the dynamics of collapse in an authoritarian regime: china ... · one response was renewed...

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The Dynamics of Collapse in an AuthoritarianRegime: China in 19671

Andrew G. Walder and Qinglian LuStanford University

Therant Sity oflzakierrehen,et. Dnive

2010002-9

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Theories of rebellion and revolution neglect short-run processes withinstate structures that can undermine their internal cohesion. These pro-cesses are evident in the rapid unraveling of the Chinese state early inthe Cultural Revolution. Portrayed in past accounts as a culmination ofstudent and worker insurgencies, an early 1967 wave of power seizureswas in fact accelerated by an internal rebellion of bureaucrats againsttheir own superiors. These led to the widespread collapse of local gov-ernments, diverting the course of the Cultural Revolution and forcingintervention by the armed forces. An event-history analysis of the dif-fusion of power seizures across a hierarchy of 2,215 government juris-dictions portrays a top-down cascade that spread deeply into rural re-gions with few students and workers and little popular protest. Theinternal rebellions were generated endogenously by events during thecourse of these upheavals, as individual officials reacted to shifting cir-cumstances that threatened their positions.

The unanticipated collapse of durable state socialist dictatorships begin-ning in the fall of 1989 led to widespread questioning of prevailing theoriesof revolution, which seemed unable to predict such events or adequately ex-plain them in retrospect (Kuran 1991; Goldstone 1995; Hechter 1995; Tilly

research reported in this article was supported by National Science FoundationES-1021134. Earlier versions were presented at Stanford University, the Univer-Oxford, and SingaporeManagement University.We thankDougMcAdam, Susan, and the AJS reviewers for their comments on earlier drafts and James Kung andLandry for their assistancewith geographical data. Lizhi Liu, XiaobinHe,Weiweiand especially Fei Yanmade important contributions to the preparation of the datairect correspondence to Andrew G. Walder, Department of Sociology, Stanfordrsity, Stanford, California 94305. E-mail: [email protected]

7 by The University of Chicago. All rights reserved.

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AJS Volume 122 Number 4 (January 2017): 1144–82

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Collapse in an Authoritarian Regime

1995b). One response was renewed attention to threshold and critical massmodels of collective behavior, which emphasize the interdependencies ofindividual decisions that shift propensities to participate (Granovetter 1978;Marwell and Oliver 1993). Applied to protests in these settings, these modelsexplained sudden escalations in protest as a function of information flows(Kuran 1989; Lohmann 1994; Oberschall 1994) or social networks (Opp andGern 1993; Opp 1994; Pfaff 1996; Pfaff and Kim 2003).

Some of these efforts implicitly treated the collapse of a regime as a func-tion of the scale of protest. State-centered theories of revolution, by contrast,insist that the impact of mass mobilization is highly contingent on the priorexistence of structural weaknesses that make regimes vulnerable to massmobilization from below. Especially important are prior cleavages amongruling elites or organizational weaknesses of the state apparatus (Skocpol1979; Goodwin and Skocpol 1989; Goldstone 1991; Goodwin 2001). Thesetheories emphasize preexisting structural weaknesses—the organization ofstate authority, fiscal strain, pressure from the international system, or divi-sion among elites. They leave unexamined the internal politics of state struc-tures during the course of the events whose outcomes they seek to explain.Popular challenges are assumed to place pressures on states: strong statesare more likely to withstand them, while structurally flawed states are morevulnerable. How and why states unravel remains unexamined and is as-sumed to follow from initial structural circumstances.

We address this gap by focusing on short-run political processes that un-fold within state structures during sustained episodes of contention. The co-hesion and resilience of the most structurally sound states can change rap-idly due to internal processes analogous to those that generate unexpectedescalations in popular protest. Individuals who staff the hierarchies of cen-tralized dictatorships have a large stake in the outcome of political conflicts.They are well connected with one another and highly attentive to signalsfrom the top of the hierarchy. They have amore immediate stake in the out-comes of political conflict than ordinary citizens who contemplate whetherto engage in protest. In authoritarian hierarchies they face pressures to re-spond to shifting circumstances. They must decide whether to enforce thedirectives of their superiors, remain noncommittal, or subtly or openly sup-port the opposition. These decisions are influenced by signals from the topof the hierarchy, and they can shift rapidly. When they do, states withoutapparent structural weaknesses can unravel with surprising speed.

CHINA’S INSURGENCIES OF 1966–67

The Chinese revolutionary state of the mid-1960s was one of the world’smost disciplined dictatorships, with an organizational capacity that enabledit to mobilize the population and remake the social and economic structures

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American Journal of Sociology

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of the country over the previous decade (Schurmann 1968; Skocpol 1979;Walder 2015, pp. 40–122). At the end of 1966, however, it disintegrated rap-idly in thewake of a popular insurgency initiated severalmonths before by itsown leader, Mao Zedong. A wave of power seizures in early 1967 overthrewlocal party committees and governments, ushering inmore than a year of fac-tional conflict and civil strife that ended in the imposition of martial law.It has long been assumed that these power seizures were the culmination

of student andworker insurgencies. The popular rebellions have been inter-preted variously as a form of charismatic mobilization inspired by faith inMao’s extraordinary authority (Andreas 2007), as a response by interestgroups to seize an opportunity to pursue their aims (Lee 1978), or as expres-sions of long-suppressed popular frustrations (White 1989). The periodseemed to conform closely to models of contentious politics that focus ongroup capacities to mobilize and shifts in political opportunity (Tilly 1978;Tarrow 1994). The state media encouraged insurgents with authoritativedirectives. Rebel groups were permitted free travel on the railway system andwere allowed to publish their own newspapers. A network of agents activelymonitored and directed insurgents behind the scenes. The armed forces andpublic security agencies were ordered not to interfere (MacFarquhar andSchoenhals 2006;Walder 2009a). Given this marked shift in political oppor-tunity, group capacity to mobilize, the disabling of state repression, and therapid spread of a popular insurgency, it was natural to assume that the pri-mary agents in this overthrowwere insurgent students and workers who chal-lenged the state from below.Evidence from new sources, however, has revealed two anomalies that

cast doubt on this assumption. The first is the extraordinarily rapid andthorough diffusion of power seizures far beyond the major cities that har-bored large student and worker insurgencies. They spread much more rap-idly and far more widely than one would expect if popular mobilizationswere the driving force. The first provincial power seizure was in Shanghaion January 6, 1967; it was praised in the national media on January 12; reb-els across China were urged to act in a similar manner by radio broadcastsof an editorial and several related articles in the Communist Party’s officialnewspaper, People’s Daily, on January 22. By the end of that month, onlynine days later, more than half of China’s 2,215 cities and counties experi-enced power seizures, and by the end of February, more than 70% (Walder2014). This is a remarkably rapid diffusion across a nation of continentalproportions, especially so because the vast majority of these jurisdictionswere rural counties with very few students or industrial workers.The second anomaly is evidence that functionaries in party-state organs—

known as “cadres”—actively rebelled against their own superiors and werean important force in power seizures. Published local histories routinelymen-tion rebel organizations in party-state offices as participants in power sei-

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zures in large cities, although they provide few details about these rebels.Unusually detailed internal investigation reports for Guangxi province pro-vide such details for the first time. They describe disruptive internal rebel-lions by state cadres at all levels of the hierarchy that were widespread byDecember 1966. In large cities and urbanized prefectures, cadre rebels ac-tively coordinatedwith student andworker rebels. In small towns and ruralcounties, they routinely seized power from their superiors unilaterally, with-out coordinating with outside insurgents (Guangxi Party Committee 1987;Walder 2016).

The materials from Guangxi suggest that bureaucrats were paradoxi-cally a driving force in the rapid collapse of the civilian state. Our purposeis to look more systematically at empirical evidence about the nationwidediffusion of power seizures. The evidence indicates that power seizures cas-caded downward through the national bureaucratic hierarchy in responseto a central directive urging rebels to seize power from local party and stateorganizations. The consequent collapse of state structures escalated civildisorder, forcing the dispatch of armed troops across most of China and ini-tiating more than a year of violent conflict that ended in martial law.

CENTRALIZED REGIMES AS AUTHORITARIAN HIERARCHIES

The bureaucratic structures of the Chinese state in mid-1966 were a highlyunlikely setting for rebellion. The hierarchy shared many of the featuresthat Max Weber attributed to a modern rational-legal bureaucracy—well-defined offices that were not the property of incumbents, careers definedas a progression through ranked posts, written rules about the powers of of-fice, and permanent records about the qualifications and performance ofindividual bureaucrats. But this bureaucracy, heir to a recent revolution thatestablished a centralized single-party state, had additional features thatWebernever observed in his lifetime.

First, the hierarchy was entirely closed: there was no plausible career al-ternative. After the formation of a state socialist economy in the 1950s therewas only one employer, and officials did not have an exit option. They lackedpersonal assets or business interests that could support a postbureaucratic ca-reer. They could not resign to take up employment in a private sector or in-dependent public institutions. They could not initiate a transfer to anotherlocation.

A second feature was close monitoring and record keeping, not just oftechnical performance but also of political dispositions and loyalty to theruling party. A separate party organization monitored individual behaviorand speech in both public meetings and private settings and recorded sus-picious actions or statements in permanent files. Almost all governmentfunctionaries were members of the Communist Party, subject to party dis-

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cipline, and were required to participate in all of the meetings and activitiesof their party branch.A third feature is often overlooked. Dictatorships are usually defined as

regimes that are harshly punitive toward subject populations, but this typealso imposed harsh discipline on its own functionaries. In prior years a se-ries of campaigns targeted officials in periodic purges. These episodes led tothe demotion and transfer to menial jobs or labor camps of cadres whosediscipline or loyalty fell under suspicion. Survival in such a hierarchy re-quired vigilance by individuals to ensure that they did not end up at thewrong end of a political investigation.The final feature was the state’s unitary and centralized structure. Unlike

loosely organized personal dictatorships or states that contain elements offederalism, all local jurisdictions were directly tied to a single national hier-archy. Two intertwined hierarchies reached from the capital to the grassroots. The central government presided directly over 28 province-level ju-risdictions, which in turn presided over a hierarchy of more than 200 pre-fectures, close to 170 cities, and more than 2,100 rural counties, each ofwhich was directly subordinate to a unit at the immediately higher level(Ministry of Civil Affairs 1998, p. 2210). Paralleling the government hierar-chy was a network of party committees whose personnel overlapped withand controlled government agencies at each level. In 1965 there were 2.4million full-time party and government functionaries in these two hierar-chies. Close to 300,000 were full-time party functionaries in 4,400 partycommittees and related offices. The remaining 2.1 million staffed govern-ment offices, and more than 80% of them were party members organizedinto 117,000 party branches (OrganizationDepartment, CCPCentral Com-mittee 2000, 16:1331, 1335–40).In sum, China’s bureaucrats were highly dependent on their positions

and had no tolerable exit option; their actions and utterances were closelymonitored and could have severely negative consequences; and they weresubjected to high demands for obedience and loyalty. Under normal cir-cumstances, one could hardly imagine a less likely group, or a less likely set-ting, that would breed an insurgency. Yet something changed near the endof 1966 to turn large numbers of cadres from disciplined and compliantfunctionaries into rebels. As we will see, the centralized and unitary natureof the hierarchy—the features that made it structurally strong—paradoxi-cally made it vulnerable to collapse from within.

THE FORMATION OF INTRABUREAUCRATIC INSURGENCIES

What could have motivated cadre rebels? As the most privileged group inthis type of socialist state, they would seem to have strong vested interests

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in the preservation of the status quo, leading them to defend their superiorsand the integrity of their agencies against outside insurgents. One could ap-ply familiar theories about social movements to the internal politics of a for-mal organization (Zald and Berger 1978; Goldstone and Useem 1999). Thisassumes that previously disaffected bureaucrats seized a political opportu-nity to act. Theories of political contention generally take the prior existenceof motivated groups as given: “relative to a particular set of social relations,individuals and groups have articulated interests that are ascertainable priorto the interactionwe are seeking to explain” (Tilly 1995a, p. 39). The analysisfocuses on the process of mobilization, leaving the formation of interests as anexogenous background assumption, without which the concept of political op-portunity would be meaningless (Walder 2009b).

To be sure, China’s state organs contained rivalries and antagonismscommon to bureaucratic organizations everywhere. Analysts have long notedtensions between old revolutionaries and party members who joined after therevolution, differences between those who rose through political credentialsand educated experts, and the rivalries between cadres with local guerillaand urban underground experience and those who arrived with the Peo-ple’s Liberation Army in its campaign of military conquest. There were alsosignificant minorities among serving cadres who had been demoted or other-wise sanctioned in prior political campaigns and who may have harbored re-sentments. Prevailing models of contentious politics would direct us to thesepreexisting antagonisms as the source of rebellions within the party-state bu-reaucracy and focus on the opening up of political opportunities to act on theseinterests.

Narrative accounts from the Guangxi investigation reports demonstratethat the internal rebellions went far beyond previously disaffected cadresand grew through two different processes generated after the onset of theCultural Revolution. The first is the individuals who were attacked in thefirst months of the Cultural Revolution, when local officials directed the cam-paign. This gave the affected cadres an urgent new motive to reverse thedamaging verdicts passed upon them and to rebel against superiors whowereresponsible. Even more consequential, however, was the widespread defec-tion of large groups of cadres who cooperated actively with their superiorsin the early phases of the campaign and who vigorously defended them forseveral months. These cadres moved to the opposition in large numbers andjoined in attacks on their superiors after October 1966. The motives for re-bellion by these two groups emerged during the course of the Cultural Rev-olution itself, the cumulative effect of individual responses to shifting circum-stances. Narrative accounts suggest that in the end it was the defection of thepreviously loyal that finally undermined the leaders of local governments(Walder 2016).

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What alters the political orientations of cadres in this case is not opportu-nity but threat, a concept thatwas prominent in early statements of resourcemobilization theory (Tilly 1978, pp. 59–60; Goldstone and Tilly 2001). Un-like these theories, ours emphasizes the impact of threat at the individualrather than the group level. Mobilizations by cadre rebels were not collec-tive responses to defend group privileges; they were the outcome of cumu-lating individual choices by cadres designed to ensure that they would notpersonally fall victim in escalating purges. In otherwords, individual cadresacted to ensure their personal survival at the expense of their superiors andany of their peers who continued to support them.The threat to individual cadres’ positions was explicit in the overt polit-

ical rationale for the Cultural Revolution—that certain officials throughoutthe country preferred moderate Soviet practices and were disloyal toMao’srevolutionary vision. The campaign potentially threatened cadres through-out the system. Some would lose their positions or worse, but it was initiallyunclearwhowould fall, and howmany. Purges began in lateMay 1966withthe attacks on an “antiparty clique” in the nation’s capital (MacFarquharand Schoenhals 2006). The leaders of provinces, cities, and counties couldnot claim that there were no “antiparty elements” in their own jurisdic-tions—this would suggest that they did not accept the campaign’s politicalrationale, potentially making them targets themselves. Official histories de-scribe party leaders at all levels of government who organized active cam-paigns of accusation against local “antiparty cliques” in August and Septem-ber 1966. These local campaigns initially focused on officials in propaganda,culture, and educational departments (Guangxi Party Committee 1987, 1:76–77). Cadreswere graded according to their perceived political reliability,withsignificant minorities classified as suspect (Guangxi Party Committee 1987,6:319–22, 326–27).There is one interest that is shared by all individual bureaucrats in this

setting and that does not change through time—to avoid losing one’s posi-tion. The onset of the Cultural Revolution potentially placed all party-stateofficials under threat, requiring them to make choices in shifting circum-stances to ensure that they did not become victims of the campaign. Someindividuals may also have been driven by an ambition to advance them-selves or by antagonism toward superiors with whom they had previousconflicts. For these individuals the unfolding campaign may have openedup political opportunities to act on preexisting interests. They may havebeen among the first movers in attacks on their superiors, responding to sig-nals that made them politically vulnerable. But even those who were somotivated had to ensure, like everyone else, that they did not themselvesbecome victims in the process. And any effective rebellion against their su-periors had to mobilize cadres who had no preexisting grievances and in-deed even those who for months had actively defended them.

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When we claim that political interests shift during the course of this ep-isode of contention, we refer to their interests in either supporting or oppos-ing their superiors. This does not represent, as in prevailing models of con-tentious politics, a choice to act on preexistingmotives. Instead, it representsa choice between political alternatives—to defend one’s superiors or to sup-port a rebellion against them. What shifts over time is individual cadres’perception that they would be more likely to preserve their position andavoid falling in the campaign if they sought to overthrow their superiorsrather than actively support them or remain uncommitted. This is a choicefaced by all cadres as the Cultural Revolution unfolded in the last weeks of1966. So long as their superiors appeared to have the backing of higher-levelorgans, there was widespread support for them. After student insurgenciesformed in August and September, party leaders mobilized cadre groupsknown as Scarlet Guards. They were in fact loyal to their superiors andaimed to deter invasions of government offices by students or workers anddefend their superiors against attacks (Guangxi Party Committee 1987, 1:454–57; 3:117–18; 6:325–26; 13:209, 370–71, 506–7). After signals from the top ofthe national hierarchy in October 1966 made it increasingly evident thatlocal leaders were vulnerable to attack for their actions in previous months,previously loyal cadres—including those who had actively defended the lo-cal leaders—began to turn on them in large numbers. To defend a superiorwho falls from power for an alleged political transgression was to risk censureand punishment by association, potentially making one complicit in one’s su-perior’s actions.

The cadre rebellions emerged shortly after a large Beijing Party con-ference held in October 1966, which assembled officials from through-out the country to hear denunciations of the recent behavior of regional andlocal leaders. Party-led purges and especially the loyal militant groups likethe Scarlet Guards were denounced as a “bourgeois reactionary line” thatsought to blunt the Cultural Revolution in defiance of Mao’s intentions. Thenew political line was rapidly communicated throughout the country. Localleaders could no longer control the process of accusation and purge. Theywere now vulnerable to criticism for their recent actions, which in almostevery instance appeared to be a manifestation of the bourgeois reactionaryline. Retaliation against accusers would only make them more vulnerable.

Whether they had joined the Scarlet Guards or simply complied activelywith campaigns against victims targeted in the initial purges, individual cad-res were forced to reassess their positions. Detailed accounts from Guangxidescribe a rapid shift within party-state organs duringNovember 1966. Scar-let Guards quickly disbanded, and some simply recast themselves as rebelgroups that targeted their superiors, claiming that theywere deceived by theirreactionary schemes. Other cadres formed new rebel organizations and chal-lenged their superiors. In one county, by the end of November 520 out of 883

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cadres had formed more than 50 rebel “fighting groups” in 29 departmentsin the county offices. In another, 22 different rebel groups formedwithin thecounty headquarters (Guangxi Party Committee 1987, 2:234; 6:328).Pressures to join the opposition mounted in December as large popular in-

surgencies formed in provincial capitals and other major cities. As the rebel-lions of students and workers grew and received the approval of the officialmass media, government employees faced the prospect that their failure tojoin in the rebellionmight be interpreted as complicity in a “reactionary” line.InDecember 1966 the leaders of cities and counties began to lose control overtheir subordinates. Rebel cadres disrupted party meetings and seized controlof the agenda, forcing their superiors to respond to charges about their allegedmisdeeds (Walder 2016).

THE DYNAMICS OF COLLAPSE

The collapse of civilian state structures—and the end point of our analysis—was marked by the declaration of a “power seizure” over an entire city orcounty government. Large popular insurgencies in several major citieshad turned streets and city squares into battlegrounds between competingfactions. Provincial governments were paralyzed by challenges from mili-tant students andworkers and by internal challenges among their own staff.If the Cultural Revolution was to do more to destroy civil order, the spread-ing chaos needed to be curbed. The solution was a new tactic: a “power sei-zure” by rebels who would claim victory in Mao’s name, permitting the im-position of order, through repression by the armed forces if necessary. TheShanghai power seizure on January 6,which quickly stabilizedChina’s larg-est city, provided a template for this maneuver. It was showered with praiseas a victory for pro-Mao rebels. Power seizures in large cities were intended tostabilize a rapidly deteriorating situation (Dong and Walder 2010; Walder2015, pp. 233–42). The result, however, was in most instances the opposite:a subsequent call for similar power seizures accelerated the collapse of localgovernments far beyond large cities, into rural jurisdictions that were not yetdisrupted by popular insurgencies.The call for rebels to seize power led to an unanticipated wave of internal

power seizures by cadres. In large cities rebel cadres joined large coalitionsof rebel forces that included workers and students. In smaller cities and es-pecially in rural counties, rebel cadres directed the entire process, in manycases seizing power unilaterally without the participation of workers or stu-dents (Walder 2016). Power seizures were hastily organized: at the highestlevels in response to the January 22 call for rebels to seize power and at lowerlevels in response to news of power seizures at the next higher level of admin-istration. The leaders of various small rebel groups in government offices formedcoalitions known as “power seizure committees” and prepared for a formal

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declaration. The power transfer typically involved a mass meeting that localleaders were forced to attend, the signing of a power transfer agreement, anda formal declaration of the overthrow of party secretaries, heads of the localgovernment, and all or most of their deputies. The transfer of power was de-clared locally via handbills, radio or loudspeaker systems, and local newspa-pers. The overthrown leaders were typically taken into custody and frequentlysubjected to violent and humiliating public “struggle sessions” (Walder 2016).

The timing of a power seizure was heavily influenced by a power seizurein the immediately higher government jurisdiction. This event spurred rebelcadres to act in order to ensure that rival rebels or outside actors did not seizepower before they did. This motivation is explicitly and repeatedly men-tioned in detailed investigation reports compiled for Guangxi Province(Guangxi Party Committee 1987, 1:77–78, 456–57, 514–18; 2:193–94; 3:339–40, 514–16; 4:169–71, 333–35, 492–96; 6:330–33; 13:309). By seizing powerrebel cadres were also signaling to the rebels who had seized power in thehierarchy above them that they shared the same political orientation.

THE DIFFUSION OF POWER SEIZURES

The wave of power seizures in early 1967 suggests a dynamic process sim-ilar to thresholdmodels of collective behavior. Dynamicmodels of social ac-tion view the decisions of actors as interdependent—they are influenced bychoices reflected in the prior actions of relevant others (Gould 1993; Hecka-thorn 1993; Kim and Bearman 1997). Threshold and critical mass theoriesemphasize the interdependencies of individual choices that shift group be-havior (Granovetter 1978; Marwell and Oliver 1993). These ideas also mo-tivate diffusion models of political events. The rate at which strikes, riots, orprotests spread across time and space is not affected solely by fixed structuralfeatures indicating group motives or capacity to act. There is a dynamic tem-poral dimension that indicates mutual influence. A period of heightened pro-test activity emerges through “imitation, comparison, the transfer of forms andthemes of protest from one sector to another” (Tarrow 1989, p. 223) and awavelike pattern in which the influence of prior actions by others eventuallydiminishes over time (Pitcher, Hamblin, andMiller 1978; Tarrow 1994; Conelland Cohn 1995;Myers 1997; Biggs 2005).

Diffusion models are fundamentally about flows of information. Actorscan be influenced by the actions of others only if they learn of them. Mostmodels are concerned with two different ways that information spreads.One is via face-to-face communication through local or distant social net-works or social movement organizations (Gould 1991; Hedström 1994; Tol-nay, Deane, and Beck 1996; Myers 1997; Soule 1997; Hedström, Sandell,and Stern 2000; McVeigh, Myers, and Sikkink 2004; Andrews and Biggs2006; Cunningham and Phillips 2007; Kim and Pfaff 2012;Wang and Soule

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2012). A second is through unconnected actors who learn of the actions ofrelevant others through the mass media (Myers 2000; Andrews and Biggs2006). These two mechanisms are referred to as relational versus nonre-lational channels of influence (Soule 1997).A second issue is how actors evaluate the information they receive and

how and why they act on it. For some analysts, the prior occurrence of anevent signals the vulnerability of authorities to a challenge and the likeli-hood that a similar challenge of their own might be successful (Tarrow1994; Conell and Cohn 1995). For others it is information about effectivetechniques of organization or strategies of protest (McAdam 1983; Minkoff1997; Soule 1997; Andrews andBiggs 2006; Traugott 2010;Wang and Soule2012).Our puzzle is primarily the second—how actors evaluate information

and choose to act. China’s cadres were tightly integrated into a centralizedhierarchy, and all local units were exposed to the same media and flows ofdocuments. Local media replicated editorial content formulated in Beijing.In such a setting the way that information is transmitted is of secondary in-terest. The real puzzle is what prompted rebel cadres to act andwhy the dif-fusion of power seizures was so remarkably rapid. Central to our explana-tion is that cadres felt compelled to seize power and control their own fate inuncertain and threatening circumstances.

EMPIRICAL IMPLICATIONS

Our claims have implications that can be tested more systematically withdata from the entire range of national jurisdictions. The first implicationis about the speed and extent of diffusion. Because intrabureaucratic insur-gencies were widespread by the end of 1966, we expect power seizures tospread rapidly and verywidely after the national call for rebels to seize poweron January 22, 1967. Because we claim that cadres were the primary agentsof power seizures in rural counties, they should diffuse widely into rural ju-risdictionswith few students andworkers andwhere therewas little evidenceof prior popular insurgencies.A second implication is that the power seizures radiated downward

through the national political hierarchy—not randomly across localitiesand not from lower jurisdictions to higher ones. Our analysis implies thatcadres had increasingly strong incentives to rebel against their superiorsby the end of 1966, independently of the strength of local popular insurgen-cies. Power seizures at higher levels of administration pressured cadre reb-els at immediately lower levels to follow suit. There should be clear evi-dence that lower-level jurisdictions experienced power seizures afterhigher levels and, more important, that the act was triggered by a power sei-zure at the immediately higher level in the bureaucratic hierarchy. A tightly

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coordinated downward pattern within the hierarchy is indirect evidencethat cadres were the primary actors in lower-ranking jurisdictions. If powerseizures were the product of popular insurgencies, wewould expect a slowerand more random, if not bottom-up pattern, as power seizures occurred ac-cording to the variable strength of local insurgencies.

Diffusion models typically invoke influences that involve imitation ofother relevant actors, in contrast to the top-down threat-driven processesthat are central to our explanation. In this case, this would mean that localcadres may have been spurred to seize power by prior power seizures innearby government jurisdictions of parallel rank. Power seizures by otheractors in a locality might have created pressures similar to those createdby power seizures at a higher level—the desire not to be left behind—or theymay have provided a successful example to emulate. Our argument does notrule out such “horizontal” influences, but we expect the top-down mecha-nisms to dominate after January 22.

DATA AND MEASURES

To test the implications of our theory, we employ data about events in 2,215county and city jurisdictions. The database was created by coding informa-tion from annals published by local governments, along with backgrounddata from these and other sources. It contains more than 99% of all countyand city jurisdictions in 1967, excluding Tibet, which is not included in thedatabase. Based entirely on local accounts, these data do not suffer from the re-gional biases in coverage by newspapers located in major media centers (Mc-Carthy, McPhail, and Smith 1996; Earl et al. 2004; Myers and Caniglia 2004).

The event of interest is a local power seizure, defined as a formal decla-ration by an insurgent group that they have occupied the party and govern-ment headquarters, deposed local party committees and government lead-ers, and assumed political control over the entire jurisdiction. Publishedlocal histories vary greatly in the level of detail that they devote to describ-ing events during these years, but power seizures were major turning pointsand are among the events most likely to be recorded.

Under the coding rules, only a power seizure over the entire jurisdictionqualifies. Seizures of radio stations, newspapers, public security bureaus, orother agencies do not. To ensure conformity to this definition, two teams ofcoders recorded the date separately for every jurisdiction. Entries that didnot match were reconciled by reexamining original sources. In all, 82% ofjurisdictions reported power seizures: 50% reported a specific date and 32%the month. The remaining 18% did not describe a power seizure that fit ourdefinition. While it is possible that some of these accounts simply failed to re-port seizures, or to report them in sufficient detail, we assume in these casesthat a power seizure did not occur.

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To track the diffusion of power seizures across cities and counties, whichare the units of analysis, each jurisdiction is coded according to its admin-istrative level. At the highest level of the hierarchy are 27 province-levelgovernments, for which we have coded the day that 25 power seizures oc-curred.2 At the next lower level are 83 prefecture-level cities (including allprovincial capitals). Below this level are 90 county-level cities and 2,042largely rural counties.For each jurisdiction, we coded background data for the years immedi-

ately before 1967. From local annals, statistical yearbooks, and tabulationsfrom the 1964 census, we recorded values for total population, urban pop-ulation, the total number of nonagricultural “workers and staff,” and thenumber of “cadres”3 We recorded the dates of reported power seizures inan intermediate level of administration between the province and countylevel—the prefecture.4 We also recorded the dates of reported events thatindicated insurgent activity and any reported data that indicated the impactof these events.

DESCRIPTIVE ANALYSIS

Table 1 describes the features of jurisdictions. Cities are vastly more urban-ized than counties, and the students and workers who fueled the insurgen-cies of late 1966 were concentrated there. Less than 9% of county popula-tions lived in urban areas; the rest lived on collective farms. The countieshad far fewer nonagricultural workers, and cadres comprised a larger pro-portion of the salaried workforce, averaging roughly half the number ofworkers. As the ratio of cadres to workers rises, they became more influen-tial as political actors.The rapid diffusion of power seizures is evident in the descriptive statis-

tics displayed in table 2. The data in the various columns indicate a top-down pattern. The higher-level jurisdictions were more likely to experiencepower seizures, and they did so earlier, although more than three-fourths ofall counties experienced power seizures by the end of March. Very smallpercentages of cities and counties had power seizures before their provincialgovernment.

Beijing and Shanghai were equal in rank to provinces. We treat their urban districts asrefecture-level cities and the counties within their boundaries as subordinate units.ianjin obtained provincial status in 1968, but in early 1967 it was a county-level citynd the seat of Tianjin prefecture within Hebei province.“Workers and staff” were salaried employees paid on a national scale for productionorkers and service personnel. “Cadres” were salaried employees paid on the nationalivil service scale, which includes political officials and office staff.Prefectures are not a unit of analysis. Dates for prefecture-level events are included in

2

pTa3

wc4

the database as variables tied to cities and counties.

1156



Given the small worker and student populations in counties, one wouldexpect much smaller and less threatening popular insurgencies than in cit-ies. Event data on insurgent activities bear this out (table 3). Popular insur-gencies developedmuchmore slowly in counties. By the end of August 1966close to 90% of cities reported the activities of student red guards, but fewerthan 60% of counties reported the same. Attacks by insurgent students orworkers on local government agencies (not including power seizures) werereported in 42.2% of cities by January 1967 but only 18.3% of counties. Themost relevant indicator is the percentage of jurisdictions that reported inva-sions of government offices by outside insurgent groups. There were seventimes more office invasions in cities than in counties. The most telling sta-tistic is the fact that only 8.2% of counties reported such office invasionsby the end of February, by which point almost 70% of counties alreadyhad power seizures. This strongly suggests that cadres, not outside insur-gents, were the driving force in county-level rebellions.

Despite limited popular insurgencies in counties, their governments weredestabilized almost as rapidly as cities. By the end of January 1967, 50.3%of cities reported that local governments were “paralyzed” and unable tofunction, only marginally higher than the 41.1% of counties. Some 85.5%of cities had power seizures by the end of February, but close to 70% ofcounties did. County governments appear to have been destabilized almostas quickly as cities, despite the modest scale of popular insurgencies.

Another striking feature of this pattern is that the diffusion of power sei-zures is only modestly affected by geographic distance. This is consistentwith known patterns of communication within the Chinese bureaucracyat that time. A directive from Beijing issued 11 days earlier ordered local ra-dio stations to discontinue local programming and to relay central programswithout alteration (Central Committee 1967). The January 22 call for rebelsto seize power was broadcast by radio in all jurisdictions, although copies

TABLE 1Demographic Characteristics of Chinese Jurisdictions, 1966

Jurisdiction LevelAverage

Population

AverageUrban

Population

Average%

Urban

AverageNumberCadres

AverageNumberWorkers

AverageRatioCadres/Workers N

Prefecture-level city . . . 853,925 636,874 70.2 27,445 235,902 .12 83County-level city . . . . 287,494 169,422 61.8 5,693 53,951 .15 90County . . . . . . . . . . . . 311,123 22,767 8.6 1,781 6,489 .48 2,042Valid N . . . . . . . . . . . . 2,214 2,212 2,212 2,058 2,136 1,999

This conteAll use subject to Univer

nt downloadsity of Chica

ed from 171go Press Te

.067.216.0rms and C

23 on Febronditions (

uary 26, 2http://www

017 01:11.journals.u

NOTE.—Data are from the county-city database.

1157

:13 AMchicago.edu/t-and-c).

TABLE2

TimingofPowerSeizuresacrossJurisdictions,1967

Level

ByEndof

January(%

)ByEndof

February(%

)ByEndof

March

(%)

MedianDate*

AverageDaysafter

Province*,y

Before

Province*,y

(%)

NoCon

firm

edPow

erSeizure

(%)

N

Province

....

....

...

86.1

89.3

89.3

January23

...

...

7.4

27Prefecture-levelcity

...

74.7

90.4

91.6

January25

2.3

16.9

8.4

83Cou

nty-levelcity

....

66.7

81.1

84.4

January25

11.9

8.9

13.3

90Cou

nty

....

....

....

47.3

69.0

77.1

January28

20.8

6.4

18.7

2,042

NOTE.—

Tibet

isnot

included

inthedatab

aseor

inthesetabulation

s.Dataarefrom

thecounty-citydatab

ase.

*The1,106casesforwhichtheexactdateof

apow

erseizure

isknow

n.

yCases

inthe25

provincesthat

experiencedpow

erseizures.



of the Communist Party’s national newspaper, People’s Daily, may have ar-rived some days later.

More directly relevant is how cadres in rural counties learned of powerseizures at the higher level of government. By the late 1950s all county gov-ernments were connected to prefectures by telephone and telegraph, andcadres routinely attended a variety of meetings at higher levels of adminis-tration (Oksenberg 1974). Narrative accounts from Guangxi describe countycadres who personally observed power seizures at the prefecture and whoimmediately telephoned their colleagues back in the county to inform themof the event. In other accounts, they rush back to their counties in order toprepare for their ownpower seizures (Guangxi PartyCommittee 1987, 3:514–18; 4:492–94).

With information from geographic databases, we recorded the distancebetween each jurisdiction and the location of the next higher level of gov-ernment (table 4). For county-level units, this was the prefecture capital;for prefecture-level units, this was the provincial capital. Table 4 makesclear that distance had only a modest impact. Despite wide variation in dis-tances, the overall percentage that experienced power seizures in each quin-

TABLE 3Indicators of Insurgent Activity, Late 1966–Early 1967, by Jurisdiction Type (%)

RedGuards,by End ofAugust

RebelGroups,by End ofJanuary

Seizure ofOfficials,byEnd ofJanuary

InsurgentInvasions ofGovernment

Offices,by End ofFebruary

Government“Paralyzed,”by End ofJanuary

PowerSeizure,byEnd ofFebruary

Cities . . . . . . 89.0 42.2 60.1 58.3 50.3 85.5Counties . . . . 59.5 18.3 25.1 8.2 41.1 69.1

ThAll use subject to

is content do University o

wnloaded fromf Chicago Pres

171.067.21s Terms and

6.023 on Febru Conditions (h

ary 26, 2017 01ttp://www.journ

NOTE.—Data are from the county-city database.

TABLE 4Prevalence and Timing of Power Seizures, by Distance from Superior Jurisdiction

Quintile (by Distance)

Average Distancefrom Superior

Jurisdiction (Miles)

Percentage withReported Power

Seizures

Median Dateof Reported

Power Seizures

1 . . . . . . . . . . . . . . . . . . . . 9.3 82.8 January 272 . . . . . . . . . . . . . . . . . . . . 28.3 84.2 January 283 . . . . . . . . . . . . . . . . . . . . 41.8 82.8 January 294 . . . . . . . . . . . . . . . . . . . . 58.4 80.6 January 295 . . . . . . . . . . . . . . . . . . . . 112.1 78.9 January 29Total . . . . . . . . . . . . . . . . . 50.0 81.9 January 28

1159

:11:13 AMals.uchicago.edu/t-and-c).


All

tile is almost identical. Jurisdictions in the least distant quintiles experiencedsomewhat earlier power seizures, while those in themost distant quintile wereslightly less likely to experience one.We have argued that January 22 was the starting point of a downward

cascade of power seizures. This signaled to cadre rebels that they were atrisk if they failed to join with student and worker insurgencies or if theyfailed to act unilaterally. The event data suggest a marked shift in the pat-tern of power seizures that began on that date. Before January 22 the powerseizures do not conform to a top-down pattern: the majority of power sei-zures occur before power seizures at higher levels of administration. Inthe subset of 1,106 cases in which the exact dates are known, 63% of cityand county power seizures before January 22 occurred before a power sei-zure over the province, 67% of prefecture power seizures occurred beforethe province seizure, and 47% of county power seizures occurred beforethe prefecture seizure. After January 22 the pattern is reversed: only 15%of counties and 17%of prefectures seized power before their province, and only18% of counties before their prefecture. The shift to a top-down pattern, andthe accelerating pace of power seizures, indicates the role of cadre rebels inconsummating power seizures across the lower reaches of the party-statehierarchy.This pattern is visually displayed by the maps in figure 1, which portray

the diffusion of power seizures as a network phenomenon.5 Jurisdictions aredifferentiated by node sizes: the largest nodes are provincial capitals,medium-sized nodes are prefecture-level jurisdictions, and small nodes are the countylevel. Jurisdictions that have experienced power seizures appear on the mapsas filled circles, and edges represent the relationship between power seizuresat higher and lower levels. When a prefecture or province power seizure pre-cedes a subsequent power seizure in a jurisdiction below it, a line connectsthe two jurisdictions, indicating a top-down process. If a power seizure occursat the lower-level jurisdiction before or without one at the higher level, thereis no line to connect them. The density of circles and lines reflects the intensi-fication of the top-down pattern over time.On January 21, one day before the national call for power seizures (fig. 1A),

the pattern of power seizures appears to be entirely random, with roughlyequal numbers of provincial capitals, prefectures, and counties across regions.There are almost no lines connecting jurisdictions. One week later, however,we see an explosion of power seizures (fig. 1B). By this point almost all pro-vincial capitals appear on the map along with large numbers of prefectures.In addition, we see numerous lines connecting jurisdictions, indicating amoreprominent top-downpattern. Asmore counties experience power seizures, thedensity of nodes and lines becomes more pronounced, as seen in figure 1C

Tibet is shaded gray to indicate that it is not included in the data set.
5
1160


FIG. 1.—Power seizures throughJanuary 21, 1967 (A); January 28, 1967 (B); February18,1967 (C); and March 31, 1967 (D).


(February 18). ByMarch 31 (fig. 1D), the density of nodes and lines approachesits maximum, by which point close to 80% of all jurisdictions have reporteda power seizure.

Figure 2 portrays the same process in greater detail for Guangxi prov-ince. On January 23, one day after the call for power seizures nationwide,there were simultaneous power seizures over the provincial governmentand the government of the capital city (Nanning). One prefecture experi-enced a power seizure on the same day. By January 26, only three days later,

1161



All

all 12 prefectures and prefecture-level cities had experienced a power sei-zure, along with 19 counties. By January 31, the power seizures had spreadto all but 17 of the 82 counties and county-level cities. Only seven county-level jurisdictions had yet to experience a power seizure by the end of Feb-ruary; two of these would have power seizures in early March, and fivewould not have one. Guangxi closely followed a top-down pattern, as powerseizures spread from higher- to lower-level jurisdictions in a rapid and com-plete manner. There were simultaneous power seizures in different levels of

FIG. 1.— (Continued)

1162



the hierarchy on eight occasions, but in no case was there a power seizure ina lower-level jurisdiction before one at the immediately higher level.

EVENT-HISTORY MODELS

Event-history models are widely applied to protest waves because they per-mit analysts to estimate the impact of structural variables and prior eventsin other units of observation (Strang and Tuma 1993; Strang and Soule1998). The fact that we know only the month of a power seizure in 32%of the cases, however, presents a problem for the estimation of event-historymodels. To employ the month as the time unit would discard valuable in-formation about the 1,106 cases in which the date of the power seizure isknown and would fail to capture the time dynamics of such rapid diffusion.To drop cases in which only the month is known would bias the results byexcluding a large block of cases in which power seizures did occur.

To recapture all known power seizures for event-history analysis, we im-pute dates for jurisdictions on the basis of clustering in prefectures—the in-termediate level of administration between the province- and county-level

FIG. 2.—Power seizures in Guangxi Province

1163



All

units. There are 252 prefecture-level units in the database, each of whichcontains an average of close to nine county-level jurisdictions. The datesof power seizures cluster within prefectures, which suggests the followingimputation strategy.6 First, we impute the missing date as the mean dateof power seizures for a given month within the same prefecture. This stepresolves 72% of the missing dates (513 out of 709 cases) and leaves 196 casesin which a prefecture’s mean date cannot be determined for a given month.Of these cases, 177 are imputed with the provincial mean date for thatmonth. A final 19 cases are imputed with the monthly mean date for the en-tire sample. The distribution of dates in the imputed sample is almost iden-tical to the distribution of dates before imputation.7

The risk set is defined as all counties, county-level cities, and prefecture-level cities. Jurisdictions enter the risk set with the first power seizure onDecember 24, 1966, and drop out when they experience a power seizure.Repeated events and right-hand censoring are not an issue. Each localitycan only experience one power seizure (the first). The last recorded powerseizure was January 14, 1968, the end point of the time clock.We conduct a nonparametric analysis to observe empirical patterns—the

survival function, hazard rate, and cumulative hazard rate—before select-ing a parametric model whose assumptions are not violated by the data.The survival function is a monotonically decreasing function over time, givenby SðtÞ 5 PðT > tÞ, which expresses the probability of not experiencing anevent up to a given time t. Kaplan-Meier survival estimates are displayed infigure 3 (top), which shows that almost all power seizures occurred in the first90 days of 1967. Lower-level jurisdictions (counties and county-level cities)have higher survival rates than prefecture-level cities, meaning that lower-level jurisdictions generally experienced power seizure events later in time. Alog-rank test confirms that the survival functions are indeed different for thesethree types of jurisdictions (x2

k52 5 64 : 83, P < .001).Next we examine the hazard rate and cumulative hazard rate. The hazard

rate is the instantaneous rate of failure given by

l tð Þ 5 limdt→ 0

P ð t < T ≤ t 1 dtjT > tÞdt

5f tð ÞS tð Þ ,

and it has an inverse U-shaped pattern that first increases and then de-creases after 50 days. A closer look at the hazard rate across jurisdictions(fig. 3, bottom) reveals that higher-level jurisdictions (prefectures and

Themean difference between the dates of prefecture- and county-level power seizures is2.1 days, and the median is three days. The dispersion within provinces is much larger:e mean difference between province and county power seizure dates is 20.3 days, ande median is seven days.Amore conservative approach of imputation based solely on prefecture dates leaves 196

6

1thth7

missing cases. This approach did not alter the findings reported below (table 5).

1164



county-level cities) are at greater risk of experiencing a power seizure ear-lier in time, whereas lower-level jurisdictions (counties) are at lower risk andexperience them later. This pattern is consistent with the survival functionacross jurisdiction levels (fig. 3, top).

The cumulative hazard rate is the integral of the hazard rate, given byKðtÞ 5 Ð 0

t lðuÞdu, and provides information about time dependence of thehazard. Given the pattern of the hazard rate, the cumulative hazard rate isS-shaped, whichmeans that the hazard first increases and then decreases overtime.

FIG. 3.—Survival and smoothed hazard estimates, by jurisdiction type

1165



All

There are a variety of event-history models that make different assump-tions about time dependence in the hazard rate. After conducting a series oftests (described in the appendix), we selected the log-logistic model for oursurvival analysis. The log-logistic distributions are appropriate for datawith nonmonotonic hazard rates, specifically those that first increase andthen decrease, such as the hazard rate observed in our nonparametric anal-ysis. Its survivor and density functions are expressed as

S tð Þ 5 1

1 1 ltð Þ1=g ,

f tð Þ 5 l ltð Þ 1=gð Þ21

g 1 1 ltð Þ1=g� �2 ,

where lj 5 expð2xjbÞ and the scale parameter c is estimated from the sam-ple.

MODEL SPECIFICATION

We specify models designed to test the top-down process of diffusion thatwe attribute to mobilizations by cadres against their own superiors. We ex-pect that counties experience power seizures later than cities—already clearfrom our descriptive analysis. We also expect the process to accelerate afterJanuary 22—a clear signal to cadres that such an action was sanctioned.There are two crucial ideas about top-down diffusion that are not convinc-ingly demonstrated by the descriptive analysis. The first is the claim thatpower seizures were triggered by a power seizure at the immediately higherlevel of administration. The second is that power seizures were triggered byinformation that other governments in the same locality had experiencedone. We do not have strong prior expectations about the latter, but we doexpect that top-down diffusion was initiated on January 22.We estimate a series of nestedmodels that introduce conceptually distinct

blocks of variables. The first block includes provincial fixed effects and es-tablishes a baseline for evaluatingmodel fit. Political events unfolded differ-ently across provinces. The first province-level power seizure occurred onJanuary 6 and the last on February 5. Two provinces did not experiencea power seizure. To account for different political dynamics across prov-inces and for other dimensions of unmeasured heterogeneity, we includeprovincial fixed effects in all models.The next model block introduces three characteristics of jurisdictions.

The first is the percentage of the population living in urban areas, whichcaptures the contrast between cities and counties. We expect more urban-ized jurisdictions to experience earlier power seizures than more rural ones

1166



because the level of urbanization is a proxy for administrative level and be-cause larger urban populations could support larger popular insurgencies.A second characteristic is the ratio of cadres to workers. One of the politi-cally significant features of rural counties is that cadres are more dominantin the urban workforce and are expected to be the primary force in powerseizures there. Overall, we would expect that a high cadre/worker ratio isassociated with later power seizures because power seizures diffuse fromthe top down. However, once we take into account the vertical diffusionpressures central to our argument, we would expect this variable to haveno net effect. This would indicate that cadre-dominant counties are justas vulnerable to power seizures as jurisdictions with large worker popula-tions. If, however, cadres were less likely to seize power where there werefew workers to fuel a popular insurgency, the variable should have a netnegative impact. The third characteristic is a jurisdiction’s distance fromthe next higher level in the hierarchy, as defined in table 4. Because ofthe rapid vertical flows of information through the mass media, telephone,and frequent cadre attendance at higher-level meetings, we expect distanceto have at most a modest impact on the timing of power seizures and no netimpact when top-down processes are taken into account.

The next block of variables contains the crucial tests of our theory. Thefirst is a dummy variable indicating the period beginning January 22. In thismodel the variable gauges the acceleration of diffusion beginning on thatday; later in the form of interaction terms it will be used to test for a shiftto a top-down process of diffusion. Two other variables directly measurethe impact of a higher-level power seizure. The first is the number of dayssince a power seizure at the higher level: coded 0 until a power seizure takesplace and increasing by 1 for each day thereafter.8 We argue that a powerseizure at the immediately higher level pressures cadres at lower levels tofollow suit. We expect that these pressures are strongest immediately afterthe higher-level power seizure and wane over time. To test this we includein the equation both a main effect and a quadratic term for number of dayssquared.

The next model block introduces a variable that indicates local or hori-zontal influence processes. Power seizures may have been hastened byknowledge of similar acts in nearby administrative units of equal rank.

8 Data for prefecture power seizures are more complete than for cities and counties. Thedates of their power seizures are known for 191 prefectures (with 1,669 cities and coun-ties). For 46 prefectures (321 cities and counties) we know only the month. For the 46missing dates, 43 are imputedwith themean dates of prefecture power seizures in a givenmonth for the province, and three cases are imputedwith themonthly mean for the entiresample. We assume that there were no prefectural power seizures for the remaining 30prefectures (223 counties and cities).

1167



All

For county-level units, this variable is coded as the percentage of county-level jurisdictions within the same prefecture that have experienced a powerseizure; for prefecture-level cities, it is the percentage of other prefecture-levelcities in the same province. The variable is coded 0 until the first power sei-zure occurs in the prefecture (or province), and the percentage increases byday up to a maximum of 100%. We do not have strong prior expectationsabout the impact of these horizontal influences; our theory emphasizes top-down processes within the political hierarchy.In the last block of variables are two interaction terms designed to gauge

a shift in diffusion processes after January 22. The first interacts the periodwith the number of days since a higher-level power seizure. The second in-teracts the period with the percentage of local jurisdictions that have expe-rienced a prior power seizure. The first gauges vertical, top-down diffusion;the second gauges local, horizontal influences. We expect that the top-downprocesses are activated on January 22 and that they will override horizontalinfluences.

FINDINGS

Table 5 presents nested equations estimated as accelerated failure time(AFT) models. Unlike event-history models that employ proportional-hazardmetrics, a positive coefficient indicates a lengthening of the time to an event,while a negative coefficient indicates a shortening of the time to an event.9 Theresults conform closely to expectations derived from our theory. The nestedmodels are designed in part to gauge which variables have the largest im-pact on model fit. Each successive model improves the fit to a statisticallysignificant degree, but by far the largest increment in the Wald v2 statisticin models 2–5 is model 3, which adds indicators of top-down diffusion.The individual coefficients conform closely to our expectations.Moreurban

jurisdictions, which are higher in the bureaucratic hierarchy, experience powerseizures earlier (models 2–5). The ratio of cadres to workers has no net effectin any of themodels. This conforms to the idea that cadres responded to top-down pressures to seize power. Power seizures were not delayed or less likelyto occur in cadre-dominant counties. The variable for distance has no effectonce we account for the top-down diffusion pattern, which indicates that thepressures from above to which cadres responded were unaffected by distancefrom the next higher level of administration.The crucial test of top-down diffusion is provided by coefficients that gauge

the impact of higher-level power seizures. The dummy variable indicating

The AFT models are expressed as ln(T)5 Xb1 ε, such that a positive coefficient indi-ates a delaying effect, that is, additional days before an event occurs.

9

c

1168


TABLE5

Log-LogisticModelEstimatesfortheTimingofPowerSeizures

(1)

(2)

(3)

(4)

(5)

Jurisdiction

feature:

Percentage

urban

....

....

....

....

....

....

..2.43***

2.59***

2.56***

2.50***

(.09)

(.14)

(.12)

(.13)

Cad

res/workersan

dstaff..

....

....

....

....

...02

.01

.01

.01e22

(.03)

(.03)

(.03)

(.03)

Distancefrom

higherlevel(logkm

)................

.04**

.03

.02

.03

(.01)

(.02)

(.02)

(.02)

Hierarchical

diffusion

:Late(after

January22)

....

....

....

....

....

.21.35***

21.33***

21.71***

(.15)

(.14)

(.20)

Dayssincehigher-levelp

ower

seizure.............

.03***

.03***

2.04

(.00)

(.00)

(.02)

Dayssince

higher-levelpow

erseizure

squared

...

2.07e23***

2.05e23***

2.05e23***

(.00)

(.00)

(.00)

Local

contagion

effect:

Prior

localpow

erseizures..

....

....

....

....

..50***

25.36***

(.12)

(1.04)

PeriodInteraction:

Dayssince

pow

erseizure

abov

e�

late

....

....

..07**

(.02)

Prior

localpow

erseizures�

late

....

....

....

..6.11***

(1.09)

Provincial

fixedeffects..

....

....

....

....

....

..Yes

Yes

Yes

Yes

Yes

Con

stan

t..

....

....

....

....

....

....

....

....

.4.08***

4.01***

4.65***

4.63***

4.86***

(.09)

(.11)

(.17)

(.16)

(.19)

Waldx2 ðkÞ..

....

....

....

....

....

....

....

....

.211.37

(25)***

56.18 (

3)***

807.51

(3)***

18.37 (

1)***

37.13 (

2)***

ln(c)

....

....

....

....

....

....

....

....

....

..2.64***

2.65***

2.98***

21.04***

2.98***

(.04)

(.04)

(.05)

(.05)

(.06)

NOTE.—

The

Waldx

2statisticin

col.1representsmod

elfitim

prov

ementcompa

redto

thenu

llmod

elwithintercepton

ly;the

Waldx

2statistics

incols.2–5

representim

prov

ementof

fitcompa

redto

themod

elin

theprevious

column.

Rob

ustSE

sarein

parentheses.N

5198,825.

*P<.05(two-tailedtest).

**P<.01.

***P<.001.



All

the period beginning January 22 is large and negative across models 3–5,which is not surprising, given the rapid spread of power seizures after thatday. The coefficient for the number of days since a higher-level power sei-zure is positive in models 3 and 4, which means that the risk of a power sei-zure is highest shortly after a power seizure at the jurisdiction above anddeclines with the number of days thereafter. The quadratic term for thisvariable is negative, which means that the effect of days since a higher-levelpower seizure eventually disappears.10 The expected median duration topower seizure is 35 days one day after a power seizure at the immediatelyhigher level, and 41 days one week after.Model 4 adds the measure of local peer effects—the percentage of local

jurisdictions that have experienced a power seizure. The coefficient is pos-itive, which means that the risk of a power seizure falls as the percentageof prior power seizures within the prefecture rises. This is not what a localcontagionmodel would predict—that prior local power seizures create pres-sures on peer units to imitate relevant others. This suggests that rapid top-down diffusion may have overwhelmed whatever peer pressures may haveoperated.Model 5 adds interaction terms with the time period for both the top-

down and horizontal diffusion measures and shows a sharp contrast beforeand after January 22. Interestingly, the coefficient for the number of dayssince a higher-level power seizure, which now signifies its impact beforeJanuary 22, is no longer statistically significant (while the sign of the coeffi-cient has reversed and is now negative). This means that before January 22the top-down pattern of diffusion had yet to take hold. The coefficient forthe interaction term, which indicates the impact of a higher-level power sei-zure January 22 and after, shows a large accelerating effect (i.e., as the num-ber of days since a higher-level power seizure rises, the expected time to theevent increases). The period interaction effect is displayed in figure 4. Forthe period beginning January 22 the estimated time to failure is lowest im-mediately after a higher-level power seizure and increases steadily there-after, roughly doubling after another 21 days. Before January 22, by contrast,there is no statistically significant effect, and the slope of the line is slightlynegative. The coefficient for the interaction term is two to three times largerthan the estimates for this variable inmodels 3 and 4, which averages effectsacross both time periods. The top-down pattern was evidently activated onJanuary 22.Note also that the coefficient for percentage of prior local power seizures

changes sign and now has an accelerating effect in the period before Janu-

The estimated effect disappears after roughly eight months. This is calculated by tak-g the derivative of our model equation for days since higher-level power seizure, whichives 0.0332 0.00014 � power seizure above based on model 3 of table 5, which equals36 days.

10

ing2

1170



ary 22, while the interaction term indicates an even larger delaying effect inthe later period. Each percentage increase in prior local power seizures de-creases the duration to power seizure by 5%before January 22 but increasesthe duration by 1% after January 22. In other words, peer influences appearto be strong before January 22, but afterward their impact is overwhelmedby top-down processes. This confirms our impressions from the visual rep-resentation of the process in figure 1.

In sum, the results are highly consistent with the argument that a top-down cascade of power seizures, driven forward by cadres in lower-leveljurisdictions, was touched off by the January 22 directive. The diffusion ac-celerated and shifted from horizontal to vertical after that date and con-formed to a top-down process that was unaffected by either the number ofcadres relative to workers or a jurisdiction’s geographic distance from higherlevels in the hierarchy. A series of robustness checks (not shown) indicate thatthe results are not affected by missing or extreme values for variables or bythe choice of model.11

FIG. 4.—Interaction between period and days since higher-level power seizure

11 The results are unaffected by the exclusion of extreme values for distance, urbaniza-tion, and the cadre-worker ratio. The only variable for which there are significant num-bers of missing values is the cadre/worker ratio (216). A model that includes a dummyvariable for those missing cases does not have a statistically significant coefficient (resultsavailable on request). Appendix table A1 shows that virtually the same results are ob-tained with a variety of different event-history estimation methods.

1171



All

CONCLUSION

We have introduced several new dimensions to the analysis of rebellion andrevolution. The first is a dynamic perspective on the resilience of centralizeddictatorships. We have analyzed these regimes as authoritarian hierarchieswith powerful mechanisms to monitor and enforce internal discipline. Be-cause these closed hierarchies made high demands for loyalty and placedagents under threat of severe sanctions, officials were highly sensitive to sig-nals from higher levels, and they reacted in order to safeguard their positions.By analyzing the behavior of cadres in a period of insecurity and threat, wehave applied to the state’s agents the kind of analysis usually reserved forpopular challenges. This contrasts with state-centered theories that leavethese short-run processes unspecified and treat them as a function of priorweaknesses in state structures. Paradoxically, the centralization that rein-forced the stability of these states accelerated defections by the state’s agentsunder conditions of uncertainty and threat.The crucial difference between our explanation and prior state-centered

theories is that there are no prior structural weaknesses that make the statevulnerable to collapse. The rebellion represented a rapid shift from disci-plined obedience to active opposition by large percentages of the state’s cad-res. While there were antagonisms and rivalries within the bureaucracythat predated this episode, the internal rebellion achieved critical mass onlyafter the leaders’ previously loyal defenders turned on them and formedrebel groups. The rebellion gained force after an October directive identi-fied local leaders as suspects in a bourgeois reactionary line, forcing their ac-tive defenders to reevaluate their stances. In this sense, the crucialmotivationsfor the cadre rebellions formed endogenously in reaction to events. Cadres didnot defend their superiors to the end—they turned on them in large numbers.This stance departs from the common assumption that the motivations

for collective action exist before the conflicts of interest. Theories about po-litical mobilization commonly treat the formation of interests as exogenous,and the analysis focuses on processes of mobilization that permit shared in-terests to find organized expression (Walder 2009b). This assumption mis-leads uswhen the interests thatmotivate collective action are formed duringthe course of sustainedepisodes of conflict (McAdam,Tarrow, andTilly 2001;Walder 2006).The way that the shifting choices of individual cadres eventually formed

an internal rebellion is reminiscent of threshold and critical mass theories ofcollective action, of which theories of preference falsification and informa-tion cascades are recent examples (Kuran 1989, 1991; Lohmann 1994). Butthere is a crucial difference: these theories assume shared interests that areexogenously given and relatively stable. The dynamic element is the propen-sity of individuals to contribute to the achievement of group ends or to act on

1172



previously concealed political preferences. In our analysis the dynamic ele-ment is the shift in the orientation of cadres from loyal defenders of their su-periors to active opponents. This involves the formation of political prefer-ences (to defend or overthrow their superiors), not shifts in the propensity toact on stable preferences. What changes are the political ends cadres sought,not decisions to act in their pursuit. In this case, the actions of individual bu-reaucrats, designed to ensure that they stayed on the right side of an escalat-ing purge campaign, collectively destroyed the structures to which their group’sinterests were inextricably tied.

The historical specifics of this case are highly unusual, but there are clearanalogs in the rapidly unfolding crises of other state socialist regimes. ThePolish Solidarity movement of 1980–81 touched off a proreform insurgencywithin the PolishUnitedWorkers Party that disabled its capacity for repres-sion and facilitated the expansion of the popular movement to the pointwhere the regime had to be restructured under martial law (Bloom 2014).In China in 1989, open divisions in the national leadership over how to re-spond to student protesters—dialogue or repression—generated expressionsof support for dialogue bymanywithin the state apparatus,most importantlyin the state-controlled mass media. This greatly magnified the scale of pop-ular protest and stimulated effective popular resistance to the first attemptto impose martial law, leading eventually to a more brutally effective secondattempt (Walder 1989, 1998; Zhao 2001). In the Soviet Union that same year,Gorbachev changed the rules of the political game in an effort to overcomebureaucratic resistance to reform. After he introduced competitive electionsfor regional assemblies, local officials, in efforts to appeal to electorates, shiftedfrom stances as entrenched opponents of reform and political liberalizationinto proponents of regional autonomy and even ethnic nationalism. The trendspread to officials in the Russian republic and soon led to the unexpected dis-memberment of the Soviet Union (Gill 1994; Brown 1996; Beissinger 2002).In all three cases the state’s agents responded to shifting circumstances inways that simultaneously magnified the scale and impact of popular protestand undermined the discipline and cohesion of the state. Without close at-tention to the internal dynamics of regimes—treating state officials them-selves as crucial political actors—it is difficult to explain such outcomes. Futureupheavals might be less surprising, and less difficult to explain retrospectively,if these internal processes are built into theories of rebellion and revolution.

APPENDIX

The tests that we performed in deciding to employ the log-logistic model areas follows. We first use a generalized gamma model to adjudicate amongseveral parametric models. The generalized gammamodel is highly flexible

1173



All

and allows for various shapes depending on the values of k and j. Flexibil-ity of thismodel is a desirable property given that it nests several parametricmodels as special cases: it is equivalent to the Weibull model when k 5 1,the exponential when k 5 j 5 1, and the log normal when k 5 0. Thismeans that we can test the values of k and j to decide whether to rejecta distribution assumption for the data.We start by estimating a generalizedgamma model, using the same covariates as in the full model of table 5(tableA1, col. 2).We useWald tests to evaluate hypotheses regarding the val-ues of k and j. The Wald tests suggest that the hypotheses regarding k 5 1(Wald v2 5 36.84, df5 1, P < .001), k 5 j5 1 (Wald v2 5 37.09, df5 2, P <.001), and k 5 0 (Wald v2 5 7.54, df5 1, P < .01) should all be rejected, suchthat the distribution assumptions of Weibull, exponential, and log normalare inappropriate. It is expected, given that our hazard rate has a nonmono-tonic shape (fig. 3). The parametric models that assume a monotonic or con-stant hazard function, such as exponential andWeibull, are hence inappro-priate. The test results leave us with the options of generalized gamma andtwo other nonnested models, Gompertz and log-logistic regressions.We use the Akaike’s information criterion (AIC) statistic to adjudicate be-

tween the three nonnested models. A general rule of thumb is that a better-fitting model to the data provides a smaller AIC statistic. The log-logistic re-gression has the smallest AIC statistic overall, suggesting that it provides thebest model fit (col. 1 of table A1). This is further corroborated when we com-pare the estimated hazard function from each parametric model (fig. A1), asthe curve from the log-logistic regression conforms most closely to the hazardfunction in nonparametric analysis.In addition, we compare the log-logistic regression to the Cox semipa-

rametricmodel (col. 7 of table A1). TheCoxmodel proves to be inappropriatefor our data, as the proportional hazard assumption is violated by some of thevariables related to regional features, such as percentage urban, cadre-workerratio, and provincial fixed effects (global test: v25 467.98, df5 33,P < .001).In other words, the effects of our covariates are not the same across regions,which is understandable given the diffusion patterns and timing differenceamong jurisdictions. On this basiswe conclude that the log-logisticmodel pro-vides the best fit for the data and is an appropriate modeling strategy for ouranalysis.

1174


TABLEA1

ComparisonofParametricandSemiparametricModels

Log

Logistic

(1)

Gam

ma

(2)

Exp

onential

(3)

Weibull

(4)

Log

Normal

(5)

Gom

pertz

(6)

Cox (7)

Jurisdiction

feature:

Percentage

urban

....

....

....

....

....

....

.2.50***

2.88***

21.01***

2.86***

2.77***

.99***

.95***

(.13)

(.18)

(.18)

(.16)

(.17)

(.17)

(.15)

Cad

res/workersan

dstaff..

....

....

....

....

..00

.04

.05

.04

.03

2.04

2.04

(.03)

(.04)

(.04)

(.03)

(.04)

(.04)

(.04)

Distance

from

higher

level(lo

gkm)..

....

....

.03

.03

.03

.02

.03

2.03

2.05**

(.02)

(.02)

(.02)

(.02)

(.02)

(.02)

(.02)

Hierarchical

diffusion

:Late(after

January22)

....

....

....

....

....

21.71***

22.60***

23.77***

22.99***

22.43***

3.90***

2.00

(.20)

(.40)

(.10)

(.35)

(.46)

(.12)

(.00)

Dayssince

higher-levelpow

erseizure

....

....

.2.04

2.07*

2.06

2.04

2.06*

.07

.01

(.02)

(.03)

(.06)

(.05)

(.03)

(.06)

(.04)

Dayssince

higher-levelpow

erseizure

squared

...

2.00***

2.00***

2.00***

2.00***

2.00***

.00***

2.00**

(.00)

(.00)

(.00)

(.00)

(.00)

(.00)

(.00)

Local

contagion

effect:

Prior

localpow

erseizures..

....

....

....

....

25.36***

24.53***

25.47***

24.34***

24.96***

5.64***

3.27***

(1.04)

(1.01)

(.67)

(.74)

(1.45)

(.67)

(.62)


TABLEA1(CONTIN

UED)

Log

Logistic

(1)

Gam

ma

(2)

Exp

onential

(3)

Weibull

(4)

Log

Normal

(5)

Gom

pertz

(6)

Cox (7)

PeriodInteraction:

Dayssince

power

seizureab

ove�

late

....

....

..07**

.10**

.09

.07

.09**

2.10

2.02

(.02)

(.03)

(.06)

(.05)

(.03)

(.06)

(.04)

Prior

localp

ower

seizures�

late

....

....

....

.6.11***

5.35***

6.28***

5.09***

5.80***

26.34***

23.52***

(1.09)

(1.05)

(.68)

(.77)

(1.52)

(.68)

(.64)

Provincial

fixedeffects..

....

....

....

....

....

.Yes

Yes

Yes

Yes

Yes

Yes

Yes

Con

stan

t..

....

....

....

....

....

....

....

....

4.86***

5.56***

6.78***

6.26***

5.24***

26.61***

(.19)

(.32)

(.19)

(.29)

(.28)

(.18)

ln(c)

....

....

....

....

....

....

....

....

....

.2.98***

(.06)

ln(r)..

....

....

....

....

....

....

....

....

...

2.17

2.21

(.09)

(.11)

j..

....

....

....

....

....

....

....

....

....

...31**

(.11)

ln(p)

....

....

....

....

....

....

....

....

....

..18*

(.08)

c..

....

....

....

....

....

....

....

....

....

..2.01***

(.00)

AIC

....

....

....

....

....

....

....

....

....

.2,480.52

2,487.14

2,561.55

2,557.90

2,503.60

2,548.00

21,869.88

NOTE.—

Coefficientsforallm

odelsap

artfrom

Gom

pertz

andCox

arepresentedin

theAFTform

at.R

obustSEsarein

parentheses.N

5198,825.

*P<.05(two-tailedtest).

**P<.01.

***P<.001.

1176


FIG.A

1.—

Postestim

ationcomparison

ofhazardfunctions.

1177


FIG.A

1.—

(Con

tinued

)



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