Francisco 2009 - Dynamics of Conflict

Dynamics of Conflict

Ronald A. Francisco

Dynamics of Conflict

123

Ronald A. FranciscoDepartment of Political ScienceUniversity of Kansas1541 Lilac LaneLawrence, KS [email protected]

ISBN 978-0-387-75241-9 e-ISBN 978-0-387-75242-6DOI 10.1007/978-0-387-75242-6

Library of Congress Control Number: 2008941246

c© Springer Science+Business Media, LLC 2009All rights reserved. This work may not be translated or copied in whole or in part without the writtenpermission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York,NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use inconnection with any form of information storage and retrieval, electronic adaptation, computer software,or by similar or dissimilar methodology now known or hereafter developed is forbidden.The use in this publication of trade names, trademarks, service marks, and similar terms, even if they arenot identified as such, is not to be taken as an expression of opinion as to whether or not they are subject toproprietary rights.

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For my favorite mathematicians,Christopher and Cynthia

Acknowledgments

This project is wholly dependent on data, so my first debt is to the institutions whoprovided the funds to code European and American data. The National ScienceFoundation (SBR-9631229), the University of Kansas General Research Fund andthe Department of Political Science at the University of Kansas were all importantcontributors to the data collecting effort. Equally as critical to the development ofour data were the students who worked on the project. The original four graduatestudents did the needed basic technical work as well as coding: Phil Huxtable, AstridObst, Uwe Reising and William Yarrow. Later graduate students coded: David Bri-choux, Federico Ferrara, Steve Garrison, Taehyan Nam and Alana Querze. In addi-tion, several (then) undergraduates coded as well: Amanda Boatright, Aimee Cox,Ian Ostrander and Erin Simpson. See the project codebook for more information:http:\\web.ku.edu/ronfran/data/index.html.

Several colleagues have aided my effort in crucial ways. Paul Johnson providedinvaluable help in virtually every aspect of the project, from estimation to format-ting and bibliographic assistance. Erik Herron read the chapter on dictatorships andprovided meaningful improvements. Philip Schrodt helped enormously in the earlystages of data coding and with dynamic models. Michael Lynch in my departmentand Ted Juhl in economics helped to unravel knotty econometric problems. MarkLichbach and Christian Davenport at the University of Maryland encouraged thedata collection and this project and I am indebted to them.

Two editors at Springer Verlag were critical to this project. Barbara Fess initiatedthe project and brought it to fruition while Jon Gurstelle saw it to completion. I thankboth of them for their contributions.

Our son, Christopher Francisco, helped me to render Chapter 1 mathematicallycorrect. It was he also who provided the solution insights into lower and upper tri-angle mysteries in Jacobian matricies. I could not have done this on my own andremain in his debt. For over a decade my wife Deborah has been stalwart throughtime-consuming data collection efforts and this project. She is an able editor who haseased the plight of every reader, as she eases every day for me. I take responsibilityfor all errors contained in this volume.

Lawrence, Kansas, USA Ronald A. Francisco

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Contents

1 Introduction to the Problem Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 A Summary of Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Model and Equilibrium Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4 Model of Choice I: Lotka–Volterra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.5 Model of Choice II: The Competing Species Model . . . . . . . . . . . . . . . 61.6 Model Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.7 Dynamic Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.8 Mathematical Outcomes Arising in Estimation . . . . . . . . . . . . . . . . . . . 81.9 Institutional Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.10 The Dynamics of Conflict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2 The Dynamic Relationship Between Protest and Repression inDemocratic Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.2 Salient Differences Among Formal Theorists . . . . . . . . . . . . . . . . . . . . 142.3 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.4 Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.5 The Relationship Between Protest and Repression in Differing

Contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.5.1 When Does Protest Generate Repression? . . . . . . . . . . . . . . . . . 152.5.2 When Do Protest and Repression Interactively Accelerate

Each Other? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.5.3 What Happens When Repression Is Absent? . . . . . . . . . . . . . . . 16

2.6 Analytic Results in Democratic Countries . . . . . . . . . . . . . . . . . . . . . . . 162.7 Survey of the West European Democracies and Illinois . . . . . . . . . . . . 272.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

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x Contents

3 The Dynamics of Protest and Repression in Dictatorships andDemocratic Transitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.2 Cases and the Context of Dictatorship . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.2.1 Mobilization Under Dictatorship and Harsh Repression . . . . . . 343.2.2 What Happens to Repression When Mobilization Grows

to a High Magnitude? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.3 Empirical Results on Dictatorship Periods . . . . . . . . . . . . . . . . . . . . . . . 363.4 Empirical Results from Transition Periods . . . . . . . . . . . . . . . . . . . . . . . 433.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4 Varied Dynamics of Bandwagon Mobilization . . . . . . . . . . . . . . . . . . . . . . 514.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.2 Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5 Dynamics and Stability in Civil Wars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655.2 The Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675.3 The Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695.4 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

6 Conclusion: Stability in Conflict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816.1 Stability is the Norm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816.2 Varieties of Repression in Democracies and Dictatorships . . . . . . . . . . 826.3 Convergence in Estimations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846.4 Correction of Time-Series Pathologies . . . . . . . . . . . . . . . . . . . . . . . . . . 846.5 When Repression is Absent or Rare . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846.6 What Have We Learned? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Chapter 1Introduction to the Problem Set

The effects of chance are the most accurately calculable,and therefore the least doubtful, of all the factors of anevolutionary situation

R.A. Fisher

1.1 Introduction

We explore the dynamics or mechanisms of conflict in this book. This is possibleat last because we have data that permit dynamic analysis. In many fields, themechanism of a physical or social process is the first factor to investigate and isusually prior to any other work. But in much of social science, the sequence hasbeen reversed, simply because the underlying data in the field of protest and repres-sion and many others for decades were insufficient for the tests necessary to finddynamic properties: equilibria, divergence, or oscillation. This kind of mathematicalwork was for the most part relegated to game theory, which needs no data. Now wehave the resources to estimate dynamic models. We do not assume rationality ofthe individuals underlying our data, but we do consider them self-interested andrisk-averse. After all, who wants to be arrested, injured, or killed in pursuit of apublic good?

The likelihood of protest participation is low. We know that not more than fivepercent of any local population ever participates in protest (Lichbach, 1995). Notmany need to take part, however, in a densely populated area, to make an impactupon the neighborhood and even public policy. Nonetheless, it is important tokeep in mind that we are for the most part working with fervid minorities in thisbook. Even bandwagon mobilization (Chapter 4) does not bring the level of mobi-lization to high levels. Game theorists have long been puzzled about why anyonewould act for a public good (Riker and Ordeshook, 1973). Lichbach extended col-lective action theory so that it would function with risk and probable inefficacy(Lichbach, 1995, 1996). We are interested in the people who decide to act, or moreaccurately, how many choose to act. They are represented in our data. We seekprincipally to find out what happens when they act and the state responds to them orsimply ignores them. This is the process or mechanism that we attempt to discover.

Why should we consider the mechanism of conflict important? The answer to thisquestion is that it is the basis of understanding two- or more-sided conflict and com-petition. We know a great deal about the correlates of conflict. We also know what

R.A. Francisco, Dynamics of Conflict, DOI 10.1007/978-0-387-75242-6 1,C© Springer Science+Business Media, LLC 2009

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2 1 Introduction to the Problem Set

happens to dissent when repression grows harsh (Francisco, 2004). But prior to theseconcerns is the fundamental problem of the dynamic process (Morrison, 1991).Unless we understand the mechanism, we can understand nothing else about theproblem. Only four things can happen in dynamic analysis: (1) stability or equilib-rium; (2) divergence, that is, exponential growth or decay; (3) oscillation, a wavefunction that signifies instability if it is not damped; and (4) a saddlepoint, or unsta-ble equilibrium. A saddlepoint result is shaped as a hyperbolic paraboloid and isonly stable at the midpoint (0,0,0) and nowhere else. It certainly makes a differenceto people in a conflict which of these mechanisms emerges. Anything but stableequilibrium brings disadvantages that might expand to severe levels. Divergencemeans sharp escalation or decay, something that is uncomfortable for a community.So too is oscillation, since things might be pleasant one moment and a cauldron ofconflict in another. The saddlepoint, as in game theory, is stable as long as noneof the instrumental players shifts position, but in the real world, position shiftsare common over time with scores of players, and then the saddlepoint becomesunstable as well. All of these alternatives to equilibrium are noxious, especially atthe local level and for participants (Kauffman, 1993). One of the more notewor-thy discoveries in this arena was the finding by the 19th century mathematiciansLaplace and Lagrange that the solar system had to have negative eigenvalues or itwould fly apart into space (Tabak, 2004). Another relevant topic is that equilibriumin game theory is different from other equilibria that we uncover with empirical data(Riker, 1982, 45). Stuart Kauffman stresses the beauty and order of the natural bio-logical world, a world infinitely more complex than is the general context of protestand repression (Kauffman, 1993).1 So complex and dynamic processes can maintainorder. Equilibrium or stability is essentially what is called a “steady-state or evolua-tive” context, one that allows transactions without great surprises or systemic shocks(Morton, 1999, 83). Another definition of statistical equilibrium is “the condition ofa macroscopic system when we observe no change over time” (Coleman, 1975, 15).Still another maintains that stability is achieved “if the system returns to equilibriumwhen it is pushed slightly away from equilibrium” (Roughgarden, 1998, 239). Inpractical terms, equilibrium best implies a return to origin, or zero, after protest,repression, or interaction is completed.

In this volume we attempt to use the proper (interval-level) data for conflictand also the standard procedures of interactive time-series data estimation. Recentpapers have attempted to do this, but have relied on ordinal data or artificialmanufacturing of interval data from the ordinal origin, for example, Carey (2006).They have also neglected the interaction inherent in protest and repression. Tomodel interaction of two sides, one needs at least two equations for estimation(see King, 1989). Below we introduce two models that will be the workhorses forestimation of parameters and discuss how we analytically estimate parameters.

1 Kauffman even suggests that in an ecosystem, many players can be frozen in Nash equilibriumwhile other players continue to adapt and evolve (Kauffman, 1993, 256). We expect that our con-texts are much simpler. Certainly we do not have to worry about the carrying capacity of theenvironment, which matters heavily for ecologists and biologists.

1.2 A Summary of Objectives 3

1.2 A Summary of Objectives

We have two primary objectives in this volume. First we seek to test interval con-flict data in dynamic analysis to explore what happens when mobilization leads toprotest and then some form of repression in a variety of institutional, contextual, andgovernment-form contexts. The problem we confront is how protest and repressiondiffer in different governmental contexts. We investigate what happens in differentforms of democracy and then what happens in dictatorship and authoritarian formsof government. An added focus is what happens when a dictatorship collapsesand political transition begins. Bandwagon mobilizations happen in both types ofgovernment. These events are different from normal protest and repression. Theyaccelerate rapidly and repression may be applied at the beginning of mobilization,the end, or not at all. We consider six of these events in Chapter 4. Civil war marksour final substantive topic. Civil war is certainly a definite and incongruous meansof conflict that warrants separate treatment. We estimate seven unrelated civil warsin Chapter 5. As noted later, the nature of civil war requires a different model formfrom the rest of the volume.

The second goal of the book is to test theory. We view this enterprise as part of theempirical tests of formal models. The theory under examination is Mark Lichbach’srational choice theory (Lichbach, 1995, 1996). Lichbach’s theory is an extensionof Mancur Olson’s (1965) collective action theory. Lichbach was able to apply thetheory to mobilization in conflict by using dozens of solutions grouped in four areas:market, community, contract, and hierarchy. We will also have occasion to test thetheory of mobilization in conflict by DeNardo. In the 1980s, he crafted a rationalchoice model for conflict mobilization (DeNardo, 1985). And in the chapter ondictatorship, we will invoke the theory of Wintrobe, who constructed a dictatorshipstrategic theory (Wintrobe, 1998). Our problem in this context is whether the theoryavailable conforms to empirical analytic results. If so, we confirm theory. If not,how not? Does theory need revision in terms of advancing the Collective ActionResearch Program (Lakatos, 1970)? So we will be mindful of theory as we proceed.

Another set of assumptions we tackle is that democracies are not repressiveand that dictatorships repress heavily. A separate question that has heretoforenot been tested empirically is whether inconsistent repression accelerates protest(see Lichbach, 1987). We have occasion to test this game-theoretic conjecture inChapter 2. We examine the German governments’ treatment of leftist protesters,mainly university students. This allows us to focus on one group for a long periodof time to see what happens between dissidents and the state when the state repressesonly occasionally.

Each system can be characterized by an underlying mechanism. It is that under-lying form of movement that provides the foundation of a process or conflict. Sci-entists study stable systems in experimental settings and then perturb them to see ifthey remain in equilibrium. The most simple example (Luenberger, 1979, 184) is avertical stick: secured at its bottom, it is in unstable equilibrium; secured at its top, itis in stable equilibrium, since any perturbation will move it, but it will always returnto the vertical position. A system that remains stable under perturbation also remains


predictable and can be studied with conventional methods (see Merkin, 1997). How-ever, a system that veers away from stability makes life more difficult for scholars,to say nothing of participants in a conflict.

In order to uncover the mechanism of our conflicts, we need models. In factwe need multiple equation models, because protest and repression as well as civilwar sides are all interactive and have reciprocal causative actions. The workhorsemodel for our context is the Lotka–Volterra (predator–prey) model, one that wasdeveloped during World War I and whose mathematical properties have been deeplyexplored (Murray, 1993). Originally, the model was designed to simulate the actionof predators and prey in the natural world. In the decades since the introductionof the model, it has been used a great deal for human conflict, wars, or especiallyprotest and repression (Tsebelis and Sprague 1989). Because the Lotka–Volterramodel focuses on one side eliminating or repressing another side, it works well forour application. We put dissidents in the prey or protest equation and the state inthe predator or repression equation. Since their relationship is dynamic and interac-tive, this mechanism captures a context that we can estimate with time-series data.For most of our chapters, Lotka–Volterra will be the model of choice. As we seelater, civil wars require a different process model. We estimate these models withempirically coded data. These estimates provide parameter estimates that are uniquefor every process of protest and repression. We know what parameters theoreticallymove a system from stability to oscillation or divergence, but we are interested inthe parameter estimates that conform to the process of conflict in every system inour sample (Morton, 1999, 137).

1.3 Model and Equilibrium Estimation

We use daily aggregated interval data to estimate our models. Because the models(see later) are exactly identified, their parameters form a square, 2 × 2 matrix. Oncewe generate parameter estimates, we can determine stability by computing this 2×2Jacobian matrix’s eigenvalues. There are other, more specific tests for equilibrium(e.g., Greene, 2003, 659 or Tsebelis and Sprague, 1989), but we use the general testfor difference equations.

Generally, in difference equations, equilibrium requires that the resulting eigen-values be numbers wholly in the real system (i.e., not complex conjugate, and noton the complex plane) and be bounded by −1 and 1 (Elaydi, 1996, 139; Goldberg,1986, 171–172).2

Many possibilities arise when a mechanism diverts from a soldier’s ideal view,that is, it is neither stable nor even asymptotically stable. If battles are intermittent or

2 Assume A is any k by k matrix. Then as n goes to infinity, the limit of An = 0 if and only if λ

is bounded by −1 and 1 for all eigenvalues λ of A. Clearly, if every eigenvalue’s absolute valueis less than one, fractional exponents moving to infinity will reduce to a limit of zero as the seriesprogresses.

1.4 Model of Choice I: Lotka–Volterra 5

periodic, then the system of civil war may oscillate, diverge exponentially, or forman unstable saddle point. In game theory, saddle points are Nash equilibria, but in acivil war’s systemic mechanism, saddle points are necessarily unstable.3

All of the possibilities that may emerge from a Jacobian matrix are known inmathematics (see Boyce and Diprima, 1977; Elaydi, 1996; Goldberg and Potter,1998; Merkin, 1997).4 The website http://www.math.iupui.edu/∼mtc/Chaos/phase.htm shows graphically everything that might happen when a system is eitherstable or unstable. Please note, though, that this site concerns differential equations,which have somewhat different requirements for stability analysis.

We have followed recommended statistical practice in ecology for estimatingour data (see Roughgarden, 1998). We use seemingly unrelated regression (SUR)with models modified from differential equations to difference equations, sincewe cannot with certainty assume continuity. Other estimation methods largelyuse instrumental variables; these are problematic within our models, especiallyin achieving normalization (see Maddala, 1977). So SUR is a more conserva-tive estimation method (see Maddala, 1977). These SUR estimations generallyresult in series convergence. We also test for and correct serial correlation andheteroscedicity.

1.4 Model of Choice I: Lotka–Volterra

Our most important consideration is to select a model that, when exactly identified,allows us to estimate analytically the relationship between protest and repression(Morton, 1999). We choose the predator–prey model because it has been successfulfor nearly a century in estimating relationships. It is a widely used model and willplay the role of the workhorse model in this volume. The Lotka–Volterra modelconsists to two simultaneous differential equations:

dPdt

= aP t − g(Pt × Rt)

dRdt

= bR + h(Pt × Rt)

where P is the number of rebels, R is the repression of the state (i.e., arrests, injuries,and deaths), a is the rate at which protest declines in the absence of repression, b isthe rate at which repression declines in the absence of protest, g is the rate at whichthe interaction of protest and coercion affects the decline of protest, and h is the rateat which the interaction of protest and repression increases repression.

The dynamic mathematical properties of the Lotka–Volterra model are bet-ter known than for almost any other mathematical model. Because of this and

3 The requirement that no one defect or change positions is unreasonable in systemic mechanisms.4 If these concepts are wholly new to a reader, then I suggest reading Acheson (1997) before theother, more technical sources.


because the model has been estimated with data, we understand a great dealabout the dynamic possibilities it presents (May, 2001; Murray, 1993). There isa general perception about a gap between formal models and empirical estimation(Morton, 1999). The situation in our context is that the model is fully dynamic, inother words, any of the four outcomes can happen in the predator–prey model. Thehypothesis inherent in this exercise is that repressive states are more likely to arrest,injure, and kill dissidents than are democratic, less repressive states. Whether this istrue depends on the estimation results we generate from our data through a dynamicmodel.

The basic reason that predator–prey presents a good model for protest and repres-sion, however, is that it has a parameter for protest arising without repression,another for repression arising without protest, in addition to two interactive param-eters, one for the change of protest and one for the shift of repression. Protest ismeasured as the number of people mobilized per day. Repression is a simple andnonweighed sum of arrests, injuries, and deaths of dissidents (generally arrests over-whelm the other categories). Because we cannot assume continuity even with dailyaggregated data (protesters go home, after all), we transform the model to differenceequations:

Pt = aP t−1 − g(Pt−1 × Rt−1) + ε

Rt = bRt−1 + h(Pt−1 × Rt−1) + ε

where P is the number of rebels, R is the repression of the state in the form ofarrest, injuries, and deaths, a is the rate at which protest declines in the absence ofrepression, b is the rate at which repression declines in the absence of protest, g isthe rate at which the interaction of protest and coercion affects the decline of protest,h is the rate at which the interaction of protest and repression increases repression,and ε is the error term.

1.5 Model of Choice II: The Competing Species Model

While the Lotka–Volterra model works best in almost all chapters, the civil warchapter is different. In a civil war, one side is not necessarily repressing the other;rather, each side tries to eliminate the other and either win the war to control thestate or secede and gain territorial independence. This is a fundamentally differ-ent mechanism, and it calls for a different model: the competing species model(Murray, 1993):

dRdt

= aRt − m(Rt × St)

dSdt

= bS t − n(Rt × St)

1.7 Dynamic Estimation 7

where R is the number of casualties and deaths of state forces caused by rebels,S is the number of casualties and deaths of rebels caused by state forces, a is therate at which protest declines in the absence of repression, b is the rate at whichrepression declines in the absence of protest, m is the rate at which the interactionof protest and coercion affects the decline of rebel forces, and n is the rate at whichthe interaction of protest and repression dampens state forces.

Once again, we employ difference equations because we cannot assume contin-uous processes, even in civil war. The civil war model thus becomes

Rt = aS t−1 − m(Rt−1 × St−1) + ε

St = bRt−1 − n(Rt−1 × St−1) + ε

where R is the number of state forces captured, injured, or killed, S is the numberof rebel forces captured, injured, or killed, a is the rise in state casualties withoutinteraction, b is the rise of rebel casualties without interaction, m is the dampen-ing of state casualties with interaction, n is the dampening of rebel casualties withinteraction, and ε is the error term.

1.6 Model Identification

Both of our models have two equations and two variables on the right side of theequal sign for parameter estimation, so both are exactly identified (seeMaddala, 1977). Exact identification provides a unique estimate for each param-eter, which is what we seek as an analytic outcome. Under-identification may pre-clude estimates and over-identification may provide multiple parameter estimates,so we have chosen models that are both appropriate for our application and exactlyidentified. For the mathematical definition of the identification assumption, seeGreene (2003, 541).

1.7 Dynamic Estimation

Because we have two equation models and they are exactly identified, we have theright situation to estimate dynamics from the data. The parameter estimates are par-tial derivatives, and because there are four in each model, we have a square 2 × 2Jacobian (i.e., partial derivative entries) matrix for each country tested. Because theJacobian matrix is square, we can take its determinant and compute eigenvalues. Bythe Fundamental Theorem of Algebra (also the Cayley–Hamilton theorem is perti-nent here), we know that we should get two eigenvalues from each model estimation,since the degree of each characteristic equation in the Jacobian matrix is two. Ifthese eigenvalues (solutions to the characteristic equation of the matrix) are realnumbers, bounded by −1 and 1, then we have stability. If the eigenvalues are realnumbers outside the range of −1 and 1, we have instability. And if the eigenvalues


are complex conjugate numbers, then we have either oscillation or exponentialgrowth or decline in the system estimated (see Elaydi, 1996; Goldberg, 1986).

All of the time-series tests in this volume had some expected difficulty withserial correlation and heteroscedicity at least in one of the equations. We ran theBruesch–Pagan test for heteroscedicity and the Bruesch–Godfrey and Durbin h testsfor serial correlation as detection measures. As a consequence, all of the parameterestimates in the chapter tables are corrected for one or both of these problems andthe eigenvalues are computed from the Jacobian matrix formed by the correctedparameter estimates. Heteroscedicity is corrected with generalized autoregressiveconditionally heteroscedicity, while serial correlation was overcome by a series ofstandard econometric techniques.

1.8 Mathematical Outcomes Arising in Estimation

Throughout, we assume still for simplicity that we are working with two equations.There are several situations we may confront in estimating dynamic models withempirical data. Two of the most important of these are on follows: (1) when theJacobian matrix (i.e., a matrix of parameter estimates) is upper or lower triangularand (2) the case of instability and oscillation. In the first problem, if the param-eter estimates not on the main diagonal are zero (or their product is very closeto zero), and consequently their t-values are near zero, then a result from linearalgebra simplifies life considerably. When the parameter estimates are exactly zero,the eigenvalues of the matrix are the same as the parameter estimates on the maindiagonal of the Jacobian matrix; if the product of the parameter estimates is verynear zero, then the eigenvalues will be very close to the diagonal entries.5

When we confront an unstable, diverging, or oscillating case, we want to usemathematics to understand the behavior of the solutions. This can be rather compli-cated, and one needs to work with both real and complex numbers. When we haveoscillation, the eigenvalues are complex numbers that are not real, and they occur incomplex conjugates; that is, if α + iβ is one eigenvalue, the other is α − iβ, where α

and β are real. As Elaydi (1996, 133) points out, the oscillation can be representedwith sine and cosine functions. Suppose we have complex conjugate eigenvaluesα ± iβ. Let r =

√α2 + β2; this is the modulus of the eigenvalues and represents the

length of the vector defined by α + iβ or α − iβ in the plane. Denote the first andsecond eigenvalues by ξ1 and ξ2. Then, the solution x(n) to the system is

5 The proof of this is straightforward: Assume a matrix A that has the forma b

0 c.

We find the determinant of λ × I − A. This new matrix isλ − a −b

0 λ − c.

Take the determinant; it is (λ − a) × (λ − c) − 0 × b = (λ − a) × (λ − c). We set this equal to0, and the eigenvalues are a and c.

1.9 Institutional Theory 9

x(n) = rn[(cos n�)ξ1 − (sin n�)ξ2] + irn[(cos n�)ξ2 + (sin n�)ξ1]

Here is an example drawn from parameter estimates in a case outside of oursample. We have complex conjugate eigenvalues and therefore an unstable system:

x(n) = (0.0000000172816 + 0.00000003733552i)4,381 + (0.000000526676

−(

0.001354438i

0.02114537

)+

(0+ 0.00000000005762306i0+ 0.00000001113676i

)

It is easy to see that this system oscillates. Also, the exponent (n) of 4,381 issufficiently large that the modulus of the complex number will be very close tozero. Note that the eigenvalues and eigenvectors are all well below one. Therefore,while this system oscillates, it dampens toward stability. The limit of the system iszero, or the origin; therefore, the system would eventually move to stability.

1.9 Institutional Theory

While our interest in conflict is paramount, we must have a broader view of protestand repression, civil war or even mobilization. Some countries have a greateramount of conflict than do others. This indicates that the form of government mightplay a role in the level of street conflict. So we will consider forms of govern-ment beyond our delineation in Chapters 2 and 3 on democracies and dictator-ships, respectively. Two kinds of theory dominate in our context. First, the numberof veto players in a government logically matters for groups (see Tsebelis, 2002;Doering, 1995). If veto players are maximized then groups usually will have accessto at least one type of veto player. Mobilization on the street should then be limited.In contrast, where there is effectively a single veto player (e.g., United Kingdomand France), access to politics is minimal and we would expect a larger amount ofmobilization at the street level.

Another theory that makes sense for us is federalism (see King, 1982). Thereare differing degrees of federalism. Austria and Germany are both federal countries,but in each local power is dwarfed by the power United States’ states, counties,and cities. Switzerland, in contrast, is a country that has greater local than cen-tral political power. More local autonomy should increase the standard deviation ofconflict in a country. Some local governments might be benign in the face of oppo-sition while others might actively engage opponents (see degree of centralizationin King, 1982, 122–123). We will test this with our four federal countries: Austria,Germany, Switzerland, and the United States as represented by the state of Illinois(see Chapter 2).


1.10 The Dynamics of Conflict

The problem set of this volume we have attempted to portray consists of(1) choosing appropriate models to estimate for the different contexts in our data;(2) estimating mechanisms of our data with appropriate models; (3) testing the-ory (especially Lichbach’s collective action theory) along the way as we generateresults; and (4) dealing with statistical and time-series econometric as well as linearalgebra problems. This chapter introduces all of these concepts. We will not revisitmost of them specifically in the rest of the volume, since we will concern ourselvesthere with the actual outcomes of data estimation.

Bibliography

Acheson, David. 1997. From Calculus to Chaos. New York: Oxford University Press.Carey, Sabine C. 2006. “The Dynamic Relationship Between Protest and Repression.” Political

Research Quarterly 59(1):1–11.Coleman, Stephen. 1975. Measurement and Analysis of Political Systems: A Science of Social

Behavior. New York: John Wiley and Sons.DeNardo, James. 1985. Power In Numbers: The Political Strategy of Protest and Rebellion. Prince-

ton, NJ: Princeton University Press.Doering, Herbert. 1995. Parliaments and Majority Rule in Western Europe. New York: St. Martin’s

Press.Elaydi, Saber N. 1996. An Introduction to Difference Equations. New York: Springer Verlag.Francisco, Ronald A. 2004. “After the Massacre: Mobilization in the Wake of Harsh Repression.”

Mobilization 9(2):107–126.Goldberg, Jack and Merle C. Potter. 1998. Differential Equations: A Systems Approach. Upper

Saddle River, NJ: Prentice-Hall.Goldberg, Samuel. 1986. Introduction to Difference Equations. New York: Dover Publications.Greene, William H. 2003. Econometric Analysis. Upper Saddle River, NJ: Prentice Hall.Kauffman, Stuart A. 1993. The Origins of Order. New York: Oxford University Press.King, Gary. 1989. Unifying Political Methodology. New York: Cambridge University Press.King, Preston. 1982. Federalism and Federation. Baltimore, MD: Johns Hopkins University Press.Lakatos, Imre. 1970. “Criticism and the Growth of Knowledge.” Chap. Falsification and the

Methodology of Scientific Research Programmmes, New York: Cambridge University Press.Lichbach, Mark I. 1987. “Deterrence or Escalation? The Puzzle of Aggregate Studies of Repression

and Dissent.” Journal of Conflict Resolution 31:266–297.Lichbach, Mark I. 1995. The Rebel’s Dilemma. Ann Arbor: University of Michigan Press.Lichbach, Mark I. 1996. The Cooperator’s Dilemma. Ann Arbor: University of Michigan Press.Luenberger, David G. 1979. Introduction to Dynamic Systems. New York: John Wiley & Sons.Maddala, G.S. 1977. Econometrics. New York: McGraw-Hill.May, Robert M. 2001. Stability and Complexity in Model Ecosystems. Princeton, NJ: Princeton

University Press.Merkin, David R. 1997. Introduction to the Theory of Stability. New York: Springer Verlag.Morrison, Foster. 1991. The Art of Modeling Dynamic Systems. New York: Wiley Interscience.Morton, Rebecca B. 1999. Methods and Models: A Guide to the Empirical Analysis of Formal

Models in Political Science. New York: Cambridge University Press.Murray, J.D. 1993. Mathematical Biology. New York: Springer Verlag.Olson, Mancur. 1965. The Logic of Collective Action. Cambridge, MA: Harvard University Press.Riker, William H. 1982. Political Equilibrium, Chap. A Reply to Ordeshook and Rae, pages 41–46.

Boston: Kluwer Nijhoff.

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Bibliography 11

Riker, Wiliam H. and Peter C. Ordeshook. 1973. An Introduction to Positive Political Theory.Engelwood Cliffs, NJ: Prentice-Hall.

Roughgarden, Jonathan. 1998. Primer of Ecological Theory. Upper Saddle River, NJ: PrenticeHalll.

Tabak, John. 2004. Algebra: Sets, Symbols and the Language of Thought. New York: Facts on File.Tsebelis, George. 2002. Veto Players: How Political Institutions Work. Princeton: Princeton

University Press.Tsebelis, George and John Sprague. 1989. “Coercion and Revolution: Variations on a Predator-

Prey Model.” Mathematical and Computer Modelling 12:547–559.William E. Boyce and Richard C. DiPrima. 1977. Elementary Differential Equations. New York:

John Wiley & Sons.Wintrobe, Ronald. 1998. The Political Economy of Dictatorship. New York: Cambridge University

Press.

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Chapter 2The Dynamic Relationship Between Protestand Repression in Democratic Countries

A bill of rights, as Jefferson remarked, was “what the peopleare entitled to against every government on earth, general orparticular, and what no just government should refuse, or reston inference.”

Hannah Arendt, On Revolution

2.1 Introduction

This chapter explores the relationship between protest and its repression in its mostfeasible contexts: democratic regimes. I catalog both past knowledge and recentempirical tests of formal theoretical conjectures using data on European democraticcountries as well as the state of Illinois in the United States. The purpose of thisjourney is to give firm basis to our present knowledge in order to determine, first,what is known and second, what remains uncertain about the dynamics of protestand repression. We also have a unique perspective of the street to evaluate humanrights and democracy. All the countries in this chapter are democratic. But howdemocratic? Do citizens retain rights, even when they exercise their rights to speechand assembly? We do not focus primarily on institutions, but at the level of thestreet.

This task is not as daunting as it may seem. Most of the previous work in the1960s through the 1980s used yearly or perhaps quarterly or monthly ordinal datarather than today’s interval daily data. Moreover, earlier work tested hypotheseswith linear or multiple regression rather than estimating multiple equation modelsfor interaction (King, 1989). If anything in politics is interactive, it is protest ofdissidents and repression by the state. We have to cover then a small amount of morevalid testing as well as new testing of theoretical conjectures by formal theorists,especially (Lichbach, 1987, 1995, 1996; DeNardo, 1985).

Our first priority is to settle the wrangling among our formal theorists. Once wehave them at peace, we state our assumptions and then consider the most impor-tant interactive questions. These require past and present tests of different contextsand countries; in our case all are in Europe, except for the state of Illinois in theUnited States. Once answered or found unanswerable, we shift to varied contexts,essentially, “what happens to y when x occurs?” All of this is designed to portray


13

14 2 Dynamic Relationship Between Protest and Repression

the complex relationship between these two highly interactive concepts—or at leastinform us of the research that must be done to discover the necessary knowledge.

2.2 Salient Differences Among Formal Theorists

The greatest conflict among the formal theorists: DeNardo’s (1985) assumption thatnearly anyone might be mobilized in a given policy space versus Lichbach’s (19951996) argument that at most five percent of any population can be mobilized. Empir-ical evidence in this instance supports Lichbach. Mobilization for dissent, even with-out repression, rarely achieves even five percent—at least until bandwagons develop,and even they rarely reach five percent of the population. DeNardo limits his incen-tives to ideology or state spatial policy, whereas Lichbach considers a full range ofselective incentives and solutions to the collective action problem. Lichbach (1987)proved with game theory that inconsistent repression accelerates protest. These areabstract arguments that we will test as closely as possible to discover what reallyhappens in a protest and repression context.

2.3 Assumptions

Our assumptions are simple and few. First, we assume that our daily interval data arevalid and reliable. Data come from 500 sources and are coded daily and sub-daily,that is, when more than one event occurs on a single day (http://web.ku.edu/ronfran/data/index.html). Second, we assume that the countries we test are representativeat least of most types of democratic countries. For presidential systems we shift tothe United States and examine 21 years of Illinois data. Finally, we have 16 yearsof daily data (except for Germany’s special case). We presume that these years,1980 through 1995, represent a typical time of context. For Eastern Europe, thisperiod covers regime transitions from communist dictatorships toward democrati-zation. Hence, all the former communist states appear in Chapter 3 with interruptedtime-series tests, before and after regime transition.

In terms of expectations of our predator–prey model estimation, truly democraticcountries should not have much conflictive interaction on the street. So, we expectthat in most cases, the parameter a would be statistically significant, and that insome cases the repression (without interaction) parameter b might be statisticallysignificant, but that neither of the interactive parameters would achieve statisticalsignificance in most cases (parameters g and h).

2.4 Cases

The vast majority of our cases come from our European NSF coding fund (seethe URL earlier). They comprise Austria, Belgium, Denmark, France, Germany,Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Northern Ireland (as a

2.5 The Relationship Between Protest and Repression in Differing Contexts 15

region), Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom.We also test 21 years of coded data from the United States, in this case, the state ofIllinois, from 1985 to 2005. Illinois allows us to test a federal, presidential form ofgovernment that does not exist in Europe.

2.5 The Relationship Between Protest and Repressionin Differing Contexts

We begin our investigation with what our formal theorists predict would happenunder the most feasible conditions; we also explore empirical questions of the rela-tionship between protest and repression.

2.5.1 When Does Protest Generate Repression?

Protest that generates or accelerates repression reduces the government’s capacity toenforce its laws and repress dissent. Even democratic countries require demonstra-tion and rally permits. Riots, ironically, are least repressed in North America, mainlybecause standard police procedure is for police to flee the scene. In Europe, however,riots emerge from the dregs of demonstrations and rallies. They are almost alwaysviolent and therefore generate squads of specially trained riot police. Repressivecountries are more concerned with threats of competing democratic organizations.Any such movement is more repressed than a few standard dissidents occupying asquare or a building.

2.5.2 When Do Protest and Repression Interactively AccelerateEach Other?

The first thing to recognize in the interactive situation is that both dissidents and thestate police think and adapt. Oliver and Myers (2003) show that social movementsand states co-evolve. With both sides adapting, repression should reduce deaths andinjuries over time, while protest becomes more conventionally difficult. Sometimes,though, states and dissidents remain fixed in their tactics. In such a context, thedissidents and police are in a dependent, Lotka–Volterra (predator-prey) interaction,with protest “dependent” on repression and vice-versa. In general, we expect thatthinking and adaptation would minimize interaction in democratic countries. Wewould expect that protest rises in the absence of repression, but that little else occurs.

There is a famous theorem, however, proved with game theory, that inconsistentrepression accelerates protest Lichbach (1987). We will test this theoretical conjec-ture empirically in this chapter. The test requires precisely the sort of data we have:interval-level protest and repression data with protesters identified. This allows usto isolate a single group, warrant that repression is inconsistent, and then look atprotest levels.


2.5.3 What Happens When Repression Is Absent?

If everyone is satisfied, little mobilization should take place. In the absence ofrepression, grievances always arise, if only because dissident entrepreneurs seek tomobilize in their own interest (Lichbach, 1995, 1996). In reality, a lack of repressionand general citizen satisfaction should translate to difficulty in mobilizing. We havenot had a situation to test this, but nearly total satisfaction and brutal repressionshould be the two contexts in which mobilization is challenging. We have seen con-sistent violent repression. Can we find a context in which trivial repression occurs?

2.6 Analytic Results in Democratic Countries

We now move to the test of the Lotka–Volterra model for all our cases. We presentthem alphabetically in Europe and then add the Illinois case from the United Statesat the end of this section. Each table provides parameter estimates, t-values and theprobability of t , as well as eigenvalues as a stability measure and the size (N ) of thedata sample.

Austria is our first case and gives us an interesting basis (Table 2.1). Not only arethe protest and repression without interaction statistically significant, but the inter-action repression parameter, h, is significant as well. We need to keep in mind thatwhile Austria is a parliamentary democracy, it is a federal system with a good deal

Table 2.1 Austria Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)

Parameter Estimate t-value p(t)

a 0.064873∗ 4.8 0.0001b 0.085409∗ 5.54 0.0001g 0.00069 0.1 0.9189h −0.0000525∗ 2.65 0.0081Eigenvalues λ1 = 0.0657683427 λ2 = −0.0009478427N = 5, 464∗indicates statistical significance

Table 2.2 Belgium Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.3065∗ 21.76 0.0001b 0.0759∗ 5.67 0.0001g 0.0151∗ 8.98 0.0001h −0.00000004837 0.57 0.5708Eigenvalues λ1 = 0.302713951 λ2 = 0.003786001N = 5, 661∗indicates statistical significance

2.6 Analytic Results in Democratic Countries 17

Table 2.3 Denmark Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.314∗ 24.32 0.0001b 0.00334 0.23 0.8165g 0.0137∗ 7.0 0.0001h −0.0000000191 0.07 0.9416Eigenvalues λ1 = 0.016220921 λ2 = 0.002820941N = 5, 884∗indicates statistical significance

of local autonomy. However, its sign is negative, indicating that interaction reducesrather than increases repression. Belgium (Table 2.2) gives us a similar impression,but in this case it indicates that protest increases with interaction. This may well bea result of ethnic conflict in Brussels (Flemish versus Walloons), rather than a basicdifficulty with police. In fact, it is likely to represent cases when police attempt tobreak up Flemish-Walloon fights.

Denmark (Table 2.3) presents results that are close to its Scandinavian neighbors.Both of the parameters in the protest equation are statistically significant, indicatingboth lively generation of protest and some interaction between police and dissidents.The sign of the interactive parameter (g) is negative (see model). This means that inDenmark, interaction between state agents and protesters dampens protest. There isabsolutely no indication of repression in Denmark. Both of the repression equationparameters are near zero and have exceptionally small t-values. Moreover, the signon the interactive (h) parameter is negative, meaning that interaction with policetends to reduce, not increase, repression.

For France, Table 2.4 shows us a remarkable situation where all parameter esti-mates of the Lotka–Volterra model are statistically significant. T -values are high,especially for the repression parameters b and h, and yet the interaction of dissidentand police is stable. What we see here might be a response to a form of government.In Western Europe, France is unique in having a unitary semi-presidential form ofgovernment, a totally new form of democracy invented by Charles DeGaulle andMichel Debre in 1958. This form of government has both a president and premier,

Table 2.4 France Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.0161∗ 8.06 0.0001b 0.0738∗ 49.99 0.0001g 0.000234∗ 2.42 0.0156h 0.000002217∗ 31.91 0.0001Eigenvalues λ1 = 0.107703078 λ2 = −0.001625248N = 5, 884∗indicates statistical significance


the latter functionally a prime minister. Premiers serve at the president’s pleasure,and the legislature in these kinds of government is weaker than in any other formof democratic government, since they are dominated by the premier. The choiceof meaningful actions for a dissident is bleak: lobbying a legislator is not helpful;the President and Premier are inaccessible, so the street is the most logical choice.Clearly, many choose the street and find police present. We return to the French casebelow after considering the rest of the established West European democracies.

The Federal Republic of Germany is a long-standing democratic country; it fea-tures a constitution with human rights as well as democratic elections and federalismthat allows a degree of local control. Again, we test the Lokta–Volterra model withdaily aggregated data on protest and related repression.

The German situation differs from most of our cases, because West and EastGermany merged in 1990. In order to estimate purely democratic data, we limitthe data to 10 years, 1980–1989. Table 2.5 shows the parameter estimates and t-statistics for coefficients of the predator–prey model. Germany appears to be trulydemocratic on the street in the relationship it fosters between protest and repres-sion. The only statistically significant parameters are a, showing the positive riseof protest in the absence of repression and b, the positive increase in repressionwithout protest. Protesters in Germany feel no compunction about moving out intothe streets. Repressive behavior on the part of police forces escalates in the absenceof protest, but at far higher levels than in the Netherlands. Neither of the interac-tive parameter estimates is statistically significant. The German profile is anothertype we expect for a democratic government: People move out on the streets withgrievances, but generally are not punished for doing so. The repression appears tobe inconsistent Lichbach (1987), but probably depends on the contexts of mobi-lization. The possibility of inconsistency allows us to test empirically an importanttheoretical conjecture proved by game theory, but not yet estimated with actual data.

The group that allows us to find out whether inconsistent repression increasesprotest in Germany is leftists. Leftists protested during every year of the decadewe have. In the early 1980s, they were repressed regularly. Thereafter, they wererepressed inconsistently with the same tactics, for example, demonstrations. Leftistprotest was one of the strongest in all of the German data. Ecological protestersdominated all other groups, but this was three years of occupying an airport site to

Table 2.5 (West) Germany Lotka–Volterra results, 1980–1989Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.2077∗ 12.36 0.0001b 0.2957∗ 17.16 0.0001g −0.000411 1.95 0.0507h 0.000000020564 0.18 0.8543Eigenvalues λ1 = 0.2071132064 λ2 = 0.0005868142N = 3, 067∗indicates statistical significance


prevent a new runway. There were 274 separate leftist protests. After 1980, abouttwo-thirds of the time leftist events drew no repression. But in one-third they did,even using the same tactics in the same place. The best way to see this is graphically.Figure 2.1 displays German leftist protest over time (1980–1989), while Figure 2.2

14 28 42 56 70 84 98 112 126 140 154 168 182 196 210 224 238 252 2660

10000

20000

30000

40000

50000

Fig. 2.1 German leftist protest, 1980–1989

14 28 42 56 70 84 98 112 126 140 154 168 182 196 210 224 238 2660

50

100

150

200

250

300

Fig. 2.2 German leftist repression, 1980–1989


depicts repression (arrests, injuries, and deaths) over the same period. It is plain tosee that protest rose substantially over this period of inconsistent repression. It isalso clear that repression was inconsistent and certainly did not match the protestgraph. The zero-order Pearson correlation between the leftists and their repressionby state agents is −0.05656. If one lags protest one day, then the same correlationshifts to 0.033. But as one can see from Figs. 2.1 and 2.2, there is an increasein protest and only episodic repression. The two time series do not correlate, butLichbach’s theorem holds in graphic reality. Figure 2.3 represents another view ofthe same data. It shows the ratio of the number of leftist protesters to the numberarrest, injured, or killed. It is clear that repression was episodic and inconsistent onthe same group using most the same tactics all the time.

Let us consider Greece, a country on the geographic margin of Europe. Ruled bya military dictatorship during the mid 1970s, Greece thereafter became a democraticparliamentary government with little repression. We have 16 years of daily dataon protest and repression starting in 1980, five years after Greek democracy wasrestored. Table 2.6 shows the results of the predator–prey mechanism operating inGreece. The parameter estimates show that both a and b are highly statisticallysignificant, whereas the interactive parameters g and h are not. Greek protest arisesfreely. Repression arises on an anniversary of student clashes against the militarydictatorship (when there are riots in Athens), but not on most other days.

Greece has more repression than the average European country, but the estima-tion results are similar to Belgium’s (Table 2.2). The most significant differenceis that the interaction term represented by g is statistically significant, indicatingmuch more interaction between dissidents and police. Much of this interaction

14 28 42 56 70 84 98 112 126 140 154 168 182 196 210 224 238 252 2660

200

400

600

800

1000

1200

1400

Fig. 2.3 The ratio of leftist German protest to repression, 1980–1995


Table 2.6 Greece Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.11459∗ 8.7 0.0001b 0.08397∗ 5.99 0.0001g 0.03767∗ 9.11 0.0001h 0.0000004723 1.61 0.1074Eigenvalues λ1 = 0.06823068 λ2 = 0.04635885N = 5, 884∗indicates statistical significance

encompasses annual fall clashes in Athens between anarchist students and riotpolice. Like many protest events, this is an anniversary of repression in the early1970s military junta. The Greek protest interaction parameter is negative (seemodel), so that even here interaction dampens action.

Iceland is a small Scandinavian country with a small population. Once again thismeans that we would not expect much interaction and that there would be littlestatistical significance. This is what Table 2.7 tells us, but it also presents us withour first linear algebra lower triangle situation. Because the interactive componentsare near zero, the eigenvalues in this special form of a Jacobian matrix are the sameas the main diagonal parameters. Iceland’s “repression” consists only of arrests; in16 years there were neither injuries nor deaths of dissidents.

Our Ireland data does not reflect conflict in Northern Ireland, which is part ofthe United Kingdom. Rather, it comprises mostly labor disputes and some socialproblems, especially abortion after rape and divorce in a country with a Catholicreligion. The Irish results demonstrate what we would expect to find in a democracy.Parameter a alone in Table 2.8 is statistically significant, with all other t-values nearthe zero mark.

Italy’s data start after most of the 1970s Red Brigade terror ended. These datatoo comprise a great many labor disputes, some social and regional problems, butno large-scale or overriding issues, save the diffuse response to government cor-ruption. In Italy’s case, only parameter b in Table 2.9 is statistically significant.Repression rises in the absence of protest, but neither of the interactive parameters

Table 2.7 Iceland Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.337∗ 26.49 0.0001b 0.0000000000000000031 0 1.0g 0.0564 0 0.9992h 0.000000000000000000542 1.61 1.0Eigenvalues λ1 = 0.337 λ2 = 0.000000000000000000031869N = 5, 884∗indicates statistical significance


Table 2.8 Ireland Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.03397∗ 2.47 0.0137b 0.0025 0.15 0.8825g 0.00375 0.14 0.8901h −0.000000133 0.05 0.9633Eigenvalues λ1 = 0.0342140095 λ2 = −0.0002741425N = 5, 884∗indicates statistical significance

Table 2.9 Italian Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.00844 0.61 0.5424b 0.10585∗ 7.61 0.0001g 0.000107 0.08 0.9354h −0.000000021 1.35 0.1772Eigenvalues λ1 = 0.009617622 λ2 = −0.001177643N = 5, 314∗indicates statistical significance

approaches statistical significance, implying that Italian police stand ready, but dolittle repression in general.

Luxembourg has almost no repression or police action. In 16 years, there wereonly three incidents of arrest or injury. From 1980 until the early 1990s, only twopeople had been arrested by police for political or labor actions. A total of 42arrests and 2 injuries in more than one and one-half decades marks Luxembourg as apeaceful democracy on the street. Because there is so little repression, the parameterestimates for the second (repression) equation are zero in Table 2.10. We thus have asecond case of the special situation of a linear algebra lower triangle matrix, whichmean the eigenvalues match the parameter estimates on the main diagonal of theJacobian matrix.

Table 2.10 Luxembourg Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.2135∗ 16.16 0.0001b 0 0 1.0000g −0.0671 0.99 0.3223h 0 0 1.0000Eigenvalues λ1 = 0.2135 λ2 = 0.0N = 5, 493∗indicates statistical significance


Table 2.11 Lotka–Volterra results, Netherlands, 1980–1995, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a −0.0218 1.57 0.1169b −0.01534 0.84 0.3988g 0.01339 0.36 0.7155h −0.000000692 0.16 0.8757Eigenvalues λ1 = 0.028940685 λ2 = 0.00714377N = 5, 884

The Netherlands has a high probability of being a country with little or no repres-sion. While riots occur even in the Netherlands, they are infrequent. Most of thestrife is typical labor relations, that is, strikes, occupations, and lockouts, little ofwhich is ever violent. Table 2.11 shows the results of the predator–prey model forthe Netherlands.

The Netherlands clearly represents a different case. Protest and repression do notarise easily or significantly (a and b), and there is no significant interaction betweendissidents and the state. The Netherlands’s results are a model what we would expectto find in a democratic, nonviolent country with little or no repression. The largestt-value in Table 2.11 is just 1.57 and it is where we would expect it, the parameter(a) that represents protest rising in the absence of repression.

Norway is quite similar to the Netherlands as well as very much like its Scan-dinavian neighbor Iceland. As Table 2.12 shows, only the parameter that representsprotest rising in the absence of repression is statistically significant. The t-valueson the other parameters are quite low, in particular given such a large sample size.This indicates that police are inactive in Norway and that there is little interactionbetween police and dissidents.

The Portuguese results show more interaction between state agents and dissi-dents. Both the parameters in the protest equation are statistically significant (aand g) in Table 2.13, while in the repression equation both the parameter estimatesand their respective t-scores are also zero or near zero. Therefore, beginning in1980, with the center-coalition of Francisco Balsemao, repression in Portugal almost

Table 2.12 Norway Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)




Table 2.13 Portugal Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.2094∗ 13.39 0.0001b 0.000122 0.01 0.9929g 0.0414∗ 6.88 0.0001h −0.00000000000148 0.0 0.9996Eigenvalues λ1 = 0.2094241 λ2 = −0.00002411757N = 5, 406∗indicates statistical significance

vanished. This result stems from the constitutional reform in 1982 in Lisbon thatseverely reduced the power of the president.

Spain’s results are similar to Portugal’s, and for similar reasons. After the deathof dictator Franco in 1975, Spain entered a transition phase with Juan Carlos asking. In 1978, several opposing parties came together to write a new, democraticconstitution. The most different aspect to the Spanish data is the presence of Basque-based terror by ETA. Interaction with terrorists and suspected terrorists accounts fora great deal of the repression. Nonetheless, it is only the parameters in the protestequation that are statistically significant wherein the interactive parameter showsdampening of protest and repression in Table 2.14.

Sweden’s results in Table 2.15 almost conform to the stereotype of a Scandina-vian country with human rights and social democracy. The only positive figure ofnote in the Swedish results is a statistically significant parameter (a), protest rising inthe absence of repression. Thus, a good deal of dissent emerges, but there is almostno interaction with state agents and little evidence of repression.

Switzerland presents us with a unique form of government, one anchored at thelocal level and governed largely by referendum voting. It is interesting that the onlysignificant parameter estimate in Switzerland’s Table 2.16 represents repression ris-ing in the absence of protest (b). This is a hallmark, it appears, of countries withhigh degrees of local control, mostly our federalist states Austria, Germany, andIllinois in the United States as well as Switzerland with a kind of hyper-federalism

Table 2.14 Spain Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.3192∗ 17.15 0.0001b 0.00003 0.0 0.9984g 0.003027∗ 11.07 0.0001h −0.000000005354 0.01 0.9928Eigenvalues λ1 = 0.3192003 λ2 = −0.000002791382N = 5, 406∗indicates statistical significance


Table 2.15 Sweden Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.5252∗ 45.51 0.0001b 0.012326 0.62 0.5325g 0.002979 0.04 0.9669h 0.000003153 0.19 0.8498Eigenvalues λ1 = 0.5252699 λ2 = −0.00006675273N = 5, 439∗indicates statistical significance

Table 2.16 Switzerland Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.003028 0.22 0.8248b 0.1279∗ 7.31 0.0001g 0.00012 0.05 0.9613h −0.000004638 0.65 0.5172Eigenvalues λ1 = 0.005712541 λ2 = −0.0002689179N = 5, 399∗indicates statistical significance

(see King, 1982). When local control is autonomous, one might expect that at leastin some locales, police would be more active than the norm. This is true of a suf-ficiently large number of cities in Switzerland, but there is no interaction evidentbetween state agents and dissidents. The Swiss data contain a good deal of youthprotest, some of it violent, in the early 1980s, but even this did not affect the resultsin the 16-year time series.

The United Kingdom has a more repressive profile than most other countries,but its repression is even less likely to involve injuries and deaths (93.17% of itsrepression is arrests). The UK has a higher death mean than other countries, but thatis almost wholly attributable to urban race riots and PIRA versus UFF and UVFterror.1

The information we seek lies in the parameter estimates. Note the signs on thecoefficients in Table 2.17. In the United Kingdom, a raises protest without repres-sion. UK constables accelerate their repression (b) in the absence of protest. We seedifferences in Margaret Thatcher’s and John Major’s UK in the effect of interac-tion of protest and repression. Interaction accelerates protest and repression. In theUnited Kingdom, g increases protest and h increases repression when there is inter-action between dissidents and police. The period covered by the UK data includesthe violence of a year-long coal strike, a newspaper printer strike and many summer

1 PIRA is the Provisional Irish Republican Army; UFF is the Ulster Freedom Fighters and UVF isthe Ulster Volunteer Force; the latter two organizations are Protestant.


Table 2.17 United Kingdom Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.2471∗ 18.54 0.0001b 0.0769∗ 4.66 0.0001g −0.00494∗ 4.67 0.0001h 0.00000063575∗ 2.23 0.026Eigenvalues λ1 = 0.248628 λ2 = −0.0015273N = 5, 301∗indicates statistical significance

youth and race riots in London, Liverpool, Manchester and other cities. Also impor-tant is that Northern Ireland is part of the United Kingdom. The United Kingdom hasno written constitution and therefore no true foundation of human rights. Perhapswe should expect that the interactive parameters might be statistically significant inthe United Kingdom. We take up the cases of France and UK below. In the meantime, though, we move eastward to the United States, where we can test the state ofIllinois from 1985 to 2005, 21 years of daily data.

It is clear from Table 2.18 that Illinois as a representative of a federal, presiden-tial democracy is at least as repressive as are the United Kingdom and France. Thet-values show much more interaction and activity in the United States than in ourmost repressive European contexts. The system of interaction in Illinois is nonethe-less stable. The eigenvalues here too are real numbers within the bounds of −1 and1. One puzzle is whether the city of Chicago, by far the largest city in the state, isresponsible for these results alone. The city has a history of racial segregation andof police brutality. One way to determine this is to test the Chicago events versusthe rest of the state. Tables 2.19 and 2.20 tell this story.

It is clear from these results that not Chicago, but the rest of the state, usuallytermed “downstate Illinois” is responsible for the repression and for all parametersrendered statistically significant. These are counter-intuitive findings, but they areempirically based and show that smaller cities and towns are much more likely tointeract with dissidents and to repress them than does a large metropolitan urban

Table 2.18 Illinois Lotka–Volterra results, 1985–2005Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.6769∗ 80.01 0.0001b 0.6652∗ 330.76 0.0001g 0.000399∗ 3.43 0.0006h 0.000225∗ 4.0 0.0001Eigenvalues λ1 = 0.071395832 λ2 = −0.003930832N = 7, 615∗indicates statistical significance

2.7 Survey of the West European Democracies and Illinois 27

Table 2.19 Chicago Lotka–Volterra results, 1985–2005Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.4797∗ 33.65 0.0001b 0.001424 0.05 0.964g −0.000034 0.06 0.9539h 0.00002∗ 5.15 0.0001Eigenvalues λ1 = 0.479699 λ2 = 0.000201009N = 7, 615∗indicates statistical significance

Table 2.20 Non-Chicago Illinois Lotka–Volterra results, 1985–2005Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.9456∗ 204.68 0.0001b 0.9949∗ 283.7 0.0001g −0.000543∗ 3.21 0.0013h −0.00000195∗ 2.74 0.0062Eigenvalues λ1 = 0.946170964 λ2 = −0.00057914N = 7, 615∗indicates statistical significance

area. In the Austrian results, three of the four parameter estimates were statisticallysignificant. Germany showed a different pattern with little interaction. Only the aand b parameters were statistically meaningful in Germany. In Switzerland, withmuch more local control, only one parameter (b) was statistically significant. So,there is some evidence that allowing local control leads to more interaction betweenstate agents and dissidents, as well as more repression. This might simply be a costof local autonomy, which on the whole is desirable in most contexts. It is most likelycaused by individual units with more energy and harsher attitudes toward dissent.While these tendencies might be controlled in a unitary state, they remain unfetteredin a federal one (see King, 1982).

2.7 Survey of the West European Democracies and Illinois

What do we know from our results summary of West European countries and thestate of Illinois? Notable from our findings are the facts that only France and theUnited Kingdom in Europe, but Illinois as a whole in the United States also meetsall the parameters of the Lotka–Volterra model with statistical significance. It isespecially likely that each of these two European countries would meet such aninteractive profile.

The United Kingdom has no constitution, unless one regards all (contradict-ing) bills of the House of Commons and House of Lords a constitution, but they


clearly are not. So, the United Kingdom lacks what all other countries have: a billof human rights at the level of an organic law. France is in a different position,with both a constitution and a bill of human rights. In 1958, Charles DeGaulle andMichelle Debre invented a new form of government: semi-presidential government.It is notable because it generates only one complete veto player: the president. TheFrench president appoints and dismisses the prime minister (a fractional veto player)and cabinet and cannot be impeached. The French executive has more control ofthe legislature than in any other West European country save the United Kingdom,where the prime minister is the only veto player. From the street perspective, at least,these two countries are the most repressive in Western Europe, largely because theyface more protest.

Let us leave our street-view and look at this situation from an institutional per-spective. The most applicable institutional perspective on democratic countries isGeorge Tsebelis’s work on veto players Tsebelis (2002). From Tsebelis’s analysisof Doering’s (1995b) investigations, the countries in Europe with the fewest vetoplayers (i.e., higher levels of policy instability and institutional control) are Greeceand the United Kingdom, with one veto player each, and France with 1.57 (somepower is imputed to the premier) veto players. Fewer than two veto players meansthat decisions can be made by one leader, as long as there is no level of oppositionthat would lead to ouster or nonre-election.

A second way to think about this problem is in terms of agenda control. As weknow from Richard McCelvey’s “chaos” theorem, whoever controls an agenda ininstitutional terms controls outcomes, unless there is policy symmetry, which is rare.The highest level of agenda control exists in the United Kingdom (0.69), followedby Ireland (0.519) and France (0.333). Ireland still suffers from the split engenderedin its civil war (see Chapter 5). It has two veto players, to UK’s one and France’s1.57. So, agenda control peaks again in our two countries, one with no constitutionand one with the original semi-presidential form of government.

The final institutional perspective for our inference comprises executive (vs. leg-islative) control (see Tsebelis, 2002). If we leave aside Switzerland, which has alocally and referendum-dominated government, the highest scores in Europe onexecutive control are for France and the United Kingdom (both 5.52). In otherwords, from an institutional perspective, France and the United Kingdom are theonly recurring countries with the fewest veto players, the highest agenda control,and the highest executive control. Indeed, another measure of central control is leg-islative control of plenary agenda and legislative output. On these measures, UKand Ireland score highest, with France and Greece the runners-up (Doering, 1995a).These are the only countries in the “high” measure of political control. But Ireland’sempirical results demonstrate that on the street, it is peaceful and does not interferemuch with standard citizen rights (abortion is an entirely different matter). Greece’sparameter estimates are all statistically significant except for h, the repression inter-active parameter. Once more, France and the United Kingdom stand out as democ-racies with repression on the street and with centralized executive control of politics.

Our point here is that our street-level results correlate with institutional investi-gations that show high levels of control of a single leader in the UK and France.In other places with high control, notably Greece and Ireland, there is much less

2.7 Survey of the West European Democracies and Illinois 29

street-level evidence of state control and limited human rights. In Greece, threeparameter estimates are statistically significant, but t-values are relatively low com-pared with France. In Ireland, we see no evidence of street-level interaction.

Since the United Kingdom’s model parameters are statistically significant, weneed to consider that Northern Ireland was part of its data mix for the 16 years, 14of which were characterized by Catholic vs. Protestant terror and state attempts toprevent and reduce it. Obviously, when Northern Ireland terror is separated fromthe United Kingdom data, we would expect differences, but not necessarily whatwe see in Table 2.21. Here, only the repression parameter estimates are statisticallysignificant, but the eigenvalues indicate instability. Figure 2.4 depicts the oscilla-tory pattern of terror and repression in Northern Ireland. This is a graph simulatedfrom the parameter estimates for Northern Ireland.2 It is apparent from the figurethat Northern Ireland terror oscillates in a tight periodicity. This makes sense ina local tit-for-tat terror exchange. We would not expect the system to diverge, butto oscillate according to the high frequency of bombings, shootings, and kidnap-pings (see Francisco, 1996). The reason for the tight oscillation is apparent from theeigenvalues. Although they are complex conjugate eigenvalues, both parts are smallfractions, which when taken to a 5,884 exponent will go to zero as a limit.

We know that Northern Ireland has damped oscillation, indicating terror. Butwhat is the explanation for small towns and rural areas in Illinois displaying arepressive form? The United States is a federal country, unlike most European coun-tries except for Germany and Austria? Police functions are delegated to the locallevel. It is apparent from our results that this delegation leads to a large amount ofpolice–dissident interaction and repression.

We can disaggregate repression among the democracies we cover in this chapter.Table 2.22 shows the daily means of arrests, injuries, and deaths for each countryand/or region. It is clear that most of the smaller countries have the least repression,especially Iceland, Luxembourg, Norway, and Portugal. France, Germany, Spain,and UK are larger countries with the most repression. Other countries, both largerand smaller, fall in between these two poles. The Illinois data show that Chicago

Table 2.21 Northern Ireland Lotka–Volterra results, 1980–1995Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.0072 0.5 0.6184b 0.1356∗ 9.3 0.0001g −0.000102 0.11 0.9150h −0.000001302∗ 2.96 0.0031Eigenvalues λ1 = 0.003599935 + 0.00093313i λ2 = 0.003599935 − 00093313iN = 5, 301∗indicates statistical significance

2 Thanks to Paul Johnson, Department of Political Science, University of Kansas for programmingR to simulate the parameter estimates of the Lotka–Volterra model.


0 200 400 600 800 1000

020

040

060

080

010

00

cbin

d(R

R, C

C)

Fig. 2.4 Simulated northern Ireland terror & repression, 1980–1995

arrests dissidents at a far lower rate than downstate Illinois, but that dissidents inChicago are slightly more probable to be injured or killed during interaction withpolice.

2.8 Discussion

We have tested 21 time-series data with the predator–prey model to investigate therelationship between protest and repression in Europe. What have we found? First,that in almost all cases, protest arises in the absence of repression. We would expectthis in a democratic setting. In most cases, the b parameter is statistically significant,indicating repression arising in the absence of protest. Second, we saw that UK inthe 16 years of Margaret Thatcher’s and John Major’s terms as prime minister, themodel’s parameters fit the predator–prey model well and completely. In France thisis the case as well, but in no other countries of democratic Europe. If nothing else,this should remind us that free and democratic states with a high degree of centralcontrol and limited chance for citizens to talk to powerful legislators have morerepression. It is striking, however, how much more repressive (in the sense of arrestsonly) downstate Illinois is compared either with Chicago or with Europe as a whole.

Third, in seven of our European cases, at least one of the interactive parameterswas statistically significant. For the most part this is g, the interactive parameter in

Bibliography 31

Table 2.22 Means of arrests, injuries and deaths per day in all cases

Country/Case Arrests Injuries Deaths

Austria 0.112127 0.0082312 0.000183Belgium 0.2447 0.067724 0.000178Denmark 0.1261 0.0161 0.0001846France 5.0118985 1.421 0.011374Germany 5.238697 0.6562763 0.0033698Greece 0.215 0.1433 0.0022692Iceland 0.0012771 0 0Ireland 0.11795 0.041651 0.000189322Italy 0.41231 0.3545352 0.0028227Luxembourg 0.0076461 0.0003641 0Netherlands 0.22628 0.1176812 0Norway 0.098952 0.0424867 0Portugal 0.0216386 0.0503052 0.0018495Spain 6.974475 0.9882 0.020229Sweden 0.3275735 0.0023897 0.000183824Switzerland 0.6930913 0.29696068 0.000555568United Kingdom 3.2749 0.20166 0.02452Northern Ireland 0.4782364 0.0801126 0.021576Illinois 44.5414314 0.0059094 0.021576Chicago 0.6787023 0.0112507 0.0010466Non-Chicago Illinois 65.5129453 0.000389332 0.000583998

the protest equation. Belgium, Denmark, Greece, and Spain have this profile. OnlyFrance and the United Kingdom generate statistical significance in both interactiveparameters, and Austria is the only European country in which the h parameter in therepression equation met statistical significance. Northern Ireland, the terror-plaguedregion of the UK and Chicago, also met this profile.

Finally, all the cases we test in this chapter are stable except Northern Ireland—all others remain in equilibrium. In tests from data over most of the world (not onlydemocracies), only Northern Ireland during its terror period generated two complexconjugate eigenvalues, essentially close to a perilously unstable equilibrium. Allother cases have been stable in the difference equations sense, that is, all eigenvaluesare real numbers and all occur between –1 and 1 (Goldberg, 1986; Elaydi, 1996). Ifwe assume that both the regime and its dissidents think and that both are averse toinjury and death, we can understand why protest and revolution might appear to bedisorderly and unstable. However, research in this chapter and elsewhere show thatorder prevails.

Bibliography

DeNardo, James. 1985. Power In Numbers: The Political Strategy of Protest and Rebellion. Prince-ton, NJ: Princeton University Press.

Doering, Herbert. 1995a. “Fewer Though Presumably more Conflictual Bills: Parliamentary Gov-ernment Acting as a Monopolist.” In Parliaments and Majority Rule in Western Europe, NewYork: St. Martin’s Press.

Doering, Herbert. 1995b. Parliaments and Majority Rule in Western Europe. New York: St. Mar-tin’s Press.


Elaydi, Saber N. 1996. An Introduction to Difference Equations. New York: Springer Verlag.Francisco, Ronald A. 1996. “Coercion and Protest: An Empirical Test in Two Democratic States.”

American Journal of Political Science 40(4):1179–1204.Goldberg, Samuel. 1986. Introduction to Difference Equations. New York: Dover Publications.King, Gary. 1989. Unifying Political Methodology. New York: Cambridge University Press.King, Preston. 1982. Federalism and Federation. Baltimore, MD: Johns Hopkins University Press.Lichbach, Mark I. 1987. “Deterrence or Escalation? The Puzzle of Aggregate Studies of Repression

and Dissent.” Journal of Conflict Resolution 31:266–297.Lichbach, Mark I. 1995. The Rebel’s Dilemma. Ann Arbor: University of Michigan Press.Lichbach, Mark I. 1996. The Cooperator’s Dilemma. Ann Arbor: University of Michigan Press.Oliver Pamela E. and Daniel J. Myers. 2003. “The Coevolution of Social Movements.” Mobiliza-

tion 8(1):1–24.Tsebelis, George. 2002. Veto Players: How Political Institutions Work. Princeton: Princeton Uni-

versity Press.

Chapter 3The Dynamics of Protest and Repressionin Dictatorships and Democratic Transitions

We have lost the measure of freedom. We have no meansof determining where it begins and where it ends.

Aleksander Solzhenitsyn, The Gulag Archipelago

3.1 Introduction

We investigate nondemocratic governments in this chapter. All are dictatorships insome form, but we do not expect that all will be equally repressive. One purpose ofthis chapter is to illustrate how different dictatorships repress at quite varied levels.Repression imposes costs for regimes. Those who can rule without a great deal ofrepression lower costs. For the most part we have communist dictatorships in East-ern Europe, plus one of the former USSR republics and also data from Burma forthe 1988–1989 democracy movement and its subsequent harsh repression. Becausethe communist governments in East Central Europe stopped being so in 1989 and1990, we also have data on the varied transition to democracy. This chapter willdevote a section to the dynamics of such democratic transitions.

The governments in this chapter do not tolerate dissent. That does not mean thatthey commit regular repression: most do not. Rather, they discourage dissent openly.Signals are sent by mass media that everyone should be pleased and quiescent. Whensomeone does challenge the regime, especially a group not representative of workersor farmers, repression is quick, harsh, and widely known, if not widely reported.That is the typical situation in the cases we have in our sample for this chapter. Ofcourse, there are instances in which dissent is not tolerated at all. The closest wecome to this situation is in some of the harshest East Central European communistcountries (e.g., Romania) as well as Burma in Asia and Belarus, a former republicof the Soviet Union.

Two theorists are most relevant for dictatorships: James DeNardo and RonaldWintrobe. DeNardo used formal models to discover what citizens might do if therewere harsh repression (DeNardo, 1985). DeNardo shows how a resolute strategyagainst a dictatorship can force concessions and create greater peril for the dictator.One empirical difficulty with DeNardo is that he assumes people are mobilizablewith ideology and that there is no limit on mobilization. We now know that five per-cent of the local population is the maximal limit of mobilization (Lichbach, 1995).Wintrobe takes the perspective of the dictator himself (there has never been a female


33

34 3 Dynamics of Protest and Repression in Dictatorships and Democratic Transitions

dictator) (Wintrobe, 1998, 106). Dictators face an array of dangers: (1) a cabal ofassociates; (2) loss of support by bureaucrats and the army; (3) a revolt of the mob;and (4) foreign intervention. Of these perils, the fourth is irrelevant because of thestrength of the USSR in our sample cases. The secret police in each country soughtto diminish the third danger and the communist bosses used the party apparatus tocontrol the military and much of the bureaucracy. That left only a coup danger, andthere were few of those in the life of the Soviet Union and its allies.

3.2 Cases and the Context of Dictatorship

We have a reasonably wide distribution of authoritarian countries to test. Almost allof them, though, occur in Europe. Our cases of dictatorships are Albania, Belarus,Bulgaria, Burma, Czechoslovakia, German Democratic Republic, Hungary, Poland,and Romania. For democratic transitions to democracy we have Albania, Bulgaria,the Czech Republic, Hungary, Poland, and Slovakia.

3.2.1 Mobilization Under Dictatorship and Harsh Repression

We know that mobilization happens even in the harshest environments. Littleactual protest occurs, but there will be many attempts of clandestine mobilization.Tilly (1986, 332) describes the pattern of mobilization under serious repression: theNazi occupation of Paris.

Open defiance was dangerous and difficult. Those Parisian energies that were not comman-deered or snuffed out by the occupying power and its French collaborators flowed mainlyinto survival, individual, and collective: the creation of escape routes, information chan-nels, black markets, and networks of mutual aid. Slowly and later, however, a few of thesehalf-hidden structures became means of collective resistance.

Many groups fought against Nazism during Hitler’s regime in Germany(Hoffman, 1977; Gill, 1994). But it was in Nazi-occupied Austria that clandes-tine mobilization found its most articulate expression. Bauer (1939), an Austriansocial democrat, published in Paris a manual for dissidents who sought to maintaintheir organizations during harsh repression under a dictatorship. Bauer’s (1939) keypoints were (1) a cadre (cell-based) structure; (2) anonymous leaders; and (3) exclu-sion of probable spies, people who have families or other affective connectionsand those who are not fully committed to the cause. In other words, a clandes-tine organization survives only by becoming invisible both to the public and to thestate. Recruited members in such associations tend to be loners; they are unattachedpeople, have few family members, and they substitute the group for true fam-ily. These predictions are consistent in history (Almond, 1954; Kornhauser, 1959)and in the late 20th century, for example, the Provisional Irish Republican Army(Toolis, 1995).

The membership of a clandestine cell-based organization cannot mobilize manypeople and cannot act publicly–terror is possible, but members cannot otherwisereveal themselves. Organizations of this type are first and foremost maintenance

3.2 Cases and the Context of Dictatorship 35

groups—they attempt to preserve themselves through the dictatorship. DeNardo(1985, 43) argues that the style of mobilization determines the strategy of actionand the number of people acting. Certainly, that is correct in clandestine recruit-ment. The nature of protected and exclusion mobilization requires few people andnonpublic acts. The implication of these facts for our analysis is that a great dealof the dissent in dictatorships is hidden from view. Mobilization is not publicized,and therefore not including in the data we coded. In the sample of countries wehave, the samizdat (clandestine newspapers) movements would not be in the eventdata. Thus, we would not necessarily expect results from the model to appear asharsh repression in all countries. In many countries, the barriers to action were wellknown and respected; street dissent was considered insane, not necessarily becauseof direct repression, but because of the loss of education opportunities, employment,or state health benefits.

With a protected leadership and membership of unattached people, the group canbe stable during a dictatorship. What happens, however, when the regime falls—especially if it does not fall to the clandestine organization? History reveals manyexamples of this sort. The clearest point is an exile leadership group that equals theclandestine organization in the home country. Almost always the exile and internalleaders contest each other’s right to legitimate power. The internal leaders seek tolead since they survived repression. If they win, they face challenging mobilizationproblems, since their organization has nonfamily oriented, unattached members.Who wants to listen to or follow a lonely and alienated person? Such was the prob-lem that confronted the German Social Democratic party faced after World WarII. Although the internal leaders won, they lost every election thereafter until theexile leaders (e.g., Willy Brandt) captured the party in 1959. Mobilization is easierwhen the exiled leaders ascend to power almost immediately. For example, when theexiled Bolshevik command took over after the Russian revolution in 1917 (Lenin,Trotsky, and Stalin) mobilization faced little difficulty.

The situation above exists in constant repression under a dictatorship. But whatabout other sorts of states that do not repress consistently–those that are autocraticand occasionally massacre their citizens? We have tested scenarios like this whenoccupations of other countries occurred (e.g., UK in Ireland, UK in India, Francein Germany, or Israel in Palestine) and also in highly repressive, if inconsistent,regimes, for example, Burma. In the wake of 31 urban twentieth-century massacresacross the world, we found impressive backlash mobilization. In almost all cases,backlash mobilization after a massacre dwarfed the original event massacre. Yet,Bayesian updating test shows that when backlash was repressed, mobilization washighly and quickly damped (Francisco, 2004).

3.2.2 What Happens to Repression When Mobilization Growsto a High Magnitude?

It depends. If the mobilization grows beyond the regime’s capacity to repress it,then there is low probability of repression. The most obvious example of this


phenomenon is what happened in the summer and fall of 1989 to most of thecountries in our sample. Especially in east central Europe, large-scale mobilizationessentially precluded repressive deployment. Too many people came to the street toargue that this was a peculiar minority. We saw this as well in 2004/2005 in Kyivduring the Orange revolution of Ukraine. If the regime is able to repress, it enjoysthe choice of acceding to or denying the dissidents’ demands. Many states findserious indecision in this case. For example, the fraudulent election in Kyrgyzstanled to huge mobilization, especially in the south of the country. The regime soughtto repress dissidents, but was unable to mobilize the forces to contain the protest,resulting in a successful nonviolent revolution.

3.3 Empirical Results on Dictatorship Periods

We turn now to the results of our tests, first on dictatorships, and then on transitionregimes. Once again our model of choice is Lotka–Volterra. As with democracies,we will tour the dictatorships alphabetically and attempt to discern patterns amongthem. We begin with Albania during the latter period dominated by communiststrongman Enver Hoxha, who died in 1985. Table 3.1 contains the results fromour tests. Albania and Romania were two communist states that defected from theSoviet bloc and nonetheless maintained communism as their governing ideology.As the results in Table 3.1 indicate, throughout the decade of the 1990s, Albaniawas a repressive autonomous communist dictatorship. It generates extremely hight-values. In spite of its high level of repressive interaction, as a system it remainedstable. Both eigenvalues are bounded by −1 and 1, in fact by an even narrow rangeof zero to one.

While the Albanian authorities attempted to seal hermetically the country fromall others, by the late 1980s, Albanian citizens were tuning into radio broadcastsfrom Yugoslavia and Italy. What they heard about Mikhail Gorbachev and the liber-alization of the USSR generated a great deal of interest. In May 1989, a conferenceof writers and artists discussed the need for open dialog and criticism of regimepolicies. A few months thereafter, a novel was published that directly attacked theSigurimi, the Albanian secret police (Ramet, 1991). Even in this most-closed soci-ety, the ideas of freedom could no longer be suppressed.

Table 3.1 Lotka–Volterra results, Albania 1980–1989, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.9996∗ 492.39 0.0001b 0.877386∗ 13.8 0.0001g −0.0476∗ 52.67 0.0001h 0.04182∗ 13.3 0.0001Eigenvalues λ1 = 0.9538059 λ2 = 0.0876141N = 3, 652∗indicates statistical significance

3.3 Empirical Results on Dictatorship Periods 37

Table 3.2 Lotka–Volterra results, Belarus, 1995–1997, weekly aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.13155∗ 4.54 0.0001b 0.16941∗ 1.99 0.0464g 0.00001 0.85 0.3935h −0.00000431 1.75 0.0810Eigenvalues λ1 = 0.1315629 λ2 = −0.00001718631N = 156∗indicates statistical significance

The parameter estimates in Table 3.2 show eagerness on the part of dissidentsand police, but relatively little interaction with one another. These results repre-sent university students clashing with police each week during the three years of1995–1997. Standard arrests, common injuries, especially bruises and sometimesbroken arms or legs occurred, but for the most part the regular repression stayedwithin these bounds. Students, dissidents with unstructured time, continued to act.But most citizens stayed home, eschewing interaction with law enforcement offi-cers. Note that interaction with the police dampens repression slightly, and not quitestatistical significantly (parameter h). These data were coded by Marc Nordberg anddiffer from the rest of our data in the sense that they are aggregated not daily butweekly.

Belarus was not destined to be this way. In the early collapse of the USSR andindependence for individual republics, Belarus was a reasonably democratic entity.It only devolved to repression when Aleksander Lukashenko was elected president.Belarus is controlled by Lukashenko, a hardline unreformed-communist dictatorwith persistent hopes to merge his country with mother Russia. So far at least, this isunrequited love. The waiting has led to a sustained status-quo in Belarus, includingregular repression of unhappy citizens. Hope for meaningful reform in Belarus dis-appeared when Vladimir Putin became President and then Premier of Russia. With-out a push from Russia, not much will happen in Belarus until Lukashenko is gone.

Bulgaria, our next case, has many similarities to Belarus, despite the distancebetween them. Like Belarus, Bulgaria was a loyal, unquestioning communist ally tothe USSR. This quiescence was accomplished by keeping the population ignorant:(1) the press and other media were barren and without information value; (2) sta-tistical information was unreliable at best; (3) censorship was virtually total, evenon books or articles that carried nuanced criticism of the regime; and (4) the statepublishing firm ignored demand and published only what the leadership wanted(Ramet, 1991). All this kept the Bulgarian populace focused on family, work, andleisure, largely ignoring politics and newspapers. The secret police ferreted out anydissent and punished it ruthlessly. The first human rights organization in Bulgariaemerged only in 1988 (Ramet, 1991).

Bulgarian results (Table 3.3) show a country without a great deal of regularrepression. Both dissidents and police rise on their own (parameters a and b),but the interaction of police and dissidents in both equations is negative and only


Table 3.3 Lotka–Volterra results, Bulgaria, 1980–1989, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.1723∗ 9.84 0.0001b 0.7756∗ 17.1 0.0001g −0.000031 0.39 0.6957h −0.00006∗ 10.9 0.0001Eigenvalues λ1 = 0.72604 λ2 = 0.0000796048N = 3, 652∗indicates statistical significance

statistically significant in the repression equation (parameter h). Todor Zhivkov wasthe loyal-to-USSR communist dictator from 1956 until his ouster in 1989. So loyalwas Zhivkov that many believe he attempted to make Bulgaria the sixteenth republicof the USSR. During early 1989, the renewed campaign of Bulgarian nationalization(mainly changing Turkish names to Bulgarian) generated substantial ill-will and amass exodus of Turks to Turkey. In July 1989, the Discussion Club for the Supportof Glasnost and Perestroika summoned up the courage to protest the treatment ofTurks in Bulgaria (Ramet, 1991). August in the fateful year 1989 brought clashesin ethnic-Turkish villages in Bulgaria with state troops (Ramet, 1991). All this washandled badly and resulted in large-scale international condemnation of Zhivkov.In November, Zhivkov was removed and replaced by Petar Mladenov, who actuallymet with and encouraged dissident organizations in mid-November 1989. Subse-quently, he allowed the formation of opposition political parties.

We have one case wholly outside the communist world. That case is Burma, oras its military dictators call it, Myanmar. The Burma data were coded by FedericoFerrara and are aggregated daily, but unlike the rest of the sample, they only cover244 days of the democratic rising in 1988 (Ferrara, 2003). The rising was repressedbrutally and creatively by seeming to accede to the dissidents’ request for the releaseof political prisoners. The prisoners released, though, were rapists, murderers, andarmed robbers. The state suspended social order for a few days to get everyoneoff the streets. Then a military coup by General Saw Maung sealed the fate of thedemocratic movement (Ferrara, 2003).

The communist regime in Czechoslovakia was headed by Slovak hardlinerGustav Husak, the leader chosen by the Soviet Union after the Alexander Dubcek-led Czechoslovakia Prague Spring in 1968 was repressed and shut down by WarsawPact troops invading the country. Husak was “elected” by the Czechoslovak com-munist party in 1969 and served until his own ouster by the new Soviet govern-ment. Milos Jakes took over as leader until he resigned during regime transitionin December 1989. The dissident movement Charter 77 began in 1977 after thesigning of the Helsinki accords and the repression of the rock group the PlasticPeople of the Universe. Charter 77 was immediately repressed. But because it sentsignature names put on its charter into diplomatic bags that were sent to the WestEuropean press, the regime could not repress the movement as it otherwise might


Table 3.4 Lotka–Volterra results for Burma, 1988, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.537906∗ 9.28 0.0001b 0.7655∗ 13.25 0.0002g 0.00052∗ 3.09 0.0022h −0.000000199∗ 5.93 0.0001Eigenvalues λ1 = 0.5386450028 λ2 = −0.0007388038N = 244∗indicates statistical significance

have without stirring up trouble across the barbed wire. The group operated in theopen, albeit under the intense scrutiny of the secret police (Ramet, 1991). By 1987,1,300 Czechoslovak citizens had signed the charter; despite the intense pressure,only 15 signatories withdrew their signatures (Ramet, 1991). Charter 77’s leaderwas Vaclav Havel, who later emerged as a transition political leader.

Much of the mobilization in Czechoslovakia was in clandestine form. Since theregime precluded jazz music, there were many jazz organizations that met in familyflats (usually rotating) each week or month. This sort of mobilization was hiddenfrom the authorities. The dissent was listening to prohibited music. But becausethis activity was so widespread, when the regime weakened in late 1989, there wasalready a good deal of latent mobilization. After all, these jazz clubs were a greatvenue for more generalized political complaining (Ramet, 1991). This erupted intoalmost a nation-wide school and university boycott in November 1989. Yet at thistime the regime had no way to repress such broad and dispersed mobilization. Thisphenomenon is part of a larger set of dictatorship problems; see Timur Kuran (1995).In the end it took only 21 days to remove the entrenched communist dictatorship andenter the new era of free elections by June 1990.

Czechoslovakia’s results (Table 3.5) show only the repression equation param-eter estimates to be statistically significant. Nonetheless, parameter h has a nega-tive sign, so interaction between protesters and police dampened repression. Evena (the parameter representing the rise of protest in the absence of repression) has

Table 3.5 Lotka–Volterra Results, Czechoslovakia, 1980–1989, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.000214 0.01 0.9900b 0.0854∗ 3.66 0.0003g 0.00014 0.02 0.9813h −0.000016∗ 2.25 0.0246Eigenvalues λ1 = 0.001198466 λ2 = −0.001000466N = 3, 652∗indicates statistical significance


an extremely low value, probably because of the prominence and visibility of theCharter 77 movement.

The German Democratic Republic (GDR) was carved out of Germany afterWorld War II. It was the Soviet Union’s occupation sector of Germany and becamethe GDR after West Germany (Federal Republic of Germany) was created in 1948and 1949. The GDR was the front-line cold war state and was a loyal member ofthe Soviet bloc. During the period from 1945 to August 12, 1961 millions of EastGermans voted with their feet and fled to the West, especially through West Berlin,a capitalist obstacle island within the territory of the GDR. This population losswas significant, in particular, because those who left were generally higher skilledthan those who stayed behind. The Berlin wall was built on August 13, 1961 andthereafter leaving the republic without permission became a capital crime. The GDRwas a repressive state, but its reputation for intolerance of protest prevented mostlarge-scale mobilization. We see in Table 3.6 that the parameter estimates of a andb are statistically significant, but none of the interactive variables is significant. Soprotest mobilization arose without repression and repression arose without protestmobilization, but there was relatively little interaction between protest and repres-sion on the streets of the GDR.

For the most part, the Stasi, East German secret police, operated in clandestinefashion and made massive arrests at various times to intimidate dissent. In January1988, the Stasi arrested more than 70 of the most prominent democratic activistsand deported them to the West (Ramet, 1991). But by this time Gorbachev hadset a liberal tone in the Soviet bloc. Remaining dissidents, led by Barbel Bohleyand Sebastian Pflugbeil, reorganized in the wake of the deportations and formed anew organization, symbolically announcing it on August 13, 1989: Neues Forum,or New Forum, originally designed as a pressure group for dialog with the regime(Ramet, 1991). This group was the rallying point for the dissent that explodedthroughout the GDR in the fall of 1989 and eventually brought down the regime.

The circumstances of Hungary’s role in the Warsaw Pact and the Soviet spherealtered in the wake of its 1956 rising against USSR-imposed communism. In con-trast to the USSR’s reaction to the Prague Spring in 1968, Soviet leaders in the1950s insisted on no departure from the base Soviet model, but gave Hungary

Table 3.6 Lotka–Volterra results, German Democratic Republic, 1980–1990, daily aggregateddata

Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.6842∗ 53.94 0.0001b 0.041508∗ 2.28 0.0226g −0.001417 0.74 0.4593h 0.0000007855 0.03 0.9728Eigenvalues λ1 = 0.684114 λ2 = 0.000087608N = 4, 017∗indicates statistical significance


a wide latitude to attempt new socialist policies, including private property at amicro-level. The intellectual dissent in Hungary was better tolerated as well than inCzechoslovakia. Consumer goods were more available, living standards higher, andcommunism not so draconian as in other parts of the bloc. Of course the Hungarianshad useful experience in trying wrench autonomy from the Hapsburg empire a half-century earlier. Nikita Khrushchev in the 1950s campaigned against the “cult ofpersonality” that had grown up around Stalin. Khrushchev was quick to put down the1956 rising, but then tolerated reform in Hungary after the “troublemakers” (thosewho rejected Soviet communism) had been taken care of. Foremost among the latterwas Imre Nagy. Janos Kadar cooperated with the USSR and put the unrepentantNagy on trial with other leaders. They were found guilty of treason and put to death.Buried in unmarked graves, these dissident leaders led to a smoldering of dissentthat only emerged in 1989 when Nagy and his friends were again unearthed andreburied with dignity.

From the mid 1960s, Hungary was a substantially more affluent and less repres-sive member of the Soviet bloc. West Europeans were allowed to set up factories inHungary led to better wages and a higher-level consumer culture. I was a student atthe University of Vienna in 1968–1969; it was literally cheaper and more pleasantfor us to spend weekends in Budapest, just 40 miles down the road. Austrian tourismwas encouraged (since Austria was a neutral country in the cold war) and developedthe economy still further. But in the 1980s, the economic growth rate in Hungaryplummeted from 4.8% annually to just 1.8%. Living standards stagnated. By thetime of Mikhail Gorbachev’s accession to the General Secretary’s position in theUSSR communist party, Hungarians were ready to press for more gains. A meetingof about 155 Hungarians in Latitekek resulted in a new organization, the DemocraticForum. These high-placed dissidents recognized the opportunity afforded them bythe liberalization in the USSR and the malaise in the Hungarian communist party(Ramet, 1991). With Hungary’s intense indebtedness to the International MonetaryFund and its signature on the Helskini Accords it was unlikely to repress smallepisodes of dissent. Kadar was forced to step down the next year, in 1988. Afterthat, reformist elements in the communist party and in intellectual circles seriously(Democratic Forum) negotiated a new path for the country. They met in roundtablefashion in early to mid 1989. Almost 300,000 attended the reburial of Imre Nagy andhis cohorts. In October 1989, Hungary ceased being a communist country and beganits foray into Western democracy. All this is reflected in Table 3.7, in which only theparameter representing the rise of protest in absence of repression is statisticallysignificant.

Table 3.8 shows results for Poland in the 10 years of 1980–1989, that is, fromthe rise of Solidarity in August 1980 to the regime transition. Here, a, b, and g arestatistically significant, signaling the probability that repression was inconsistent.Note that both protest and repression rise in the absence of each other. Interactiverepression between state agents and dissidents lowers repression levels. So, we see aprofile of a communist dictatorship that was in control much of this time period, butthat even when it lost control, for example, August 1980–December 13, 1981 andfrom the mid-1980s until 1989 it was not a particularly repressive country. That is


Table 3.7 Lotka–Volterra Results, Hungary 1980–1989, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)



Table 3.8 Lotka–Volterra results, Poland, 1980–1989, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.193008∗ 6.93 0.0001b 0.366∗ 14.09 0.0001g 0.000001857 1.96 0.0501h −0.000038∗ 2.73 0.0065Eigenvalues λ1 = 0.1960115 λ2 = −0.00004152066N = 3, 466∗indicates statistical significance

not to say that Solidarity and the human rights organization KOR were not repressed,only that the street interaction between dissidents and regime was not as active andextreme as we see in other east central European states.

The problem that military and communist authorities faced in Poland was a poorand deteriorating economy and proven widespread public support for a movementit continued to repress. When Mikhail Gorbachev assumed the summit of the com-munist party in the Soviet Union, the harsh repressive tactics of General Jaruzel-ski appeared increasingly out of place. Strikes were rampant, the regime could notstop them, and could not supply the population’s needs. Eventually, the regime wasforced to make the humbling gesture to Lech Walesa to curtail labor strife.

Our next case is wholly different in character. Romania was a communist dic-tatorship that shunned the leading role of the USSR. The principal dissident orga-nization was “Free Romania”, a worker-based antiregime group that nonethelesssupported Gorbachev’s reform communism. But this was necessarily a clandestinemovement (Ramet, 1991). It found a natural alliance with the Hungarian minorityin Romania. Free Romania used dissident Hungarian printing facilities and thesetwo groups formally protested the regime’s state intent to destroy seven to eightthousand villages in Romania (Ramet, 1991).

It is clear that Romania was a repressive state with a good deal of interactionbetween state agents and dissidents. The data underlying Table 3.9 run from 1980to 1995, in other words, through the transition time that occurred elsewhere in theregion. Regime transition in Romania was intensely violent, far more so than for any

3.4 Empirical Results from Transition Periods 43

Table 3.9 Lotka–Volterra results, Romania, 1980–1995, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.0934∗ 7.03 0.0001b 0.4537∗ 30.32 0.0001g 0.00072∗ 3.58 0.0003h −0.0000030293∗ 10.81 0.0001Eigenvalues λ1 = 0.096775585 λ2 = −0.003372556N = 5, 844∗indicates statistical significance

other east European country. Romania defected from the Soviet bloc in 1958, afterthe first decade of the cold war. As such, it was an autonomous communist dictator-ship run by Nicolae Ceausescu. In the mid-1980s, the economy faltered badly andled to numerous power outages and brownouts. In 1987, workers were emboldenedto go out on the street to protest the austerity program implemented by the state.During the December holiday period of 1989, elements of the military joined withdissidents to capture, try, and execute Ceausescu and his wife. An estimated 10,000total of Romanians died in the subsequent civil war.

3.4 Empirical Results from Transition Periods

We now investigate how the results change when communism gave way and democ-racy grew from 1990 to 1995. For most of our cases such a change did take place,save for Belarus, Burma, and Romania. Belarus and Burma experienced no transi-tion, while Romania’s came somewhat later than other countries in the region. Wewould expect that there would be less repressive interaction in the transition period,although mobilization increases in such a context, so more repression might occur.

Our first transition case is Albania. Because Albania was not part of the Sovietbloc, its transition occurred differently and was not part of the contagion that spreadfrom Poland and Hungary through the GDR to Czechoslovakia and finally Bulgaria.Unrest broke out in northern Albania in January 1990. Within five months, therewere clear signs of liberalization. A look at the data underlying Table 3.10 willshow how much dissent emerged before the entrenched communist elites decided togrant concessions.

It is clear from Table 3.10 that a good deal of interaction took place after regimetransition between dissidents and police. The only parameter estimate that is notstatistically significant (b) is repression rising in the absence of protest. This is astreet-level indication of democratic transition, but the negative sign of the itera-tion of protesters and police in the protest equation (g) shows that protest growsunder interaction. The near-zero parameter estimate of interaction in the repressionequation (h) indicates the small rise of repression under dissident-police interaction.The change from the dictatorship era is that the parameter estimate (b) represent-


Table 3.10 Lotka–Volterra results for Albania, 1990–1995, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.9086∗ 94.4 0.0001b −0.0101 0.4 0.6874g −0.003838∗ 2.97 0.0030h 0.000008564∗ 5.46 0.0001Eigenvalues λ1 = 0.9086427 λ2 = −0.00004180486N = 2, 191∗indicates statistical significance

ing repression arising without protest loses statistical significance. Also, t-valuesdecline greatly in Albania between Enver Hoxha-dominated dictatorship to the1990–1995 transition, marked by the beginning event of the first formation of anopposition political party in 1990.

The fall of Todor Zhivkov in November 1989 inaugurated the Bulgarian politicaltransition. On the 17th of November, 100,000 Bulgarians massed in front of theNational Assembly in Sofia to protest corruption and dictatorship and support Gor-bachev’s reform agenda (Ramet, 1991). The new regime, clearly faced with a moreactive population than ever before, began to see the light and mend its ways. Meet-ings with newly mobilized opposition parties were held and brought the promise offree elections. The order for the Turkish to change their names to Bulgarian wasrescinded on 29 December (Ramet, 1991). Bulgarian nationalists were not pleasedby the recension, so the new regime changed its national day from the day commu-nism began to the day it overthrew Turkish rule for independence (Ramet, 1991).As Table 3.11 demonstrates, only the parameters in the protest equation are statisti-cally significant. It is a mark of some progress that the parameters in the repressionequation have t-values near zero.

Our Czechoslovak and Czech Republic tables (Tables 3.12 and 3.13) tell apeaceful story about regime transition under the presidency of former dissident-playwright Vaclav Havel. In the 1980–1989 era only the repression parameter esti-mates achieved statistical significance. In the transition period, those parameter

Table 3.11 Lotka–Volterra results for Bulgaria, 1990–1995, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.6159∗ 34.85 0.0001b 0.00327 0.14 0.8899g 0.0184∗ 5.71 0.0001h 0.0000001006 0.76 0.4502Eigenvalues λ1 = 0.615977 λ2 = −0.00009666985N = 2, 191∗indicates statistical significance


Table 3.12 Lotka–Volterra results for Czechoslovakia, 1990–1992, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.002036 0.05 0.9573b −0.0000000000000000167 0 1.000g −0.07814 0.12 0.9067h 0.0000000000000000000136 0 1.000Eigenvalues λ1 = 0.002036 λ2 = −0.0000000000000006409186N = 665

Table 3.13 Lotka–Volterra results for the Czech Republic, 1993–1995, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.063254 1.67 0.0945b −0.0000000000000000214 0 1.000g 0.003576 0 0.9979h 0.000000000000000000108 0 1.000Eigenvalues λ1 = 0.063254 λ2 = −0.00000000000000000131827N = 701

estimates lose statistical significance and result in a table with no significance. Thiscase is different from all of our others in the sense that Czechoslovakia stayed uni-fied from 1990 through 1992, but split apart in 1993. Vaclav Klaus and VladimırMeciar became respective presidents of the Czech Republic and Slovakia. As aconsequence, Slovakia and the Czech Republic are together until 1992, but haveseparate three-year samples from 1993 to 1995. We have so little repression orstate-dissident interaction in Czechoslovakia, the Czech Republic, and Slovakia(Table 3.16) that we once again confront the linear algebra lower triangle context,where the eigenvalues are close to the main diagonal parameter estimates becauseof zero or near-zero parameter estimates in the Jacobian matrix (see Chapter 1).

The portrait of the street emerging in Prague and its surroundings is a peacefulone. It is noteworthy that the impetus for reform came from the art and culturalworld, not the hard-core human rights community. In January 1989, the same monthin which Charter 77 leader and playwright Valav Havel was imprisoned, the culturalministry replaced on library shelves titles of Czech authors that had been banned fordecades (Ramet, 1991). New, critical films were released. All this liberalized cul-tural activity was probably designed to serve as minimal accession to Gorbachev’sreform policies. After all, how many people read library books or attend art films?The really important repression, of active dissidents, did not retreat. In fact, we nowknow that Gorbachev was unhappy with the Czechoslovak leadership and sought todepose the dictator Milos Jakes (Ramet, 1991). Once mobilization reached a masslevel, the communist regime was doomed. There would be no help from Moscowin 1989. The dictatorship surrendered timidly. Within weeks, a broad spectrum of


political parties had emerged and Havel stood with Dubcek in front of hundreds ofthousands of cheering citizens. From this point, repression essentially vanished.

The Hungarian transition was perhaps the easiest in all the countries we investi-gate. The old regime was ossified, the communist party increasingly a joke to mostinformed citizens; a young cadre of leadership emerged in this context and sim-ply shifted from communism to democracy, without muss or fuss. As in the CzechRepublic, repression, never strong in Hungary, also vanished. It is interesting thatin many transition cases we find a shift from major to minor repression, while inothers, especially the Czech Republic and Hungary, shift from minor to none. Asnoted earlier, the communist party essentially admitted its inability to manage theeconomy. So from November 1988 to April 1989, the process of transformation wascomplete, and Hungary was the first to demolish the “iron curtain” of barbed wirethat separated it from Austria. This act, in turn, led the flight of hundreds of EastGermans vacationing in socialist Hungary, which eventually led to the fall of theentire Soviet bloc in east central Europe.

It is clear that Hungary, which had the least repression during the communistperiod, has almost none during the transition to democratic rule. As in Chapter 2,we have a lower triangle linear algebraic situation with near-zero parameters inTable 3.14. The first eigenvalue is the same as the parameter estimate a and thesecond eigenvalue is close to the value of the parameter estimate h, just a bit fartheraway from zero. The pattern for Hungary is much like the least repressive WestEuropean democracies. Only the parameter denoted the rise of protest in the absenceof repression (a) is statistically significant. All communist dictatorships were notequally repressive. Nor were repressive communist dictatorships necessarily repres-sive in political transitions. Nonetheless, the less repressive the country in dictator-ship, it is generally true that it will also be less repressive in the movement toward anew political system.

Poland continues our parade of impressive declines of repression. Unlike Hun-gary, Poland (Table 3.15) has no parameter estimate achieving statisticalsignificance. Like Hungary, Poland has one of the unusual (but common in thetransition sample) lower triangle matrix contexts in linear algebra. Once again wehave two zero t-values and two fractional ones.

After Solidarity took over the government, repression more or less ceased. Onceagain we confront the lower triangle phenomenon. Even the police and security

Table 3.14 Lotka–Volterra results for Hungary, 1990–1995, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.0792∗ 3.61 0.0003b 0.0000000000000000202 0.0 1.0000g 0.001888 0.0 0.9991h 0.000000000000000000163 0.0 1.0000Eigenvalues λ1 = 0.0792 λ2 = −0.0000000000000000006445354N = 2, 191∗indicates statistical significance


Table 3.15 Lotka–Volterra results for Poland, 1990–1995, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.000000000000000000445 0 1.0000b 0.026688 0.85 0.3928g −0.0000000000000000000000813 0 1.000h −0.00023 0.15 0.8814Eigenvalues λ1 = 0.00023 λ2 = −0.00000000000000000000943383N = 2, 191

forces supported the popular legitimacy of Solidarity, something palpably obviousin Poland during the 1980s. The Poles selected a semipresidential government,with Lech Walesa as the first president and Tadeusz Mazowiecki as prime min-ister. Mazowiecki worked fast to dismantle a state-run economy and convert it tofree-enterprise. Poland had no major social divisions. Most Jews had been killed inthe Holocaust; those who left largely did not return. Everyone was either nominallyRoman Catholic or not, but there were not so many other choices. So from the outsetof the transition, there was no reason for security beyond normal police criminalfunctions.

If the Czechoslovak revolution were the velvet one, then the split between theCzech Republic and the Slovak Republic is called the velvet divorce. Once theSoviet yoke was removed from east central Europe, longstanding autonomy move-ments emerged or shifted from western to east central Europe. The Slovak Indepen-dence Movement, an emigre organization based in Munich, moved to Bratislava inthe spring of 1990. Part and parcel of the removal of communism was the relatedidea of independence for the Czech Republic and Slovakia. Manifold Slovak inde-pendence parties formed, all with the fervent wish to finally, after centuries ofHapsburg and Soviet rule, to have independence (Ramet, 1991). Vladimir Meciar,an aspiring prime minister without a country, was happy to accommodate all thesewishes.

Although the Slovak Republic started out under a former communist ambitiouspolitician (Meciar), it quickly met the standard criteria for democratic consoli-dation. Independent political parties formed and contested free elections. From1994 they have cooperated in coalition governments that have exhibited stability(Wolchick, 2008). Although parties remain organizationally weak, they are working

Table 3.16 Lotka–Volterra Results for Slovakia, 1993–1995, daily aggregated dataPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.000015 0 0.9997b 0.00000000000837 0 1.0000g 0.000015 0 1.000h 0.00000000000000199 0 1.0000Eigenvalues λ1 = 0.00001499999 λ2 = 0.0000000000199N = 705


through the consolidation that is necessary. The electoral law, of course, determineshow many parties will eventually remain. Slovakia uses the largest remainder pro-portional representation formula, so parties have the incentive to split to gain moreseats in the legislature. Both the Czech and Slovak Republics maintain a five percentthreshold to keep extremist or frivolous parties at bay.

One of the reasons that the Slovak Republic’s transition was so peaceful anddevoid of coercion (Table 3.16) was the decision taken in 1990 to dissolve thepolitical function secret police in all of Czechoslovakia and to deprive it of anyrepressive resources. The unified general transition under Havel also expanded theforeign investment by allowing greater that 49% ownership. Both these measuresled to a benign Slovak Republic after 1993 (Ramet, 1991).

3.5 Conclusion

Dictatorships are rarely benign and usually terrible, but we have seen many of themtransform into peaceful and nonrepressive countries in this chapter. In general, dicta-torships are not uniformly repressive. The worst countries in our dictatorship samplewere Albania, Belarus, Bulgaria, Burma, Czechoslovakia, Poland, and Romania. Intransition, Albania remained repressive, Bulgaria slightly so, and in Czechoslovakia,the Czech Republic and Slovakia, it disappeared. In Hungary there was little repres-sion under communism and none thereafter. Belarus, Burma, and Romania remainedrepressive and for the most part the former two have not reformed at all.

Repression in an authoritarian state that has consistent yet reasonably mildrepression can either see very little mobilization or alternatively small and con-sistent protest. Also observe that in all these results in tables, the eigenvalues aremembers of the real number system (as opposed to complex conjugate numbers);thus, they signify that the interaction of dissidents and the police stays in equilibrium(Elaydi, 1996; Goldberg, 1986; Merkin, 1997; Francisco, 2000). As Tilly (1986, 4)puts it, “conflict, not disorder.” All these estimations were calculated with SASModel using seemingly unrelated regression with correction for serial correlationand heteroscedicity when necessary.

Do these parameter estimates make a difference for the mechanism betweenprotest and repression? Which of our dictatorships is most repressive? The con-testants are for this distinction are only Albania, Burma, and Romania, the onlycountries for whom all parameters are statistically significant. Judging by t-values,Albania wins hands down (see Table 3.1). But while repression was almost con-sistent, it was not terribly violent. The maximum arrested at any given time wastwo, and the maximum killed was 21. No one was injured by police in Albania,or at least none was reported. In the Burma case we have only deaths, since theregime mostly shot people dead. The mean of deaths per day in Burma is 26.3443,a large number of people killed per day. From this perspective, Burma is clearly themost repressive. What about Romania? Its mean arrest number per day is 0.725; forinjuries, it is 0.491; and for deaths, 0.1539. This is a logical decline from arrests,injuries to deaths. The maximum arrests in Romania were 1,049; maximal injuries

Bibliography 49

765; and highest incident of deaths, 610. Based on the foregoing evidence, Burmawould have to be judged the worst violator of human rights in our rouge gallery ofdictatorships.

Of the countries in the transition sample, only Albania and Bulgaria show clearsigns of continued repressive interaction. The unexpected and truly felicitous newsis that by far the majority of the east central European cases are not only not repres-sive, but most actually meet the requirements of the lower triangle linear algebracontext, which are rarely met (see Tables 3.12, 3.13, 3.14, 3.15 and 3.16). To havean entire class of countries meet, these requirements in the same time period isremarkable. Czechoslovakia, the Czech Republic, Hungary, Poland, and the Slo-vak Republic all have near-zero parameter estimates and at least some zero-levelt-values. In this sense, these are cases devoid of repression, less intrusive on thestreet than are even most democratic west European countries in Chapter 2. Theysay that old habits die hard; not so in these cases. It is a remarkable turnaround fromcommunist dictatorship to total noninterference at the street level. The forces whotook over in the wake of the communists, in some cases communists themselves,created policies totally different from the palpably recent past. Their citizens enjoyfreedom denied to them for nearly a half-century.

Bibliography

Almond, Gabriel. 1954. The Appeals of Communism. Princeton: Princeton University Press.Bauer, Otto. 1939. Die illegale Partei. Paris: Editions “La Lutte Socialiste”.DeNardo, James. 1985. Power In Numbers: The Political Strategy of Protest and Rebellion. Prince-

ton, NJ: Princeton University Press.Elaydi, Saber N. 1996. An Introduction to Difference Equations. New York: Springer Verlag.Ferrara, Federico. 2003. “Why Regimes Create Disorder: Hobbe’s Dilemma during a Rangoon

Summer.” Journal of Conflict Resolution 47(3):302–325.Francisco, Ronald A. 2000. “Paths to State Repression.” chap. “Why are Collective Conflicts

’Stable’?”, pages 149–172, Lanham, MD: Rowman and Littlefield.Francisco, Ronald A. 2004. “After the Massacre: Mobilization in the Wake of Harsh Repression.”

Mobilization 9(2):107–126.Gill, Anton. 1994. An Honorable Defeat: A History of German Resistance to Hitler. New York:

Henry Holt.Goldberg, Samuel. 1986. Introduction to Difference Equations. New York: Dover Publications.Hoffman, Peter. 1977. The History of German Resistance, 1933–1945. Cambridge, MA: The MIT

Press.Kornhauser, William. 1959. Politics of Mass Society. New York: The Free Press.Lichbach, Mark I. 1995. The Rebel’s Dilemma. Ann Arbor: University of Michigan Press.Merkin, David R. 1997. Introduction to the Theory of Stability. New York: Springer Verlag.Ramet, Sabrina P. 1991. Social Currents in Eastern Europe. Durham, NC: Duke University Press.Tilly, Charles. 1986. The Contentious French. Cambridge, MA: Harvard University Press,.Timur Kuran. 1995. Private Truths, Public Lies: The Social Consequences of Preference Falsifica-

tion. Cambridge, MA: Harvard University Press.Toolis, Kevin. 1995. Rebel Hearts: Journeys Within the IRA’s Soul. New York: St. Martin’s Press.Wintrobe, Ronald. 1998. The Political Economy of Dictatorship. New York: Cambridge University

Press.Wolchick, Sharon L. 2008. “The Czech and Slovak Republics.” In Central and East European

Politics: From Communism to Democracy, Lanham, MD: Rowman and Littlefield.

Chapter 4Varied Dynamics of Bandwagon Mobilization

With every step, the crowd swelled like a river being fed bytributaries. At first it was only students, but then more andmore adults joined in. The crowd thickened, occupying entireavenues. The trams were forced to stop running, becausepeople overflowed onto the tracks in the street. Everyone washollering and shouting happily as more and more peopleshared in the increasing excitement. Every window facing thestreet had someone hanging out of it, waving madly. After allthe years of sullen, silent May Day marches, there wassomething magical about a large spontaneous demonstration.I kept looking around, soaking it all in, feeling that I was in adream.

Andrew S. Grove1

4.1 Introduction

What happens when dissident mobilization accelerates so rapidly that it overwhelmsthe state’s coercive or control capacity? This turn of events is called bandwagonmobilization. We know almost nothing about its dynamics, other than the fact that itshows high levels of acceleration. Bandwagon mobilization occurs when the Rebel’sdilemma is solved and the State’s dilemma (mobilizing sufficient control forces)remains confounding (Lichbach, 1995, 1996). If we assume that in many statesthere is large-scale latent discontent, an event that shows that the state’s repressivecapacity is constrained can accelerate mobilization. Such an event focuses the atten-tive population on the state’s coercive ability. When the minimum-winning mass ofdissidents are able to shut down the repression or overwhelm the state’s coercivecapacity, the door is open to bandwagon participants. The process of bandwagonmobilization usually begins when a typical mobilization grows larger and is eithernot repressed, or the state struggles to repress or contain it. When the populationas a whole sees this process unfold, they are likely to pay closer heed, since oftenbandwagon mobilizations have the potential power to change a regime. Bandwagonmobilization is only instance when we expect the total mobilization in local areas toexceed five percent of the population (Lichbach, 1995).

1Swimming Across: A Memoir. New York: Warner Books, 2001. Describing a scene in the 1956Hungarian rising.


51

52 4 Varied Dynamics of Bandwagon Mobilization

There is little true bandwagon theory in the field of protest and repression. Cer-tainly Granovetter’s (Granovetter and Soong, 1983; Granovetter, 1978) work is rele-vant, because it concerns how mobilization occurs when dissidents have preferencethresholds. Nonetheless, those who have attempted to model the dynamic band-wagon behavior are mostly economists. The work began with telephone demand(Artle and Avernous, 1973; Rohlfs, 1974). These pioneers essentially maximizedsocial welfare functions to model the growth of clamor for telephones. They foundthat bandwagon demand is always logistic in form. This is encouraging, because weknow that mobilization must almost always assume a logistic form.

The economists work wholly within a non-conflict (except for price) environ-ment. We at least have dissidents demanding a public good that the state rejectsinitially. The state, for its part, would seek to minimize a social welfare functionrepresenting the dissidents’ public good. Even Harvey (1996), who writes aboutbandwagon mobilization after women’s suffrage does not have to consider conflict.Also relevant are so-called iceberg models, e.g., Brookmeyer (2006). The idea hereis that the state knows about the consistently active dissidents, but not how manycitizens support them. Once mobilization accelerates logistically to a high slope,the state can do nothing because its coercive capacity is overwhelmed. Timar Kuranoffers propagation models to consider the changes of public opinion Kuran (1995).Kuran considers bandwagon mobilization in a manner close to Granovetter (1978):“Each new person on this upward bandwagon induces additional people to climbon, until the entire population is on board (Kuran, 1995, 71).” Kuran considers onlypublic opinion, not mobilization, which means we need to scale back the idea ofthe whole population. So models and theories are available to us in this realm, butthey require evaluation for their appropriateness for the dynamics of bandwagonmobilization.

The iceberg and propagation models are both univariate procedures. They workwell to show the speed of mobilization to simulate Granovetter (1978) thresholdmodel of preferences, but they are not interactive. Perhaps with bandwagon mobi-lization an interactive model is less important, but the inherent risk in even band-wagon mobilization is repression. As we will see, repression is most likely at thebeginning or end of the bandwagon period, but not in the middle. Below we presentthe raw graphs of daily aggregated bandwagon mobilization. There is a significantamount of graphical variance in our sample cases.

The model we use instead is the standard Lotka–Volterra (predator-prey) simul-taneous equation model (see Chapter 1). This is a widely used model in ecologyand has also been used for protest and repression Tsebelis and Sprague (1989).We do not expect the interactive parameters g and h to be statistically significant,because bandwagon mobilization only occurs in absence of risk. The standard modelis depicted in the tables below for ease in deciphering the meaning of parameterestimates.

4.2 Cases

The cases we investigate comprise: (1) the events of May, 1968 in France; (2) therise of Solidarity and its initial repression in Poland (1980–1981); (3) the fall of

4.3 Results 53

1989 in the German Democratic Republic; (4) the fall of 1989 in Czechoslovakia;(5) the fall of 1989 in Bulgaria; and (6) the fall of 1989 to January 1991 in Romania.We range in time from 1968 until 1991, a relatively short interval, but the one forwhich we have data. We have no reason to believe that the dynamics of previouseras would differ substantially. While the east European cases were part of a systemand obviously interrelated, neither the events of May, 1968 in France nor the rise ofSolidarity in Poland was involved in the 1989 contagion.

Everyone who has researched bandwagon mobilization has always found a logis-tic curve, whether in conflict or economics. That is one thing that we know must betrue, for whatever its detailed shape, all mobilization has the distinctive, yet contin-uous, three-phase pattern.

4.3 Results

The more one looks at French long time series of strike data, the more one is frus-trated by an exasperating gap. That gap represents the 1968 extraordinary events ofMay (Mathieu, 2008). The French government records only blanks for this period,for they never truly measured the massive mobilization that took place. The band-wagon mobilization in France accelerated rapidly after university students began tostrike. They demanded a more modern curriculum and more power for students andlower-ranking faculty. Young factory workers rejected the sloth of their trade unionsand demanded reform. As is typical in bandwagon mobilization in democratic con-texts, public opinion shifted quickly to support of the movement. On 18 May 1968,55 percent of French citizens supported the student and young worker claims, 60percent wanted a new form of society, and 50 percent approved the student andworker strikes. It is this sort of community support that helps to sustain bandwagonmovements.

Table 4.1 shows the statistical results from the Lotka–Volterra model. Note thatthe only parameters to achieve statistical significance represent dissident and statemobilization arising without interaction. The interaction parameters, especially g,which is at the Verge of statistical significance. We will see this pattern in mostcases of bandwagon mobilization: there is almost no statistical indication of inter-action between protesters and the state. In a way, this is part of the definition ofbandwagons. They cannot arise under substantial risk. Also note that the eigenvalues

Table 4.1 France’s Lotka–Volterra results, May 1968Pt = a Pt−1 + g(Pt−1 × Rt−1)Rt = bRt−1 − h(Pt−1 × Rt−1)

Parameter Estimate t-score p(t)

a 0.8669∗ 12.66 0.0001b 0.2783∗ 2.05 0.0429g −0.000000021527 1.97 0.0512h 0.000000737 1.35 0.1818Eigenvalues λ1 = 0.8694 λ2 = 0.0000004739N = 103∗indicates statistical significance


6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 1020

2000000

4000000

6000000

8000000

10000000

12000000

14000000

Fig. 4.1 French March–June 1968 mobilization

of the Jacobian matrix indicate complete stability. Both are positive and within therequired bounds of −1 to 1.

The mobilization figure begins in March when the incipient events began. Theacceleration begins in May and continues through most of the month. The figureends in mid-June when repression reappeared. Once DeGaulle restored gasoline toservice stations at the end of May, most of the bandwagon mobilization halted. Thelogistic form is evident in the upswing of mobilization during May.

The movement in France was sustained throughout the month of May by adapta-tion of the students and young workers. They made bargains with nearby farmers tobuy food directly and therefore more cheaply, and they also made makeshift livingaccommodations on the left bank of the Seine. The movement ended when CharlesDeGaulle, out of the country visiting Germany, was advised to put gasoline backinto gas stations. He did, and that ended the movement, as French people could onceagain drive. There was some clearing out of dissident concentrations by police, butthere were few injuries.

Most of the rest of our cases are related. They occurred because in 1985, MikhailGorbachev became General Secretary of the Communist Party in the USSR. AfterHungary and Poland had reformed their governments, Gorbachev did not act. Infact, in the early fall of 1989, the Warsaw Treaty Organization met and formallydissolved the Brezhnev Doctrine, which held that the WTO had the right to intervenemilitarily when any member faced a challenge to maintain socialism. The fact thatthe USSR had neither intervened in Hungary nor Poland and that Gorbachev scoldedthe GDR’s hard-line leader Erich Honecker on its 40th anniversary was a signal todissidents in the entire bloc that the constraints of mobilization had lowered a greatdeal. Timur Kuran has noted that dictatorship creates false public preferences. It is

4.3 Results 55

only when the dictatorship weakens that it discovers how weak its political supporthas been Kuran (1991, 1995).

Poland’s case is different from the rest of our East European sample. It modelsthe rise of Solidarity, the only free trade union in the Soviet bloc that was approvedon August 1, 1980 and led to rapidly accelerating mobilization. Within a month,Solidarity had Fashion Solidarity, Steel Solidarity, Rural Solidarity, Mining Solidar-ity, and countless other branches. It grew from the workers in the Gdansk shipyardon the first day to 20 million members after two months. By the end of the fall, thestate began to signal opposition to Solidarity. The mobilization was stunning andsurprised the regime in Warsaw as much as anyone else. In 1981 strikes increased,as did the demands of strikers for still more freedom. When the firefighters’ academystruck in Warsaw in December, the regime appeared powerless. In its stead came acommunist military coup led by General Jaruselski. The military leaders imposedmartial law, proscribed Solidarity and its human rights affiliate KOR on December13, 1981. It took a full two weeks of severe repression to stop mobilization anddissident reaction.

The parameter estimates in Table 4.2 are typical for bandwagon mobilization.Since bandwagons form unexpectedly, the state is rarely prepared to deal withthem, even if it possessed sufficient coercive capacity to oppose dissent. It is notsurprising then that a is the only parameter estimate that achieves statistical signif-icance. After all, it represents the rise of dissent in the absence of repression, andthat is what bandwagon mobilization represents. There is likely to be neither muchrepression nor interaction with state forces that generally watch in amazement asmobilization rises exponentially. The Polish case is unusual in the sense that initialmobilization accelerated exponentially and then at its conclusion was crushed withviolence, but took a long time to subside completely. Nonetheless, we do not seethese dynamics reflected in the parameter estimates. As a consequence, the interac-tive parameter estimates reflect nothing happening. Repression in these cases almostalways appears at the beginning of the bandwagon briefly or at its conclusion (as inPoland) in an attempt to regain state control. For the most part, these front and endrepressions do not affect parameters for the whole bandwagon mobilization.

Figure 4.2 shows the shape of mobilization in Poland. Note that the logistic formbegins early on, in August 1980 when Solidarity was approved as a free trade union

Table 4.2 Poland’s Lotka–Volterra results, August 1, 1980–December 31, 1981Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.7974∗ 15.56 0.0001b −0.18202 0.11 0.9149g 0.000158 0.43 0.6665h 0.0000003636 0.11 0.915Eigenvalues λ1 = 0.7973639 λ2 = 0.00003643141N = 153∗indicates statistical significance


8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128 136 144 1520

100000

200000

300000

400000

500000

600000

700000

800000

Fig. 4.2 Solidarity mobilization in Poland, 1980

8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128 136 144 1520

100

200

300

400

500

600

700

Fig. 4.3 Polish repression of solidarity, 1980

by the government. The spikes toward the middle and end reflect the attempt of thePolish government to stop Solidarity and preclude it. For this reason, it is helpfulin the Polish case also to look at repression graphically (Fig. 4.3). The repressionin 1980 was done early after the decision to try to halt a free trade union. It did notsucceed, as Fig. 4.2 shows.

Table 4.3 depicts the German Democratic Republic bandwagon mobilization.Astute readers might recognize that the eigenvalues are the same numbers as the

4.3 Results 57

Table 4.3 German Democratic Republic Lotka–Volterra resultsPt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.2233∗ 2.5 0.0139b 0.412474 1.51 0.1341g 0.0 0 0.9974h 0.000000792 0.89 0.3771Eigenvalues λ1 = 0.2233 λ2 = 0.000000792N = 122∗indicates statistical significance

a and h parameter estimates. This is because, as we saw in Chapters 2 and 3, theupper right cell of the Jacobian matrix (g) is zero, we have another linear algebracontext of a lower triangle matrix, rendering the eigenvalues equal to the value ofthe main diagonal cells. Mostly, this simply confirms that there is little interactionbetween state forces and dissidents in bandwagon mobilizations. In the GDR it wasthe combined action of an orchestra conductor in Leipzig and the supposed lastline of protection in the Socialist Unity Party, the ruling communist part of EastGermany.

There is no great puzzle about the East German case and the cessation of repres-sion in October 1989. Hundreds of thousands massed in Leipzig, facing lines ofpolice. Kurt Masur, director of the Leipzig Gewandhaus Orchestra, phoned thePolitburo in East Berlin and convinced the authorities to withdraw forces. One ofthe most persuasive reasons Masur cited was the surrender of the Kampftruppen(battle troops) of the Socialist Unity Party (the communist party in the GDR). Battletroops were young men who served as the last bastion in the defense of socialismwith weapons issued by the party. These young men wanted nothing to do with therepression of masses of demonstrating citizens.

After Poland and the GDR the rest of the bandwagon mobilizations in EasternEurope were part of a system of the Warsaw Treaty Organization. The USSR hadkept these countries on a short leash and had demanded of all that they not waverfrom socialism. But when USSR General Secretary Gorbachev agreed to nonin-tervention, there was little that the dictators in each country could do about massinsurrection. In Czechoslovakia even the rumor that a student had been shot deadgenerated a massive general strike of secondary and university students. Dispersedin their individual homes, they could not be repressed effectively. Finally, the regimeof General Secretary Husak was forced to accede to dissident demands, and that ledto the release of prisoner Vaclav Havel and a reuniting of Alexander Dubcek andHavel in front of 300,000 Czechoslovakian citizens in November 1989. As Table 4.4shows, we have once again only parameter a statistically significant.

Bulgaria was the most loyal member of the Warsaw Pact and a fairly typical com-munist dictatorship. There was strong repression of the Turkish minority in Bulgariaand quite slavish following of the USSR’s orders of any day. In the beginning of1989 mass numbers of ethnic Turks fled into Turkey, but by the late autumn the


Table 4.4 Czechoslovakia’s Lotka–Volterra results, September 1 – December 31, 1989Pt = a Pt−1 − g(Pt−1 × Rt−1)Rt = bRt−1 + h(Pt−1 × Rt−1)


a 0.325∗ 3.75 0.0003b −0.1414 0.09 0.9307g −0.000146 0.01 0.9969h −0.00000045 0.07 0.9428Eigenvalues λ1 = 0.3250635 λ2 = −0.000064N = 122∗indicates statistical significance

Table 4.5 Bulgaria’s Lotka–Volterra results, October 15, 1989–December 31, 1989Pt = a Pt−1 + g(Pt−1 × Rt−1)Rt = bRt−1 − h(Pt−1 × Rt−1)


a −0.09421 0.15 0.8788b 0.995774∗ 3.69 0.0004g 0.433663 0.94 0.3515h 0.000022 0.46 0.6443Eigenvalues λ1 = −0.7059189 λ2 = −0.52343N = 78∗indicates statistical significance

dictator Zhvivkov was ousted and replaced by Petar Mladenov. Opposition politicalparties were authorized in December 1989. This was a nonviolent transition thatled to a long period of political conflict over the level of democracy. But we endedour data series after the Zhvivkov regime resigned. It was only then that Bulgariabegan its torturous journey toward democratic government. The Bulgarian results inTable 4.5 measure a shift from parameter a to parameter b as the only statisticallysignificant coefficient.

As Fig. 4.6 shows, the mobilization in Bulgaria looks a good deal more episodic,but even so it is in logistic form. The major government concessions came in thewake of the spiked mobilization in center of the figure (see the number of 36 onx-axis). The Zhvivkov regime did not so readily surrender, and even after it did,the transition leaders were from the communist party, which maintained dissidentmobilization through December 1989. In fact, if one looks closely at Fig. 4.6, it isapparent that another logistic bandwagon mobilization began (see after number 52on x-axis). This one was smaller, which we would expect from existing theory, forexample, Granovetter (1978 and DeNardo (1985). Both Granovetter and DeNardoclaim that after a state makes policy concessions, mobilization should dampen, sincemany more people’s preference thresholds have been satisfied. Throughout, theBulgarian mobilization, there were only two days where any repression occurred.This is important, because it is difficult to generate bandwagon when dissidents arefalling from state repression.

4.3 Results 59

7 14 21 28 35 42 49 56 63 70 77 84 91 98 105 112 1190

200000

400000

600000

800000

1000000

Fig. 4.4 GDR mobilization, September–December 1989

7 14 21 28 35 42 49 56 63 70 77 84 91 98 105 112 1190

1000000

2000000

3000000

4000000

5000000

Fig. 4.5 Czechoslovak mobilization, October–December 1989

It is apparent in Fig. 4.7 that there was only one instance of repression in Bul-garia, and that came near the beginning of the bandwagon time series. So, there wasno repression to impede the second and subsequent bandwagon mobilizations.

Romania’s case is clearly different from the previous others. While it too remainsin stable equilibrium, all its parameter estimates in Table 4.6 achieve statistical sig-nificance. Obviously, there was a great deal of interaction in the Romanian case.We also allowed a longer time series in this case because it took a long time for the


4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 760

20000

40000

60000

80000

100000

120000

Fig. 4.6 Bulgarian mobilization, 1989

4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 760

10

20

30

40

Fig. 4.7 Bulgarian repression, 1989

interaction to settle down. This was the only East European revolution that includedviolence between the state and the dissidents. This violence remained active afterthe surrender of the previous regime. In fact, this was a case of regicide, where theold dictator Ceausescu and his spouse were executed after a revolutionary coup.

Romania stayed outside of the Warsaw Pact and was not a client state of theUSSR. Nonetheless, it was a communist dictatorship of a harsh character that tol-erated no dissent. The dissidents had to rely on disaffected members of the regime

4.3 Results 61

Table 4.6 Romania’s Lotka–Volterra resultsPt = a Pt−1 + g(Pt−1 × Rt−1)Rt = bRt−1 − h(Pt−1 × Rt−1)


a 0.1685∗ 3.5 0.0005b 0.1827∗ 2.99 0.0029g 0.00135∗ 5.58 0.0001h 0.0000125∗ 8.33 0.0001Eigenvalues λ1 = 0.169951375 λ2 = −0.001438875N = 416∗indicates statistical significance

before the bandwagon could begin. Perhaps it is because this is a longer time-seriesthan our other cases, but more likely it is the violence that characterized Romania tomake all parameters statistically significant. It is our only such case, and thereforerepresents an outlier.

Romania was the only east European case we have where the regime excludeditself from the Soviet bloc. The other members of the bloc fell without (or withoutmuch) violence, but Romania looked nowhere else for policy guidance. The countrywas also poorer both in standards of living and education than our other cases. Asin Bulgaria, it took longer for acceptable transition officials to succeed. Our timeseries runs from November 15, 1989 to January 19, 1991. As Fig. 4.7 shows, therewere a series of bandwagon mobilizations in Romania.

Fig. 4.8 Romanian mobilization, 1989–1991


21 42 63 84 105 126 147 168 189 210 231 252 273 294 315 336 357 378 3990

200

400

600

800

1000

1200

1400

Fig. 4.9 Romanian repression, 1989–1991

In order to understand the episodic mobilization in Romania, it helps to havea view of the repression, which was stronger in Romania than in any of our othercases. Figure 4.9 shows Romanian repression, heavy at the outset, but then recurringoccasionally along the time series until late 1990, when it decreased significantly.Romania is a different case because repression never ceased fully during the band-wagon period. Figures 4.8 and 4.9 show how waves of mobilization rose most justafter repression was high. This is the sort of backlash protest we see under normalconditions. In this, the most interactive case we have, there is much less evidenceof logistical mobilization. More spikes of mobilization emerge toward the end ofthe period in the figures than at the outset, after repression virtually disappeared.Romania represents a much more interactive bandwagon than we see in any othercase. This accounts for the results shown in Table 4.6, our single situation where allthe model’s parameter estimates are statistically significant.

4.4 Discussion

We know now that all of these bandwagon mobilizations are stable, but also thattheir dynamics vary a good deal. All arise logistically, but tend to end either sud-denly or to peter out in extended fashion. Beginnings are short and long. Sustainedmobilization is also both short and long. Can we infer anything from the statisticalresults and graphical evidence about the dynamics of bandwagon mobilization? Letus proceed systematically.

4.4 Discussion 63

First, what accounts for the acceleration rate of the bandwagon? The two mostimportant factors are the level of repression and the probability of making a dif-ference. If repression is present, mobilization will arise slowly; if it is hidden butavailable, the rate of mobilization should be higher; and if it is nonexistent or over-whelmed, the rate of mobilization should be highest. Typical of bandwagon mobi-lizations is repression at the outset or arising suddenly to end mobilization. If itoccurs at the outset, then once it subsides or dissolves, especially if the state admitsit was wrong to repress, bandwagons can start; after all, if the state admits it waswrong, it is a weak state and therefore vulnerable to dissent. Bandwagon dissidentsact only when it appears that a regime transition might occur.

If the repression is nonexistent until the end of the mobilization period, thenbandwagons are logically possible as well. If mobilization swells beyond the state’sexpectation and its coercive capacity, sometimes it attempts to increase its repressionability behind the scenes. If it is able to generate sufficient coercion ability, then itwaits until the bandwagon begins to falter and moves to shut it down. This is whathappened in France in 1968.

Second, what halts a bandwagon? Obviously severe repression can do the trick,but bandwagons typically overwhelm state capacity. Endings of bandwagons havenot been analyzed, or even much considered, but we have data and graphical infor-mation, so we can think about this problem systematically. There are several pos-sibilities: (1) the regime grants sufficient concessions to satisfy most of the dissi-dent population; under this circumstance, DeNardo and probably Granovetter wouldsay that most would simply resume their normal lives (DeNardo 1985; Granovet-ter 1978); (2) the regime augments its coercion capacity and begins to apply it;bandwagon dissidents do not tolerate repression and would go home, which waswhat happened in Burma in 1989 (see Chapter 3); and (3) the state grants conces-sions in intervals with some repression; this is the situation that obtained in Bulgaria,the GDR and Romania; it leads to several bandwagons, but usually with smallermobilization in each; if the regime gives up, mobilization rises; if it represses, band-wagons halt.

Of the bandwagon ending possibilities, the first is represented by Czechoslo-vakia, Poland and France. In the Czechoslovak case, the Husak regime really didsurrender to dissent. Once Vaclav Havel and Alexander Dubcek stood together inPrague in front of hundreds of thousands of citizens, mobilization stopped. ThePolish Solidarity mobilization arose swiftly and then once all the divisions werecreated, street mobilization stopped. The French government gave some indicationthat it would implement reforms, but it did not. Bandwagons did not arise after it wasclear that the DeGaulle regime had stonewalled the young dissidents. Bandwagonsare difficult to restart after mobilization has collapsed totally.

We have no instances of the second bandwagon ending possibility in our sample,mainly because after the fact these instances are not considered bandwagons. Agood example is what happened in Burma after the 1988 election was annulledby the military junta (see Ferrara, 2003). There was massive mobilization at levelsbeyond even the Burmese government’s ability to repress. So the regime, sayingit would accede to the dissident demand to free political prisoners, actually freed


rapists, armed robbers, and murderers. The population instantly left the streets andretreated to their homes, locking themselves in. Another example is represented bythe quote at the outset of this chapter. The Hungarian revolution was definitely abandwagon mobilization, but its repression by the Soviet Red army dominates ourimpressions today.

As noted earlier, we have three instances of the third possibility: piecemealconcession to dissident demands. Here we expect that the bandwagons wouldsubside and regenerate, albeit at lower levels, and that appears to happen in thefigures (4.4, 4.6 and 4.7) representing these cases. Once the regime itself surrenders,bandwagon dissidents are more likely to be present than are standard protesters,since bandwagons are for the future and one’s place within it.

Bibliography

Artle, Roland and Christian Avernous. 1973. “The Telephone System as a Public Good: Static andDynamic Aspects.” Bell Journal of Economics and Management Science 4(1):89–100.

Brookmeyer, Ron. 2006. Statistics: A Guide to the Unknown, chap. Modeling an Outbreak ofAnthrax, pages 197–209. Belmont, CA: Thomson.

DeNardo, James. 1985. Power In Numbers: The Political Strategy of Protest and Rebellion. Prince-ton, NJ: Princeton University Press.

Ferrara, Federico. 2003. “Why Regimes Create Disorder: Hobbe’s Dilemma during a RangoonSummer.” Journal of Conflict Resolution 47(3):302–325.

Granovetter, Mark. 1978. “Threshold models of collective behavior.” American Journal of Sociol-ogy 83(6):1420–1443.

Granovetter, Mark and Roland Soong. 1983. “Threshold Models of Diffusion and CollectiveBehavior.” Journal of Mathematical Sociology 9:165–179.

Harvey, Anna L. 1996. “The Political Consequences of Suffrage Exclusion: Organizations, Insti-tutions, and the Electoral Mobilization of Women.” Social Science History 20(1):97–132.

Kuran, Timur. 1991. “Now Out of Never: The Element of Surprise in the East European Revolutionof 1989.” World Politics 44(1):7–48.

Kuran, Timur. 1995. Private Truths, Public Lies: The Social Consquences of Preference Falsfica-tion. Cambrdige, MA: Harvard University Press.

Lichbach, Mark I. 1995. The Rebel’s Dilemma. Ann Arbor: University of Michigan Press.Lichbach, Mark I. 1996. The Cooperator’s Dilemma. Ann Arbor: University of Michigan Press.Mathieu, Lilian. 2008. “The Spatial Dynamics of the May 1968 Demonstrations.” Mobilization

13(1):83–97.Rohlfs, Jeffrey. 1974. “A Theory of Interdependent Demand for a Communication Service.” Bell

Journal of Economics and Management Science 5(1):16–37.Tsebelis, George and John Sprague. 1989. “Coercion and Revolution: Variations on a Predator-

Prey Model.” Mathematical and Computer Modelling 12:547–559.

Chapter 5Dynamics and Stability in Civil Wars

Both [North and South] deprecated war, but one of them wouldmake war rather than let the nation survive, and the otherwould accept war rather than let it perish, and the war came.

Abraham Lincoln

5.1 Introduction

Interstate and colonial wars have now mostly vanished, leaving civil wars as themost common form of armed conflict. We know a great deal about the correlatesof civil wars, but almost nothing about their underlying dynamics, such as theirunderlying structures or mechanisms. Are they stable? If these conflicts stay inequilibrium, what sort of equilibrium is it? If they are unstable, how? Is there anoscillation pattern or a divergent path toward infinite growth or total decay? Howfar and how fast does the war move away from a stable point or plane? These arethe questions this chapter attempts to answer. The concept of stability is prior inimportance to almost anything else, even the correlates of civil wars. It is surprisingthen that there is no theory predicting the dynamics of civil war. Certainly, somepapers have attempted to model the dynamics of at least civil war duration, but notthe mechanism of the war itself (DeRouen and Sobek (2004)). We work instead bynecessity from a series of inductive conjectures.

In order to understand the critical nature of stability, it is necessary to considereach civil war as a system. Both sides act against one another, mostly without directcommunication. Nonetheless, they interact almost constantly on the battlefield.

A civil war could remain stable (i.e., in equilibrium) if the conflict casualtiesduring most of the war formed a white-noise random pattern or was relatively con-stant in casualties over time. That civil wars might periodically dip into stabilityduring sieges is clear, but even some battlefield conditions might promote equilib-rium. Great-great grandfather Allen Francisco, a lieutenant in the 126th regiment ofthe Illinois Volunteers, fought with the Union army during what he called “The WarBetween the States.” Late in the war General Grant sent my ancestor’s and two otherIllinois regiments to Arkansas to clear out patches of remaining confederates. Aftera long, trench-based fire fight, Lt. Francisco realized that both sides had probablyrun out of ammunition. So he shouted across the trenches simply to ask the rebelsif they were out. A yell came back, “yes!” The default military policy at the timewas a bayonet charge, but this was late in the war, and the Union army had often


65

66 5 Dynamics and Stability in Civil Wars

eschewed pursuit of rebels lacking ammunition. So great-great grandpa Allen sug-gested that everyone simply turn around and go home. The rebels quickly agreed,and that battle ended peacefully. This suggests to me that rational soldiers and theirlower-grade officers often resort to tactics that protect both sides from further harm.The individual soldier’s preferences should lead to stability in a time-series.

This view is bolstered in many other accounts of civil wars. Younger (1968) forexample, points out that in the Irish civil war:

. . .there was among many Republicans a lack, not of conviction or courage, but of heart inthe fight. They wanted to make their protest as urgently as they could and to keep on makingit; they wanted to stop the Provisional Government from working the Treaty and buildingup an administration. They did not want to take life if they could avoid it, and neither didmost of the Provisional Government troops, and so flights of bullets hurtled through the airharmlessly as migrating birds. The air above Ireland was crisscrossed with busy bullets withno particular object in view.

These soldier-based views increase the likelihood that civil wars remain in equilib-rium. Soldiers are risk-averse; their casualties are generally far lower than are civil-ian casualties in a civil war, which are often nine times higher (see Mason, 2004).

Nonetheless, there are also many reasons to predict that civil wars would beunstable. After all, unlike normal, everyday protest, civil wars last a long time. Theirorganization arises from battles, not more common street mobilization. Civil warstherefore should be cyclical, since no army fights a battle continuously. Armies andterrorists are paid fighters; hence, they are likely to fight when required and to beidle most of the remainder of the time. All these factors lead one to believe thatunlike most protest, civil wars oscillate and remain unstable.

This might seem like a purely academic concern. In part it certainly is, but sincecivil wars absorb the highest human costs, suffering is likely to be even greaterin unstable wars. A conflict with expanding oscillation creates the condition forcitizens approaching Olson’s (1993) roving bandits. Raids and battles are unpre-dictable. A quiet village in the morning becomes a cauldron of battle in the after-noon. Stability renders a war less harmful, both to troops and to civilians. Highlevels of instability would certainly help to explain why civil wars have emergedas the worst form of social conflict. The effects of civil wars extend far beyondthe battlefield: the actual process conflict loses predictability both for commandersand citizens; civilian casualties always outnumber military losses; and civil warsgenerate the highest proportion of casualties of all types of conflict. Readers canfind in E.L. Doctorow’s The March (2005) a gruesome, horror-filled account of anapparently unstable civil war.

Most of the published literature on civil wars concerns the correlates of war:their causes, endings, and an array of factors that affects them. We know that thereare civil wars spawned by greed versus those born of grievance (Collier and Hoef-fler, 2001; Regan and Norton, 2005; Ron, 2005; Dunning, 2005; Fearon, 2005;Humphreys, 2005). We have learned that mountain ranges inhibit movement, yetincrease mobilization; ethnic rivalries fuel conflict, but poverty dominates ethnicconflict as a spur to mobilizing rebellion (Fearon and Laitin, 2003). Both eth-nic homogeneity and foreign military intervention raise the level of casualties incivil wars (Lacina, 2006). Veto players are critical in the duration of civil wars

5.2 The Cases 67

(Cunningham, 2006). Military victories often lead to genocide but reduce the proba-bility of recurrence of war. Intervention and negotiated settlement preclude postwargenocide, but bring a much higher probability of recurrence (Licklider, 1995).

We know much less about the fundamental mechanisms of these conflicts. StevenGarrison (2002, 2008) has shown that most civil wars begin as tactical co-evolution:both sides adapt to one another. Either one side defeats the other early on or bothadapt at the same rate, allowing the conflict to continue and to escalate. It is thisco-evolution and two-sided adaptation that bolsters the conjecture that civil warsare unstable. After all, if both sides augment their capability over time, we wouldexpect casualties to increase sharply.

This chapter investigates the most fundamental mechanism of civil wars: theirequilibrium (see Bendor and Swistak, 1997; Adam, 2003). Do they oscillate orinflate exponentially as expected by many observers, or is conflict constrained suchthat stability is maintained, as foot soldiers would prefer? If civil wars vary in termsof stability, divergence, or oscillation, what accounts for such differences? Are geo-graphically widespread wars more likely to oscillate, whereas spatially containedones remain stable? Or do wars that end suddenly with a military victory showstability, while wars that have a long period of final battles (such as the US or theRussian civil war) do not? Were that to be the case, then an unfinished, still violentcivil war (such as Colombia) is far more likely to be unstable.

5.2 The Cases

We explore the question of stability and dynamics with seven dissimilar (in type,scope, space, and time) civil wars: (1) the Archidamian war (the first 10 years ofthe ancient Greek Peloponnesian war [431-421 BCE]); (2) the US civil war from1861 to 1865; (3) the Russian civil war, including the Allied intervention and thePolish-Russian war, 1917–1920; (4) the Irish civil war, 1922–1923; (5) the Spanishcivil war, with German and Italian intervention, 1936–1939; (6) the El Salvador civilwar, 1979–1991; and (7) the ongoing Colombian civil war, for which we have datafrom 1988 to 2004.

The principle of most-different systems (Przeworski and Teune, 1970) formedthe basis for our case selection. While all these conflicts are civil wars, they occurat different times and in different regions. Moreover, each has a unique cause thatsparked the fighting. While the population of the countries differs greatly, size isalways uniquely relative to the conflict. In a small war, rebel casualties should belower, but so also should state casualties. It is their relation to one another that mat-ters, not absolute size. Geographic scope matters much more. The larger the physicalsize of a country, the more probability that there are multiple fronts. Multiple frontscomplicate matters and generate higher likelihood of a more diffuse and oscillatingconflict. In order to provide an orientation to the character of these varied conflicts,we present a short capsule description of each civil war.

Sparta attacked the city–state of Athens in ancient Greece in 431 BCE. The first10 years of this classic Peloponnesian civil war is called the Archidamian war after


the name of the original warring Spartan leader. It is one of the best documentedancient conflicts, which enabled us code it. We include it here because we are inter-ested in civil war mechanisms. Were ancient conflicts, before firearms even existed,different in their dynamic processes than more modern wars? There were certainlygreater periods with no events in this war, but also extremely intensive conflict withheavy casualties.

The US civil war occurred in the nineteenth century because groups in the Southsought to retain their position as slave-holding elites. Although abolitionists in theNorth actively opposed this state of affairs, South Carolina initiated the war at thefederal Fort Sumpter in the spring and summer of 1861, after which a cascade ofsouthern states left the Union. In the early years, the war might have gone either way,but eventually, the Union amassed more resources and military troops than could theConfederates. In April 1865, General Lee surrendered to General Grant, followedlater by Johnston’s surrender to Sherman. The last battle of the war occurred inTexas in April 1865, and the war ended resolutely when the last skirmish took placein May in Missouri.

A far different state of affairs served as backdrop in Russia. The Russian civilwar was the result of the Bolsheviks’ refusal to accept a free-election result inthe Constituent Assembly. As the Bolsheviks destroyed the assembly, conservativeWhite forces, leftist Social Revolutionary groups (who had won a majority of seats)and ethnic Cossacks and Chechen groups organized to fight them, as did the West-ern allies of World War I, Japan, and the Poles later in the war. In the end, LeonTrotsky was able to mobilize and discipline the Bolshevik forces and garner suffi-cient resources to defeat every foe except the Poles, who signed a peace treaty thatallowed the communists to shut down the only remaining White opposition in 1920.

The Irish civil war emerged from a fight between factions of the Irish Repub-lican Army. When the British offered Ireland “free state” status in the form of atreaty on December 6, 1921, most IRA members saw it as a victory over Britishimperialism. Many others saw the continued British presence in the six counties ofUlster as unacceptable. Tempers flared in the spring and summer of 1922 when afull-scale civil war began. It lasted barely a year, but the consequences of the warhave dominated Irish politics to this day.

The Spanish civil war also began as a response to an election victory: the 1936triumph of communists, socialists, and anarchists. The military, with which the newgovernment clashed, rose from a base in Morocco and soon controlled most of west-ern and northern Spain. General Franco ascended to the apex of command in 1936and brought in large-scale military aid from Nazi Germany and fascist Italy. Demo-cratic European governments sympathetic to the Republicans in Madrid remainedneutral during the civil war. This posture doomed the Republicans; the governmentwas forced to yield all the gold in Spain’s treasury in order to obtain poor qualityweapons from the USSR. The Republicans further reduced their probability of win-ning when the communists violently attacked both socialists and anarchists. Slowlyat first, and then working systematically, the Nationalist fascists defeated the Gali-cians and Basques in the north, then the Catalonians within and around Barcelona,and finally Madrid itself in the spring of 1939.

5.3 The Data 69

It should be clear that the Archidamian, US, Russian, Irish, and Spanish civilwars are independent of each other and not linked in any way. If our Latin Americancases relate to each other, it would be a remote connection and one that should notaffect the underlying mechanism of the wars. El Salvador’s civil war began withmilitary subversion of an election in 1979, escalated quickly to a civil conflict andended with a negotiated agreement in early 1992. This was a war punctuated bydeath squads (comprised of police and military troops) murdering teachers, unionofficials, and peasants in the cities, as well as three horrific 1981 massacres in ruralvillages, while the leftist forces remained entrenched in caves and jungle sanctu-aries. Everywhere, citizens experienced many terror tactics, especially kidnapping,bombing, and raids on prisons and police stations.

In South America, the Colombian conflict represents our only case of a continu-ing civil war. This war began 40 years ago, progressed in fits and starts, then accel-erated sharply in 1989, around the time the El Salvador battle in central Americawas winding down. The Colombian civil war’s great escalation began when leftistguerrillas undertook a set of market and contract solutions (Lichbach, 1995) of col-lective action with cocaine cartels. The guerrillas protected the cocaine business inexchange for large sums of money to pay leftist fighters and to procure weapons.This caused the United States to step up aid to the Colombian government. Theseaugmented resources sustained both sides and considerably worsened the conflict.

5.3 The Data

Civil war data required for stability tests were unavailable until recently. The stan-dard forms of existing data (e.g., the Correlates of War) preclude the kinds of testswe need to conduct. Typically civil war data heretofore are aggregated yearly and inordinal form. We need interval capture and casualty data aggregated daily in orderto determine the dynamics of interaction on the smallest available time scale. Theseven civil wars investigated in this chapter are the only ones I am aware of that offeror yield the requisite form of data. The Archidamian war was coded with six historybooks, of which Thucydides was clearly the most important. I used six referencebooks compiled by military historians to code the US civil war. Even though theConfederates burned all their military records at the end of the war, this case wasthe easiest to code. The Russian civil war required almost twenty volumes repre-senting all facets of the conflict. The brief Irish civil war was covered well by twomilitary histories and one anti-treaty general’s memoirs. The Spanish civil war took10 histories as well as two sampled microfilmed newspapers, the Times of Londonand the New York Times. The data for our El Salvador case comes from Steve Garri-son’s coding from Lexis-Nexis. Presented in raw form, these data require extensivework to clean and expand event series. Nonetheless, they have events sufficientlydetailed to aggregate daily. All these data, including El Salvador, are linked in theiroriginal format on my data site (see http://web.ku.edu/ronfran/data/index.html). OurColombian data were generously provided from a project led by Michael Spagat, a


professor of economics at the University of London. These dense, archival codeddata run from 1988 to 2004 but include only aggregated injuries and deaths: thereare no data on captured troops.

We have sufficiently dense interval data on all of our civil wars for daily aggrega-tion. This may seem surprising in such durable conflicts. Most people are unawarethat in civil wars battles or skirmishes occur virtually every day in various loca-tions and on different fronts. Such was the case in all of our civil conflicts. Thedata density allows us to use mainstream mathematical and statistical methods toanalyze stability and equilibrium; in fact, because the data are so dense and notnearly as episodic as most history textbooks indicate, we have a higher probabilityof stability.

5.4 Models

This chapter’s principal goal is to test the dynamics and stability of civil wars.The putative opposite of stability is divergence, oscillation or what Mandelbrot andHudson (2004) call wild randomness. In their terms, we seek to find regularity, but“sometimes this regularity can be direct and awesome, at other times strange andwild” (Mandelbrot and Hudson, 2004, 30). The kind of regularity we seek is at bestwhite-noise random. We could perform spectral analysis (see Hamilton, 1994) totest for white-noise randomness, but this is a univariate procedure. We are interestedin interactive, system-level effects. For the complete mechanism, we must turn to aproper system of equations model.

The model appropriate for civil wars should match the goals of each side. Aswe noted in Chapter 1, the best choice for a model is based on the principle ofcompetitive exclusion (Luenberger, 1979, 328). The competing-species model isbased on this principle and is therefore the structure of choice when investigatingcivil wars, since it reveals the mechanism of two opponents trying to defeat oneanother. Its underlying thesis is the principle of competitive exclusion, preciselywhat each side in a civil war seeks to accomplish. This model allows us to discoverstability in its differential-equations sense, although we use difference equationsbecause we cannot assume continuity. We compute the eigenvalues of the parameterestimates (i.e., partial derivatives) in the Jacobian matrix of the competing speciesmodel to evaluate the mechanism of the war system; this is an appropriate structuregiven our data (Murray, 1993).

Because this is the only chapter that uses this model, we introduce it again. Thecompeting species model is an ordinary differential-equations model:1

1 The standard competing species model in biology divides terms on the right side of the equationby K, a measure of the ecological carrying capacity. Since there is no significant limit on carryingcapacity in civil wars, we assume K = 1.

5.5 Results 71

d R

dt= a Rt − m(RtSt) (5.1)

d S

dt= bSt − n(St Rt)

where, R is the rebel-induced state casualties; S is the state-induced rebel casual-ties; a is the state casualties in the absence of interaction; m is the the decline ofrebel and state casualties with interaction; b is the rebel casualties in the absence ofinteraction; and n is the the decline of state and rebel casualties with interaction.

Since we use interval data aggregated daily, we cannot assume continuity. Evenin civil wars, fighting is not continuous. Soldiers, unlike protesters, do not returnhome at night, but neither do they (usually) fight both day and night. Therefore,we need to transform the above model into a system of difference equations. Weare interested in how the resolute loss of fighters affects each force. It makes littlesense to employ the total number of troops on each side as a variable in civil waranalysis; the number rises in great battles and falls in skirmishes and quiescence.A better method is to consider the actual loss of soldiers. So, as noted earlier, weuse the number of state forces that were lost for rebel power and conversely howmany rebel casualties the state caused. The other side must be defeated in a civilwar; therefore the objective is to eliminate as many opposing troops as possible.

With these principles in mind, the tested difference equation model becomes(with R, S and the parameters retaining the meaning designated in the differentialequation model above):

Rt = aSt−1 − m(Rt−1St−1) (5.2)

St = bRt−1 − n(Rt−1St−1)

The parameter estimates emerging from each test form a Jacobian matrix. Ourtwo exactly identified equations yield a square matrix that allows us to computedeterminants as well as eigenvalues and eigenvectors. Our principal indicators ofstability are eigenvalues that arise from the Jacobian matrices. The interpretation ofthese eigenvalues is similar in the worlds of difference and differential equations,but not identical. See Chapter 1 for details.

5.5 Results

Do battles happen occasionally with great casualties, rendering the mechanism ofcivil wars unstable? Or can soldiers maintain sufficient control of the war so that itis able to maintain equilibrium?

We test the entire systemic mechanism of civil wars. We take our conflictschronologically, starting with the ancient Greece Archidamian war. Tables for eachcivil war indicate parameter estimates, t-values, and the probability that the t-score.The eigenvalues from the Jacobian matrix are also displayed in each table.


Table 5.1 Competing species test results for the Archidamian war (431-421 BCE)Rt = aSt−1 − m(Rt−1∗St−1)St = bRt−1 − n(Rt−1∗St−1)

Parameter Estimate t-ratio p(t)

a 0.2459∗ 13.47 0.0001b 0.3221∗ 17.61 0.0001m 0.00016 1.92 0.0547n 0.000212∗ 2.07 0.0387Eigenvalues λ1 = 0.2461096 λ2 = 0.00000241681N = 3, 209∗indicates statistical significance

Table 5.1 shows the ancient Archidamian war results. While this is a conflictalmost one and one-half millennial old, it fit the competing species model well.Three parameter estimates are statistically significant and the fourth (m) is close tosignificance. The eigenvalues portray a conflict with much interaction, but a fullystable mechanism. Phase-plane plots of this war system would show motion onlytoward the origin.

It is somewhat remarkable that a conflict that had no firearms, no mechanizedweapon system and no motorized ships still fit the model parameters well. Thisindicates that like size, primitive weapons systems do not deter battles and killing.Since both sides used arrows as weapons, the soldiers were often almost as far apartas in more modern wars.

Our next case is the US civil war. As we will see in the Spanish civil war, therebels in the US civil war showed more initiative and activity in the absence ofinteraction than did the Union forces. Interaction accelerated Confederate casualties,even while it damped Union losses. The US data indicate a quiescent war in Virginia,an active one in the south and west during during the early years of the war, andalmost complete Union dominance everywhere from 1864 onward. The system’seigenvalues (Table 5.2) indicate stability. In fact the eigenvalues easily meet thestability standard for difference equations.

The US civil war is a study in extraordinary mobilization (on the Union side) toelude quick defeat (by the rebel South). The Union lost a great many more troops

Table 5.2 Competing species test results for US civil war (1861–1868)Rt = aSt−1 − m(Rt−1 × St−1)St = bRt−1 − n(Rt−1 × St−1)


a 0.1559∗ 3.84 0.0001b 0.2629∗ 6.8 0.0001m 0.0000349∗ 6.39 0.0001n 0.0000086812∗ 2.01 0.0446Eigenvalues λ1 = 0.1559588 λ2 = −0.000050153N = 1, 599∗indicates statistical significance

5.5 Results 73

over the course of the war, but its huge resource advantage fueled its victory, albeit instages. Victory in Virginia in April 1865 was followed by the surrender of Johnstonto Sherman and then finally to triumph in the west. The course of the war, and evenits uneven ending stayed within the bounds of stability.

The Russian civil war is the most complex in our sample of large-scale civil con-flicts. More armies faced the Bolsheviks than faced any other government. The Bol-sheviks were challenged by several White armies, Social Revolutionaries, Chechens,Cossacks, Americans, British, French and Japanese intervenors as well as a full-scale series of battles with Poland. In 1920 the Poles fought the Bolsheviks to a draw,after which the Bolsheviks secured a peace treaty with Poland, and then set aboutdefeating the remaining clutches of White armies around Petrograd and in Ukraine.They had already driven all the foreign Western troops out, and chased the Japanesefrom Siberia. Leon Trotsky marshaled his forces well after the first year of the warwhen outcomes were seriously in doubt. The Bolsheviks mobilized resources andtroops to a higher level than any of their many opponents. When the war ended,it ended resolutely with White forces running for boats in Crimea. From late 1919there was little doubt that the Bolsheviks would defeat the international challengersin Siberia as well as the array of White forces in the west. These victories, saveagainst the Poles, were decisive. Stability makes sense in this type of varied, butabrupt resolution of a conflict, and that is what we found. In our statistical tests,all these players were arrayed against the hegemony of the Bolsheviks. This was amulti-front war fought over huge distances and for three years. These characteristicsseem to imply chaos, but the Russian civil war is not unstable (Table 5.3). Instead itis stable, in other words its phase-plane is damped to the point that the war wouldmove into stability at the origin.

The Irish civil war is the shortest of our seven conflicts, with the active phaselasting only about one year. Nonetheless, it was a multi-front war with a great deal ofviolence, especially in the summer of 1922. By the fall of 1922 the anti-treaty rebelshad been sufficiently vanquished that they henceforth fought a guerrilla campaign.We would expect such a war to be more likely stable than unstable, and that is whatit is. The eigenvalues are bounded by −1 and 1; nonetheless, it is curious that onlyone of the model parameters in Table 5.4 is statistically significant, and that is a, therebel-induced state casualties. But as in the US civil war, the state had overwhelming

Table 5.3 Competing species test results for the Russian civil warRt = aSt−1 − m(Rt−1∗St−1)St = bRt−1 − n(Rt−1∗St−1)


a 0.00189 0.05 0.9636b 0.7535∗ 8.03 0.0001m 0.000425 0.51 0.6077n −0.000881∗ 4.5 0.0001Eigenvalues λ1 = 0.01929293 λ2 = −0.01651193N = 1, 036∗indicates statistical significance


Table 5.4 Competing species test results for Ireland (1922–1923)Rt = aSt−1 − m(Rt−1∗St−1)St = bRt−1 − n(Rt−1∗St−1)


a 0.349∗ 5.23 0.0001b 0.002534 0.06 0.9509m −0.000938 1.4 0.1625n −0.00348 0.37 0.709Eigenvalues λ1 = 0.3490067 λ2 = −0.003487N = 595∗indicates statistical significance

resources. The fact that the conflict’s intense phase was short, followed by low-levelskirmishes and guerrilla tactics probably accounts for the worst model fit in ourseven conflicts. The Irish civil war is the only one in our sample that did not last atleast one thousand days.

The Spanish civil war saw a great deal of foreign intervention, albeit only onthe side of the fascists. General Franco was able to mobilize aid from his allies,while the leftist government could not force France and the United Kingdom out oftheir neutrality. The mechanism of the war is stable; the entire system remained inequilibrium. Parameter b in Table 5.5 represents rebel casualties in the absence ofinteraction. Interaction accelerated the casualties on the state side, rather than damp-ing them as the competing species model specifies. But the system’s eigenvaluesindicate the same sort of convergence we saw in the other cases. Both eigenvaluesare positive and bounded by zero and one.

El Salvador received some resources for the military government, but little forthe rebels. The war was largely confined within the El Salvador northern “red zone”and had few major battles. It was a guerrilla-based conflict and it remained in equi-librium as it wound down toward a negotiated settlement.

The El Salvador civil war is a paragon of equilibrium (Table 5.6). Its eigenval-ues both indicate stability, indicating that the system stays in equilibrium. Why thestability? First, it was a long war. The data begin at its commencement and endnear the negotiated cessation of active conflict. Second, while its casualties were

Table 5.5 Competing species test results for the Spanish Civil War (1936–1939)Rt = aSt−1 − m(Rt−1∗St−1)St = bRt−1 − n(Rt−1∗St−1)


a 0.052905 1.12 0.2612b 0.6323∗ 22.55 0.0001m −0.00000992 0.16 0.8765n 0.000178∗ 3.69 0.0002Eigenvalues λ1 = 0.05302369 λ2 = 0.00005930696N = 1, 006∗indicates statistical significance

5.5 Results 75

Table 5.6 Competing species test results for the El Salvador Civil WarRt = aSt−1 − m(Rt−1∗St−1)St = bRt−1 − n(Rt−1∗St−1)


a 0.004843 0.32 0.7497b 0.0804∗ 5.15 0.0001m 0.00048 1.44 0.1491n 0.000416∗ 4.1 0.0001Eigenvalues λ1 = 0.0092254315 λ2 = −0.00396315N = 4, 450∗indicates statistical significance

among the lowest of the seven cases we considered, much of the killing was doneby state-sponsored death squads. Interspersed in the data are three brutal massacresthat literally left no one to lash back at the state. Third, the war was fought on twofronts: (1) in major cities as a one-sided conflict at night (via death squads) and (2)in rural areas as an occasional two-sided conflict on the battlefield by day.

The Colombian civil war is the longest-running conflict we consider. For overfour decades the insurgents have battled Colombian state forces. Our data comprisethe most violent and active era of the whole conflict, from 1988 to 2004. Three ofthe model parameter estimates are statistically significant in our analysis, showing agood deal of intense interaction. Only the n parameter is insignificant in Table 5.7,and that is an artifact of serial correlation correction. The fact that even Colombia,an unfinished, long-term civil war, is stable is an important finding. This is the caseone might predict would move out of equilibrium and oscillate. But it stays in stableequilibrium. Why? It appears that the most basic reason is that all the diverse civilwars we tested are stable. That even Colombia is appears to be a victory of the footsoldier over commanders. It is well known that Colombian army conscripts sell riflesto insurgents and that the two sides often compromise with one another locally.

What is our score at this point? We have seven civil wars in secure stability.We could not have more consistent empirical results. While different conflicts metdifferent parameters with statistical significance, our principal question is answered:civil wars are stable; they remain in equilibrium for the whole of the conflict. The

Table 5.7 Competing species test results for colombia (1988–2004)Rt = aSt−1 − m(Rt−1∗St−1)St = bRt−1 − n(Rt−1∗St−1)


a 0.2296∗ 15.66 0.0001b 0.2271∗ 16.04 0.0001m 0.001734∗ 2.63 0.0085n 0000181 0.21 0.8307Eigenvalues λ1 = 0.23163848 λ2 = −0.00185748N = 5, 138∗indicates statistical significance


Table 5.8 Model fit by civil war (statistical significance)Rt = aSt−1 − m(Rt−1∗St−1)St = bRt−1 − n(Rt−1∗St−1)

Civil war a b m n

Archidamian x x xUS x x x xRussia x xIreland xSpain x xEl Salvador x xColombia x x x

preference of the foot soldier seems to prevail. Let us examine the model fit byparameter for each of our conflicts (Table 5.8).

Table 5.8 tells an interesting tale about the dynamics of civil wars. Parameter bis statistically significant in every case except Ireland, the shortest conflict. So themost likely occurrence in civil war mobilization is the state rising in the absenceof interaction. The state is more active than the insurgents in Russia, Spain, andEl Salvador in the absence of interactive conflict. The state and its enemies wereequally likely to capture, injure, and kill each other in the Archidamian, US andColombian civil wars (parameters a and b). Only in Ireland was the a parameterstatistically significant without its state counterpart. The interactive terms also showdifferences. The parameter measuring the state-induced rebel casualties equationinteraction (n) is statistically significant in the US, Russia, Spain, and El Salvadorcases. Only in the Archidamian and Colombian civil wars are the rebel-induced statecasualties equation parameter of interaction (m) confirmed statistically without itsstate-induced rebel casualties equation interaction parameter significant as well.

5.6 Discussion

Both sides of a civil war seek to win; but barring victory, the worst outcome is adefeat or negotiated settlement that provides territory or special rights to the oppos-ing side. Without an outright military victory, peace is a hazard. This is what impelsa civil war to co-evolve. It causes both the state and its opponents to mobilize fight-ers, resources and better weapons. In large measure, a civil war is a different kind ofconflict from those we generally study in protest and repression. Most modern civilwars do not begin in a fully military fashion as did the US and Spanish civil wars,but rather as persistent protest against a state. A state that is unable to deter, co-optor defeat its dissent, incurs a higher probability of a civil war.

It is a great surprise to find that all of our seven civil conflicts are stable andin complete equilibrium. Almost any veteran can attest that aside from momentsof high intensity, most of the time war is intensely boring. Civil wars offer lessdown time, less predictability, and usually more intense battles than do other kindsof fighting. These factors make civil wars the worst form of conflict human beings

5.6 Discussion 77

have developed. Colombia’s civil war wages on in its fifth decade. As an unfinishedwar, it is surprising that it operates like any other case in our diverse sample. TheUS civil war ended unevenly, with battles raging even a month after Grant acceptedLee’s surrender at Appomattox. Yet despite this messy conclusion, US civil warremains in equilibrium.

The Russian civil war was a wide-ranging contest of many foes against resoluteBolsheviks. Its theater ranged over an incredibly wide arena, from Poland and Pet-rograd to Crimea and Siberia and the Pacific ocean. Denikin’s White army and theforeign intervenors were defeated in 1919, Poland was subdued sufficiently to agreeto a truce in mid-1920, and the Wrangel White army collapsed against Bolshevikpressure in the autumn of 1920. Weak and lacking resources, the opponents suc-cumbed. Mostly fighting only on one or two fronts at time the Bolsheviks claimed adecisive military victory.

Resources matter. The side that can mobilize and assemble the greatest resourceshas a good chance to win a civil war. But resources do not affect stability as muchas they might appear. Conflicts in our sample that mobilized the most resources aremost likely, in relative order, the United States, Russia, Archidamian, Colombia,Spain, El Salvador, and Ireland. Only one side successfully mobilized resourcesin Spain. El Salvador and Ireland were clearly the “cheapest” wars in terms ofresources used. A naval blockade was imposed along the entire Atlantic and Gulfcoasts during the US civil war. And although the British and Confederates were ableto breach the blockade, in general it held and deprived the south of urgently neededmilitary goods and food. It is fitting that Robert E. Lee surrendered during the siegeof Petersburg, Virginia when he could not fight his way outside Grant’s forces andthe crippling naval blockade. The Bolsheviks, after a slow start, ramped up theirresources and fighter mobilization quickly. Trotsky ran the Bolshevik army in thefield in an armored train, which allowed him to move easily from one theater of oper-ation to another. The Bolsheviks could also meet payrolls more easily than couldtheir opponents. One should not minimize the difficulty of finding soldiers who willfight with no compensation. The mobilization in ancient Greece was relatively easyand surprisingly mobile. Since the war was fought in a relatively small area, allsurrounded by sea, it was not difficult to move large numbers of troops quickly toa new fighting venue. The present Colombian civil war is the model for resources:(1) the drug cartels have paid insurgents to protect them; (2) the United States sendsthe arms needed by the state, but troops too readily sell rifles and ammunition to theinsurgents; and (3) the insurgents kidnap wealthy citizens and hold them for ransom.It was the insurgent military that found necessary resources in its fascist allies, evenwhile the government foundered on the neutrality of the UK and France.

El Salvador and Ireland used the fewest resources. To be sure, the military regimein San Salvador secured large-scale help from the United States, some of which wasconfiscated by the rebels. But El Salvador was a classic guerrilla war against anestablished military leadership that sought to use air power and artillery against adisbursed enemy and mostly succeeded in killing civilians. Ireland’s difficult shed-ding of British imperialism led to violent confrontation of two Irish nationalist sides.The pro-treaty, provisional government side managed to secure the resources of the


departing British military, which gave it an immense advantage from the outset ofthe war.

Perhaps one of the reasons we have found consistent and total stability is that wedo not have civilian casualties, which are uniformly higher than military casualtiesin civil wars. Consider General Sherman’s comments in the US civil war: “[wewill] make old and young, rich and poor, feel the hard hand of war, as well as theirorganized armies.” (Weigley, 1973, 149). Civilians certainly suffer more harm fromcivil wars than do soldiers (Mason, 2004). If we could obtain accurate and valid dataon civilian casualties we could undertake stability tests, but these are data collectedby no one, in large measure because the harms are diffuse and often hidden fromofficials.

I am far more inclined to view stability as a consequence of almost constantconflict. History texts emphasize the major battles in several of the civil wars inour sample. Only in coding these conflicts does one realize how important dailyskirmishes are in the portrait of the total war. Often there are small conflicts apartfrom major battles even as these battles rage. These violent interactions emergeconsistently and create a kind of asymptotic global minimum to the data time series.As a consequence, the time series appears as a rather stable series with a few spikesthat account for major battles.

The surprising finding from this study is how much stability occurs in civil wars.That all of our cases would be stable is unexpected. All of this is consistent with thebasic results of stability tests: most human macro-level conflicts remain in equilib-rium (Francisco, 1995, 1996).

Bibliography

Adam, John A. 2003. Mathematics in Nature. Princeton, NJ: Princeton University Press.Bendor, Jonathon and Piotr Swistak. 1997. “The Evolutionary Stability of Cooperation.” American

Political Science Review 91(2):290–307.Collier, Paul and Anke Hoeffler. 2001. “Greed and Grievance in Civil War.” World Bank.Cunningham, David E. 2006. “Veto Players and Civil War Duration.” American Journal of Political

Science 50(4):875–892.DeRouen, Karl R. Jr. and David Sobek. 2004. “The Dynamics of Civil War Duration and Out-

come.” Journal of Peace Research 41(3):303–320.Dunning, Thad. 2005. “Resource Dependence, Economic Performance and Political Stability.”

Journal of Conflict Resolution 49(4):451–482.Fearon, James D. and David D. Laitin. 2003. “Ethnicity, Insurgency, and Civil War.” American

Political Science Review 97(1):75–90.Fearon, James D. 2005. “Primary Commodities and Civil War.” Journal of Conflict Resolution

49(4):483–507.Francisco, Ronald A. 1995. “Coercion and Protest in Three Coercive States.” Journal of Conflict

Resolution 39(2):263–282.Francisco, Ronald A. 1996. “Coercion and Protest: An Empirical Test in Two Democratic States.”

American Journal of Political Science 40(4):1179–1204.Garrison, Steven. 2002. The Long and Terrible Road: The Evolution of Political Protest to Civil

War. Ph.D. thesis, University of Kansas.

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Garrison, Steven. 2008. “The Road to Civil War: An Interactive Theory of Political Violence.”Defence and Peace Economics .

Hamilton, James D. 1994. Time-Series Analysis. Princeton, NJ: Princeton University Press.Humphreys, Macartan. 2005. “Natural Resource Conflict and Conflict Resolution.” Journal of Con-

flict Resolution 49(4):508–537.Lacina, Bethany. 2006. “Explaining the Severity of Civil Wars.” Journal of Conflict Resolution

50(2):276–289.Lichbach, Mark I. 1995. The Rebel’s Dilemma. Ann Arbor: University of Michigan Press.Licklider, Roy A. 1995. “The Consequences of Negotiated Settlement in Civil Wars, 1945-1993.”

American Political Science Review 89(3):681–690.Luenberger, David G. 1979. Introduction to Dynamic Systems. New York: John Wiley & Sons.Mandelbrot, Benoit and Richard L. Hudson. 2004. The (mis)Behavior of Markets. New York: Basic

Books.Mason, T. David. 2004. Caught in the Crossfire: Revolution, Repression and the Rational Peasant.

Lanham, MD: Rowman and Littlefield.Murray, J.D. 1993. Mathematical Biology. New York: Springer Verlag.Olson, Mancur. 1993. “Dictatorship, Democracy and Development.” American Political Science

Review 87(3):567–576.Przeworski, Adam and Henry Teune. 1970. The Logic of Comparative Social Inquiry. New York:

John Wiley & Sons.Regan, Partick M. and Daniel Norton. 2005. “Greed, Grievance, and Mobilization in Civil Wars.”

Journal of Conflict Resolution 49(3):319–336.Ron, James. 2005. “Paradigm in Distress? Primary Commodities and Civil War.” Journal of Con-

flict Resolution 49(4):443–450.Weigley, Russell F. 1973. The American Way of War: A History of the US Military Strategy and

Policy. Bloomington: Indiana University Press.Younger, Carton. 1968. Ireland’s Civil War. London: Frederick Muller.

Chapter 6Conclusion: Stability in Conflict

Popular revolutionary violence was not some sort of boilingsubterranean lava that finally forced its way onto thesurface . . . and then proceeded to scald all those who steppedin its way. . . . (I)t is better to think of the revolutionary elite asrash geologists, themselves gouging open great holes in thecrust of public discourse and then feeding angry matterthrough the pipes of their rhetoric out into the open.

Simon Schama, Citizens: A Chronicle of the FrenchRevolution

The surprising result of our investigations is almost universal stability. In a widevariety of conflictual contexts, stability was the norm. The single exception wasterror in Northern Ireland, which oscillated, but in so damped a fashion that it tooreturns to the origin and a bed of stability. We did not expect this at the outsetof the project. It certainly seemed that civil war would oscillate, moving from bigbattle to big battle with little in between. But the data show that there is much moreaction between large-scale battles than we suspected. There are skirmishes almostevery day between scouting parties and front lines and with individual regiments.All these conflicts smooth the data between the really large fighting and their after-maths. Civil war is the worst form of human conflict, but it is stable. Neither did wethink that bandwagon mobilization would be stable, since it arises exponentially andgenerally collapses exponentially as well. Yet as we saw in Chapter 4, bandwagonmobilizations with widely varying graphs are nonetheless in equilibrium. In thischapter we summarize the most important findings in the project. We try as wellto find important commonalities beyond the stability that lies at the core of ourconflicts.

6.1 Stability is the Norm

Why does stability stand out so clearly as a universal concept in conflict? Thefindings tell us that stability exists, but nothing about the reasons that caused it.Foremost among the likely reasons for stability is that human conflicts are createdby and conducted by humans. As Hardin (1995) noted “How many people wantto be ardently political all the time?” Certainly no one, or least few wish to be inconflict all the time either. Normal life is a fierce competitor to conflict. Most people


81

82 6 Conclusion

have jobs, families, and other responsibilities. After all, civil society alone has manyrequirements and conflict is certainly not one of them. One of the reasons we refuseto assume continuity is that most protesters go home at night and sleep. They do notcontinuously protest. In another project, we found that protesters were even loatheto give up their weekends to demonstrate. The most likely demonstration times overmost of North America and Europe are during lunch from Monday to Friday. Sowhen a rally begins, it is likely to end as soon as its point is made. A few radicalsmay remain, but the vast majority of dissidents find other activities more enjoyableand apparently compelling.

We have discovered in other contexts that conflict is not only stable, but is usuallywhite-noise random. Mark Lichbach has noted that this occurs naturally; as oneconflict starts and proceeds, another does two days later. The first one ends andanother begins three days after the second one. This is precisely how a white-noiserandom and stable conflict situation would be organized. It does happen that way: ahunger strike ends two days after a corporate lockout occurs; a boycott starts threedays after the lockout begins. We have documented these patterns in the data thatunderlie this project and in a wide variety of conflict. Most instances of conflict,then, remain in equilibrium and exhibit a white-noise randomness pattern. Conflictis not a cauldron; it is a means to an end in political disagreement.

6.2 Varieties of Repression in Democracies and Dictatorships

We have seen, especially in Chapters 2 and 3, that not all democracies are equallyrepressive on the street and that even dictatorships vary a great deal in the amount ofstreet repression they create. While one might believe that unitary democratic coun-tries are more likely to repress, federal systems actually have more repression. Thisis in part because our data represent the whole of each country. We discover protesteven in rural or outlying areas. Austria, for example, showed three parameters thatwere statistically significant, as did Belgium, where linguistic differences and con-flict have bifurcated the country’s political structure. Both these countries allow agood deal of local control. Denmark, in contrast, has only parameter estimates in theprotest equation statistically significant. In Germany, a federal country, we foundonly the noninteractive parameters statistically significant. Greece, a less-developedcountry in our sample, had parameter estimates near to Belgium’s. But Iceland andIreland showed little repression or even interaction with dissidents. Iceland was oneof our early examples of linear algebra lower triangle context, which means thatcritical parameter estimates are near zero. Italy and Luxembourg each had but oneparameter significant, while the Netherlands showed neither interaction nor statisti-cal significance. Norway had one noninteractive parameter statistically significant,while the Iberian states shared both parameters in the protest equation statisticallysignificant. Sweden and Switzerland, meanwhile, matched the Norwegian resultsclosely. In none of these countries do we find much repression or interaction.

In contrast, we have France and the United Kingdom with all parameters inboth the protest and repression equations statistically significant. As we noted in

6.2 Varieties of Repression in Democracies and Dictatorships 83

Chapter 2, the fact that each country has only one veto player explains a good dealof the interaction on the street. If the legislature is a silly place to lobby, and onecannot talk to the leader of the country, the street is one of the only options availablefor political action. This is one possible explanation. If it is large countries thathave these properties, then why are they absent in Germany? There is little elsein common between France and UK; they have totally different political systemsand UK does not even have a constitution, although both countries maintain unitarysystems. So the absence of veto players is the best explanation from my perspective.

If democracies are not uniform in their levels of repression, then what about dic-tatorships? During and after Enver Hoxha’s rule, Albania was intensely repressive,claiming some of the largest t-values I have ever seen. Meanwhile Belarus (withonly weekly data) and Bulgaria were not noticeably more repressive on the streetthan are many West European democracies. Burma, in 1988, however, matched thebrutality of Albania. Czechoslovakia’s results show that both repression equationparameters are statistically significant, indicating that the regime was active againstdissidents on the street. The German Democratic Republic was much more subtle;except for a large t-score, one might mistake its results for a West European democ-racy. Hungary looks, if anything, better than a West European democracy. Clearly,the period after the rising in 1956 left Hungary a peaceful and politically carefulcountry. Poland battled against Solidarity almost the entire decade of the 1980s; itsthree significant parameter estimates show activity on the street, but not nearly asactive and violent as in Albania and Burma. Romania is a repressive regime; all theparameter estimates are statistically significant. Yet, its t-values are far higher thanthose in France and UK, indicating much more frequent repressive action on thestreet.

Nor were democratic transitions equally peaceful and democratic. Romaniabefore 1995 did not complete a total regime transition and continued to be repres-sive, even as a non-communist state. Albania continued to repress during its transi-tion, although its t-values decreased markedly. Bulgaria’s results continued to makeit look like a West European state. But the real drama was reserved for the othertransition countries in the sample. We were amazed by the astonishing reductionof repression in Czechoslovakia, the Czech Republic, Hungary, Poland, and Slo-vakia. All these are examples of the linear algebra lower-triangle phenomenon. Theyshow some of the most remarkable changes in regime behavior we have witnessedanywhere. To the great relief of the populations, pacification followed the largelynon-violent revolutions in east central Europe.

Bandwagon mobilizations varied greatly. The only finding they had in commonwas stability. From France in 1968 to the 1989 east central Europe revolutions allhad widely varying graphs, but all were found to be stable. This would be less sur-prising if our sample included only east central Europe revolutionary mobilizations.But we also included the quite separate and major French May 1968 mobilization.And Poland’s 1980–1981 Solidarity mobilizations was not linked directly to 1989.They too are stable.

The most variance we introduced into samples was in our investigations of civilwars. From ancient Greece to current Colombia we had major (Archidamian, US,

84 6 Conclusion

Russian, Spanish, Colombian) civil wars as well as smaller (Ireland, El Salvador)but in all cases, covering (quite unevenly) three millennia, we found systemic equi-librium.

6.3 Convergence in Estimations

Whenever one attempts to estimate time-series data, the technical problem of con-vergence to efficient estimates (when the gradient is zero) must be addressed (seeGreene, 2003). For the most part we experienced no difficulty in gaining conver-gence, even in the shortest time-series of all, the bandwagon mobilizations. Weencountered convergence difficulties only two or three times, usually in long time-series. We then attempted other estimation techniques, even ordinary least squares,to determine whether the parameter estimates we found were close to correct. Weconfronted no serious problems in doing this, which was surprising, given the num-ber of time-series we estimated.

6.4 Correction of Time-Series Pathologies

Time-series estimation always faces the dual hazards of serial correlation of variableerror terms and heteroscedicity. The former of these is by far the most serious pathol-ogy in time-series estimation, but we tested for and corrected for heteroscedicityas well (see Gelman and Hill, 2007 or Greene, 2003). In fact, all the parameterestimates appearing in this volume are corrected for both of these pathologies. Thatwe used seemingly unrelated regression (SUR) was helpful, because then we couldcompare SUR parameter estimates with corrected parameter estimates determinedequation by equation. For the most part, the SUR parameter estimates were close tothe corrected ones. Nonetheless, we entered only corrected parameter estimates andused these in the eigenvalue stability tests.

6.5 When Repression is Absent or Rare

Beyond nearly universal stability, the real surprise from our tests is the numberof cases with almost no repression. For these cases we invoked the linear algebralower or upper triangle concept. Remarkably, these occurred infrequently in long-democratic countries, and mainly in the democratic transitions from communistdictatorships. The new rulers in these countries, at least in the north part of eastcentral Europe, were judiciously conservative in arrest and police violence. In thepolitical mayhem that accompanies transition, it is a tribute to these countries thatthey were even less repressive than the Scandinavians. Would that the whole worldcould act this way!

Bibliography 85

6.6 What Have We Learned?

Conflict is stable, but what else emerged from the investigation of many diverseconflicts? Certainly, the lack of repression in surprising contexts and the necessity toinvoke the lower triangle concept was something we never anticipated. Also, in thecourse of our investigations we were at last able to test a long-standing conjecture byMark Lichbach that inconsistent repression accelerates protest (see Lichbach, 1987).We found in German leftist protest and repression that inconsistent repression didin fact accelerate protest. This is a graphical demonstration that should be testeddifferently and in more than one case. Yet it is empirical confirmation of a discoveryfrom a formal model. Now that data in interval form are available, much more ofthis sort of testing can be conducted on many conjectures drawn from formal theory.

The fact that we had interval data drawn directly from multiple sources enabledus to invoke levels of estimation and mathematics that are only available to theseforms of data. Such data are difficult and expensive to code, but the wealth of appli-cations they open justifies much of the time and expense of coding. This volume wasrestricted to Europe, Burma, and one state in the United States for the simple reasonthat daily interval data exist only in these settings. If we had more such data ondeveloping countries such as India, China, Brazil, and African countries, we mightsee instability and different outcomes. Certainly, the experience we have had in awide variety of contexts that included revolution, though, indicates that we shouldexpect stability even in these actively changing environments.

Bibliography

Gelman, Andrew and Jennifer Hill. 2007. Data Analysis Using Regression and Multi-level/Hierarchical Models. New York: Cambridge University Press.

Greene, William H. 2003. Econometric Analysis. Upper Saddle River, NJ: Prentice Hall.Hardin, Russell. 1995. One for All: The Logic of Group Conflict. Princeton, JN: Princeton

University Press.Lichbach, Mark I. 1987. “Deterrence or Escalation? The Puzzle of Aggregate Studies of Repression

and Dissent.” Journal of Conflict Resolution 31:266–297.

Index

AAcheson, David, 5Adam, John A., 67Albania, 34, 36, 43–44, 48–49, 83

Hoxha, Enver, 36, 44Sigurimi, 36

Almond, Gabriel, 34America, 15, 69, 82Archidamian civil war, 67–69, 71–72, 76, 83,

84Athens, 67–68Sparta, 67–68

Arendt, Hannah, 13Artle, Roland, 52Austria, 9, 14, 16, 24, 27, 29, 31, 34, 41, 46, 82Autocracy, 35Avernous, Christian, 52

BBandwagon mobilization, 3, 51–64Bauer, Otto, 34Belarus, 33, 34, 37, 43, 83Belgium, 14, 16, 17, 20, 31, 82

Flemish, 17Walloons, 17

Bendor, Jonathon, 67Boyce, William E., 5Brookmeyer, Ron, 52Bruesch–Godfrey test, 8Bruesch–Pagan test, 8Bulgaria, 34, 37, 38, 43, 44, 53, 57, 59, 60, 61,

63, 83Burma, 33, 34, 35, 38, 39, 43, 63, 83, 85

CCarey, Sabine C., 2Ceausescu, Nicolae, 43, 60Chicago, 26, 27, 29, 30Coevolution, 67

Coleman, Stephen, 2Collier, Paul, 66Colombia, 67, 75, 76, 77, 83Colombian civil war, 67, 69, 75, 76, 77, 84Competing species model, 6–7, 70–71, 72, 74Convergence, 5, 74, 84Correlates of War data, 69Cunningham, David E., 67Czechoslovakia, 38–39, 40, 44, 45, 46, 47,

63, 83Charter 77, 38–39, 40, 45Helsinki accords, 38–39Jazz, 39Klaus, Vaclav, 44Plastic People of the Universe, 38–39Prague, 38–39, 40, 45, 63

Czech Republic, 34, 44, 45, 46, 47, 83

DData, 1–10, 13, 14–15, 18, 21, 23, 24, 25, 26,

29, 35, 37, 39, 42, 43, 44, 45, 46, 53,69–70, 71, 72, 74–75, 81, 82, 83, 84, 85

interval, 2, 4, 14, 70, 71, 85ordinal, 2, 13

Democracy or democratic, 3, 6, 9, 13–31,33–49, 53, 57, 8 68, 82–84

DeNardo, James, 3, 13, 14, 33–34, 35, 58, 63Denmark, 14, 17, 31, 82DeRouen, Karl R. Jr, 65Dictatorship, 3, 9, 14, 20, 33–49, 54–55, 57,

60, 82–84DiPrima, Richard C., 5Divergence, 1, 2, 4, 67, 70Doctorow, E.L., 66Doering, Herbert, 9, 28Dubcek, Alexander, 57Dunning, Thad, 66Durbin h test, 8Dynamics, 1, 7, 10, 13, 33–49, 51–63, 65–78

87

88 Index

EElaydi, Saber N., 4, 5, 8, 31, 48Electoral law, 47El Salvador civil war, 67, 74, 75

death squads, 69, 75red zone, 74

Equilibrium, 2, 3, 4–5, 31, 48, 59, 65, 66, 67,70, 71, 74, 75, 76, 78, 81, 82, 84

Exponential, 2, 5, 8, 55, 67, 81

FFearon, James D., 66Federalism, 9, 18, 24Ferrara, Federico, 38, 63Fisher, R.A., 1France, 9, 14, 17, 26, 27, 28, 29, 30, 31, 52–53,

54, 63, 74, 82, 83Debre, Michel, 17, 28DeGaulle, Charles, 17, 28, 54, 63May 1968, 52–53, 83

Francisco, Ronald A., 2, 23, 29, 35, 48, 65, 78

GGarrison, Steven, 67Gelman, Andrew, 84German Democratic Republic (GDR), 40, 43,

54, 57, 59, 63Bohley, Baerbel, 40Honecker, Erich, 54Kampftruppen, 57Neues Forum (New Forum), 40Pflugbeil, Sebastian, 40Socialist Unity Party, 57Stasi, 40

Germany or Federal Republic of Germany, 9,14, 18, 24, 27, 29, 31, 34, 40, 57, 68,82, 83

Brandt, Willy, 35ecological protesters, 18leftists, 18, 19, 20, 68, 69, 74, 85

Gill, Anton, 34Goldberg, Jack, 5Goldberg, Samuel, 4, 8, 31, 48Gorbachev, Mikhail, 36, 40, 41, 42, 44, 45, 54,

57Granovetter, Mark, 52, 58, 63Grant, General, 65, 68, 76, 77Greece, 14, 20, 21, 28, 29, 31, 67, 71, 77, 82,

83Greene, William H., 4, 7, 84Grove, Andrew S., 51

HHamilton, James D., 70Hardin, Russell, 81Harvey, Anna L., 52Havel, Vaclav, 39, 44, 45, 48, 57, 63Hill, Jennifer, 84Hoeffler, Anke, 66Hoffman, Peter, 34Hudson, Richard L, 70Humphreys, Macartan, 66Hungary, 34, 40, 41, 42, 46, 49, 54, 83

1956 rising, 40–41Democratic Forum, 41

Husak, Gustav, 57

IIceland, 14, 21, 23, 29, 31, 82Illinois, 9, 13, 14, 15, 16, 24, 26, 27–30, 31, 65

Chicago, 26, 27, 29, 30, 31Downstate, 26, 30

Institutional theories, 9International Monetary Fund, 41Ireland, 14, 21, 26, 28, 29, 30, 31, 35, 66, 68,

74, 76, 77, 81, 82, 84Irish civil war, 66, 67, 68, 69, 73, 74

anti-treaty, 69, 73pro-treaty, 77

Italy, 14, 21, 31, 36, 68, 82Red Brigade terror, 21

JJacobian matrices, 71Jakes, Milo, 38, 45Johnson, Paul, 29

KKhrushchev, Nikita, 41Kornhauser, William, 34Kuran, Timur, 39, 52, 54

LLacina, Bethany, 66Laitin, David D., 66Lakatos, Imre, 3Lee, Robert E., 68, 76, 77Lenin, Vladimir, 35Licklider, Roy, 67Lincoln, Abraham, 65Logistic, 52, 53, 54, 55, 58, 62Lotka–Volterra (or predator–prey) model, 4,

5–6, 15, 16, 17, 18, 20–28, 36–47, 52,53, 55, 57, 58, 61

Lower triangle, 21, 22, 45, 46, 49, 57, 82, 83,85

Index 89

Luenberger, David G., 3, 70Lukashenko, Aleksander, 37Luxembourg, 14, 22, 29, 31, 82

MMcCelvey, Richard, 28Maddala, G.S., 5, 7Mandelbrot, Benoit, 70Mason, T. David, 66, 78Masur, Kurt, 57Mathieu, Lilian, 53Maung, Saw (Burmese General), 38Meciar, Vladimir, 47Merkin, David R., 4, 5, 48Mladenov, Petar, 38, 58Mobilization, 1, 3, 9, 14, 16, 18, 33, 34–36, 39,

40, 43, 45, 51–63, 66, 72, 76, 77, 81,83, 84

Morton, Rebecca B, 2, 4, 5, 6Murray, J. D., 4, 6, 70Myanmar, see BurmaMyers, Daniel J., 15

NNagy, Imre, 41Netherlands, 14, 18, 23, 31, 82Nordberg, Marc, 37Northern Ireland, 14–15, 21, 26, 29–30, 31, 81

PIRA, 25UFF, 25UVF, 25

Norton, Daniel, 66Norway, 15, 23, 29, 31, 82

OOliver, Pamela E., 15Olson, Mancur, 3, 66Ordeshook, Peter C., 1Oscillation, 1, 2, 4, 8, 29, 65, 66, 67, 70

PPoland, 42, 47, 52, 53, 55, 56, 63, 83

Jaruzelski, General, 42KOR, 42, 55Mazowiecki, Tadeusz, 47Solidarity, 42, 47, 52, 53, 55, 56, 63, 83Walesa, Lech, 47

Polish-Russian war, 67Political transition, 3, 44, 46Portugal, 15, 23, 24, 29, 31

Balsemao, Francisco, 23Potter, Merle C., 5Putin, Vladimir, 37

RRamet, Sabrinia P., 36, 37, 38, 39, 40, 41, 42,

44, 45, 47, 48Regime transition, 14, 38, 41, 44, 63, 83Repression, 3, 14, 15, 18, 19, 33, 34–35, 85

harsh, 33, 34–35inconsistent, 3, 14, 15, 18, 20, 85

Riker, William H., 1, 2, 55Rohlfs, Jeffery, 52Romania, 33, 34, 36, 42–43, 48, 53, 59–62, 83

Free Romania, 42Ron, James, 66Roughgarden, Jonathan, 2, 5Russian civil war, 67, 68, 69, 73, 77

Bolsheviks, 35, 68, 73, 77Chechens, 68, 73Cossacks, 68, 73Whites, 68, 73, 77

SSaddle point, 5Schama, Simon, 81Semi-presidential government, 17, 28, 47Sherman, William, 68, 73, 77Slovakia, 34, 44, 45, 47, 48, 83Sobek, David, 65Solzhenitsyn, Aleksander, 33Soong, Roland, 52Spagat, Micheal, 69Spain, 15, 24, 29, 31, 68, 76, 77

Carlos, Juan, 24Franco, Francisco, 24, 68, 74

Spanish civil war, 24, 67, 68, 69, 72, 74, 76, 84Sprague, John, 4, 52Stability, 2, 4, 5, 7, 9, 16, 47, 54, 65–78, 81–85

See also EquilibriumStalin, Joseph, 35, 41Sweden, 15, 24, 25, 31, 82Swistak, Piotr, 67Switzerland, 9, 15, 24, 25, 27, 28, 31, 82

TTabak, John, 2Teune, Henry, 67Thucydides, 69Tilly, Charles, 34, 48Toolis, Kevin, 34Transition, see Political transition; Regime

transitionTrotsky, Leon, 35, 68, 73, 77Tsebelis, George, 4, 9, 27, 28, 52Turks (in Bulgaria), 38, 57

90 Index

UUnitary, 17, 27, 82, 83United Kingdom, 9, 15, 21, 25, 26, 27, 28, 29,

31, 82House of Commons, 27–28House of Lords, 27–28Major, John, 25, 30Thatcher, Margaret, 25, 30

Upper triangle, 84US, 67, 68, 69, 72, 73, 76, 77, 83–84US civil war, 67, 68, 69, 72, 73, 76, 77USSR or Soviet Union, 33, 34, 36, 37, 38, 40,

41, 42, 54, 57, 60, 68

VVeto players, 9, 28, 66, 83

WWarsaw Pact, 38, 40, 57, 60Weigley, Russell F., 78Wintrobe, Ronald, 3, 33, 34Wolchick, Sharon, 47

YYounger, Carlton, 66

ZZhivkov, Todor, 38, 44

Francisco 2009 - Dynamics of Conflict

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