Where revolution or guerrilla

22

The Sample, the Variables, and the Coding

war brought in new leaders, as in the Soviet Union and Yugoslavia, we coded them as "unconstitutional." During communist rule, exactly what constitutional change of leaders means is open to question. A central committee vote may ratify a majority in the Politburo against the leaders. Still, where a vote appears to have occurred, as in Khrushchev's ouster, the case was treated as one of constitutional change. There is a high correlation between the constitutional or the unconstitutional exit of a leader and his successor's constitutional or unconstitutional entrance. But the correlation is not perfect, because a leader may exit constitutionally and a provisional leader, who was not coded, may then enter. The next "real" leader may come in unconstitutionally. A leader may be driven from power but a legislature may be convened and vote in "constitutionally" a new leader. We coded for leaders still in power, for leaders losing power constitutionally, and for leaders losing power extraconstitutionally. We have no illusions about the difficulties of these codings. In order to obtain a fuller understanding of the distributions of risk of losing power, entrances to and exits from power are examined over time. The temptation has been resisted to distinguish between electoral and nonelectoral systems in order to scale risks in the different types of systems. Nor were all the possible combinations of voluntary/involuntary, electoral/nonelectoral, and constitutional/nonconstitutional analyzed. It' There are systems in which elections take place but do not usually lead to a change in leadership. These are not fully competitive elections, and they abound in Africa, Latin America, and the Middle East. In military regimes, leaders may voluntarily give way too, as General Olusegun Obasanjo did in Nigeria in 1979. In parliamentary systems, a leader may lose his majority within his own party and may have to leave, which is not a voluntary retirement. In nonelectoral systems or in noncompetitive electoral systems, a leader may not retain "enough" support in a critical constituency, as occurred in Khrushchev's case. These are not voluntary retirements, but they are not fought to the political death. Thus, in theory, risk could be assessed in different kinds of systems and we could try to account for whether leaders truly choose to retire for personal reasons or whether they fear they

The Sample, the Variables, and the Coding

23

may lose the contest, as Lyndon Johnson may well have felt. However, we cannot know all the "voluntary" exit situations well enough to accurately code for retirements across the whole sample. And, to repeat, systems move in and out of being "constitutional" or "electoral." The solution is to analyze the global sample, focusing both on individuals' exits and entrances and on coding for constitutionallnonconstitutional exits and entrances, not focusing on types of systems or coding for voluntary or involuntary retirements. Although leaders may return to power, and some in the sample do, we treat loss of power as a full exit from office. When a leader goes out of office in the analysis, the clock starts all over for a new period of time in power; the second, third, or fourth returns to power are not cumulated.

Foreign Intervention Interventions from abroad can be highly consequential for leaders. In Africa, certain leaders, such as Patrice Lumumba in the Congo, have been brought down through external interventions or through a combination of civil war and foreign meddling. Some have been kept in power or reinstated through military interventions by outsiders. Mobutu Sese Seko in Zaire, Leon M'ba in Gabon, Jomo Kenyatta in Kenya, Julius Nyerere in Tanzania, and Milton Obote in Uganda all benefited from direct interventions. French commitments to Felix Houphouet-Boigny in the Ivory Coast and to Leopold Senghor in Senegal undoubtedly raised the stakes for would-be opponents and especially made military coups less probable in those countries than in places where French commitments were smaller or had been abandoned. Similarly, Soviet and Cuban military personnel make less likely a coup against Mengistu Haile Mariam in Ethiopia so long as they continue to support him. A powerful neighbor can affect a leader's longevity. Soviet support for Castro has been important for his personal power as well as for the maintenance of a communist regime in Cuba. South Africa's support for individual leaders of small neighboring states-Botswana, Lesotho, and Swaziland-made prospects worse for would-be military or civilian rivals. South African hostility toward a leader can also make life much more

24

The Sample, the Variables, and the Coding

difficult, as it did for Chief Lebua Jonathan in Lesotho. Vietnam has had a large influence in Cambodia and Laos. Clearly, foreign troops used as a praetorian guard for the leader can enhance his prospects for retaining power. In Eastern Europe, communist rule has rested heavily on Soviet military might. The Soviet Union has been a consequential actor in affecting factional politics in many East European countries although it has not always achieved its goals in the internal politics of these countries. Stalin failed to replace Tito in Yugoslavia, and it is not clear that the Soviet leadership, itself probably split in many cases, always obtained the leader it preferred in other countries. The Soviet Union has intervened massively in Afghanistan, but whether it wants to or can maintain Najibullah indefinitely remains to be seen. Military and economic aid has been important in keeping U.S. allies in power, too. The Philippine's president Coraz6n Acquino might well have fallen to a military coup in 1989 if U.S. planes had not provided air cover. Earlier, when the United States decided no longer to support Marcos in the Philippines, his chances of staying in power were diminished. In 1963 the United States may have been involved in removing Ngo Dinh Diem in South Vietnam, and in 1989 it directly removed General Manuel Noriega in Panama. But U.S. military aid in the 198o's to the opponents of the Sandinista government in Nicaragua was not sufficient to topple Sandinista leaders by military means; nor was U.S. support for a succession of South Vietnamese leaders sufficient to maintain them. Thus external intervention is an important variable in affecting leaders' ability to stay in power. It would be possible to code for direct, large-scale interventions that installed or removed leaders, as in Panama. But it would be very difficult to measure covert external support and its importance for most leaders. Thus we prefer not to attempt direct measurement of the external variables at all.

The Time Frame As indicated in Table 2, we decided to start counting leaders beginning with their countries' formal independence in Africa,

The Sample, the Variables, and the Coding

25

TABLE 2

The Subsamples, by Period of Time Up to Region

1900

-

Middle East Africa

Asia Latin America N. America, Europe, and Australasia Total

-----

19011945

Post1945

------

Total (censored)

---- ----

-·-

442

12 6 53 291

103 145 168 286

115 151 231 1,019

21 473

431 793

288 990

740 (71) 2,256 (299)

0 0 10

-

(34) (54) (48) (92)

- - - ----·--

·-

the Middle East, and Latin America. In Latin America this meant going back to the early nineteenth century for many countries, which gave a much larger sample for one region and afforded the opportunity to make nineteenth- and twentieth-century comparisons. However, the analysis of the Latin American sample was mostly limited to post-World War II. If all the nineteenthcentury Latin American cases had been used in the subsample analyses, some of the country variables, such as rate of economic growth, could not have been employed easily. 11 For African countries that were independent well before the 195o's, some arbitrary decisions were made: for Liberia the starting point begins with President William Tubman in 1944; for Ethiopia it is Emperor Haile Selassie entering in 1930; for Libya it is King Idris in 1951; and for Egypt it is King Fouad in 1923. For leaders in the Asian sample, the date of independence was taken if the country had been a colony in the twentieth century. We took the post-Meiji period in Japan, and we went from the Republican period on in China (post-1912). Post-World War II was the starting date for the two Koreas. Although different time periods were chosen to begin counting leaders for Asian countries that were not colonies, no bias is introduced by the starting points. Each starting point was chosen for a break with the past and the start of a new era, somewhat equivalent to the independence starting dates for Africa and the Middle East and for former colonies in Asia. For Eastern Europe we began with the inception of communist rule, which is also the starting point for the Soviet Union.

26

The Sample, the Variables, and the Coding

This was done largely because the post-World War II period gives a convenient time period for Eastern Europe that was close to the post-colonial periods for the Third World regions aside from Latin America. For Western Europe, North America, Australia, and New Zealand the sample was developed starting from the twentieth century. However, for many of the analytical comparisons we use the post-World War II period also. Thus Europe and Latin America do not weigh that heavily on the total sample. Formal independence may not have been the best date to have chosen for the developing countries. The size of the sample would have been increased if effective home rule had been chosen. South Africa was governed by South Africans well before 1934. A number of Caribbean and South Pacific island states had leaders who had contested for power prior to formal independence. Yet consistency prevailed over other considerations here. The dependent variable is straightforward: it is the risk of losing power over time. We need measures of duration in power. Duration of leadership is measured in years: if a leader was in power less than one full year, we coded it as "o"; if the leader was in power fourteen months, one year was coded; if the leader was in power twenty-seven months, two years was coded, and so on. There may be some bias in our sample toward leaders in power zero years, for although we tried to avoid entering provisional leaders, some individuals may have been included inadvertently who never intended to compete for power, and thus it is possible that more leaders leave power at an early stage than should be in the sample.

Coding Explanatory Variables The independent variables were designed to account for variations in the risks of losing power over time. They were derived from both the literature on regime stability and our own ideas as to what variables might account for leadership longevity. These variables are divided into two categories. Some pertain to the characteristics of individuals, others pertain to characteristics of countries. Some variables are straightforward and easily specified, and others are less clear or not so easily captured by sum-

The Sample, the Variables, and the Coding

27

mary measures. Not all variables are employed for the global sample. Some make sense only for certain regional analyses. Age is a factor we would expect to be relevant for durability. Leaders who come to power at relatively young ages will have, other things being equal, a longer period before they physically and mentally weaken. On the other hand, younger leaders may not have acquired the requisite skills or knowledge about their political systems that relatively older leaders may have acquired. The presumption here is that age and experience are positively correlated. By recourse to many sources, the age of a leader at entry into power could usually be found. The age of 126 leaders could not be found, and these were treated as missing observations in the statistical analysis. We initially thought that the first leaders of new states would have longer tenures and lower risks of falling from power than subsequent leaders. We coded dichotomously for leaders of former colonial countries, coding for first and subsequent leaders who came to power after independence. 1' This hypothesis was based on the notion that first leaders had a special legitimacy as "Fathers of the Country" or first leaders of new systems-such as Lenin in the Soviet Union. It was also based on the idea that many of the leaders who had led the anticolonial struggles that helped them to acquire this legitimacy had a lengthy preindependence period in which to develop leadership skills, acquire information about followers and opponents, and develop political networks. This was true for some revolutionary leaders, too. We also thought that many countries which came to independence after World War II had, for a time, important resources from the former colonial country. Some inherited substantial hard-currency reserves, and former colonial officers stayed on to help organize and administer. However, for every one of these reasons to believe that first leaders had advantages, there were reasons to believe the opposite, if not generally speaking, at least for many cases. Some leaders simply received power from a colonial administration; there was little struggle and no evidence that the leader had won support. Indeed, the leader might have been seen as a tool of the colonial regime. This could be argued for some of the former French colonies in Africa. Elsewhere the anticolonial struggle had been extremely bitter but the indigenous leadership itself

28

The Sample, the Variables, and the Coding

became fractured during the struggle. This was true for Angola and, to a lesser extent, for Algeria. It could be hypothesized that leaders who emerged from anticolonial warfare might not have a set of skills that would serve them well for the period of independent rule. This could be true but still legitimacy might accrue for a long time because of a history of civil war and/ or anticolonial war. Mao in China, Machel in Mozambique, and Castro in Cuba are examples. The skills of the revolutionary leader might or might not serve one well for continuing in power. Indeed, the hypothesis predicting lower risks for first leaders was confirmed, as we shall see in Chapter 5 when the subsamples are analyzed. First leaders have about half the risks of leaders who were not the "Fathers of the Country." Another leader characteristic that we coded was whether the leader came to power and lost power through constitutional or unconstitutional means. 13 Obviously, analysts of political leadership are concerned with the legitimacy of leaders. Leaders who come to power constitutionally might be presumed to have more legitimacy than those who do not. This statement, however, requires qualification. Most first leaders of newly independent states come to power constitutionally. Of course, revolutionary seizure of power may occur, and it also may bring legitimacy. Moreover, many military coups have been received enthusiastically in Africa, the Middle East, and Latin America. Thus the relationship between constitutional entrance and legitimacy needs to be proved. We coded under leaders' characteristics whether the individual was military or civilian before becoming supreme leader. 14 If this were a study of military regimes, then a military leader would be defined as one who comes to power by virtue of a place in the military chain of command. But for the purposes of analyzing leadership change, we want to know whether a leader had a significant part of his career in the military and whether a place in the armed forces was consequential for rising to the top. This variable is not hard to code for most countries in the twentieth century. But the main power base of a number of leaders remains a combination of military and civilian elites. Some military leaders cease to be entirely dependent on armed forces for their maintenance in power. This was true for Anwar Sadat and

The Sample, the Variables, and the Coding

29

Muhammad Mubarak in Egypt and for the military leaders of Iraq and Syria, who have had strong ties to the Baath parties. Judgments must be made as to whether or not military background was important in achieving power. Thus both Charles de Gaulle and Dwight Eisenhower are coded as military men. Harry Truman, who served in the military, is coded as a civilian. There are difficulties in coding, especially for nineteenth-century Latin America, where many men who were essentially civilians took military titles or were awarded such titles. In the twentieth century, military men often put away their uniforms most of the time. This was true for Juan Peron in Argentina. Possibly we have coded too many military leaders, accepting titles in Latin America that were awarded to civilian political leaders. Many studies of developing countries have examined the importance of military rule, not to mention the making of coups. Cross-national aggregate data studies have looked at the effect of military rule on development patterns and on public policy outputs. 15 There are studies of military rule and political stability, but we are not aware of systematic studies as to whether military rulers are more durable per se than civilian rulers. Obviously, some of the rulers longest in power have been military men, but some have been dynastic rulers and some have been civilians without dynastic ties. We test for the importance of military background on duration in power. Finally, a dummy was created for those leaders who were in power more than once. Several hypotheses justified inclusion of this variable. It was hoped that the performance of "repeaters" would provide clues regarding the salience of skill and learning to holding on to power. First, if learning were important to power maintenance, then leaders might be expected to improve their performance each successive time in power. Second, inclusion of the dummy in the model would test the hypothesis that a leader's ability to regain power after having lost it indicated uncommon political skills and resources.

Country

Variables

A number of country variables are introduced. These variables are straightforward in the sense that a label or a number can be put on them. This does not mean, however, that the vari-

30

The Sample, the Variables, and the Coding

abies that pertain to all countries have been specified well. Our aim is to take conventional variables from the regime-stability literature and expose them through the hazard-models analysis to see if they account for variability in leadership duration. The nature of colonial rule might be thought to be important for the kinds of leadership that emerged after independence. We coded for British, French, Portuguese, and other colonial experiences for Africa, the Middle East, and Asia. The trouble with this coding is that, within different colonial powers' administrations, a great deal of variation existed. The most significant differences conventionally have been presumed to appear from the distinctions between indirect and direct rule attributed to the British and French, respectively, or from the failure to prepare leaderships that many historians have claimed describes Belgian and Portuguese rule in Africa. While some analysts have seen effects of the impact of particular colonial rulers on the patterns of independence politics, others have questioned the importance of the distinctions between British and French rule especially.'b We coded for colonial background and tested for its effects on the subsample of countries for which this variable is relevant. Countries were divided by both economic-growth levels and income levels. Per capita-income level was specified as a continuous variable according to the World Bank's 1973 and 1980 estimates of per capita GNP. Per capita income does not adequately capture the full cross-national variation in purchasing power, or income distribution, and using the 1973 and 1980 country estimates introduced a bias for the leaders of countries that have changed income categories over the longer periods of the study. The importance of the latter problem is reduced by also differentiating countries by growth level. Here a continuous variable was specified using the World Bank's estimates of annual GNP growth from 1965 and 1983 for the countries represented in the sample. Greater disaggregation may have been called for, as growth levels were not always uniform over the period of study. The farther back one pushes in time, the more troublesome the problem is. The difficulty is that our hazard model does not allow us to use a changing income or growth variable for each year of our interest. This gives us an intractable problem.

The Sample, the Variables, and the Coding

31

We use the income growth and levels of income variables because a large literature on preconditions for democracy, on political stability, and on centralization of power examines the relationship of these economic variables to the political ones. 17 Thus it seemed useful to utilize levels of development and rates of growth as independent variables to explain variance in leadership durability despite all the problems involved in variable specification. A large literature examines the relationship between ethnic structure and ethnic conflict and political outcomes. Analysts of regime stability as well as analysts of political leadership have been concerned with ethnic identity and ethnic conflict. 18 Increasingly, students of both industrial and developing countries have come to the conclusion that levels of ethnic tension are not correlated neatly with levels of development, and ethnic conflict has come to be seen as a nonlinear phenomenon. Communal strife waxes and wanes in complicated ways related to the specific issue areas that become conflictual in a society, the ways that these issues are defined and resolved by political leaders, demographic and social change in a society, and other variables. Nonetheless, whatever the conception of the growth of or diminution of "ethnicity" in a society, most observers have believed that it is more difficult to rule ethnically heterogeneous societies than ethnically homogeneous ones. It is held especially difficult to preside over heterogeneous societies that are highly conflictual. 19 Thus, we coded for aspects of a country's degree of ethnic homogeneity and also for levels of ethnic conflict. We coded for high, medium, and low degrees of ethnic conflict over time in all countries. But degree of ethnic homogeneity is measured by different subvariables depending on the country. In the Middle East, the share of the population that is Muslim is taken. This ignores Sunni-Shia distinctions, although these are captured by the degree-of-conflict variable. Still, we miss other important ethnic distinctions in the Middle East. Similarly, in Africa the share of population that is Islamic is coded in specific countries. We hoped to pick up in the Islamic variable not only an "ethnicity factor" but also a cultural one. It has been asserted that Islamic countries are relatively hierarchical and given to accepting the authority of rulers. 20 We tested for this via the vari-

32

The Sample, the Variables, and the Coding

able "share of population Islamic." But we did not code for the share of population in African countries that is from a major ethnic/language group. Counting ethnic/language groups in Africa does not necessarily give a good purchase on "ethnicity." Many large ethnic language groups are divided by clan, lineage, or regional distinctions. Students of Kenya may find Kikuyu acting as a group vis-a-vis non-Kikuyu, but they would not argue that Kikuyu are a homogeneous political grouping any more than students of Nigeria would argue this for Yorubas or Ibos. It would have been interesting to see if individual rulers from dominant ethnic/language groups were more or less likely to stay in power for longer periods. This effort was not made because we did not believe that we could specify the leader's "ethnicity" accurately enough for our analysis. Membership in a particular ethnic/language group was not specific enough for our purposes because it would have ignored ethnic subgroups as well as religion and place of origin. Thus the ethnicity variables used are crude ones that do not capture well either ethnicity or ethnic conflict, but for the purposes of our study they give us a first pass at these issues. For Latin America, demographic data on Indian or mestizo shares of various populations did not give consistently good comparisons across countries. In the Caribbean, the issue was not usually people of indigenous Indian populations but what share of the total population were people of African origins, including those whose descendants can be traced to Europe and to East Asia. We did code for degree of ethnic conflict and used the proportion of the population of European descent for coding ethnic heterogeneity in Latin America and the Caribbean. Eastern and Western Europe, North America, Australia, and New Zealand were also coded for degree of ethnic conflict. Violence hardly seemed a fire that would forge a strong and coherent leadership after independence, although Frantz Fanon argues for its positive effects in creating new men and strong organizations. 21 Nor was evidence overwhelming that elites who won civil wars were particularly cohesive. Violence in Algeria, Zaire, Mozambique, Angola, Vietnam, and Kampuchea proved to be bitter legacies for these societies and arguably made them

The Sample, the Variables, and the Coding

33

more difficult, not easier, to rule. Still, we wanted to test for the relationship between violence and leadership duration. We coded for whether violence was present at high, medium, or low levels for first leaders as they came to power or immediately before the colonial regime gave way. Latin America, Europe, North America, Australia, and New Zealand were excluded from this analysis, which concerned only Asia, the Middle East, and Africa and, again, involved judgments based on secondary sources. We introduced two size variables that distinguish countries: size of country and size of population. Size was measured in square kilometers; population was measured in millions of people. The idea was to introduce variables thought to be relevant for discussing governability of countries and viability of countries. Assertions have been made from Aristotle on whether large countries are more or less likely to be governable, be democratic, have a high degree of political participation by their citizens, and be sustainable economically. 22 We were interested to see whether size variables had strong correlations with leadership turnover. These two variables were also combined in an interaction term to test for the relevance of countries' population densities. Literacy is another variable that has been seen as consequential for governability. Arguments have been produced that countries with low literacy are easier to rule because their populations are tradition-bound and passive. As they become more literate, it is argued, they demand more from leaders. Yet, arguments have also been made to the effect that democracy is highly correlated with literacy. 23 Our own view is that there are many intervening variables between literacy and regime stability and specifically between literacy and leadership turnover. Data were collected on the median length of stay in power of all the leaders in a given leader's country. 24 More than the socioeconomic variables, this variable would provide direct evidence regarding a country's governability because it captured the longterm country trend in leadership stability, whereas the socioeconomic variables were assumed to have complex and heavily mediated relationships with political stability and leadership tenure. Indeed, the country median duration variable could

34

The Sample, the Variables, and the Coding

serve as a proxy for all the institutional and socioeconomic factors that undermine or enhance power maintenance in a given country. We fully realize that we have not tried to code for and to model all the variables found in the regime-stability literature. We have excluded some that are often mentioned in the stability literature, such as rate of urbanization, population growth rate, and government spending as a share of the gross domestic product. To some extent, we worried about good data across time for these variables, and we were skeptical that adding country variables would improve the explanation for leadership duration. Our study puts time at the center of the analysis. Time appears as an independent variable not only for time trends but also as date of entry when leaders take power. Particular time periods may be difficult for certain kinds of leaders to stay in power or for leaders in countries characterized by specific international trade or other patterns. A period of falling commodity prices may make resources harder to acquire for leaders who rule states in which those commodities are produced. Rising oil prices may have negative effects for rulers of oil-importing states. The 198o's were a difficult decade for leaders who presided over debt-management problems, whether they inherited the debts or helped to create them. Other difficulties associated with particular periods include contagion effects for rebellion, high rates of disease incidence, and poor weather. A specific decade or even year may appear as a consequential variable. Thus we coded for the year a leader entered power or exited from power. To repeat a point made earlier, here time is a variable that is a proxy for variables not directly measured, such as weather, commodity prices, and diseases. And, finally, time intervals are a key explanatory variable for us. We explore the intervals at which leaders leave power to see the impact of length of time in power on the risks of losing power in the future. Here, the time (duration) variable may reflect the winnowing out of weak leaders or the improvement of skill and building of networks of surviving leaders. It is important that we be able to test for the interactive effects between time and other variables that we have described. That is, do the effects of particular independent variables change over

The Sample, the Variables, and the Coding

35

the length of time that a leader is in power, or do they remain constant? For example, it could be hypothesized that a military leader coming to power might have particular liabilities stemming from a lack of specific political skills or allies due to his military past but that if that leader survives for five years, he has then acquired the requisite skills or allies and his risks would fall sharply. The impact of the military variable changes over time. Or, it could be argued that a leader who comes to power unconstitutionally has higher risks at the start of his rule, which may be perceived as illegitimate, but then people grow accustomed to him and risks fall. We might argue that risks rise over time for military leaders because they are given grace periods to "clean up the mess" left by predecessors but that their legitimacy is dependent on results. The point is that there are reasons to believe that specific variables themselves have different impacts on risks over time. To explain more formally what we mean by time trends and by interaction effects, we now turn to the analytical techniques and models that are employed.

CHAPTER

3

Modeling Leader Longevity Political scientists have long used multivariate regression techniques to study a wide array of political phenomena. Often, an observed outcome or event is explained in terms of specific variables. To cite an example, several scholars have examined the prevalence of coups d'etat in the Third World in terms of the specific socioeconomic characteristics of different countries or the impact of military spending on development in the Third World. 1 In these cases, the event of interest is the coup, but it could be any other regular occurrence to which one can attach a fixed probability, from the reelection of American congressional members to the outbreak of war. With the important exception of the literature on the causes of war, political scientists have rarely treated time as an explanatory variable, whether a proxy variable for unmeasured variables or a "pure" variable.' There are few attempts by political scientists to model the interactions between time and the explanatory variables that are used. Economists and demographers often estimate regression equations employing a time variable. Rebecca Blank, for example, asks: How does past time on welfare affect spell on welfare in the present?' She, like others who do duration analysis, raises the heterogeneity issue: it may be that longer welfare usage stems from the innate characteristics of the individuals who remain longer on welfare. This is the unmeasured heterogeneity problem to which we refer in more detail below. The concepts of duration, dependence, and heterogeneity are hard to unlink in Blank's study. Length of time on welfare might alter motivation to get off welfare. In our study, length of time in power may increase (or decrease) motives to

Modeling Leader Longevity

37

stay in power. It might also increase (or decrease) skills that we cannot measure. Nonetheless, economists see length of time on welfare or duration a person is unemployed as variables in their own right. Political scientists understand outcomes and events as changes over time, and it is often presumed that time and its passage is central to many social processes. Political scientists and political observers regularly make use of concepts such as honeymoons or grace periods, thresholds, or cycles. These are all concepts which assume that the passage of time is relevant in some critical way. But few political scientists have used survival analysis to test for the effects of time on political outcomes. • Political scientists tend to see time as a proxy variable for unmeasured occurrences. These may be changes over time due to the heterogeneity of the sample-for example, skills increaseor because some events have produced particular outcomesfor example, economic growth produces more legitimacy for luders. 5 But if we subtract the effect of all the variables our theories tell us are necessary to test, and we are left with a net time effect, then we can refer to this, terminologically, as a "pure" time effect. It is possible that advantages for leaders have cumulated over time and thus their risks fall the longer they remain in power. The issue is: How reductionist do we become in our analysis? We show time effects. It may be that the time effects capture heterogeneity in the sample. It may be that they capture variables we have not taken into account or specified well. Or it may be that time in power does cumulate to advantages that could be spelled out through complicated netting out of different relationships and events happening across time. There is nothing mystical about time; time does not have unique causal properties. However, there are, in short, time effects. In the research of other disciplines within the social sciences, "event-history" methodologies have been refined to analyze data on events and the passage of time for a variety of subjects." These techniques were developed for demography and mortality analysis first, and subsequently for a host of other kinds of event-history data: divorce, job promotion, unemployment, and spacing times between births. 7 Even earlier, the biomedical sciences used similar techniques to study the remission rates

38

Modeling Leader Longevity

of cancer victims and called them survival analysis or lifetime analysis." In engineering, they have been used to study machine and product life; for example, to test the life expectation of light bulbs." Event-history analysis methods are particularly suited for data that exhibit two characteristics: the presence of censored observations, which are those that are not observed for the full period of risk; and the salience of explanatory variables that change over time, or have effects that change over time. The standard statistical techniques of multiple regression do not handle these two elements well, which can lead to important biases. We start this chapter with an example of the problems faced by more conventional regression analysis, then move on to a brief review of the event-history analysis methods we will use in subsequent chapters.

An Example Modeling regime stability or "predicting coup d'etat" has been attempted by a number of scholars. To analyze regime changes in Africa, for example, Robert Jackman has used a multivariate Ordinary Least Squares (OLS) regression model of the following general kind: 1"

(1)

where Y equals an additive index in which plotted, attempted, and successful coups are weighed according to their political importance, and X, ... X; correspond to a variety of explanatory variables, such as level of cultural pluralism, level of social mobilization in principle, and any socioeconomic variable thought to affect the incidence of coups. This approach is reasonable for exploratory work, but it is inadequate in several ways. Most important for our concerns are the difficulties in handling time periods. The period of study, 1960 to 1975, poses problems because not all of the countries were in the sample for the same amount of time, since they did not all gain independence at the same time. This may introduce biases in the analysis. A country that amassed a composite coup score of fifteen in ten years is considered the same as one that

Modeling Leader Longevity

39

takes fifteen years to reach that score. 11 Our own analysis indicates that many African regimes enjoyed a grace period for leaders for several years right after independence. It should also be noted that the dependent variable in the coup equation, cumulative coups, cannot be negative: no country can experience fewer than zero coups. Yet the OLS model can predict negative values for the dependent variable.' 2 Another problem results from the necessity of cutting off the analysis at some arbitrary point. The sample has to end at some point, but bias may also be introduced; a coup on January 1, 1976, is not taken into account at all in the regression, for example, even if the instability that led to it was presumably present in the time frame under study. This tack may be justified if a very long period is under study, or if there is some natural point at which to end the study. Historical discontinuity, such as a world war or the passage of a new constitution, would be an example. Biases are more likely to occur when the time frame is small and individual observations weigh heavily on the sample, but the researcher will often have little choice over the suitable time frame. Clearly, some way of dealing with the fact that the time frame has to be closed at some necessarily arbitrary point would bring an important advantage to the analysis. As we will see, event analysis provides a way of censoring data in order to grapple with this issue. The standard approach for studying coups, represented by Jackman's regression analysis, cannot handle the possibility that between 1960 and 1975 several of the explanatory variables may have changed in value. In Jackman's study, for example, one value for variables such as political participation or social mobilization is assigned to each country. Between 1960 and 1975, however, these countries may have undergone substantial sociopolitical changes, and the value of these variables, as well as their impact on coups, may well have changed in fundamental ways. Within OLS, however, there are no easy ways of handling such difficulties. To give unrelated examples: it is plausible to suppose that educational level is much more highly correlated with income for people just out of school than it is ten years later. In demography, it is commonly argued that certain social characteristics

40

Modeling Leader Longevity

have a large effect on the mortality rates of small children and a relatively small effect later in life. Single-equation OLS techniques cannot capture these changes in the effects of explanatory variables. Hazard models, however, allow us to test for the importance of changes over time. Standard regression analysis of coups cannot tell us when coups are most likely to occur. One country may have ten coup attempts in three years, then twelve years without any coup attempts. The statistical analysis will not differentiate that country from another that may have had ten coups spread out evenly over the fifteen years. There are reasons to think that interesting political phenomena are not being adequately captured by this approach. Coups might be more difficult in the six months after an unsuccessful attempt, for example, or it might be harder to topple a leader who has been in power five years than one who has ruled six months, regardless of the socioeconomic characteristics of the country in question. In other words, the approach may fail to account for important dynamics within the data. If nothing else, this seems wasteful of available information. Clearly, integrating the passage of time into the model would enrich the study of coups d'etat.

Appeal of the Event-History Approach The methods known collectively as event-history analysis, of which survival analysis is a subgroup, allow the investigator to grapple with changes over time from a different analytical perspectiv~. We are interested in understanding why some leaders manage to hold on to power longer than others. Our unit of analysis is not states, but rather state leaders themselves. Our dependent variable is the risk of losing power of individual leaders. The loss of power of individual leaders is the event we are interested in explaining. The oldest method for analyzing eventhistory data is the life table. More recently, statisticians have developed increasingly sophisticated techniques from the basic principles behind the life table. It is possible to adapt eventhistory methodologies for our purposes by treating a leader's time in power as a life and his loss of power as the equivalent

Modeling Leader Longevity

41

of death, allowing us to perform demographic analysis or our sample of leaders, using both life tables and hazard models.

Life Tables Life tables describe the distribution of survival times experienced by homogeneous groups for subjects, when these survival times are subject to censoring. 13 They provide a way of describing the survival rates of homogeneous groups of subjects and allow comparison of one group with another. To make comparisons, a hazard function h(t) is created and defined as the time-dependent risk of loss of power. In our study, it is the conditional probability that a leader who is known to be in power at least until time twill lose power before time t + 1. Let r( t) be the number of leaders in power and thus "at risk" between times t and t + 1. Finally, let m (t) define the number of leaders who lose power between t and t + 1. Then, h ( t) is defined as h(t)

=

m(t) r(t)

for discrete events.'• Given a sample of state leaders, it is possible to construct a distribution of hazards over time; the leader who lost power in year x contributes to the hazard rate for that year, and so on until a rate is calculated for each year of rule. Thus, life tables are purely descriptive and do not allow us to formally predict how long any given leader will stay in power. They do constitute an exploratory tool to first evaluate the effect of time on the pattern of risk faced by leaders.

Censoring The life-table method provides a deceptively simple way of dealing with complications that arise from censoring. Censoring is defined as the presence of certain observations that are not observed for the full period of risk. ' 5 This can happen in several ways. One type of censoring refers to a situation in which the period of study or time frame has been fixed in advance. Any subjects still at risk at the end of the period are then censored

42

Modeling Leader Longevity

out of the study. For example, we might have limited our study to the first ten years of power. At the end of the ten years, some non-negligible percentage of leaders are still in power. Bias would obviously result if it were assumed that all these leaders had lost power at the end of the ten years, since some of them have lasted much longer. In practice, we have not fixed any upper limit on time in power, so this type of censoring is not relevant for our study. "Random censoring" is more relevant to our study. It is what statisticians have called the situation when censoring times vary across individuals. That is, observation ends at the same time for all individuals, but begins at different times. Jackman, for example, cannot distinguish the countries that were at risk for the whole period from those that received independence later and could not have had coups during the entire period. In our case, random censoring has occurred in two ways. First, leaders still in power as of October 1987, when we ceased to observe, were treated as censored observations. Some, like Mobutu in Zaire and Kim II Sung in North Korea, have been in power a long time. Others, like Zine el Abidine Ben Ali in Tunisia, achieved power recently. Bias would be introduced if we treated Alfredo Stroessner in Paraguay as being in power twenty-plus years and Ben Ali as being in power the one year. But bias would be introduced potentially if we had limited our study to leaders already out of power. For Africa and the Middle East, the sample of leaders still in power is a significant share of all post-independence leaders. It is convenient to assume that leaders still in power today are no different in their likely length of stay in power from leaders already out of power. In the presence of censored observations, we need a method that will adjust the estimates in some way. The most commonly used procedure is to adjust the hazard of Equation (2) in the following way: h(t)

=

, m(t) r(t) - c(t)l2

where c(t) denotes the number of observations censored in period t. In effect, the censored observations are assumed to be at

Modeling Leader Longevity

43

risk for half of the interval in which they exit from the sample. The adjustment is arbitrary but reasonable for many circumstances. Other adjustments that make different assumptions about censoring are possible, depending on the likely distribution of censoring within each interval, but the general principles remain.

Natural Deaths Event-history methods require that censored observations be independent of the length of time before the event of interest occurs. We censored the leaders that exited from the sample through natural deaths. It makes sense to treat natural deaths as censored observations, since we are interested in the political ability to stay in power. Usually it can be assumed that death from natural causes is unrelated to the leader's ability to sustain his grip on power had he stayed alive; if so, censoring is justified. On the other hand, aging and illness could be considered to impinge on a leader's ability to sustain a grip on power. Possibly a leader's physical capacities have been inadequate for the tasks necessary to maintain power, helping to precipitate loss of power. Habib Bourguiba and Haile Selassie come to mind. In certain cases it is possible to imagine that leaders would have been toppled soon had death from natural causes not occurred. The exigencies of power might hasten death from high blood pressure or stroke. This may have occurred in Lenin's case. In such circumstances, exit through censoring would replace regular exit through loss of power and would thus not be justified. In practice, however, it is exceedingly difficult to make these judgments. We did not do so. We treated leaders who died from natural causes both as a censored observation and as a loss of power in the analysis to see if it made a difference to patterns of risk. Sensitivity analysis was employed to test the degree to which we were violating the assumption of independence, and the results were satisfactory. The results presented treat death from natural causes as censored observations because they are independent of the political events under examination.

44

Modeling Leader Longevity

Parametrizing the Life Table Life tables are purely descriptive and do not allow us to formally predict the statistical probability of the loss of power by a leader outside the cohort for which we have estimated hazards. Thus, statisticians want to estimate the underlying hazard function, which is described by a given life table, and to fit the data into some functional form. This allows determination of the statistical significance of the observed pattern of risk. For example, we can test whether the hazard function is monotonically decreasing over time, as opposed to being constant over time. A number of different survival distributions have been used by statisticians to test for the likely underlying shape of the hazard function." The first and simplest survival distribution is the exponential distribution. Its hazard function is constant and is unaffected by time. We have good reason to suspect that the hazard for leaders in our sample is not independent of time, but exponential hazard function provides a logical first test and will be used throughout the analysis as a null model to be compared with other models. In the class of survival distributions called extreme-value distributions, the Gompertz distribution has been used widely by demographers because its hazard function is practical and easy to handle.' The Gompertz hazard function takes the form 7

h(t) = Be",

where h(t) denotes the hazard or the risk of a given event occurring at time t, and B and c are the two parameters to be estimated. If cis positive, then the equation slopes upwards and the hazard increases over time; if c is negative, then the slope is downwards and risks are a decreasing function of time. If c is zero, then the equation reduces to the B parameter, and the hazard is constant and thus independent of time. In other words, the exponential hazard function is a special case of the Gompertz. That turns out to be a useful property because we can apply statistical tests to the c parameter to formally assess the null hypothesis of constant risk. The Gompertz hazard function is monotonic. That is, the haz-

Modeling Leader Longevity

45

ard may either increase or decrease over time, but it cannot change directions, and the rate of change remains constant over time. Other, more sophisticated distributions allow for the possibility of a U-shaped or inverted U-shaped pattern of risks, notably the lognormal and log-logistic distributions. The investigator's choice of a distribution depends on a number of considerations. The Gompertz distribution fits our complete sample and most of the subsamples of leaders adequately, and the assumption of monotonically decreasing risks was a reasonable one to make although it is not the only assumption one could make about a pattern of leaders' risks. The life tables did suggest an underlying decrease in risk. Indeed, no other single distribution fits the different samples equally well, and we did not think any analytical purpose would be served by utilizing different types of hazard functions for each subsample. Moreover, the functional form one chooses has important effects on the estimated coefficients," and there are no formal tests with which to compare the goodness of fit of models estimated with different distributions. Although the results of fitting the Gompertz functional form to the data are reported below, we focused on a more flexible semi-parametric approach for most of our analysis. These methods of analysis are now discussed.

Multivariate Hazard Models So far, the models we have discussed have been limited to making the risk of losing power a function of time alone, or more precisely of the length of time the leader is in power. We can estimate and make statistical statements about the nature of the hazard function derived from life tables, its shape and scale. But a requirement of the life-table analysis is that the sample of leaders be homogeneous. While different estimated distributions can be compared for homogeneity so that subgroups within the overall sample can be compared, this univariate approach does not allow us to estimate the net effects of different factors on the overall hazard rate. If the samples for which we estimate specific distributions are not homogeneous and include subgroups whose characteristics increase (or decrease) their risk

46

Modeling Leader Longevity

significantly, the shape of the hazard function with respect to time may be misleading. The characteristics that we hypothesized in Chapter 2 affect the level of risk need to be netted out before the "pure" effect of time can be known, noting that "effect of time" means that the net effect of many factors we cannot measure are working so that leaders' advantages are accumulating and their risks are declining. For this analysis we turn to multivariate regression techniques. Multivariate hazard models are not unlike the OLS equation shown in Equation (1): the hazard is estimated as a function of a number of explanatory variables. The coefficients that are estimated provide an indication of the effect, if any, that the variables have on the hazard rate. T-statistics are available to test for the significance of the coefficients. Hazard models are innovative insofar as they allow for time to have an effect, as we shall see below. To incorporate time and time effects, sophisticated likelihood-estimation techniques have been devised that set them apart from more traditional regression techniques, but they are well beyond the scope of this chapter. One of the advantages of the semi-parametric models estimated below is that they make very limited assumptions about functional form. The underlying hazard is allowed to be any function of time and does not have to be specified in advance. Given that theoretical considerations do not offer any clear justification to prefer one functional form over another, this added flexibility is a valuable feature of semi-parametric models. If and when exploratory research indicates that a specific functional form is appropriate, then parametric models can be estimated. The three models we have estimated differ from each other in the assumptions that they embody about the effects of the variables on the level of risk over time. Discrete time intervals have to be chosen before estimation. Duration is defined as the time in years from entry into power and is broken down into K intervals. The intervals have to be chosen so as to adequately capture the variation in the pattern of risk across the time distribution, while remaining parsimonious enough to have few intervals. After some experimentation, we divided the distribution into six intervals: entry into power to 2 years, 2-4, 4-6, 6-9, 9-13, and

Modeling Leader Longevity

47

more than 13 years. The three classes of hazard models we have defined can be described in the following manner. We define hik as leader i's risk of losing power in period k. The simplest model is called the "constant-rate" or "exponential" model, because it is based on the assumption that the risk of losing power is constant across time for leaders with the same vector of characteristics Xi. Thus, in the constant-rate model, (5)

Ln(h,) =a+ X',b =a+ b,x, + b2 X 2 + ... + b,x""

The log of the hazard is taken to make the equation linear. In this model, a is some constant that effectively captures the underlying risk of losing power. The vector of covariates X, then shifts this risk up or down by some constant multiplicative factor. Time does not have any effect on the estimated hazard, and any effect that the independent variables have on the hazard is unrelated to time. If the assumptions embodied in the constant rate model turn out to be correct, and the passage of time is of little consequence, then the advantage of using hazard models as opposed to more conventional multivariate regression methods is limited to their ability to handle censored data. The constant rate model is graphed in Panel A of Figure 1. Throughout the time distribution, the estimated hazard is assumed to be constant at h( t) *. A hypothetical leader with characteristic X, sees his risk increased by * * throughout the distribution. It is assumed that there is no causal relationship between time and the risk of losing power. In a second model, we define the so-called proportionalhazards model. Here, a is allowed to change values in each of the k periods defined: (6)

Ln(hik)

= ak = ak

+ X'ib + b,x, + h

2

X2

+ ... + b,x,.

The risk of losing power in period k is e"'ex·,,., with e"• as an underlying risk that is time-specific and different in each of the K periods. It is in this sense that the model can be termed semiparametric. It can accommodate any estimated level of risk in each of the intervals.

48

Modeling Leader Longevity

A) The Constant-Rate Model The underlying risk is estimated at h (t) • for the entire time distribution, and the leader with a given set of characteristics Xi sees his risk increased by the same factor •• throughout the distribution.

...

..

}

Time

0 B) The Proportional-Hazards Model Only one set of covariates is estimated, as in the constant-rate model, but a different underlying risk is now estimated for each period of time defined. The leader with characteristics X; sees his risk increased by a factor of •• throughout the distribution.

••+.

.r.

:::-

.J..

·-'

.c:

••+. ..l.J ..

0

Time

C) The Time-Dependent Model The model in Panel B is now extended by allowing the possibility that the characteristics Xj have a different effect on the risk level in each period; in period t, it increases risk, whereas it decreases risks in period 2 and has no effect in period 3.

:::.c:

I

••

...,_.

•••

t ••• •h

'

••• Time

Fig.

1.

Hazard models.

Proportional-hazard models can be estimated parametrically with distributions such as the Gompertz, and they are justified if the researcher is not concerned with time effects on the overall hazard rate, or if the underlying pattern of risk over time follows a predictable path that is well captured by a parametric form. Neither of these conditions were met in our case, since we are particularly interested in time effects and since the underlying pattern of risk, while close to being monotonically decreasing, undergoes several kinks that are interesting. By allowing the risk level to move without restriction in each period defined, we achieve a better fit and can better capture the dependence of several of the explanatory variables on time duration.

Modeling Leader Longevity

49

The model is said to be proportional in the sense that individual i's characteristics are assumed to have the same effect in each time period, although the overall estimated hazard may be different because of pure time effects. The proportional-hazards model is depicted in Panel B of Figure 1. In period 1, the first time interval defined, the underlying hazard is denoted by h(1); in period 2, a different underlying hazard is estimated by the model, at h(2), and so on. The leader with characteristic X; again sees his risk increased by * * throughout the distribution, but now his overall hazard rate is no longer constant, because the underlying period-specific risk is different in each period. Finally, a third type of model maintains the framework of the proportional-hazards model but extends it by assuming that the effects of covariates on the risk of losing power can vary over time:

where the duration subscript on the coefficient indicates that the effect of the variable changes in each period, even if the variable remains the same. We call these models "time-dependent" hazard models because they allow for the possibility of interaction between the passage of time and the explanatory variables. The time-dependent model is depicted in Panel C of Figure 1. The difference with the proportional model is that now the hypothetical leader with characteristics X; may have increased risks in period 1, decreased risks in period 2, and no significant change from the underlying risks in period 3· 19 Note that we can posit a variation on this time-dependent model in which the variable changes value over time, but its effect remains the same; we would indicate this by putting the subscript on the covariate and not on the coefficient. Or we could posit that variables vary over time and their effects are time dependent as well, and put subscripts on variables and coefficients. For example, in the analysis in subsequent chapters, we test the hypothesis that age, obviously a time-varying variable, has a time-dependent effect on the risk of losing power. To illustrate, perhaps a leader comes to power at the age of 75 and is respected or venerated for age and wisdom. But perhaps another leader has been in power for twenty years, is now 75, and is seen as aged and stale.

50

Modeling Leader Longevity

A general model could include all the above assumptions, including constant and time-varying variables with time-dependent and time-independent effects on an underlying hazard rate, which is constant or different in each period. The difficulty is not in defining the models but rather in estimating them. Methods have been developed to construct likelihood functions that estimate the parameters of models embodying any of the assumptions about time effects described above. These are well described elsewhere. zo

Unobserved Sources of Heterogeneity: The Problem of Self-Selection A basic rule of most multivariate regression techniques is that a badly specified model will lead to biases in the estimated parameterS.21 In particular, biases can be expected if relevant variables are left out of the estimated model, because some of the variation in the dependent variable will be left unexplained. This problem is exacerbated in event-history analysis because the bias is likely to be reflected in the estimates of the effect of time on the estimated hazard rate. Statisticians have called this the problem of "unobserved sources of heterogeneity." In effect, variation between individual observations due to some factor left out of the analysis will not be distinguishable from the effect of time on the hazard rate. It is worth repeating here our cautions about unobserved heterogeneity of the sample. Unobserved heterogeneity will usually result in estimates of a declining hazard rate." An illustration will make this process clearer. It may be that a leader's height is strongly positively correlated with length in power, but we have not suspected this and have not included height as a variable in the hazard model we are estimating. We could then expect a higher turnover rate in the first several years as the short leaders are ousted from office. After they are selected out of the sample, the turnover rate would decline, other things being equal, because only the taller leaders would remain and they are more likely to maintain power. The observed decline in the hazard rate would then not be related to the special difficulties we associate with the first few years in power. Instead, it may be equally

Modeling Leader Longevity

51

difficult for leaders to maintain power regardless of how long they have been in power, but by the time they are past the first few years, only the taller leaders remain in power. The solution to this problem would be to integrate the sources of heterogeneity into the model. To pursue our illustration, we could add a variable for leader's height into the hazard model we estimate. It is unrealistic to expect that all sources of heterogeneity will be accounted for and the number of variables that can be tested for is large, even if the investigator is guided by theory, not fancy. The problem is not as severe if the investigator is only interested in the effects of the explanatory variables on the hazard rate and not in the underlying risk in any given period. The estimated coefficients may be biased, but rarely misleadingly so. It should also be noted that the problem is only present if the estimated hazard rate is decreasing. The problem is more severe if time is central to the analysis and if it is thought that time interacts with other variables, as is the case in our study. The issue of being unable to separate out the effects of the passage of time from those of the different covariates has attracted much theoretical discussion. 23 Any hazard rate estimated to decline over time could be the result of unobserved heterogeneity, and in practice it will be difficult to prove otherwise. To provide another, and this time highly relevant, example, it may be objected that our analysis of leader longevity leaves out the most important factor: leadership skills. As discussed in Chapter 1, the length of time a leader stays in power may reflect his leadership skills, and the reason we observe a decline in the hazard rate over time is that the leaders of lesser ability are selected out of the sample faster than the more skillful leaders. This is largely an intractable problem, unfortunately, because there are no ready independent measures of leadership skills. In theory we could give leaders IQ tests for what they know about their own societies, we could measure how they do on these tests over time, or we could design better measures of leadership skills but probably not be able to test for them in the real world. To define skills in terms of length of stay in power would be tautological. How to measure leadership skill is less an issue of statistical theory than of political analysis. It is impos-

52

Modeling Leader Longevity

sible to refute the contention that an estimated decreasing pattern of risk over time is only the result of a hidden selection process operating within the data and left uncovered because of model misspecification. Chapters 4 and 5, however, provide support for thinking that leaders' characteristics matter quite apart from skills or ability. The analysis that follows shows the importance of structural variables whatever may be the importance of behavioral ones.

CHAPTER

4

Analyses of the Sample A global sample of 2,256 leaders from 167 countries was collected, and it is reproduced in the Appendix. Chapter 2 described the criteria employed to include or exclude individual leaders and to determine the duration of their tenure in power. This chapter describes how leaders' risks of losing power evolves over the time they are in power; it also develops a series of models of the kind described in Chapter 3 to explain the patterns of risks observed. This analysis will allow us to see if leaders' risks of losing power are constant across time for a given vector of individual and country characteristics or if the risks vary in each time period. One way of answering this question is by taking the descriptive data from the life tables and ordering them into an underlying distribution. The aim is to see if there is an underlying risk that is captured by a parametric function. As was suggested in Chapter 3, the Gompertz hazard equation fits the data adequately for this purpose and allows us to test the data for a pattern of monotonically decreasing risks. It is also important to determine whether the different variables that describe country and leader characteristics vary across time in their effects on the underlying risk of losing power. Thus, for example, the hypothesis is tested that age, obviously a time-varying variable, has a time-dependent effect on the risk of losing power. Of course, a variable need not be time-varying for its effects to be dependent on time. A variable can be posited whose value is constant over time but that has an effect on risk levels that depends on time and thus changes in the course of the leader's period of rule. The dummy for nonconstitutional entry is such a variable. The whole sample and several subsamples are analyzed in

54

Analyses of the Sample

order to examine regional and time-period variations. Intensive analysis of particular regions is undertaken where it makes sense to test specific hypotheses that do not apply to the full sample. For example, Latin America is the only region that provides a large sample in both the nineteenth and twentieth centuries. Thus we can test for changing patterns of risk over a long time period in Latin America. When the proposition is tested that first leaders after independence have lower risks than subsequent leaders across all time periods, it does not make sense to include the early nineteenth-century Latin American leaders in the sample because the differences are too large in first leaders across this time span. Most European countries were not new nations in the nineteenth century. Thus there are aspects of leadership durability that can best be examined within more homogeneous subsamples. This is done in the next chapter.

The Distribution of Risks: The Life Table The full sample contains leaders from widely different types of political systems as well as from countries differentiated by size, levels of income, rates of growth, and other factors. What TABLE

3

The Frequency Distribution for Years in Power Full years in power

Q; Ul

.0

0

300

0 Q;

.0

E

::l

z

200

100

0

0

2

8-9

3

10-14 15-19 20-24

> 25

Full Years in Power

Fig.

2.

Distribution of time and power (full sample).

do we learn from analysis of the full sample? The simplest description is a frequency distribution of numbers of leaders in power for various lengths of years. This can be expressed in tabular and in graph form. Table 3 and Figure 2 allow us to see, not unexpectedly, that few leaders have stayed in power for long periods. More interestingly, there seems to be a decline in the rate at which leaders lose power over time, although the decline is not monotonic. For example, more leaders fall from power in a fourth year than in a third year. Table 4 provides evidence of major differences among the regions. The interpretation of the table is straightforward. It shows that 969 leaders (or 43 percent of the 2,256 leaders) were still in power at the end of two full years of office for the full sample. The length of tenure is much shorter in Latin America and in North America, Europe, and Australasia than in the other regions. While a full 30 percent of the observations are still in power at the end of eight years in Africa and the Middle East, in Latin America only 5 percent of the observations are left, which

56

Analyses of the Sample TABLE

4

Total Number of Leaders Left Over After X Years, and as a Percentage of the Total

Full years in power

Asia

(%)

(%)

969 542 222 110 47 30

2 4 8 14 20 24 Sample size --

Full sample

2,256

(43) (24) (10) (5) (2) (1)

109 76 41 25 9 5 231

(47) (33) (18) (11) (4) (2)

N. America, Middle East Europe, and Latin America and Africa Australasia (%) ('k) -------··-

461 191 47 18 9 7 1,019

(45) (19) (5) (2) (1) (1)

168 137 81 42 18 10 266

(63) (52) (30) (16) (7) (4)

(%)

231 138 53 25 11 8

(31) (19) (7) (3) (1) (1)

740

---

is half the rate of the full sample. A full 81 percent of the observations in Latin America and in North America, Europe, and Australasia lose power before the end of four years in power, while only 48 percent do so in Africa and the Middle East.' With these data on leadership spells, statistical models can be constructed to analyze the risk of losing power. We will see that the fact of shorter tenures for leaders in the more developed countries rather than the less developed countries will drive many results discussed below. This finding is counterintuitive precisely because many analysts confuse leadership risk and turnover with system instability. The more developed countries have parliamentary systems where leaders turn over relatively frequently. In Italy and France in specific periods, the turnover has been great but no large-scale system instability was concomitant with leadership turnover. In Chapter 3 it was noted that life tables describe the distribution of survival times experienced by homogeneous groups of subjects when these survival times are censored. The survival rates of a set of homogeneous observations (that is, we assume no differences in skill levels or abilities of leaders) can be compared. The life tables generate distinct hazard functions defined as the time-dependent risk of loss of power. The life tables are purely descriptive, but they provide a way for seeing the effect of time on the pattern of risk faced by leaders. The hazard function created from the life table is graphed !n Figure 3 to

Analyses of the Sample

57

show the pattern of risk as it evolves over time in power. Figure 3 shows that from a hazard rate of .33-in other words leaders faced a one-in-three chance of losing power in their first year in power-the hazard rate fell to .15 in the third year, rose briefly, and then fell to almost insignificant levels. How significant is this decline? A Gompertz distribution was used to parametrize the hazard function. It takes the following form: h(t) = Beet, where h(t) denotes the hazard rate at time t, B is a parameter that sets the hazard level at entry (when t = o, the right-hand side of the equation reduces to the B term), and cis a parameter that determines the slope of the function. In other words, c captures the rate at which the risk of losing power changes over time. As we noted in Chapter 3, other distributions might have been used, but the Gompertz distribution is useful because it allows us to test the proposition that the pattern of risk is decreasing monotonically by applying statistical tests to the c parameter. Figure 3 juxtaposes the estimated hazard from the life table 0.4

,,,. 0.3

I I

----

Gompertz Life table

I I

I I £

..c::

0.2

Fig. 3· The risk of losing power: The entire sample of leaders.

58

Analyses of the Sample

with the estimated Gompertz hazard distribution for the same data. The following Gompertz function was estimated for the full sample of leaders: (8)

h(t) = .}e- o8t (33-6)(14.0)

Chi-square: 273·9·

The t-statistics are reported in parentheses and indicate that both estimated coefficients attain the desired significance levels. Applying a one-tailed t-test to the second parameter confirms that the coefficient is significantly less than zero and thus that risks are a decreasing function of time. A Chi-square statistic is also reported; it compares the goodness of fit of the estimated equation to that of a null model of a constant hazard rate across time. The statistic allows us to reject the null model with more than 99·9 percent confidence and to accept the alternative of a monotonically decreasing hazard rate. Clearly, the estimated Gompertz equation fits the data reasonably well. Still, the Gompertz does not capture the sudden rise in the risks in the fourth year of rule that the life-table estimates demonstrate. Further analysis described below reveals that this surge of risks is largely the result of an incidence of four-year electoral terms in the Latin American sample. As a result, the Gompertz equation overstates the observed level of risk up to the third year and then underestimates it for the next three years. Chapter 3 noted that a drawback of using a parametric modeling approach is that this approach is likely to miss some of the interesting characteristics of the distribution studied. The parametric form used has an impact on the estimated risks in each time period. Had we resorted to another distribution, the fitted hazard equation might not have the same appearance. Another distribution might have captured the fourth-year surge in risks but not, say, the high risks in the first two years. The choice of parametric forms is thus in part a strategic one, reflecting the preferences and research objectives of the researcher. We sought to capture the underlying pattern of risk as closely as possible, since we wanted to focus on the relationship between risk and time. We thus preferred to use semi-parametric rather than parametric approaches when we turned to multivariate modeling. Added freedom and extra precision were provided

Analyses of the Sample

59

not by imposing any underlying pattern to the risk estimates, but rather by allowing them to move randomly from period to period.

A Proportional-Hazard Model The life table and Gompertz hazard equation provide information on the relationship between time and risk. It is reasonable to assume that different factors have an effect on the risk of losing power and may themselves have varied effects over time. We thus begin a multivariate analysis of the risk of losing power, employing the independent variables that were discussed in Chapter 2. Table 5 provides information on the mean values of the major variables we use and how they vary between data sets. The table shows the proportion of observations taking on the different values of the dummy variables; for example, it shows that in the complete sample, some 29 percent of the leaders were military and 71 percent were civilian, while these proportions were 6 percent and 94 percent, respectively, in the North America, Europe, and Australasia data sets. The table provides mean values for the continuous variables as well as their standard deviations. It shows that the average age of leaders on entry was 51 in Latin America with a standard deviation of 10.3 years and that there is a 43 percent literacy rate among the population in the African and Middle Eastern countries. If we assume that the hazard function is constant over all durations and is unaffected by any covariates, then the estimated hazard is simply the number of events or losses of power divided by the number of years of exposure. Even though we have already rejected the assumption of constant risks, such a null model was estimated to serve as a base of comparison with other models. Its estimated rate was found to be .215, implying that 21.5 percent of the leaders still alive at the beginning of each period of rule lost power in that period. The model generated a Ln likelihood statistic that measures how well it fits and makes possible formal comparisons with more elaborate models. 2 Several proportional-hazard models were fit for the complete sample of leaders. First, a preliminary model assumes there are no covariates of the risk of losing power but estimates a separate

TABLE

5

Sample Proportions and Means of the Covariates Across Regions ---

--·---

Variable

Complete sample

------

Latin America

Middle East and Africa

Asia

N. America, Europe, and Australasia

--------·

..

DUMMIES

Still in power Constitutional exit Nonconst. exit Natural death

.07 .62 .24 .06

.03 .57 .34 .06

.25 .22 .45 .08

.14 .57 .23 .06

.04 .86 .05 .05

Constitutional entry Nonconst. entry

.75 .25

.65 .35

.61 .39

.77 .23

.93 .07

Military Civilian

.29 .71

.43 .57

.36 .64

.27 .73

.06 .94

Low ethnic tension High ethnic tension

.88 .12

.91 .09

.61 .39

.75 .25

.98 .02

Single entries Multiple entries

.71 .29

.77 .23

.86 .14

.71 .29

.58 .42

Entry< 1900 Entry 1901-1945 Entry> 1945

.21 .35 .44

.43 .29 .28

.00 .07 .93

.04 .23 .73

.03 .58 .39

3.5 (3.9) 55 (12.2) 1956 (24.8) 1212 (1767) 2.7 (2.7) 921 (2121) 135 (263.3) 74 (27.5)

1.9 (2.6) 56 (9.2) 1939 (24.9) 3647 (1556) 2.9 (0.9) 1155 (3218) 30 (46.5) 97 (4.7)

CONTINUOUS

Country Median duration (standard deviation) Age at entry (SO) Date of entry (SO) Per capita income* (SO) GNP growth rate** (SO) Country size*** (SO) Population**** (SO) Literacy rate (SO)

2.7 (3.0) 52 (10.9) 1930 (42.7) 1717 (1854) 2.29 (1. 99) 958 (2251) 34 (97.0) 80 (23.2)

51 (10.3) 1908 (48.5) 675 (397) 1.90 (1. 78) 914 (1580) 19 (28.9) 79 (17.3)

5.8 (5.4) 47 (12.4) 1966 (13.9) 777 (1867) 1.7 (3.3) 610 (704) 17 (22.8) 43 (22. 7)

No. of countries No. of observations

167 2,256

33 1,019

67 266

2.4 (1.3)

----------

--

Totals may not sum to 1 because of rounding errors. * 1973 U.S. dollars. **In per capita terms, measured from 1965 to 1983. ***In thousands of square kilometers. ****In millions.

NoTE:

34 231

33 740 ----

Analyses of the Sample

61

TABLE 6 Full Sample Proportional-Hazard Models

Variable

Ill 121 Duration Individual effects only traits

Nonconstitutional entry (D) Military (D) Multiple entries (D) Country median duration Date of entry Age at entry Latin America (D) N. America, Europe, and Australasia (D) High ethnic tension (D) Per capita income, 1973 GNP growth rate, 1965-1983 Country size Population Population density Literacy rate Duration 0-1 Duration 2-3 Duration 4-5 Duration 6-8 Duration 9-13 Duration > 13 - Ln likelihood (No. of parameters)

[3] Country traits

1.205** 0.860** 1.366**

[5] Preferred

1.249** 0.842** 1.095

1.219** 0.843** 0.834** 0.996** 1.016**

1.435**

0.846** 0.997** 1.015** 1.205

1.949**

1.665**

1.525**

0.871

0.863*

0.935**

0.993**

1.024 0.959** 1.000 0.999 1.002

1.021 0.957** 1.000 0.999 1.001

0.841 ** 0.995** 1.022**

0.954* 0.966**

.285 .190 .298 .179 .081 .062

.251 .178 .294 .189 .090 .072

.181 .139 .243 .173 .089 .066

.198 .156 .278 .198 .104 .079

.227 .180 .316 .219 .114 .086

4,798

4,671

4,491

4,452 (21)

4,463 (14)

(6)

(11)

(1 6)

All duration effects were found to be significant at statistic for the null model was estimated at 4,974. (D): Dummy variable. *Significant at .o5level, two-tailed test. **Significant at .01 level, two-tailed test.

NOTE:

[4] Comprehensivc

.01

level. The - Ln likelihood

hazard rate for each period. It is presented in Column 1 of Table 6. Analytically equivalent to a life table, it presents the ratio of events to exposure (or number of deaths divided by the total number of years leaders were at risk) in six discrete time periods. For example, an estimated 28.5 percent of the leaders at risk lost power in both the first and second year of office, whereas

62

Analyses of the Sample

after year 13, 6.2 percent lost power each year. The rates decrease each year with the exception of the third time period, Years 4- 5· These estimated rates are significant at the .o1 level and again allow us to reject the null hypothesis of constant risks. Second, variables pertaining to the leaders' individual traits were grouped and estimated in a model called the "individualtraits" model. It is presented in Column 2 of Table 6. This model included five variables assumed to affect duration in power: a dummy for nonconstitutional entry; a dummy for military leaders; the date of entry when the leaders came to power; the age of the leader; and a dummy for leaders who were in power more than once. Third, the variables pertaining to the socioeconomic traits of the country of the individual leaders were grouped in a column we labeled the "country-traits" model. This is Column 3 of Table 6. The aim is to examine the salience of country-wide phenomena. The country characteristics model included the following ten variables: a dummy for countries with a high level of ethnic tension; 1973 per capita income; rate of GNP growth between 1965 and 1983; country size; population; population density; level of literacy; a dummy for Latin America; a dummy for North America, Europe, and Australia; and the median duration of leaders' tenures in each country. 3 We then put all the variables together in a "comprehensive" model. The results are presented in Column 4· Finally, we removed the seven variables that had no or only marginal significance from the comprehensive model and reestimated a final "preferred" model with the remaining eight variables. Its results are presented in Column 5 of Table 6. Before analyzing these results, a brief word is necessary on interpreting the coefficients. For the o-1 or dummy variables, indicated by "D" in the table, if 1 is subtracted from the coefficient and the result is multiplied by 100, then the resulting number provides the percentage increase or decrease in the annual level of risk for the leaders that possess the characteristic of the dummy. For example, the dummy variable for leaders entering power nonconstitutionally has an estimated coefficient of 1.205 in the individual-traits model, implying that nonconstitutional entry results in a 20 percent increase in the risk of losing power

Analyses of the Sample

63

during every time interval, as compared to the leaders entering power constitutionally. Dummy variables can all be interpreted this way, with a coefficient of above 1 implying an increase in risks and a coefficient under 1 a decrease in risks. The interpretation of the coefficients for the continuous variables is slightly different. If 1 is subtracted from the coefficient and the result is multiplied by 100, then the resulting number gives the percentage change in the hazard rate in any given year of rule due to a unit change in the variable. Again, a coefficient greater than 1 implies an increase in risks; lower than 1 implies a decrease for risks. For example, the coefficient of ·995 for date of entry in Column 2 implies that hazard rates have decreased over time at the rate of ·5 percent a year. (Other things being equal, a leader entering in 1965 was .5 percent less likely to lose power at any point during his tenure than a leader arriving in 1964.) 4

Results It is necessary to assess the significance of the different variables in the models and to test hypotheses about the impact of specific country and individual characteristics on the risk of losing power. It is also necessary to assess which assumption about the relationship between time and hazard rates is justified. Recall that a constant-rate hazard is one where a leader with a given set of characteristics sees his risk increased by the same factor throughout the distribution. In the proportionalhazards model, only one set of covariates is estimated, as in the constant-rate model, but a different underlying risk is now estimated for each period defined. There are six duration-specific rates estimated for each of these multivariate models and listed at the bottom of Table 6. These rates can be thought of as the underlying risk of losing power in a given period, once the impact of all the estimated covariates has been netted out. In the individual-traits model, the underlying risk of losing power in the first period is .251, and this goes to .189 in the fourth period. A leader with specific characteristics will see his risk increased by a fixed factor throughout the distribution. For example, given that being a military leader decreases risk by 14 percent throughout the duration in power, military

64

Analyses of the Sample

leaders will, other things being equal, have risks of .216 and .163 in the first and fourth periods, respectively. 5 We see that the individual-traits model fits the data significantly better than the null model or the "duration-effects-only" model. All the estimated coefficients are significant at the .01 level and have the expected signs. Entering power nonconstitutionally increases risks sharply, as we have noted above. Interestingly, the age of the leader at entry is positively correlated with risks; each extra year adds 2.2 percent to the risks of losing power. To test the possibility of a nonlinear relationship between age and risks, we also added the square of the age at entry to the estimated model, but this did not significantly increase the log likelihood function. Leaders in office more than once faced risks 36.6 percent higher than leaders in power only once. The multiple leaders' much higher risk of losing power reflects the fact that they are typically present in parliamentary regimes with rapid leadership turnover. We discuss leaders who return to power two or more times at greater length below. Here it should be noted that of those who return to power more than once, a significant number are in Greece, Italy, France, Portugal, and Spain in the twentieth century during their parliamentary periods, or periods when presidents and parliaments coexisted. It is true that Mexico in the first half of the twentieth century and other Latin American countries with presidential systems and ones where many military leaders came to power also have many leaders who returned to power. However, the sample is driven by parliamentary systems where coalitions were fluid and cabinet leaders turned over frequently. In some countries-for example, Portugal and Greece during parts of the nineteenth and twentieth centuries-system-wide instability existed. In Italy in the 197o's and 198o's, leadership turnover was not accompanied by a great deal of social instability; protests and violence were not evident in Italy during this period. There is no strong correlation between leadership turnover and regime instability, any more than lengthy duration in power guarantees system stability. One of the more surprising results of the individual-traits model is the statistical significance of the date-of-entry variable. The cumulative impact of a .5 percent decrease each year in risks

Analyses of the Sample

65

(implying that a leader coming to power in 1850 faced half the risks of a leader coming to power in 1950) is very large. Theresult is more acceptable if account is taken of the greater leader turnover that characterizes Latin America, the region that provides most of the sample's observations who entered power before 1945, and of the lower levels of leader turnover prevailing in Africa, Asia, and the Middle East, the regions that provide a large share of the later observations. We analyze at greater length below these differences in risks across time periods and regions. Here suffice it to say that these regional differences probably account for this finding, and they point to the pitfalls of modeling such a large and varied data set. Nonetheless, "date of entry" is telling us that overall risks have declined. And, indeed, over time leadership stability has increased in Spain, Portugal, Greece, and France, as well as in Latin America. A final point on the individual-traits model concerns the dummy for military leaders. The estimated coefficient indicates that military leaders face a 14 percent lower risk than civilian leaders. Because military leaders enter power often through nonconstitutional coups, it is often assumed that they face higher risks. However, once the effect of type of entry is netted out, the opposite is true. It is unconstitutional entry, not military characteristics, that drives the risks for military leaders. We thus reestimated the same equation, minus the dummy for type of entry, to see what the impact would be on the coefficient for military. The model's statistical fit was worse and the coefficient was no longer significantly different from zero. This confirms that military leaders who enter power nonconstitutionally faced different risks than constitutional military leaders. The failure to differentiate them clearly in the model left unexplained variance that worsened the fit. The country-traits model is presented in Column 3 of Table 6. As might be expected, the variable for median country duration is highly significant and negatively correlated with risks. The annual risk of losing power decreases by 15.9 percent with every year increase in the median duration in power of all leaders in a given country. Another significant result is the high degree of significance for the regional dummies for Latin American leaders, on the one hand, and leaders from North America, Europe,

66

Analyses of the Sample

and Australasia, on the other, even after the median duration of leaders in these countries has been taken into account. Leaders from these two regions face a 94·9 and 43·5 percent increase in risks, respectively. In part these higher risk levels in the more developed regions of our sample can be accounted for by the shorter leader tenures in parliamentary regimes, particularly in those with regular multiparty elections. This may account for the high risk levels found for the European sample, but not for those in Latin America, where nonconstitutional takeovers, coups, and revolutions have been as common as in Africa and the Middle East. The high risks faced by Latin American leaders must rather be understood as resulting from particular characteristics of the socioeconomic or political systems there that are not fully captured by our current set of covariates. These characteristics seem to make power consolidation more difficult, even though Latin America does present us with some authoritarian leaders that have lasted more than two decades. We explore this at greater length in Chapter 5· The other estimated coefficients of the country-traits model are ambiguous. Two of the estimated coefficients, GNP growth rate and level of ethnic conflict, are found to be not significantly different than zero. We might expect that leaders in countries with a high level of ethnic conflict face a higher risk of losing power than leaders in countries with a low level of ethnic tension. That we get a finding of lower risks albeit of borderline statistical significance occurs in part because countries without a high degree of ethnic tension-such as France, Italy, Portugal, and Greece-have a great deal of leadership turnover in their democratic periods. Japan, too, a country with low levels of ethnic tension, has had many leaders who served for short periods. It is true also that in many countries with high degrees of ethnic tension individual leaders often have had very long periods in power, such as Selassie in Ethiopia, Mobutu in Zaire, Tubman in Liberia, Gafaar Nimeiri in the Sudan, and Tito in Yugoslavia. Ethnic tension is no guarantee of short duration of leaders in power. We will look at ethnic tension and leadership duration again in a more homogeneous Third World sample. Leaders in larger countries face lower risks. Population and population density do not appear to have an effect on risks. "Per

Analyses of the Sample

67

capita income" does seem to have the expected effect, though the impact on the hazard is fairly minor; the coefficient at ·935 implies that every thousand-dollar increase in GNP decreases risks by 6. 5 percent. This result provides some evidence that leadership stability does increase with per capita income, once account is taken of the particular characteristics of leadership turnover in Latin America and in the countries of North America, Europe, and Australasia. The problems with the full country-traits model are not unexpected because of the ways that the variables were specified. Data availability led us to pick a single value for each observation for the variables literacy, GNP growth rate, and per capita income, despite the fact that these change over time. Thus, we used GNP per capita in 1973, not only for those leaders in power in the early 197o's but also for the leaders in power at the turn of the century. Ideally, a different value would be entered for each year of power, or at least for each of the periods defined by the model. We followed the latter approach in specifying the age of the leader, so that we have an observation for each period of rule (age at entry, age at year 2, age at year 4, and so on). We also coded the level of ethnic tension as changing over time. But consistent macroeconomic time series for most of the countries in the sample are not available before World War II, sometimes considerably later, and the computer cost of such a specification, even if the data were available, was prohibitive. Our approach may have introduced biases. If it is true that there is a strong positive correlation between GNP level and the risk of losing power, our specification would not be satisfactory. The countries with a rapid rate of growth that have gone from being poor and with leader instability to rich and with lower risks for leaders would only introduce noise into the estimation procedure. The significance of the date-of-entry variable discussed above suggests that socioeconomic change has had an impact on leader longevity. As countries have grown wealthier over time, risks have declined. We tried to account for this possibility by also including GNP growth rate as a variable that might capture the impact of socioeconomic change. This variable was admittedly not specified perfectly either, since it covered GNP growth only between 1965 and 1983.

68

Analyses of the Sample

Some of the counterintuitive results and the low level of significance found may reflect the lack of salience of these socioeconomic factors to the risk of losing power, but it may be that correlation has not been observed because of the poor specification of our variables. A better test for these country-level variables is provided when we limit our sample to those who come to power after World War II, as we do below. The "comprehensive" and "preferred" models described in Table 6 largely confirm the findings of the first two models discussed. It is comforting that the coefficients change little from model to model, suggesting that these coefficients are fairly robust. "Date of entry" and "age at entry" provide particularly stable coefficients. Two variables do lose their significance in the more comprehensive model. The estimated coefficient for the dummy for Latin America has a reduced level of significance. Though not reported in the table, it remains significant at the 10 percent level, and since it is broadly similar to the coefficient estimated in the more parsimonious model, the reduction in significance levels may simply reflect an estimation difficulty in a large model with several relatively collinear variables. More interestingly, the dummy for multiple leaders loses its significance once the variable for country median duration is included in the model. Multicollinearity may also be a problem here. Multiple leaders occur overwhelmingly in the Latin American and European regions, where risks are higher and leadership spells shorter. The result does provide tentative evidence that leaders in power more than once are not exceptional and that their ability to reach the pinnacles of power on more than one occasion is a consequence of the type of political system they operate in, rather than an indication of any particular skills on their part. The other reason for including multiple leaders in power, to determine whether or not leaders learn in power, is addressed below. First, however, it is necessary to confront the issue of governability.

Governability Are some countries or regions more difficult to govern than others? A cursory look at the data suggests that while some

Analyses of the Sample

69

countries clearly exhibit greater or lesser leadership stability than others, in almost every country there are cases of both long-lasting and short-tenured leaders. Country-level variables like GNP level, ethnic tension, or colonial background are employed in order to test the hypothesis that the observed variation in risk levels actually results from systemic differences between countries. It would not be surprising if some countries were characterized by shorter leadership tenures than others. The pattern of decreasing risk that is observed might result from heterogeneity at the country level, not at the individual level. Therefore country-level variables are included in the estimated models in order to account for such heterogeneity. We cannot be sure, however, that the country-level variables chosen fully capture across countries the systemic differences in political structure and culture or the country dynamics that might create variation in risk levels. We suspect country differences are mediated by complex factors that are difficult to quantify precisely. The variable "country median duration" was specified to serve as a proxy for the different institutional factors, such as constitutional limits on executive terms, for socioeconomic factors not measured directly, or for less tangible factors pertaining to national political culture and history, all of which might constrain the length of stay in power of leaders. In the country-traits model, the variable proved to have strong explanatory power, suggesting that length of leadership spells does vary across countries. The variable provides plausible evidence of the constraints on long-term power maintenance that exist in some countries. The same model indicates that country variations are not well captured by the socioeconomic variables that were directly specified. Median stays in power by country have several disadvantages as a measure of governability, however, and render these results ambiguous. Most notably, the eighteen countries that have had only one leader could not be included in the calculations, yet their leaders have typically been in power well above the average length of time, which must bias the results." In addition, leadership stability patterns may change over time, and the country median does not capture a country's entire history. An additional method was used to assess the importance of country-level risk to the overall variation of risk over time ob-

70

Analyses of the Sample 7 The Correlation Between a Leader's Time in Power and His Predecessor's TABLE

------

---------------

Correlation coefficient "r"

Sample size

Africa The Middle East Asia Latin America N. America, Europe, and Australasia

.024 .233 .389 .118

104 94 199 986

.229

709

Complete sample

.182

2,092

Region

served. The correlation can be calculated between a leader's length of stay in power with that of his immediate predecessor. A high level of correlation would provide some evidence that country-level factors are salient even if we are not observing them properly. Simple correlation coefficients were calculated for each of the regional subsamples and are reported in Table 7· These provide rough "governability" indices aggregated here for regions, not for countries. For the entire sample, a correlation coefficient of .182 implies a weak relationship between the variables. But this disguises the diversity among regions and countries. The estimated correlation coefficients indicate insignificant levels of correlation in Africa and Latin America and much higher levels in the Middle East and particularly in Asia. In the latter two samples, countryspecific risks are not unimportant, even if these results are ambiguous. The level of correlation appears low, given the high statistical significance of the country-median-duration variable. There are several difficulties with these statistics that reduce their usefulness. First, a bias may be introduced by the loss of the first observation for each country. Since first leaders did not have a predecessor, they only provide data for the calculation as the predecessor of the second leader. This decreases sample size; it may also bias the results since first leaders appear to be so different from subsequent ones, particularly in the regions of the Third World. Second, there is no way to handle censored observations, ei-

Analyses of the Sample

71

ther natural deaths or the leaders still in power today, whose duration in power get arbitrarily cut off. These problems are exacerbated for the countries in the sample with a short time frame, since first leaders may still be in power (in which case the country does not figure in the calculations at all) or have occupied most of the time frame. 7 Another reason for these slightly different results is that the distribution of leadership spells is highly skewed, with the majority of leaders in power less than three full years. The median length in power is thus lower than the average and does not reflect the degree of intracountry variation in length of tenure, which is picked up in the correlation coefficient. Qualified by these comments, the results are ambiguous but they do not provide evidence that country-level factors are driving the variation in risk levels that we are observing. Thus, while we find little strong evidence that leadershipstability patterns are conditioned by the country-level socioeconomic factors that were specified, it does appear that other characteristics of some countries make long tenures less likely. The high statistical significance of the median duration variables not surprisingly provides evidence that some countries are "easier" to govern. However, the low level of correlation between a leader's time in power and that of his predecessors suggests that these country medians cover wide variations in leaders' durability and do not adequately capture the substantial number of leaders who buck the odds and stay in power a long time.

A Time-Dependent Hazard Model for the Full Sample We estimated a "time-dependent" hazard model with all of the variables discussed. By estimating a different coefficient for every time period for each of the explanatory variables, we can test the hypothesis that these variables have a time-varying impact on the risk of losing power. In other words, a variable may have a positive impact early in the duration in power, but no impact later on, or it may have a negative impact. We thought it possible that several variables would have such a time-varying impact. For example, age might be an asset in the first few years of power, since older leaders might be perceived as wise and statesmen-like. Yet, over time, age might become a liability,

72

Analyses of the Sample

since older leaders might be perceived as lacking in charisma, or they might lose the physical and mental energy necessary to dominate the political game. Similarly, nonconstitutional leaders might be thought to lack legitimacy in the first few years of power and so face higher levels of risk of losing power early on, but after some years, if they were still in power, their mode of entry would not matter. Table 8 presents a time-dependent model with all fifteen explanatory variables. Age is the only variable with significant coefficients in five of the six time periods. Most of the variables have significant coefficients in three or fewer of the time intervals. This may suggest that effects are time-dependent and not proportional, but just as likely it reflects the small number of observations in the latter intervals, making estimation more haphazard. Most variables' coefficients suggest the presence of proportional risks. Thus, the coefficients for age are significant in each time period, but they are not significantly different from each other. Age seems to be slightly positively correlated with the risk of losing power throughout the duration in power. Older leaders do face slightly higher risks in each time period of their rule. Similarly, date of entry, high ethnic tension, and multiple entries seem to have constant effects that do not vary over time. The impact of the other variables are more ambiguous but may possibly indicate nonproportional risks. The estimated coefficient for date of entry indicates a dampening effect on risks, along the level found in the proportional-hazard models, but only in the first two time periods. The GNP growth rate was significant only in the fourth time period, indicating a 10.4 percent decrease in the latter. The only variables with clearly nonproportional effects appear to be the dummy for nonconstitutional entry into power and the variable for country median duration. From a doubling of risks in the first period, the impact of nonconstitutional entry eventually shows a halving of risks after twelve years in power. A plausible explanation can be given. Nonconstitutional entries often involve violence and occur in political systems characterized by instability. Nonconstitutional-entry leaders often lack legitimacy and are vulnerable to counter-coups; these factors tend initially to increase the risk of losing power. Many do lose power,

Analyses of the Sample

73

8 Full Sample Time-Dependent Hazard Model TABLE

Periods (in years) Variable

Nonconstitutional entry (D) Military (D) Multiple entries (D) Country median duration Date of entry Age Latin America (D) N. America, Europe, and Australasia (D) High ethnic tension (D) Per capita income GNP growth rate Country size Population Population density Literacy rate Duration effect

0-1

----

2-3 ~~-

4-5

6-8

- - - -- ----

9-13

>13

1.981 ** 0.786**

1.145 0.876

0.644** 0.734**

0.616** 0.836

0.907 1.489

0.473** 0.826

0.893

1.233

1.079

1.358

2.116

2.505

0.722** 0.997** 1.014** 0.906

0.765** 0.992** 1.015* 0.524**

0.925** 0.999 1.015** 4.080**

0.876** 0.998 1.022** 2.228*

0.936 0.994 1.008 1.125

0.998 1.003 1.032 ** 1.468

1.611 *

1.012

1.299

1.282

0.866

1.640

0.889 0.937 1.031 0.973 1.000 0.999 1.002

0.671 * 0.999 1.016 0.959 1.000 0.999 0.997

0.937 0.984 1.060 0.988 0.999 1.000 1.006

1.002 0.977 0.896** 0.998 0.998 1.000 1.008

1.704 0.971 0.955 0.999 0.999 1.001 1.007

1.498 1.000 0.979 0.941 1.000 0.999 0.993

0.190**

0.263**

0.169**

0.176**

0.077**

0.063**

- Ln likelihood for

model: No. of parameters:

4,242 96

(D): Dummy variable. *Significant at .05 level, two-tailed test. **Significant at ~'n level, two-tailed test.

but for those who surive, risks soon decrease. After time, the leader creates networks of repression or becomes more legitimate. In either case, or for other reasons, risks go way down. The coefficients for country median duration indicate a significant decrease in risks for the first four periods and then little effect after the end of the eighth year in power. Indeed, only the dummy for nonconstitutional entry and the age variable have an impact on the underlying level of risk in the latter half of the time distribution. This is a striking finding, particularly in light of the clear decrease in risk levels after the eighth year. It suggests that leaders who retain power that long reach a point

74

Analyses of the Sample

where their country's governability, its political traditions, and its socioeconomic characteristics cease to matter very much. A leader is able to use past duration in power to become relatively invulnerable.

Estimating Models from Subsamples The full sample of leaders contains 167 countries and stretches out over almost two centuries (the first observation enters power in 1801, the last in 1987). It is certainly plausible that some of the relationships between leader traits or country characteristics and the risk of losing power have changed over time, or they may vary over culture or level of economic development. Several results from the models estimated with the full sample suggest this to be the case. The risk of losing power seems to have decreased over time, judging from the significant coefficients for date of entry, implying it might be worthwhile to strata the data by century. In addition, fitting models to more homogeneous subsamples may provide better tests for the socioeconomic variables. Because of the problems in our specification of the socioeconomic variables discussed above, the observations these nations provide the sample may be hiding strong correlations between risks of losing power and socioeconomic variables. Some countries that are now industrial or semi-industrial had rapid leadership turnover when their levels of economic development were much lower. We wanted to analyze our largest sample to see whether particular country or leader traits leapt out across widely different systems. That is, across time and place, could we find that certain kinds of leaders-civilian or military, young or old, those who came in constitutionally or nonconstitutionally-would have lower risks? Could we find that there was confirmation for the proposition that the pure effects of time or the net effects of time would be important? That is, would risks decline (or rise) over an extended period of time? The answer is that duration effects are very important and that risks decline over time with the exception of a blip at around four years. We emphasize that we still have not decided whether this is because "smart" lead-

Analyses of the Sample

75

ers stay and "dumb" ones get weeded out early or because leaders build networks, learn more, become more effective after they are lucky enough to stay for some period of time, or are able to stay in power because of some other unobserved factors.

Repeaters We have referred more than once to the problem of heterogeneity in the sample. This is a problem that has occupied econometricians who have emphasized the need to allow for unobserved individual characteristics of heterogeneity in a sample. We had hoped to use repeaters in our sample, that is, leaders who have come to power more than once, to provide insights into the heterogeneity of our sample (see Table 9). The 271 leaders who were in power more than once provide a useful subsample of leaders with which to test hypotheses about leadership skills and learning. First, leaders in power more than once might be assumed to have special political skills and resources. Contradictory evidence was provided by the estimated proportional-hazard models, however, since the dummy for repeaters consistently increased risks significantly in the individual-traits model. Moreover, the variable lost its significance in the estimated comprehensive model once country characteristics were added, notably the variable for country median duration. This suggests that any special skills that these leaders' TABLE 9

Sample of Multiple Entries -~~----

---

-----~----

Total

115 151 231 1,019

10.4 4.0 13.0 9.7

740 2,256

17.2 12.1

Number of entry --·--

3d

---·-----

Region

1st

2d

Middle East Africa Asia Latin America N. America, Europe, and Australasia Total

102 145 194 887

12 6 30 99

5 23

2 5

3

546 1,874

127 274

40 69

14 21

8 11

~~-----

---~~--------

4th

5th

6th

3

4

7th

2 3

-~

Repeaters as percentage of total

76

Analyses of the Sample

multiple spells use to reach power is less important than the structural characteristics of regimes that give rise to leaders who return to power. Second, the repeaters subsample allows us to test whether or not prior experience in office gives a special advantage, either because leadership skills have been honed the first time, or because the political resources accumulated during the previous term retain some of their value at the outset of the present term in office. Either factor consists of "learning" in the sense that we have used that term. By focusing on repeaters, we can attempt to distinguish learning and variation in learning ability across different leaders from sample heterogeneity at entry, that is, differences in skills that leaders have when they begin to rule. It is not easy to distinguish learning from skills. We cannot hold constant the learning that takes place between terms in office. The actual time out of power may have been spent actively preparing the return to power, accumulating resources, and learning from past mistakes. Charles de Gaulle, for example, manipulated events in the waning days of the French Fourth Republic before his return in 1958. His political power remained strong outside of office despite the decade-long hiatus, in part because of his prestige as a war hero, but also because he assiduously developed his political base. A different example is Milton Obote of Uganda, who returned to power in 1980. He had retained little of the political capital that he had acquired from his eight years in office after independence, and his resource bases had not survived the Idi Amin years. He was reimposed by Tanzanian power. In many of the parliamentary regimes of Western Europe, leaders' reentrance to power and length of tenure results from a mixture of coalitional dynamics and their political skills. These leaders have political resources that can survive extended stays away from power since they often lead political parties, hold cabinet posts, or hold local and state offices. Parliamentary leaders in Western Europe have often enjoyed the benefits of cabinet positions or mayoralties and party leadership when out of power. Most repeaters in Third World countries exist in systems in which executive power is rarely shared with opposition fig-

Analyses of the Sample TABLE

77

10

The Correlation Between a Leader's Time in Power and His Time in Power in Previous Tenure

Sample

Africa, Asia, and the Middle East N. America, Europe, and Australasia Latin America Complete sample ---

Correlation coefficient

Sample

"r"

size

.082

56

.213 .086

194 132

.175

382

---

ures and there are rarely subnational offices that provide safe havens. Taking these difficulties into account, it is still interesting to determine the extent to which the length of a leader's previous tenure can predict the length of his current stay in power. Correlation coefficients were calculated for all the leaders who had previously been in power (see Table 10). Weak positive correlation was found in each regional case." Interestingly, the level of correlation is weaker than that found between a leader and his immediate predecessor (see Table 7). In part, this may suggest that leaders really do not learn from past mistakes, or that political networks developed in a previous term are no help for subsequent tenures. More likely, it reflects the high rate of leadership turnover prevailing in parliamentary regimes that resort to the same leaders on more than one occasion in revolving-door cabinets based on fragmented electoral support. We could not, in the end, separate repeaters from their environment because we did not have good enough time-varying country characteristics that we could analyze against time spells for leaders who return to power.

Conclusions This chapter has analyzed the global sample of 2,256 leaders, employing models that focus on individual traits and country traits. The individual traits are not behavioral ones. That is,

78

Analyses of the Sample

there are no variables that measure or describe how leaders act or how skilled they are. But there is a good deal of explanatory power received from examination of characteristics that leaders possess. Leaders who came to power in the second half of the twentieth century have had lower risks than earlier leaders. And, this pattern continues to hold, such that leaders who came to power in the 197o's have had lower risks than those who entered in the 196o's. Partially, this pattern is driven by the large Latin American sample, which shows a great deal of turnover in the nineteenth and early twentieth centuries. Also, the new states that were created after World War II have lower leadership turnover, and leadership stability has increased in many European countries. The age of a leader is positively correlated with risks. Leaders who return to power have higher risks than those who come to power only once. This arises from the fact that the leaders who repeat typically do so in parliamentary systems with rapid turnover, rather than stemming from any feature of the leaders themselves that we have measured. It is not true that military leaders face higher risks than civilians. Once the fact that many military leaders enter power unconstitutionally is netted out, military risks are lower, not higher. · Although the country-median-duration variable is highly significant, there is more explanatory power to be obtained from leaders' characteristics than from country characteristics. No doubt, the specification of country variables leaves much to be desired. We do find, as just noted, higher risks for leaders within Latin America, North America, Europe, and Australasia as compared to Africa, the Middle East, and Asia. That leaders in countries with relatively high degrees of ethnic conflict face relatively lower risks may appear counterintuitive. This occurs because countries with relatively lower levels of ethnic conflict often exhibit high leadership turnover-for example, France, Portugal, Italy, Greece, and Japan. As countries have grown wealthier over time, risks for leaders have declined. The significance of the country-median-duration variable suggests that some countries are more likely to produce long-lasting leaders than others. This is hardly surprising, given that the dis-

Analyses of the Sample

79

tribution of leaders is heavily skewed toward leaders in power a short time. Our other results suggest that long-lasting leaders are difficult to predict from country characteristics; they typically appear to be leaders who came in unconstitutionally and who survived in defiance of the odds. Interestingly, none of our other specified variables affect their risk of losing power except for age. Old leaders eventually die, but they are very hard to eliminate. We now turn to subsamples to analyze leaders' risks in what are more homogeneous political and economic contexts and in what are more compact periods of time to see if we can add to our analysis of time and power. Indeed, in the next chapter we will strengthen the view that duration in power emerges as a powerful predictor of future time in power.

CHAPTER

5

Analyses of the Subsamples The full sample of leaders contains 167 countries and stretches out over almost two centuries. The first observation enters power in 1801, the last in 1987. In the previous chapter several noteworthy aspects of the data were revealed and it was shown that characteristics of individual leaders explain a greater proportion of the risk of losing power than do national socioeconomic characteristics. Nonetheless, it was shown that not all countries are characterized by the same level of leadership stability and that some countries are easier to govern. However, this governability factor affects individual leaders' risks of losing power only in the early years of power. The sample's size and diversity complicated specification of the socioeconomic variables. It is plausible that relationships between leader or country characteristics and the risk of losing power have evolved over time, or that they vary over culture and level of economic development. Several results from the models estimated with the full sample suggest this to be the case. Risk levels were found to vary significantly across the different regions. Low risks, and by implication long periods of rule, occur in nonelectoral regimes, typically in Asia, Africa, and the Middle East. The risk of losing power has decreased over time, judging from the significant coefficients for date of entry. This suggested that it would be worthwhile to organize the data by century. Fitting models to more homogeneous subsamples might provide better tests for the socioeconomic variables. Because of the problems in the specification of the socioeconomic variables discussed above, the observations these nations provide the sample might be hiding strong correlations between the risk of losing power and those variables.

Analyses of the Subsamples

81

We fit models for several subsamples to better understand these issues. First, basic Gompertz hazard functions are estimated for the different regional samples as well as for several of the subsamples of the complete data set. This provides insights into differences in the samples. Second, several of the subsamples are examined in more detail to test our hypotheses. The sample of leaders is limited to those in all countries who came to power after 1945 to provide a better test of the significance of the socioeconomic variables. Then, leaders who came to power after 1945 in Latin America, Africa, Asia, and the Middle East are examined. This sample allows us to test several new variables not applicable to the developed countries, such as "first leaders" after independence. In addition, it allows us to focus on a set of countries in which nondemocratic political systems are the norm and power consolidation might be assumed to follow a different pattern than in the democratic regimes of North America and Europe.

The Gompertz Hazard Functions A Gompertz was estimated for each of the regional subsamples, for the subsamples divided by period (before and after 1945), for nonconstitutional exits only, and for subsamples treating natural deaths in different ways.' These subsample hazard functions are listed in Table 11. The resulting Gompertz distributions for several of the subsamples are graphed as Figure 4· Figure 5 provides a comparison between the life tables and Gompertz distributions for the Latin America sample, for the North America, Europe, and Australasia sample, and for the post-1945 Third World sample. T-tests are reported for the estimated coefficients and a Chisquare statistic provides goodness of fit measures for each estimated modeU Every regional equation estimated is significant, but the Latin America and Asia samples, and to a lesser extent the Middle East and Africa samples, clearly fit the data less satisfactorily. While the Chi-squares for the estimated equations for Latin America and Asia are reasonably high, their lower level compared to the Chi-squares for the equations for the other samples indicates that there might be a problem with the assumption of monotonicity. The imperfect fit of these two equa-

82

Analyses of the Subsamples TABLE 11

Gompertz Hazard Functions for Different Subsamples Sample (N)

Estimated equation

(1]

Full sample (2,256)

h(t)

=

.3r

081

Chi-square

273.9

REGIONAL SAMPLES

N. America, Europe, and Australasia (740) Latin America (1,019) Asia (231) Africa and the Middle East (266)

[2] [3] (4]

h(t) h(t) h(t)

= =

.43e- 121 .29e- 031 .24e- 10I

154.4 16.9 42.3

(5]

h(t)

=

.llr 041

10.4

=

OTHER SAMPLES

After 1945 (990) Third World (686) Developed countries (799) Leaders with single term (1,615) Leaders with multiple terms (642) Treating natural deaths as regular exit (2,256) Eliminating leaders exiting through natural deaths (2, 122)

[7] [8]

h(t) h(t) h(t)

= = =

.22e- 071 .2oe- 071 .44e- 121

81.4 57.9 158.9

[9]

h(t)

=

.27e-

081

222.7

[10] h(t) = .36e- 041

8.7

[6]

(11] h(t) = .3oe-

[12] h(t)

=

061

209.9

.32e- 071

191.7

NoTE: T-tests indicate that all of the estimated parameters were significantly different from zero at the .01 level of significance for a one-tailed test. Each model uses up 1 degree of freedom. The critical value for greater explanatory power than the null model is a Chisquare value of 6.63 for the .o1 level of significance.

tions is confirmed by examining the t-tests on the estimated c parameter. Each of the subsample Gompertz hazard functions was tested for a decreasing slope by making sure that the negative sign on the c parameter was significantly different from o. Again, the levels of significance were high, with the exception of the Middle East, Africa, and Latin America. The poor fit of the Africa and Middle East sample is in part explained by the sample size and in part by the lower level of risks throughout the distribution, which flattens out the curve and makes the hazard appear almost constant. For Latin America, the very low coefficient suggests an almost flat curve. Its statistical significance levels are lower than for the other samples, albeit well above critical levels. When the Gompertz distribution is graphed alongside the life table for the Latin America sample, the problem becomes evident. As is shown on the upper panel of Figure 5, the Gompertz

Analyses of the Subsamples

83

simply does not fit well for Latin America. After the sixth year, the pattern of risk does achieve monotonicity, but the entire distribution clearly does not. The bulge in Latin America does show the survival of leaders to years 4-6 at higher rates than leaders in other regions. A final point on these regional hazard functions is worth making. In Table 11, the B parameter provides an estimate of the level of risk in the first year. Interestingly, that level shows much variation between the different samples. Thus, in the North America, Europe, and Australasia sample, the B parameter is .43, whereas it is only .11 for the Africa and Middle East sample and .22 for the post-1945 sample. Although Figure 4 does indicate that over time all the different regions level off to the same level of risk, the entire distributions do indicate wide disparities in risk levels. In particular, the lower level of risks for Africa and the Middle East is striking. This underscores the distinction drawn in Chapter 1 between political stability, which is arguably lower in Africa than anywhere else, and the ability of individual 0.45

.....------------------------~

0.30

---- ---

1 (I)

0.15

0

2

4

6

8

10

12

14

- - - N. America. Europe, and Australasia - - - -- Latin America - - - The Middle East -·-Africa -·-·-Asia

-- -- ----- ' 16 18 Years

20

22

24

26

28

30

Fig. 4· The risk of losing power: Gompertz distributions for the differ-

ent regional samples.

84

Analyses of the Subsamples 0.7

A) Latin America ( N = 1,019)

0.6

'I II I I ~ I I II I \/I I

0.5

h ( t) 0.4 0.3

Gompertz - - Life table

0.2 0.1

-------

0 0.6 B) N. America, Europe, and Australasia ( N = 740)

0.5 0.4

h ( t)

I

0.3

'

0.2

'

0.1 0 C) The Third World Post-1945 (N=686)

0.23 0.21 0.19 0.17 0.15

h ( t) 0.13 0.11 0.09 0.07 0.05 0.03 0.01 0

6

12

18

24

30

Years

Fig. 5· The risk of losing power: The subsamples.

leaders to hold on to power, which appears to be quite good in comparative terms in Africa. Gompertz hazard functions were estimated for the subsample of leaders in power only once and for those in power more than once_ As might be expected, the equations are quite different. The equation estimated for repeaters fits poorly, and the c coeffi-

Analyses of the Subsamples

85

cient is not different from zero at the .01 level of statistical significance, suggesting that the equation traces out a virtually flat line and thus a constant hazard rate that is not affected by time. This finding suggests that the risk of losing power faced by repeaters is similar to that found by Claudio Cioffi-Revilla for cabinets in postwar Italy.' It is essentially random across time. It was concluded in the previous chapter that repeaters probably have no special characteristics that differentiate them from other leaders once the characteristics of the political systems in which they tend to appear are taken into account. This is an issue that we discuss below, when we analyze the more homogeneous subsample of countries in developed countries that provide most of the repeaters in the sample. A diagnostic test was conducted to determine whether or not it matters for results if natural deaths are treated as censored observations. In Table 11, Equation 1, natural deaths are treated as censored observations, whereas in Equation 11 natural deaths are treated as regular exits. In Equation 12, all leaders who died from natural causes are pulled out from the data set. The estimated parameters and their respective t-tests indicate that the three distributions are virtually indistinguishable from each other. This result suggests that the decision to treat natural deaths as censored observations introduced no bias into the results. Although these different Gompertz equations suffer from the defects of all univariate statistics, they are useful in highlighting some of the differences between the geographical regions and the time periods represented in the sample. Not only are risk levels different in important ways across these subsamples before they all level off to the same low level at the tail end of the distribution, but, interestingly, the rate of risk decline also varies enormously. It is necessary to turn to multivariate hazard models to attempt to explain these differences.

Proportional-Hazard Models for the Different Subsamples Different models were estimated for several subsamples of the entire data set. The basic characteristics of these subsamples are described in Table 12. First, all the observations entering power before 1945 were removed and the 990 remaining leaders

TABLE 12

Proportions and Means of the Covariatcs for Different Subsamples --

~--

PostVariable -------~

---~

1945 -

Developed countries

Third World

Latin America

~----

--~

DUMMIES

Still in power Constitu tiona! exit Nonconst. exit Natural death

.17 .52 .25 .06

.19 .39 .36 .07

.04 .86 .05 .05

.03

Constitutional entry Nonconst. entry

.74 .26

.64 .36

.94 .06

.65 .35

Military Civilian

.23 .77

.33 .67

.08 .92

.43 .57

Low ethnic tension High ethnic tension

.89 .21

.71 .29

.98 .02

.91 .09

Single en tries Multiple entries

.88 .22

.83 .17

.58 .42

.77 .23

.57 .34 .06

.14 .86

First leaders Subsequent leaders CONTINUOUS

Median duration (standard deviation) Age at entry (SD) Date of entry (SD) Per capita income* (SD) GNP growth rate** (SD) Country size*** (SD) Population''"'** (SD) Literacy rate (SD) No. of countries No. of observations

2.4

62 (27.0)

1.8 (2.5) 56 (9.3) 1938 (25.1) 3646 (1497) 3.06 (1.0) 1097 (3104) 37 (50.8) 97 (4.6)

51 (10.3) 1908 (48.5) 675 (397) 1.90 (1.8) 914 (1580) 19 (28.9) 79 (17.3)

134 686

33 799

33 1,019

3.5 (3.7) 52 (10.9) 1967 (11.8) 732 (1 971) 2.15 (2.4) 925 (2459) 34 (97.5) 73 (27.8)

4.0 (3.9) 51 (11.4) 1968 (11.4) 663 (1347) 1.79 (2.6) 753 (1491) 33

167 990

(111)

(1.3)

Totals may not sum to 1 because of rounding errors. Sec the text for definition of the samples. *1973 U.S. dollars. **In per capita terms, measured from 1965-1983. ***In millions of square kilometers.

NOT b:

****In millions.

Analyses of the Subsamplcs

87

were labeled the "post-1945" sample. Next a "Third World" sample was defined, consisting of 686 leaders from the countries in Latin America, Asia (excluding Japan), Africa, and the Middle East who came to power after 1945. Third, the 799leaders from all the industrialized countries of Western and Eastern Europe, North America, and Asia were grouped and called the "developed-countries" sample. Finally, the Latin America data set of 1,019 leaders was separated out since it seemed to exhibit distinct properties. Table 13 estimates the preferred model for these different subTABLE 13

Proportional-Hazard Models: Preferred Models for Subsamplcs --~

-

-------

Variable

-----

-

Nonconstitutional entry (D) Military (D) Country median duration Date of entry Age at entry High ethnic tension (D) First leaders (D) Literacy rate Latin America (D) N. America, Europe, and Australasia (D) Eastern bloc (D) Duration Duration Duration Duration Duration Duration

---------

(1] Post1945

[3]

Third World

Developed countries

0.849** 0.990** 1.016**

[4] Latin America --

--

--~---~

1.299*

1.287* 0.812*

0.693**

1.324**

0.864** 0.989** 1.016**

0.770** 0.993** 1.018**

0.907** 0.998** 1.011 ** 0.720**

0.527** 0.972** 1.318** 1.335** 0.565**

0-1 2-3 4-5 6-8 9-13 > 13

0.153 0.113 0.183 0.129 0.082 0.079

Chi-square (No. of parameters) ------~--

[2]

389 (12) ---

0.189 0.130 0.240 0.171 0.110 0.095 275 (12)

0.415 0.368 0.347 0.382 0.151 0.165

0.238 0.176 0.527 0.332 0.169 0.054

467 (12)

239 (11) ---

--

All duration effects were found to be significant at .01 level. Chi-square statistics compare estimated model with the null model for that data set. (D): Dummy variable. *Significant at .05 level, two-tailed test. **Significant at .01 level, two-tailed test. NOTE:

88

Analyses of the Subsanzples

samples. We estimated models with the different variables, using the same approach that was followed for the entire data set in Chapter 4· Several new variables relevant to each of these subsamples were added. We kept the best-fitting models for each sample. For example, we estimated models with the dummy variable for multiple leaders but do not report them because the estimated coefficients were never more than marginally significant. The fitted models are presented side by side for the convenience of the reader. In general, the different subsamples share striking similarities. Note, for example, the stability of the estimated coefficients for age and date of entry, which practically do not vary from model to model. Note also how similarly the duration effects change over time across the different models, even if risk levels vary, particularly in the first intervals. There are several interesting differences in the subsamples that are worthy of exploration.

The Post-1945 Sample The post-1945 sample included 990 observations from all the regions (see Table 14). The results of analysis are presented in Column 1 of Table 13. This sample was thought to be the best test for the salience of the socioeconomic variables, since it keeps only the observations of the past four decades and the socioeconomic variables are more appropriately specified than for the full model. In fact, among the country-level characteristics, the socioeconomic indicators have little explanatory power. Their estimated coefficients had at best marginal significance and were not retained in the preferred model. Only the regional dummies for Latin America and the developed country sample of North America, Europe, and Australasia have significant estimated coefficients that indicate a relatively higher degree of risks for leaders from those regions. The estimated coefficient of the variable for country median durations indicates a statistically significant reduction in risks throughout the time duration in power for leaders from countries whose leaders have historically enjoyed higher-than-average median spells in power. Along with the

Analyses of the Subsamples

8g

TABLE 14 The Frequency Distribution for Years in Power: Leaders After 1945

_"

_______ Full years in power

------

-'

....

#

Name (Country)

Entry

Years in power

Year

Mode

Age

Exit mode

2 2 0 0 2 0 0 2 5 0 3 5 4 0 0 4 26 0 1 0 1 0 0 0

1848 1851 1853 1853 1853 1855 1855 1855 1858 1863 1864 1867 1872 1863 1876 1880 1884 1911 1911 1913 1913 1914 1914 1914

0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 0 1 1 1 1

56 49 42 50 58 49 65 43 51 53 32 60 47 40 46 47 54 48 38 55 68 43 55 34

1 1 3 1 1 1 1 1 3 3 1 2 3 3 1 3 1 1 1 3 3 3 3 3

Mil itary

Number Leader of order entries

Latin America (cont_) 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193

Herrera (Mexico) Arista (Mexico) Ceballos (Mexico) Lombardini (Mexico) Santa Anna (Mexico) Carrera (Mexico) Juan Alvarez (Mexico) Comonfort (Mexico) Juarez (Mexico) Iglesias (Mexico) Maximilian (Mexico) Juarez (Mexico) Lerdo (Mexico) Ormachea (Mexico) Diaz (Mexico) Gonzalez (Mexico) Diaz (Mexico) de La Barra (Mexico) Madero (Mexico) Lascurain (Mexico) Huerta (Mexico) Carbajal (Mexico) Carranza (Mexico) Gutierrez (Mexico)

1 1 0 1 1 1 1 1 0 0 1 0 0 0 1 1 1 0 0 0 1 0 1 1

31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

3 1 1 1 7 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1

\J1

N

1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225

Gonzalez Garza (Mexico) Lagos Chazaro (Mexico) Carranza (Mexico) de la Huerta, Adolpho (Mexico) Obregon (Mexico) Calles (Mexico) Portes (Mexico) Ortiz (Mexico) Rodriquez (Mexico) Cardenas (Mexico) Avila Camacho (Mexico) Aleman (Mexico) Ruiz Cortines (Mexico) Lopez Mateos (Mexico) Diaz Ordaz (Mexico) Echeverria (Mexico) Lopez Portillo (Mexico) de la Madrid (Mexico) de la Cerda (Nicaragua) Herrera (Nicaragua) Zepeda (Nicaragua) Nunez (Nicaragua) Buitrago (Nicaragua) Orozco (Nicaragua) Perez (Nicaragua) Sandoval (Nicaragua) Guerrero (Nicaragua) Ramirez (Nicaragua) Pineda (Nicaragua) Chamorro (Nicaragua) Castellon (Nicaragua) Estrada, J.M. (Nicaragua)

0 0 5 0 4 4 2 2 2 6 6 6 6 6 6 6 6 5 3 2 1 2 0 1 0 2 2 2 2 1 1 1

1915 1915 1915 1920 1920 1924 1928 1930 1932 1934 1940 1946 1952 1958 1964 1970 1976 1982 1825 1830 1835 1837 1843 1843 1844 1845 1847 1849 1851 1853 1854 1855

1 1 1 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0

0 0

1 0

30 36 56 49 40 47 37 53 43 39 43 44 61 48 53 48 52 48 45 50 n.a. n.a. 46 n.a. n.a. n.a. n.a. n.a. 51 47 n.a. n.a.

3 3 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 2 1 1 1 1 1 1 1 1 2 2 3

1 0 1 1 1 0 0 0

1 1 1 0 1 0

0 0 0 0 1 1 1 0 0 0 0

1 0

0 0 1 0 0

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1 2 1 1 1 1 1 1 1 1 1 1 1

'

1

....

\.11 VJ

#

Name (Country)

Years in power

Entry Year

1 10 4 4 4 4 4 4 2 16 0 0 5 2 2 1 1 0 2 2 3 0 19 6

1856 1857 1867 1871 1875 1879 1883 1887 1889 1893 1909 1911 1911 1917 1921 1923 1925 1926 1926 1929 1933 1936 1937 1956

Mode

Age

Exit mode

Hilitary

34 45 n.a. 41 45 n.a. 47 65 51 40 n.a. n.a. 33 45 59 63 65 54 49 62 58 51 40 40

3 1 1 1 1 1 1 2 3 3 3 3 3 1 2 1 3 3 1 1 3 1 3 3

1 1 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 1 0

Number Leader of order entries

Latin America (cont.) 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249

Walker (Nicaragua) Martinez (Nicaragua) Guzman (Nicaragua) Cuadra (Nicaragua) Chamorro (Nicaragua) Zavala (Nicaragua) Cardenas (Nicaragua) Carazo (Nicaragua) Sacasa, Roberto (Nicaragua) Zelaya (Nicaragua) Madriz (Nicaragua) Estrada, J.J. (Nicaragua) Diaz (Nicaragua) Chamorro Vargas (Nicaragua) Diego Chamorro (Nicaragua) Martinez Bartolo (Nicaragua) Solorzano (Nicaragua) Chamorro Vargas (Nicaragua) Diaz (Nicaragua) Moncada (Nicaragua) Sacasa, Juan (Nicaragua) Jarquin (Nicaragua) Somoza G.Anastasio (Nicaragua) Somoza Debayle, Luis (Nic)

1 1 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 1

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1

'"'

\Jl ~

1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281

Shick (Nicaragua) Guerrero Gutierrez (Nicaragua) Somoza, D. Anastasio (Nicaragua) Ortega, Daniel S. (Nicaragua) Arosomena, Justo (Panama) Fabriega (Panama) Calvo (Panama) De Obaldia (Panama) Guardia, s. (Panama) Diaz, M. (Panama) d Arango (Panama) Amador (Panama) Obaldia (Panama) Arosemena, P. (Panama) Porras (Panama) Valdes (Panama) Urriola (Panama) Porras (Panama) Chiari, Rudolfo (Panama) Arosomena F. (Panama) Arias (Panama) Alfaro (Panama) Arias, H. (Panama) Arosomena, Juan (Panama) Boyd (Panama) Arias, A. (Panama) Guardia, R. (Panama) Jimenez (Panama) Diaz Arosomena (Panama) Chaniz Pinzon (Panama) Chiari, Roberto (Panama) Arias, A. (Panama)

3 0 11 6 0 0 2 2 2 0 1 4 1 2 4 1 0 6 4 2 0 1 4 3 0 1 4 3 1 0 0 1

1963 1966 1967 1981 1855 1856 1856 1858 1860 1862 1903 1904 1908 1910 1912 1916 1917 1918 1924 1928 1931 1931 1932 1936 1939 1940 1941 1945 1948 1949 1949 1949

1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 1 1 1

53 66 34 36 38 n.a. n.a. n.a. n.a. n.a. n.a. 29 63 74 56 n.a. n.a. 62 54 56 45 49 46 43 57 38 42 58 73 57 54 47

2 3 3 0 1 1 1 1 1 3 1 1 2 1 1 2 1 1 1 3 3 1 1 2 1 3 1 3 2 3 3 3

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

39 40 41 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2

H

Vl Vl

....

#

Name (Country)

Years in power

Entry Year

Mode

Age

1951 1952 1953 1954 1955 1956 1960 1964 1968 1968 1968 1968 1981 1982 1982 1811 1840 1841 1862 1870 1871 1874 1877 1878

1 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 1 0 0 0 1 1 0 0

69 44 56 46 43 52 55 59 n.a. 63

Exit mode

Mil itary

1 3 3 3 1 1 1 3 1 1 3 2 3 3 0 2 1 2 2 3 3 1 1 1

0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 1 1 0 0 0

Number Leader of order entries

Latin America (cont.) 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305

Arosomena, A. (Panama) Remon Cantero (Panama) Guizado (Panama) Remon Cantero (Panama) Arias Espinosa (Panama) Guardia, E. (Panama) Chiari, Roberto (Panama) Robles (Panama) Del Valle (Panama) Robles (Panama) Arias, A. (Panama) Torrijos (Panama) Florez (Panama) Dario Paredes (Panama) Noriega (Panama) Francia (Paraguay) Patino (Paraguay) Lopez Carlos (Paraguay) Lopez Francisco (Paraguay) Rivarola (Paraguay) Jovellanos (Paraguay) Gill (Paraguay) Uriarte (Paraguay) Barreiro (Paraguay)

1 1 0 0 1 4 4 4 0 0 0 12 0 0 5 29 0 21 7 1 3 2 1 1

66

39 n.a. 48 n.a. 45 n.a. 51 36 34 62 34 n.a. n.a.

0

29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 1 2 3 4 5 6 7 8 9

1 1 1 2 1 1 2 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1

VI

0\

1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337

Saguier (Paraguay) Caballero (Paraguay) Escobar (Paraguay) Gonzalez Juan (Paraguay) Moringo, M. (Paraguay) Egusquiza (Paraguay) Aceval (Paraguay) Carvallo (Paraguay) Escurra (Paraguay) Gaona (Paraguay) Baez (Paraguay) Ferreiria (Paraguay) Gonzalez Navero (Paraguay) Gondra (Paraguay) Albino Jara (Paraguay) Rojas (Paraguay) Pena (Paraguay) Gonzalez Navero (Paraguay) Schaerer (Paraguay) Franco Manuel (Paraguay) Montero (Paraguay) Gondra (Paraguay) Paiva (Paraguay) Ayala Eusebio (Paraguay) Ayala Eligio (Paraguay) Riart (Paraguay) Ayala Eligio (Paraguay) Guggiari (Paraguay) Gonzalez Navero (Paraguay) Ayala Eusebio (Paraguay) Franco, Rafael (Paraguay) Paiva (Paraguay)

0 6 4 3 0 4 3 0 1 1 1 1 2 0 0 0 0 0 4 2 1 1 0 1 1 0 4 3 0 4 0 2

1880 1880 1886 1890 1893 1894 1898 1902 1902 1904 1906 1906 1908 1910 1911 1911 1912 1912 1912 1916 1919 1920 1921 1921 1923 1924 1924 1928 1931 1932 1936 1937

0 1 0 0 1 0 0 1 0 1 1 1 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0 1 0 1 1

n.a. 32 n.a. n.a. n.a. n.a. 55 n.a. n.a. 58 44 60 47 39 33 41 55 51 39 45 n.a. 49 44 47 56 33 59 44 70 58 40 60

3 1 1 3 1 1 2 1 3 3 3 3 1 3 1 3 3 1 1 2 1 3 3 1 1 1 1 3 1 3 3 1

0 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 3 2 1 2

....

VI '-1

.....

#

Name (Country)

Entry

Years in power

Year

Mode

Age

Exit mode

Mil itary

1 7 0 0 0 1 3 23 1 0 0 0 6 4 1 0 1 1 0 0 1 3 0 0

1939 1940 1948 1948 1949 1949 1950 1954 1821 1823 1823 1823 1823 1829 1833 1834 1834 1835 1836 1836 1837 1838 1841 1841

0 0 1 0 1 1 1 0 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1

51 43 n.a. 51 46 48 67 42 43 36 28 44 40 44 38 41 39 29 41 44 42 53 48 34

2 3 1 3 3 3 1 0 3 1 1 1 3 1 3 1 3 1 3 3 3 1 3 3

1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Number of Leader order entries

Latin America (cont.) 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361

Estigarribia (Paraguay) Morinigo Higinio (Paraguay) Frutos (Paraguay) Gonzales Natalicio (Paraguay) Rolon (Paraguay) Molas Lopez (Paraguay) Chavez (Paraguay) Stroessner (Paraguay) San Martin (Peru) La Riva Aguero (Peru) Sucre Antonio (Peru) Torre Tagle (Peru) Bolivar (Peru) Gamarra (Peru) Obregoso (Peru) Bermudez (Peru) Obregoso (Peru) Salaverry (Peru) Obregoso (Peru) Santa Cruz (Peru) Obregoso (Peru) Gamarra (Peru) Menendez (Peru) Torrico (Peru)

42 43 44 45 46 47 48 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 3 1 4 2 1 1

V1 C/J

1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393

Vidal (Peru) Vivanco (Peru) Menendez (Peru) Castilla (Peru) Echenique (Peru> Castilla (Peru) San Roman (Peru) Pezet (Peru) Prado (Peru) Balta (Peru) Gutierrez (Peru) Pardo (Peru) Prado (Peru) Pierola (Peru) Garcia Calderon (Peru) Montero (Peru) Iglesias (Peru) Caceres (Peru) Bermudez Mor. Rem. (Peru) Borgono (Peru) Caceres (Peru) Pierola (Peru) Lopez de Romana (Peru) Candamo (Peru) Pardo y Barreda (Peru) Leguia y Salcedo (Peru) Billinghurst (Peru) Benavidez (Peru) Pardo y Barreda (Peru) Leguia y Salcedo (Peru) Sanchez Cerro (Peru) Benavidez (Peru)

0 1 0 6 3 7 0 2 2 3 0 4 3 1 0 2 2 4 3 0 0 4 4 0 4 4 1 1 4 11 1 6

1842 1843 1844 1845 1851 1855 1862 1863 1865 1868 1872 1872 1876 1879 1881 1881 1883 1886 1890 1894 1894 1895 1899 1903 1904 1908 1912 1914 1915 1919 1930 1933

1 1 0 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0

42 37 51 47 43 57 60 53 38 52 n.a. 38 49 40 47 49 61 49 58 58 57 56 52 59 40 44 51 37 51 55 36 56

3 1 1 1 3 1 2 3 1 3 1 1 3 3 3 3 3 1 2 1 1 1 1 2 1 1 3 1 3 3 1 1

1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1

17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 2 2 1 2

,.... Vl ~

.....

#

Name (Country)

Years in power

Entry Year

5 3 7 6 0 0 0 5 6 4 5 2 4 0 2 0 0 5 5 3 4 2 6 19

1939 1945 1948 1956 1962 1962 1963 1963 1968 1975 1980 1985 1983 1979 1979 1981 1982 1982 1979 1984 1973 1980 1981 1962

Mode

Age

Exit mode

Hilitary

Number Leader of order entries

Latin America (cont.) 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417

Prado y Ugarteche (Peru) Bustamente y Rivero (Peru) Odria (Peru) Prado y Ugarteche (Peru) Haya de la Torre (Peru) Perez Godoy (Peru) Lindley Lopez (Peru) Belaunde (Peru) Velasco Alvardo (Peru) Morales Ber. Francisco (Peru) Belaunde (Peru) Garcia (Peru) Simmonds (St. Kitts) Compton (St. Lucia) Louisy (St.Lucia) Cenac (St.Lucia) Pilgrim (St.Lucia) Compton (St. Lucia) Cato (St. Vincent/Grenadine) Mitchell (St. Vin/Grenadine) Arron (Suriname) Chin (Suriname) Bouterse (Suriname) Williams (Trin/Tobago)

0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0

50 51 50 67 67 59 n.a. 50 58 54 67 36 47 53 62 55 36 56 64

53 37 43 n.a. 50

1 3 1 1 3 3 1 3 3 1 1 0 0 1 1 1 1 0 1 0 3 1 0 2

0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0

49 50 51 52 53 54 55 56 57 58 59 60 1 1 2 3 4 5 1 2 1 2 3 1

1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1

0\

0

1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449

Chambers (Trin/Tobago) Robinson (Trin/Tobago) Fructeroso Rivera (Uruguay) Oribe (Uruguay) Fructeroso Rivera (Uruguay) Joaquin Suarez (Uruguay) Francisco Giro (Uruguay) Flores (Uruguay) Bustamante (Uruguay) Pereira (Uruguay) Prudencio Berro (Uruguay) Cruz Aquirre (Uruguay) Flores (Uruguay) lorenzo Batlle (Uruguay) Gomensoro (Uruguay) Ellauri (Uruguay> Varela (Uruguay) Latorre (Uruguay) Vidal (Uruguay) Santos (Uruguay) Tajes (Uruguay) Herrera y Obes (Uruguay) ldiarte Borda (Uruguay) Lindolfo cuestas (Uruguay) Batlle y Ordonez (Uruguay) Williaman (Uruguay) Batlle y Ordonez (Uruguay) Viera (Uruguay) Brum (Uruguay) Serrato (Uruguay) Campisteguy (Uruguay) Terra (Uruguay)

5 1 5 3 4 9 1 1 1 4 4 1 3 4 1 2 1 4 2 4 4 4 3 6 4 4 4 4 4 4 4 7

1981 1986 1830 1835 1838 1843 1852 1854 1855 1856 1860 1864 1865 1868 1872 1873 1875 1876 1880 1882 1886 1890 1894 1897 1903 1907 1911 1915 1919 1923 1927 1931

1 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0

49 60 46 43 54 62 61 46 70 62 57 63 57 58 62 39 38 36 53 35 34 49 50 60 53 44 61 43 36 55 68 58

1 0 1 3 3 1 3 1 1 1 1 3 1 1 1 3 3 1 1 3 1 1 3 1 1 1 3 1 1 1 1 1

0 0 1 1 1 1 0 1 1 0 0 0

1 1 1 0 0 1 0 1 1 0

0 0

0 0 0

0 0 0 0 0

2 3 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1

.... ....

0\

#

Name (Country)

Years in power

Entry Year

Mode

Age

Exit mode

Mil itary

Number Leader of order entries

Latin America (cont.) 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473

Baldomir (Uruguay) Amezaga (Uruguay) Berreta (Uruguay) Batlle Berres Luis (Uruguay) Martinez Trueba (Uruguay) e Echegoyen (Uruguay) Nardone (Uruguay) Haedo (Uruguay) Harrison (Uruguay) Crespo (Uruguay) Giannattasio (Uruguay) Beltran (Uruguay) Penades (Uruguay) Heber (Uruguay) Usher (Uruguay) Gestido (Uruguay) Pacheco Arceo (Uruguay) Bordaberry (Uruguay) Demichelli (Uruguay) Mendez Manfredini (Uruguay) Alvarez Armallino (Uruguay) Sanguinetti (Uruguay) Paez (Venezuela) Vargas (Venezuela)

5 4 0 4 4 1 1 1 1 1 0 0 1 0 0 0 4 5 0 5 3

2 5 1

1938 1943 1947 1947 1951 1959 1960 1961 1962 1963 1964 1965 1965 1966 1966 1967 1967 1971 1976 1976 1981 1985 1830 1835

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0

54 62 72 50 66 54 n.a. n.a. n.a. n.a. 70 50 n.a. 50 n.a. 66 47 43 80 72 56 49 40 49

1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 3 3

3 1 0 1 3

1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0

31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 1 2

'""

0' N

1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505

Marino (Venezuela) Vargas (Venezuela) Soublette (Venezuela) Paez (Venezuela) Soublette (Venezuela) Monagas, J. Tadeo (Venezuela) Monagas, J. Gregario (Venenzuela) Monagas, J. Tadeo (Venezuela) Castro, Julian (Venezuela> Tovar (Venezuela) Gual (Venezuela) Paez (Venezuela) Falcon (Venezuela) Guzman Blanco (Venezuela) Linares Alcantra (Venezuela> Guzman Blanco (Venezuela) Crespo (Venezuela) Guzman Blanco (Venezuela) Rojas Paul (Venezuela) Andueza Palacio (Venezuela) Crespo (Venezuela) Andrade (Venezuela) Castro, Cipriano (Venezuela) Gomez (Venezuela) Marquez Bustillos (Venezuela) Gomez (Venezuela) Perez (Venezuela) Gomez (Venezuela) Lopez Contreras (Venezuela) Medina Angarita (Venezuela) Betancourt (Venezuela) Gallegos (Venezuela)

0 1 2 4 4 4 4 3 2 1 0 3 5 7 2 5 2 2 2 2 6 1 8 7 7 7 2 4 5 4 2 1

1835 1835 1837 1839 1843 1847 1851 1855 1858 1860 1861 1861 1863 1870 1877 1879 1884 1886 1888 1890 1892 1898 1899 1909 1915 1922 1929 1931 1936 1941 1945 1947

1 1 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0

47 49 48 49 54 63 56 71 n.a. 57 77 71 43 41 47 50 34 57 59 49 47 58 41 51 n.a. 65 60 74 52 44 37 63

3 1 1 1 1 1 1 3 1 1 3 1 3 1 3 1 1 1 1 3 1 3 3 1 1 1 1 1 1 3 1 3

1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 0 0

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

1 2 1 2 2 1 1 2 1 1 1 3 1 1 1 2 1 3 1 1 2 1 1 1 1 2 1 3 1 1 1 1

H

0\

\.;,)

......

#

Name (Country)

Years in power

Entry Year

Mode

Age

Exit mode

Mil itary

Number Leader of order entries

Latin America (cont.) 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516

Delgado Chalbaud (Venezuela) Suarez Flamerich (Venezuela) Perez Jimenez (Venezuela) Larrazabal (Venezuela) Sanabria (Venezuela) Betancourt (Venezuela) Leoni (Venezuela) Caldera Rodriquez (Venezuela) Andres Perez (Venezuela) Herrera Campins (Venezuela) Lusinchi (Venezuela)

2 2 5 0 0 5 5 5 5 5 4

1948 1950 1952 1958 1958 1959 1964 1969 1974 1979 1984

1 1 1 1 1 0 0 0 0 0 0

48 32 34 47 47 51 58 53 52 53 59

3 3 3 3 1 1 1 1 1 1 0

1 0 1 1 0 0 0 0 0 0 0

35 36 37 38 39 40 41 42 43 44 45

1 1 1 1 1 2 1 1 1 1 1

39 2 1 0 0 0 3 0 0

1945 1985 1901 1903 1904 1904 1905 1908 1909

0 0 0 0 0 0 0 0 0

37 67 52 47 37 56 49 46 50

2 0 1 1 1 1 1 1 1

1 0 0 0 0 0 0 0 0

1 2 1 2 3 4 5 6 7

1 1 1 1 1 1 2 1 3

North America, Europe and Australasia 1517 1518 1519 1520 1521 1522 1523 1524 1525

Hoxha (Albania) Alia (Albania) Barton (Australia) Deakin (Australia) Watson (Australia) Reid (Australia) Deakin (Australia) Fisher (Australia) Deakin (Australia)

..,..0\

1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557

Fisher (Australia) Cook (Australia) Fisher (Australia) Hughes (Australia) Bruce (Australia) Scullin (Australia) Lyons (Australia) Page (Australia) Menzies (Australia) Fadden (Australia) Curtin (Australia) Forde (Australia) Chifley (Australia) Menzies (Australia) Holt (Australia) McEwan (Australia) Gorton (Australia) McMahon (Australia) Whittam (Australia) Fraser (Australia) Hawke (Australia) Renner (Austria) Mayr (Austria) Schober (Austria) Seipel (Austria) Ramek (Austria) Seipel (Austria) Streeuwir (Austria) Schober (Austria) Vaugoin (Austria) Ender (Austria) Buresch (Austria)

3 1 1 7 6 2 7 0 2 0 3 0 4 16 1 0 3 1 2 7 4 1 0 0 2 1 2 0 1 0 0 0

1910 1913 1914 1915 1923 1929 1932 1939 1939 1941 1941 1943 1945 1949 1966 1967 1968 1971 1972 1975 1983 1918 1920 1921 1922 1924 1926 1929 1929 1930 1930 1932

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

48 53 52 51 40 53 58 59 45 46 58 53 60 55 58 67 57 63 56 45 54 48 58 47 46 43 50 55 55 57 55 53

1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 2 3 4 5 6 7 8 9 10 11 12

2 1 3 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1

,_. 0\ \Jt

H

#

Name (Country)

Years in power

Entry Year

Mode

Age

Exit mode

Military

1932 1934 1938 1945 1945 1953 1961 1964 1970 1983 1986 1899 1907 1908 1911 1918 1918 1920 1921 1925 1925 1926 1931 1932

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

40 37 46 75 43 62 63 53 59 54 49 56 50 57 51 66 51 51 48 54 57 56 69 72

1 1 3 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Number Leader of order entries

North America, Europe and Australasia (cont.) 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581

Dollfuss (Austria) Schuschnigg (Austria) Seyss-Inquart (Austria) Renner (Austria) Figl (Austria) Raab (Austria) Gorbach (Austria) Klaus (Austria) Kreisky (Austria) Sinowatz (Austria) Vranitzky (Austria) de Smet de Nayer (Belgium) de Trooz (Belgium) Schollaert (Belgium> de Broqueville (Belgium) Cooreman (Belgium) Delacroix (Belgium) de Wiart (Belgium) Theunis (Belgium) van der Vyvere (Belgium) Poullet (Belgium) Jaspar (Belgium) Renkin (Belgium) de Broqueville (Belgium)

2 3 1 0 7 8 3 5 13 3 1 7 0 3 7 0 2 1 3 0 0 5 1 2

13 14 15 16 17 18 19 20 21 22 23 1 2 3 4 5 6 7 8 9 10 11 12 13

1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2

0\ 0\

1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613

Theunis (Belgium) van Zeeland (Belgium) Janson (Belgium) Spaak (Belgium) Pierlot (Belgium) f van Acker (Belgium) Spaak (Belgium) van Acker (Belgium) Huysman (Belgium) Spaak (Belgium) Eyskens, G. (Belgium) Duvieusart (Belgium) Pholien (Belgium) van Houtte (Belgium) van Acker (Belgium) Eyskens, G. (Belgium) Lefevre (Belgium) Harmel (Belgium) van den Boeynants (Belgium) Eyskens, G. (Belgium) Leburton (Belgium) Tindemans (Belgium) van den Boeynants (Belgium) Martens (Belgium) Eyskens, H. (Belgium) Martens (Belgium) Dimitrov (Bulgaria) Chervenkov (Bulgaria) Zhikov (Bulgaria) Laurier (Canada) Borden (Canada) Meighen (Canada)

0 2 0 0 6 1 0 0 0 2 0 0 1 2 4 2 4 0 1 4 1 4 0 2 0 6 1 4 34 15 8 1

1934 1935 1937 1938 1939 1945 1946 1946 1946 1947 1949 1950 1950 1952 1954 1958 1961 1965 1966 1968 1973 1974 1978 1979 1981 1981 1947 1949 1953 1896 1911 1920

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

61 42 65 39 56 47 51 48 75 52 44 50 66 45 56 53 47 54 47 63 58 52 59 43 48 45 57 49 42 55 57 40

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2 1 0 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 1 2 3 1 2 3

2 1 1 1 1 1 2 2 1 3 1 1 1 1 3 2 1 1 1 3 1 1 2 1 1 2 1 1 1 1 1 1

H

0\

'-l

.....

#

Name (Country)

Years in power

Entry Year

Mode

Age

1921 1926 1926 1930 1935 1948 1957 1963 1968 1979 1980 1984 1984 1960 1974 1974 1977 1975 1948 1953 1968 1969 1900 1901

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0

52 46 52 60 61

Exit mode

Mil itary

1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 2 0 0 2 1 1 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Number Leader of order entries

North America, Europe and Australasia (cont.) 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637

King (Canada) Meighen (Canada) King (Canada) Bennett (Canada) King (Canada) St. Laurent (Canada) Diefenbaker (Canada) Pearson (Canada) Trudeau (Canada) Clark (Canada) Trudeau (Canada) Turner (Canada) Mulroney (Canada) Makarios (Cyprus) Sampson (Cyprus) Makarios (Cyprus) Kyprianou (Cyprus) Denktas (Cyprus) Gottwald (Czech.) Novotny (Czech.) Dubcek (Czech.) Husak (Czech.) Sehested (Denmark) Deuntzer (Denmark)

4 0 3

5 13 8 5 5 11 0 4 0 3 13 0 2 10 12 5 14 0 18 1 3

66

62 66

49 40 61 55 45 57 39 71 55 51 52 49 47 56 58 56

4 5 6 7 8 9 10 11 12 13 14 15 16 1 2 3

4 5 1 2 3 4 1 2

1 2 2 1 3 1 1 1 1 1 2 1 1 1

0\ 00

1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669

Christensen (Denmark) Neergaard (Denmark) Holstein (Denmark) Zahle (Denmark) Bernstein (Denmark) Zahle (Denmark) Liebe (Denmark) Friis (Denmark) Neergaard (Denmark) Stauning (Denmark) Madsen-Mygdal (Denmark) Stauning (Denmark) Buhl (Denmark) Scavenius (Denmark) Buhl (Denmark) Kristensen (Denmark) Hedtoft (Denmark) Eriksen (Denmark) Hedtoft (Denmark) Hansen (Denmark) Kampmann (Denmark) Krag (Denmark) Baunsgaard (Denmark) Krag (Denmark) Jorgensen (Denmark) Hartling (Denmark) Jorgensen (Denmark) Schluter (Denmark) Stahlberg (Finland) Relander (Finland) Svinhufud (Finland) Kallio (Finland)

3 0 0 0 3 6 1 0 4 2 2 13 0 2 0

2 3 2 1 5 1 5 3 1 1 1 17 7 5 6 6 3

1905 1908 1909 1909 1910 1913 1920 1920 1920 1924 1926 1929 1942 1942 1945 1945 1947 1950 1953 1955 1960 1962 1968 1971 1972 1973 1975 1982 1919 1925 1931 1937

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

59 54 70 53 66 57 60 50 66 51 50 56 61 45 64 50 44 48 50 49 50 48 48 57 50 59 53 53 54 42 70 64

1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 0 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2

0

0 0

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4

1 1 1 1 1 2 1 1 2 1 1 2 1 1 2 1 1 1 2 1 1 1 1 2 1 1 2 1 1 1 1 1

..... (j\

\C

#

Name (Country)

Years in power

Entry Year

Mode

Age

1940 1944 1946 1956 1982 1899 1902 1905 1906 1906 1909 1911 1911 1912 1913 1913 1913 1914 1915 1917 1917 1917 1920 1920

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

51 77 76 56 59 53 67 63

Exit mode

Mil itary

Number Leader of order entries

North America, Europe and Australasia (cont.) 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693

Ryti (Finland) Mannerheim (finland) Paasikivi (Finland) Kekkonen (Finland) Koivisto (Finland) Waldeck-Rousseau (France) Combes (France) Rouvier (France) Sarrien (France) Clemenceau (france) Briand (france) Monis (France) Caillaux (France) Poincare (France) Briand (France) Barthou (France) Doumergue (france) Viviani (France) Briand (France) Ribot (France) Painleve (France) Clemenceau (France) Millerand (France) Leygues (france)

3 1 10 25 5 2 2 1 0 2 1 0 0 1 0 0 0 1 1 0 0 2 0 0

66

65 47 65 48 52 51 51 50 51 53 75 54 76 61 62

1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 3 1 1 2 1 1

""' ''"l

0

1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725

Briand (france) Poincare (france) Francois-Marsal (france) Herriot (france) Painleve (france) Briand (france) Herriot (france) Poincare (france) Briand (france) Tardieu (france) Chautemps (france) Tardieu (france) Steeg (france) Laval (france) Tardieu (france) Herriot (france) Paul-Boncour (france) Daladier (france) Sarraut (france) Chautemps (france) Daladier (france) Doumergue (france) Flandin (france) Bouissin (france) Laval (france) Sarraut (france) Blum (france) Chautemps (france) Blum (france) Daladier (france) Reynaud (france) Petain (France)

1 2 0 0 0 0 0 3 0 0 0 0 0

1 0 0 0 0 0 0 0

0 0 0 0

0 1 0 0 1 0 1

1921 1922 1924 1924 1925 1925 1926 1926 1929 1929 1930 1930 1930 1931 1932 1932 1932 1933 1933 1933 1934 1934 1934 1935 1935 1936 1936 1937 1938 1938 1940 1940

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0

59 62 50 52 62 63 54 66 67 53 45 54 62 48 56 72 59 49 61 48 50 72 45 61 52 64 64

0

52

0 0 0 0

66

54 62 84

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

4 2 1 1 2 5 2 3 6 1 1 2 1 1 3 3 1 1 1 2 2 2 1 1 2 2 1 3 2 3 1 1

H

'-J H

....

#

Name (Country)

Years in power

Entry Year

Mode

Age

1942 1944 1946 1946 1946 1947 1947 1948 1948 1948 1949 1950 1950 1951 1951 1952 1952 1953 1953 1954 1955 1956 1957 1957

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

59 54 62 47 74 59 61 51 62

Exit mode

Mil itary

Number Leader of order entries

North America, Europe and Australasia (cont.) 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749

Laval (France) De Gaulle (France) Gouin (France) Bidault (France) Blum (France) Ramadier (France) Schuman (France) Marie (France) Schuman (France) Queuille (France) Bidault (France) Queuille (France) Pleven (France) Queuille (France) Pleven (France) Faure (France) Pinay (France) Mayer (France) Laniel (France) Mendes-France (France) Faure (France) Mollet (France) Bourges-Maunory (France) Gaillard (France)

2 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0

64

50 66

49 67 50 44 61 58 64

47 47 51 43 38

3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

52 53 54 55 56 57 58 59 60 61 62 63 64

65 66

67 68

69 70 71 72 73

74 75

3 1 1 1 3 1 1 1 2 1 2 2 1 3 2 1 1 1 1 1 2 1 1 1

'l

tv

1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781

Pflimin (France) De Gaulle (France) Pompidou (France) Giscard d'Estaing (France) Mitterand (France) Chirac (France) Ebert (Germany) Scheidemann (Germany) Bauer (Germany) Kapp (Germany) Muller (Germany) Fehrenback (Germany) Wirth (Germany) Cuno (Germany) Stresemann (Germany> Marx (Germany) Luther (Germany) Marx (Germany) Muller (Germany) Bruning (Germany) Papen (Germany) Schleicher (Germany) Hitler (Germany) Donitz (Germany) Adenauer (Germany) Erhard (Germany) Kiesinger (Germany) Brandt (Germany) Schmidt (Germany) Kohl (Germany) Ulbricht (E. Germany) Honecker (E. Germany)

0

11 5 7 4 1 0

0 0 0 0 0 1 0 0 1 1 2 1 2 0 0 12 0 14 3 2 4 8 5 22 16

1958 1958 1969 1974 1981 1986 1918 1919 1919 1920 1920 1920 1921 1922 1923 1923 1925 1926 1928 1930 1932 1932 1933 1945 1949 1963 1966 1969 1974 1982 1949 1971

0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

51 68 58 48 65 54 37 54 49 62 44 68 42 46 45 60 46 63 52 45 53 50 44 54 63 66 62 56 56 52 56 59

1 1 2 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 1 1 1 1 1 0 1 0

0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0

76 77 78 79 80 81 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2

1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1

....

'.J VJ

.....

#

Name (Country)

Years in power

Entry Exit mode

Year

Mode

Age

1899 1901 1902 1903 1903 1903 1904 1905 1905 1909 1909 1910 1910 1915 1915 1915 1915 1916 1916 1916 1917 1917 1920 1921

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

55 56 57 59 59 59 59 61 61 65 60

1 1 1 1 1 1 1 1 1 1

68

1 1 1 1 1

Mil itary

Number Leader of order entries

North America, Europe and Australasia (cont.) 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805

Theotokis (Greece) Zaimis (Greece) Deliyiannis (Greece) Theotokis (Greece) Rallis, I. (Greece) Theotokis (Greece) Deliyiannis (Greece) Rallis, 1. (Greece) Theotokis (Greece) Rallis, I. (Greece) Mavromichalis (Greece) Dragoumis (Greece) Venizelos, E. (Greece) Gounaris (Greece) Venizelos, E. (Greece) Zaimis (Greece) Skouloudis (Greece) Zaimis (Greece) Kalogeropoulos (Greece) Lambros (Greece) Zaimis (Greece) Venizelos, E. (Greece) Rallis, I. (Greece) Kalogeropoulos (Greece)

2 1 0 0 0 1 0 0 3 0 0 0 4 0 0 0 0 0 0 0 0

3 0 0

46 48 51 70 79 71 63 65 72 53 76 54

1

1

1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 1 1 2 1 3 2 2 4 3 1 1 2 1 2 2 1 3 1 1 4 3 4 1

.... '-)

1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837

Stratos (Greece) Protopapadakis (Greece) Triantophilakos (Greece) Gonatas (Greece) Venizelos, E.(Greece) Kafandaris (Greece) Papanastasiou (Greece) Koundouriotis (Greece) Pangalos (Greece) Koundouriotis (Greece) Zaimis (Greece) Venizelos, E. (Greece) Papanastasiou (Greece) Venizelos, E. (Greece) Tsaldaris, P. (Greece) Venizelos, E. (Greece) Plastiras (Greece) Othonaos (Greece) Tsaldaris, P. (Greece) Kondylis (Greece) Demerdzis (Greece) Metaxas (Greece) Korizis (Greece) Tsouderos (Greece) g Papandreou, G. (Greece) Plastiras (Greece) Voulgaris (Greece) Damaskinos (Greece) Sophoulis (Greece) Tsaldaris, K. (Greece) Maximos (Greece) Tsaldaris, K. (Greece)

0 0 0 1 0 0 0 2 0 2 0 2 0 0

0 0 0 0 2 0 0 4 0 0 0 0 0 0 0 0 0 0

1922 1922 1922 1922 1924 1924 1924 1924 1926 1926 1928 1928 1932 1932 1932 1933 1933 1933 1933 1935 1935 1936 1941 1941 1944 1945 1945 1945 1945 1946 1947 1947

0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0

50 62 67 46 60 51 58 69 48 71 83 64 56 68 64

69 49 54 65 56 59 65 56 59 56 51 61 54 85 62 74 63

1 1 3 1 1 1 1 3 3 3 3 3 1 1 1 3 3 1 1 1 2 2 2 3 1 1 1 1 1 1 1 1

0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0

1 0 0

1 0 1 0 0 0 1 0 0 0 0

0 0

25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

1 1 1 1 4 1 1 1 1 2 5 5 2 6 1 7 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 2

....

'1 Vl

.....

#

Name (Country)

Years in power

Entry Year

Mode

1948 1949 1950 1950 1950 1951 1952 1952 1955 1955 1963 1963 1965 1965 1965 1966 1967 1973 1980 1981 1949 1956 1056 1914

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0

Age

Exit mode

Mil itary

2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 3 1 1 1 0 1 3 0 1

0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0

Number Leader of order entries

North America, Europe and Australasia (cont.) 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861

Sophoulis (Greece) Diomedes (Greece) Venizelos, S. (Greece) Plastiras (Greece) Venizelos, S. (Greece) Plastiras (Greece) Kioussopoulos (Greece) Papagos (Greece) Stefanopoulos (Greece) Karamanlis (Greece) Pipinelis (Greece) Papandreou, G. (Greece) Athanassiadis-Novas(Greece) Tsirimokos (Greece) Stefanopoulos (Greece) Paraskevopoulos (Greece) Papadopoulos (Greece) Ghizikis (Greece) Karamanlis (Greece) Papandreou, A. (Greece) Rakosi (Hungary) Nagy (Hungary) Kadar (Hungary) Magnusson (Iceland)

1 0 0 0 1 0 0 2 1 8 0 1 0 0 1 0 6 0 1 6 6 0 31 8

88

74 56 56 56 57 60 69 56 48 64

75 72 58 67 66 48 56 62 62 57 60 46 55

57 58 59 60 61 62 63 64

65 66 67 68

69 70 71 72 73

74 75 76 77 78 79 80

2 1 1 3 2 4 1 1 1 1 1 2 1 1 2 1 1 1 2 1 1 1 1 1

'-J 0\

1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893

Eggerz (Iceland) Thorlakson (Iceland) Thorhallson (Iceland) Asgeirsson (Iceland) Jonasson (Iceland) Thors (Iceland) Thordarson (Iceland) Thors (Iceland) Stefansson (Iceland) Thors (Iceland) Steinthorsson (Iceland) Thors (Iceland) Jonasson (Iceland) Jonsson (Iceland) Thors (Iceland) Benediktsson (Iceland) Hafstein (Iceland) Johannesson (Iceland) Hallgrimsson (Iceland) Johannesson (Iceland) Groendal (Iceland) Thoroddsen (Iceland) Hermansson (Iceland) Palsson (Iceland) de Valera (Ireland) Griffith (Ireland) Cosgrave, w. (Ireland) de Valera (Ireland) Costello (Ireland) de Valera (Ireland) Costello (Ireland) de Valera (Ireland)

2 3 4 1 3 0 2 2 2 0 3 2 2 0 4 6 0 3 4 10 0 3 3 0 2 0 7 15 3 3 2 2

1922 1924 1927 1932 1934 1942 1942 1944 1947 1949 1950 1953 1956 1958 1959 1963 1970 1971 1974 1978 1979 1980 1983 1987 1919 1922 1922 1932 1948 1951 1954 1957

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

57 47 38 38 38 50 63 52 53 57 57 61 60 56 67 54 55 58 49 65 55 69 55 40 37 50 42 50 57 69 63 75

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 2 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

81 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 1 2 3 4 5 6 7 8

1 1 1 1 1 1 1 2 1 3 1 4 2 1 5 1 1 1 1 2 1 1 1 1 1 1 1 2 1 3 2 4

.....

'-1 '-1

#

Name (Country)

Years in power

Entry Year

Mode

Age

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

60 49 53 60 54 55 57 56 62 79 75 63 63 59 66 62 69 71 61 78 57 51 80 48

Exit mode

Mil itary

Number Leader of order entries

North America, Europe and Australasia (cont.) 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917

LeMass (Ireland) Lynch (Ireland) Cosgrave, L. (Ireland) Lynch (Ireland) Haughey (Ireland) Fitzgerald (Ireland) Haughey (Ireland) Fitzgerald (Ireland) Haughey (Ireland) Saracco (Italy) Zanardelli (Italy) Giolitti (Italy) Fortis (Italy) Sonnino (Italy) Giolitti (Italy) Sonnino (Italy) Luzzatti (Italy) Giolitti (Italy) Salandra (Italy) Boselli (Italy) Orlando (Italy) Nitti (Italy) Giolitti (Italy) Bonomi (Italy)

7 6 4 2 1 0 0 4 0 0 2 1 0 0 3 0 1 3 2 1 1 1 1 0

1959 1966 1973 1977 1979 1981 1982 1982 1987 1900 1901 1903 1905 1906 1906 1909 1910 1911 1914 1916 1917 1919 1920 1921

1 1 1 1 1 1 1 1 0 1 1 1 1 1

1 1

1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

9 10 11 12 13 14 15 16 17 1 2 3 4 5 6 7 8 9 10 11 12 13

14 15

1 1 1 2 1 1 2 2 3 1 1 1 1 1 2 2 1 3 1 1 1 1 4 1

'"'

'l

~

1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949

Facta (Italy) Mussolini (Italy) Badoglio (Italy) Bonomi (Italy) Parri (Italy) de Gasperi (Italy) Pella (Italy) Fanfani (Italy) Scelba (Italy) Segni (Italy) Zoli (Italy) Fanfani (Italy) Segni (Italy) Tambroni (Italy) Fanfani (Italy) Leone (Italy) Moro (Italy) Leone (Italy) Rumor (Italy) Colombo (Italy) Andreotti (Italy) Rumor (Italy) Moro (Italy) Andreotti (Italy) Cossiga (Italy) Forlani (Italy) Spadolini (Italy) Fanfani (Italy) Craxi (Italy) Fanfani (Italy) Goria (Italy) Ospelt (Liechtenstein)

0 20 0 1 0 7 0 0 1 1 1 0 0 0 2 0 4 0 1 1 1 1 1 3 1 0 1 0 3 0 0 0

1922 1922 1943 1944 1945 1945 1953 1954 1954 1955 1957 1958 1959 1960 1960 1963 1963 1968 1968 1970 1972 1973 1974 1976 1979 1979 1981 1982 1983 1987 1987 1921

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

61 39 72 71 55 64 51 46 49 46 70 50 50 59 52 55 47 60 53 50 53 58 58 57 51 54 56 74 49 79 44 n.a.

1 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 1

1 1 1 2 1 1 1 1 1 1 1 2 2 1 3 1 1 2

1 1 1 2 2 2

1 1 1 4 1 5 1 1

H

'-]

\0

#

Name (Country)

Years in power

Entry Year

Mode

Age

Exit mode

Military

Number Leader of order entries

North America, Europe and Australasia (cont.) 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973

Schadler (Liechtenstein) Hoop (Liechtenstein) Frick (Liechtenstein) Batliner (Liechtenstein) Hilbe (Liechtenstein) Kieber (Liechtenstein) Brunhart (Liechtenstein) Eyschen (Luxembourg) Mongenast (Luxembourg) Loutsch (Luxembourg) Thorn A. (Luxembourg) Kauffmann (Luxembourg) Reuter (Luxembourg) Prum (Luxembourg) Bech (Luxembourg) Dupong (Luxembourg) h Bech (Luxembourg) Frieden (Luxembourg) Werner (Luxembourg) Thorn G. (Luxembourg) Werner (Luxembourg) Santer (Luxembourg) Olivier (Malta) Mintoff (Malta)

6 17 16 7

4 4 9 16 0 0 1 1 6 1 11 16 4 0 15 5 5 3 7

13

1922 1928 1945 1962 1970 1974 1978 1889 1915 1915 1916 1917 1918 1925 1926 1937 1953 1958 1959 1974 1979 1984 1964 1971

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

n.a. 33 35 34 32 43 33

48 n.a. 37 43 n.a. n.a. n.a. 39 52 66 56 46 46 66 47 53 55

1 1 1 1 1 1 0 2 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 3 4 5 6 7

8 1 2 3

4 5 6 7

8 9 10 11 12 13

14 15 1 2

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1

'"'

CIJ 0

1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Bonnici (Malta) Adami (Malta) Pierson (Netherlands) Kuyper (Netherlands) de Meester (Netherlands) Heemskerk (Netherlands) van der Linden (Netherlands) de Beerenbrouck (Nether) Colijn (Netherlands) de Geer (Netherlands) de Beerenbrouck (Nether) Colijn (Netherlands) de Geer (Netherlands) Gerbrandy (Netherlands) i Schermerhorn (Netherlands) Beel (Netherlands) Drees (Netherlands) Beel (Netherlands) de Quay (Netherlands) Marijnen (Netherlands) Quay (Netherlands) Cals (Netherlands) Zijlstra (Netherlands) de Jong (Netherlands) Biesheuvel (Netherlands) den Uyl (Netherlands) van Agt (Netherlands) Lubbers (Netherlands) Seddon (New Zealand) Yard (New Zealand) Massey (New Zealand) Coates (New Zealand)

2 0 4 4 2 4 4 6 0 3 3 6 1 4 0 2 10 0 4 1 2 1 0 4 1 4 4 5 13 6 12 3

1984 1987 1897 1901 1905 1908 1913 1918 1925 1926 1929 1933 1939 1940 1946 1946 1948 1958 1959 1963 1963 1965 1966 1967 1971 1973 1977 1982 1893 1906 1912 1925

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

56 53 48 64

54 56 67 45 56 56 56 64

59 55 51 44 62 56 58 46 62 51 48 52 51 54 46 50 48 50 56 47

1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2 1 2 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 1 2 3 4

1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 2 1 1 2 1 1 1 1

I

1

..... .....

00

.....

Name (Country)

#

Years in power

Entry Year

Mode

Age

1928 1930 1935 1940 1949 1957 1957 1960 1972 1972 1974 1975 1984 1873 1880 1884 1884 1889 1892 1893 1895 1898 1902 1903

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

72 61 63 56 56 53 75 56 60 39 47 54 42 65

Exit mode

Mil itary

1 1 2 1 1 1 1 1 1 2 1 1 0 1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Number Leader of order entries

North America, Europe and Australasia (cont.) 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029

(New Zealand) Forbes (New Zealand) Savage (New Zealand) Fraser (New Zealand) Holland (New Zealand) Holyoake (New Zealand) Nash (New Zealand) Holyoake (New Zealand) Marshall (New Zealand) Kirk (New Zealand) Rowling (New Zealand) Muldoon (New Zealand) Lange (New Zealand) Stang (Norway) Selmer (Norway) Schweigaard (Norway) Sverdrup (Norway) Stang (Norway) Steen (Norway) Stang (Norway) Hagerup (Norway) Steen (Norway) Blehr (Norway) Hagerup (Norway) j ~ard

1 5 4 9 7 0 3 11 0 1 1 8 3 7 3 0 5 2 1 2 :.'

4 1 1

64

46 58 55 55 59 42 61 55 50

5 6 7 8 9 10 11 12 13

14 15 16 17 1 2 3 4 5 6 7 8 9 10 11

2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 3 1 2 1 2

~

N

2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061

Michelsen (Norway) Lovland (Norway) Knudsen (Norway) Konow (Norway) Bratlie (Norway) Knudsen (Norway) Halvorsen (Norway) Blehr (Norway) Halvorsen (Norway) Berge (Norway) Mowinckel (Norway) Lykke (Norway) Hornsrud (Norway) Mowinckel (Norway) Kolstad (Norway) Hundseid (Norway) Mowinckel (Norway) Nygaardsvold (Norway) Quisling (Norway) Gerhardsen (Norway) Torp (Norway) Gerhardsen (Norway) Lyng (Norway) Gerhardsen (Norway) Borten (Norway) Bratteli (Norway) Korvald (Norway) Bratteli (Norway) Nordli (Norway) Brundtland (Norway) Willoch (Norway) Brundtland (Norway)

2 0 1 2 0 6 1 1 0 1 1

1 0 3 0 0 2 5 5 6 3 8 0 2 5 1 1 2 5 0 4 1

1905 1907 1908 1910 1912 1919 1920 1921 1923 1923 1924 1926 1928 1928 1931 1932 1933 1935 1940 1945 1951 1955 1963 1963 1965 1971 1972 1973 1976 1981 1982 1986

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

48 59 60 63 56 71 48 74 51 72 54 54 69 58 53 49 63 56 53 48 58 58 58 63 52 61 56 63 48 41 54 46

1 1 1 1 1 1 2 1 2 1 1 1 1 1 2 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

12 13

14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

1 1 1 1 1 2 1 2 1 1 1 1 1 2 1 1 3 1 1 1 1 2 1 3 1 1 1 2 1 1 1 2

..... 00

VJ

.... #

Name (Country)

Years in power

Entry Year

Mode

Age

Exit mode

1946 1948 1956 1956 1970 1980 1981 1910 1911 1911 1912 1913 1914 1914 1915 1915 1915 1915 1916 1917 1917 1918 1919 1919

0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1

42 56 50 51 57 53 58 67 48 44 48 42 63 43 69 52 47 44 50 46 59 35 61 37

1 2 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 3 1

Military

0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0

Number Leader of order entries

North America, Europe and Australasia (cont.) 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085

Gomulka (Poland) Bierut (Poland) Ochab (Poland) Gomulka (Poland) Gierek (Poland) Kania (Poland) Jaruzelski (Poland) Braga (Portugal) Chagas (Portugal) da Vasconcelos (Portugal) Leite (Portugal) Costa (Portugal) Bernandino Machado (Port) Azvedo Coutinho (Portugal) Pimenta de Castro (Portugal) Chagas (Portugal) de Castro, J. (Portugal) Costa (Portugal) de Almedia (Portugal) Costa (Portugal) Pais (Portugal) Barbosa (Portugal) Relvas (Portugal) Pereira (Portugal)

2 7 0 14 9 1 6 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0

1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 3 1 1 1 1

00

+>-

2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117

Sa Cardoso (Portugal) Pereira (Portugal) Baptista (Portugal) da Silva (Portugal) Granjo (Portugal) de Castro, A. (Portugal) Ribeiro·Pinto (Portugal) Bernadino·Machado (Port) Barros·Queiros(Portugal) Granjo (Portugal) Coelho (Portugal) Maia·Pinto (Portugal) Cunha Leal (Portugal) da Silva (Portugal) Ginestral Machado (Portugal) deCastro, A. (Portugal) Rodrigues Gaspar (Portugal) Dos Santos (Portugal) Guimaraes (Portugal) DaSilva (Portugal) Pereira (Portugal) DaSilva (Portugal) Cabecades (Portugal) Gomes da Costa, M.(Portugal) Carmona (Portugal) Freitas (Portugal) Ferraz (Portugal) da Costa·Olivera (Portugal) Salazar (Portugal) Caetano (Portugal) Spinola (Portugal) da Costa Gomes, J.(Portugal)

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 32 5 0 0

1919 1920 1920 1920 1920 1920 1920 1921 1921 1921 1921 1921 1921 1922 1923 1923 1924 1924 1925 1925 1925 1925 1926 1926 1926 1928 1929 1930 1936 1968 1974 1974

1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 1 1

55 38 54 48 39 42 40 70 n.a. 40 64 n.a. 34 50 n.a. 45 59 39 49 53 43 53 43 63 57 59 59 n.a. 43 62 64

59

1 1 2 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 1 3 1 1

1 0 1 0 0 1 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 1 1

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

1 2 1 1 1 1 1 2 1 2 1 1 1 2 1 2 1 1 1 3 3 4 1 1 1 1 1 1 1 1 1 1

H (Xi

Vl

.....

#

Name (Country)

Years in power

Entry Year

Mode

Age

1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

53 58 51 42 46 44 59 46 46 47 68 74 59 55 50 72 57

Exit mode

Mil itary

Number Leader of order entries

North America, Europe and Australasia (cont.) 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141

Goncalves (Portugal) De Azevedo (Portugal) Soares (Portugal) Eanes (Portugal) Sa Carneiro (Portugal) Balsemao (Portugal) Soares (Portugal) Cavaco Silva (Portugal) Georghiu-Dej (Romania) Ceausecu (Romania) Azcarraga y Palmero (Sp) Mateo Sagasta (Spain) Silvela (Spain) Fernandez Villaverde (Sp) Maura y Montaner (Spain) Azcarraga y Palmero (Sp) Fernandez Villaverde (Sp) Montero Rios (Spain) Moret y Prendergast (Sp) Lopez Dominguez (Spain) Moret y Prendergast (Sp) de Aguilar (Spain) Maura y Montaner (Spain) Moret y Prendergast (Sp)

1 0 3 2 0 2 2 2 18 22 0 1 0 0 1 0 0 0 0 0 0 0 2 0

1974 1975 1975 1977 1980 1981 1983 1985 1947 1965 1900 1901 1902 1903 1903 1904 1905 1905 1905 1906 1906 1906 1907 1909

73

67 77 68 78 54 71

1 1 1 1 2 1 1 0 2 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

50 51 52 53 54 55 56 57 1 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 2 3

00

0\

2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173

Canalejas y Mendez (Spain) Figueroa y Torres (Spain) Garcia Prieto (Spain) Dato y lradier (Spain) Figueroa y Torres (Spain) Garcia Prieto (Spain) Dato y Iradier (Spain) Maura y Montaner (Spain) Garcia Prieto (Spain) Figueroa y Torres (Spain) Sanchez de Toea (Spain) Allende Salazar (Spain) Dato y Iradier (Spain) Allende Salazar (Spain) Maura y Montaner (Spain) Sanchez Guerra y Martinez (Spain) Garcia Prieto (Spain) de Rivera (Spain) Berenguer (Spain) Azmar-Cabanas (Spain) Alcala Zamora (Spain) Azana y Diez (Spain) Lerroux y Garcia (Spain) Semper Ibanez (Spain) Lerroux y Garcia (Spain) Chapaprieta (Spain) Portela (Spain) Azana y Diez (Spain) Casares Quiroga (Spain) Martinez Berro (Spain) Giral y Pereyra (Spain) Largo Caballero (Spain)

2 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 1 0 0 1 0 0 0 0 0 0 0

1910 1912 1912 1913 1915 1917 1917 1918 1918 1918 1919 1919 1920 1921 1921 1922 1922 1923 1930 1931 1931 1931 1933 1934 1934 1935 1935 1936 1936 1936 1936 1936

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0

0

56 49 53 57 52 58 61 65 59 55 67 63 64 65 68 63 63 53 57 71 54 56 67 53 68 64 68 61 52 53 57 67

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

1 1 1 1 2 2 2 3 3 3 1 1 3 2 4 1 4 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1

H

CXi '-1

#

Name (Country)

Years in power

Entry Year

Mode

Age

Exit mode

Hilitary

Number Leader of order entries

North America, Europe and Australasia (cont.) 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197

Negrin (Spain) Franco (Spain) Arias Navarro (Spain) Suarez (Spain) Calvo-Sotelo (Spain) Gonzalez (Spain) von Otter (Sweden) Bostrom (Sweden) Ramstedt (Sweden) Lundeberg (Sweden) Staaf (Sweden) Lindman (Sweden) Staaf (Sweden) Hammerskjold (Sweden) Swartz (Sweden) Eden (Sweden) Branting (Sweden) de Geer (Sweden) von Sydow (Sweden) Branting (Sweden) Trygger (Sweden) Branting (Sweden) Sandlar (Sweden) Ekman (Sweden)

1 38 0 5 0 5 1 2 0 0 0 5 2 3 0 2 0 0 0 1 1 0 1 2

1937 1937 1975 1976 1982 1982 1900 1902 1905 1905 1905 1906 1911 1914 1917 1917 1920 1920 1921 1921 1923 1924 1925 1926

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

50 45 67 44 56 40 67 n.a. 53 63 45 44 51 52 59 46 60 66

48 61 66

64 41 54

3 2 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

47 48 49 50 51 52 1 2 3 4 5 6 7 8 9 10 11 12 13

14 15 16 17 18

1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 3 1 1

""'

00 00

2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229

Lindman (Sweden) Ekman (Sweden) Hamrin (Sweden) Hansson (Sweden) Pehrsson-Bramstorp (Sweden) Hansson (Sweden) Erlander (Sweden) Palme (Sweden) Falldin (Sweden) Ullsten (Sweden) Falldin (Sweden) Palme (Sweden) Carlsson