Exceptional Politics: Why Regimes Declare States of Emergency

INSTITUTE

Jose A. Aleman, Dong Wook Lee, Dwayne Woods

July 2021

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Disclaimer: V-Dem does not do quality control and therefore does not endorse the content of the papers, which is the responsibility of the authors only. Exceptional Politics: Why Regimes Declare States of Emergency

Jose A. Aleman ([email protected]) Professor, Political Science Department, Fordham University

Dong Wook Lee ([email protected]) Assistant Professor, Department of Political Science, Adelphi University

Dwayne Woods ([email protected]) Professor, Department of Political Science, Purdue University

Abstract The political world is full of heterogeneity. The relationship between regime type and the enactment of a state of emergency is indicative of such heterogeneity. Almost all political regimes have enacted states of emergency at some point but presumably for different reasons. Exploring such heterogeneity requires conceptual and empirical models that incorporate micro-level strategic decisions, institutional factors, as well as spatial and temporal non-linearities. The effect of regimes in determining the timing and purpose of states of emergency is examined in this article. We distinguish between the onset of a state of emergency and its continuation. This yields the novel finding that executive constraints primarily affect the second phenomenon but not the first. We also observe that because they experience more internal conflict, multiparty dictatorships more frequently declare emergencies. However, personalism – or the quest by an incumbent to make power less collegial and more concentrated – interacts significantly with multiparty autocracies to delay the onset of (and decrease the incidence of) emergency governance.

Keywords: States of exception, self-coups, autocratization, personalism

On October 10th, 1972, South Korea’s president, former army general Park Chung-hee, declared martial law, suspending the constitution, and dissolving the National Assembly. Park proclaimed a new constitution, Yushin (or revitalization), giving him sweeping executive and legislative powers, the right to appoint one-third of all seats in the country’s unicameral legislature and all Supreme Court justices (Chang, 2015: 42), and the ability to face an indirect election by an electoral college with no limits on reelection. Once ratified in a referendum, the Yushin constitution effectively turned Park’s self-coup into a legal dictatorship. Seven years later, on October 26th, 1979, President Park died at the hands of his own CIA (KCIA) chief. Although Prime Minister Choi Kyu- hah became acting president pursuant to the Yushin Constitution, Major General Chun Doo-hwan declared martial law on May 17th, 1980, installing a military junta and inaugurating a new dictatorship. The Korean case illustrates how emergency declarations can be used to introduce new autocratic regimes. In Turkey, however, authoritarian power grabs – such as increasing control over the media and the judiciary and violating civil rights – were occurring even before emergency rule was declared. The 2016 state of emergency thus functioned as a mechanism to consolidate power in an already competitive authoritarian regime (Conrad & Ritter, 2019: 151). In fact, the ‘autocratisation process enabled the broad use of emergency powers and simply served to consolidate further authoritarian rule’ (Lührmann & Rooney, 2020: 1). In Taiwan, we observe yet another pattern related to the longevity of emergency rule use when it becomes an institutional feature of autocracy. Essentially, the country was under martial law from 1949 to 1987, the ‘longest unbroken stretch of martial law’ in the world (Greitens, 2016: 75) and a period coinciding with the single-party rule of the Nationalist (or Kuomintang) party. These vignettes prompt a question. What type of opportunity structures do declarations of emergency (i.e. martial law, the state of siege, and the state of emergency) open up for regimes? Political scientists have begun to explore why democracies invoke emergency decrees (Lührmann & Rooney, 2020). Less work exists on nondemocracies. Thus, a pertinent question is how states of emergency relate to regime types. Our study contributes empirically to a predominantly theoretical literature on emergency rule – a literature that has mostly shed light on the suspension of civil rights and liberties (Ferejohn & Pasquino, 2004; Honig, 2009; Loevy, 2016). We do so by carrying out the first systematic,

comparative analysis of emergency declarations from 1951 to 2006.1 While most political regimes invoke states of exception, we uncover some spatial and temporal clustering for this phenomenon. We examine these clusters descriptively first. We then look at the conditional effect of regime type subject to personalism (Frantz et al., 2020; Gandhi & Summer, 2020) on states of emergency. Our interaction model estimates show that multiparty dictatorships more frequently declare emergencies since they experience more internal conflict. However, once consolidated as more personalistic regimes, these regimes delay the onset of emergency declarations and evoke them less frequently. This study also distinguishes between the onset of a state of exception and its continuation. Bjørnskov and Voigt (2018a: 110) portray emergencies as being the product of a very specific ‘political opportunity structure’2 – constitutional provisions that allow for the use of states of emergency, and weak or ineffective checks and balances on the executive. Our analysis reveals however that constraints on the executive primarily affect the second phenomenon but not the first. We model the heterogeneity evident in states of exception while accounting for variation across spatial and temporal clusters.

What is an emergency declaration? Emergency measures are usually defined as legal measures that invoke derogations from normal constitutional order (Keith & Poe, 2004: 1075-1076). In principle, they are summoned to facilitate an effective governmental response to a crisis (Richards & Clay, 2012). However, such derogations can entail suspensions or restrictions of fundamental rights (Richards & Clay, 2012), expansion of executive authority vis-à-vis other branches (Bjørnskov & Voigt, 2018a; 2018b), and creation of special administrative or judicial mechanisms. As Honig (2009: 67) points out, emergency declarations entail the ‘redistribution of the existing powers of governance from proceduralised processes to discretionary decision.’ The roots of emergency declarations can be traced back to the Roman Republic and its use by Roman consuls. The consuls accorded a dictator nearly absolute power to act independently of typical republican principles. However, its contemporary understanding and link to regime and constitutional legitimacy emerged during the French Revolution. It was with the decree of a state of

1 The only other systematic analyses of the determinants of emergency rule, the studies by Bjørnskov and Voigt, do not employ complete data on emergencies. Instead, they use measures of political unrest to proxy for them in years in which their dataset registers no emergency declarations (Bjørnskov & Voigt, 2018a: 115; Bjørnskov & Voigt, 2020: 586). 2 We borrow this term from the social movements literature. 4

emergency by the French Constituent Assembly that the idea of exceptional circumstances granting a regime the legitimacy to suspend normal constitutional guarantees arose. Basically, the dichotomy arose in which ‘a state of peace’ differed from a ‘state of siege,’ ‘where under the latter, all the functions entrusted to civilian authority for maintaining order and internal policing pass to the military commander, who exercises them under his exclusive responsibility’ (Sheeran, 2013: 496). In the 19th century, the idea of exceptional powers gained a constitutional footing with the adoption of these powers by the executive during times of ‘state of siege’ in the 1848 constitution of the Second French Republic and the suspension of habeas corpus at the onset of civil war in the United States. The use of a state of emergency to ensconce authoritarian rule in a constitutional framework occurred during the Weimar Republic. As Ginsburg & Huq (2018: 40) note, ‘the Weimar Constitution was never abrogated; instead, Hitler exercised emergency powers pursuant to the Enabling Act [of 1933] through to the end of the war.’ In the post-World War II era, states of emergency have been used extensively and by different political systems. For example, President Truman declared a state of emergency in response to the conflict in Korea in 1950 (Sheeran, 2013: 298). Charles De Gaulle was granted exceptional powers under a state of emergency in 1958 to counter a military putsch in Algeria. Other instances followed. ‘Latin America…provides perhaps the most dense [sic] thicket of historical examples [during the autocratic wave of the 1960s and 1970s] of both coups and misused emergency powers’ (Ginsburg & Huq, 2018: 52).3 Between 1985 and 2014, ‘some countries invoked a state of emergency at least once’ (Ginsburg & Huq, 2018: 58). Today, ‘some 90% of constitutions in force’… ‘have some provisions on emergency powers’ (Bjørnskov & Voigt, 2018b; Ginsburg & Huq, 2018: 58). However, ‘emergency provisions often provide little specificity of which actions short of open warfare warrant the declaration of a state of emergency’ (Lührmann & Rooney, 2020: 5). The result is that their purpose and duration vary considerably. In the Middle East and North Africa, for example, some autocratic regimes ‘maintained state of emergency laws for decades’ (Barany, 2016: 134). For data on emergency declarations, we rely on version 10 of the Varieties of Democracy (V-Dem) project, which includes information on whether an emergency was declared in a specific

3 A good example is Chile. Upon taking power, the Chilean armed forces declared a state of siege that was ‘understood as “a state or time of war.” The state of siege was renewed every six months until March 1978, when it was replaced by a “state of emergency”’ (Policzer, 2009: 187). ‘From March 11, 1981 … [until the transition to democracy in 1990] Pinochet kept the country perpetually under both a state of emergency… and a state of danger … He also declared a state of siege twice’ (Hilbink, 2007: 136). 5

country-year to cope with various types of events including interstate wars and natural disasters (Coppedge et al., 2020: 213). We only consider emergencies invoked in response to overt domestic conflict in the form of a terrorist attack (Bjørnskov & Voigt, 2020), civil war, or mass protest.4 To generate this data, country experts were asked, for each situation, whether an emergency was declared/existed in a given country-year or not, resulting in a series of dichotomous scales. These expert codings were then used to generate the average country-year values for each emergency type. Because the resulting indicators range from 0 to 1 and do not follow a normal distribution, they cannot be modeled as continuous variables even if transformed. We thus opted to create a dichotomous variable taking a value of 1 if the underlying indicator’s value is greater than 0 or 0 otherwise. This binary coding minimizes error that results from differences in human judgment and focuses attention on the existence (or not) during a given year of a state of emergency in a country.

Emergencies by regime type Although ‘there is no substantial difference between democracies and nondemocracies regarding [de jure] emergency powers’ (Bjørnskov & Voigt, 2018b: 114), the frequency of declaring a state of emergency could still vary by regime type. This is because constitutions have increasingly placed more emphasis on the separation of powers while simultaneously granting, in their emergency provisions, more discretion to executives (Bjørnskov & Voigt, 2018b: 108). We thus explore what patterns, if any, link regimes to the de facto use of emergencies. For time-series cross- sectional data on regime types, we use the Autocracies of the World (AoW) Dataset (Magaloni et al., 2013). This panel dataset distinguishes among five mutually exclusive types of political regimes: democracies, military regimes, monarchies, single-party dictatorships, and multi-party dictatorships (Magaloni et al., 2013: 4). The AoW follows in the footsteps of another major data project on autocratic politics, the Autocratic Regimes Dataset (Geddes et al., 2014). The Autocratic Regimes Dataset builds on Geddes’ (1999) deductively derived classification of authoritarian regimes as single-party, military, personalistic, or hybrid. It also adds two categories not originally coded by Geddes: monarchies and oligarchies. As Svolik (2012, 29) observed, however,

4 ‘Violent conflict is a signal that politics may not be fully institutionalized’ (Gerring et al., 2021: 610). More generally, overt internal conflict is a sign that institutions are not working well (Gerring et al., 2021: 611). These events, although emanating from below, sometimes reflect or arise in response to elite conflicts and divisions. 6

[t]he ‘types’ of dictatorship that emerge … (1) are neither exclusive nor collectively exhaustive and (2) require difficult classification judgements that weigh incommensurable aspects of authoritarian politics. [original emphasis].

The AoW improves on Geddes et al. (2014) in three ways. First, unlike some extant data, the AoW features no hybrid classifications. Great efforts were made to determine the underlying institutions that control power in a given country- year. Second, the common category of ‘personalist regime’ is removed. Theoretically, the AoW works under the assumption that ‘personalism’ (the degree to which a regime is tied to one specific individual) is a varying feature rather than a category in and of itself. ... Third, the AoW corrects numerous inconsistencies found in existing regime classification datasets, as well as filling in many points of missing data.5

Figure 1 provides a sense of the geographic distribution and intensity of emergency rule by regime type from 1950 to 2010. The lattice graph confirms the ubiquity of states of exception across political regimes. This makes a compelling case for not decoupling nondemocracies from democracies in our analysis. [ Figure 1 about here]

Combining our data with the extant literature, a few stylized facts emerge. First, the average within-country non-emergency spell, or the time between two emergencies, is 9 years. Second, when the frequency by regime type of states of exception (counted as incidence) is taken into consideration, monarchies had the lowest incidence as a share of emergencies (5.34% of 2,346 total episodes), with single-party regimes following behind (22.09%). Military regimes had the highest frequency (26.11%), followed by democracies (23.81%) and multiparty regimes (22.66%). An important question is whether emergencies occur because of political unrest (Bjørnskov & Voigt, 2018a: 111), and/or because power has not been sufficiently consolidated. We return to this nexus between emergencies and instability in the following section. The previous observations allow us to formulate our first hypothesis regarding the relationship between political regimes and emergency declarations. Controlling for other variables that could prompt regimes to declare emergencies, we posit that compared to their democratic

5 https://cddrl.fsi.stanford.edu/research/autocracies_of_the_world_dataset. The AoW Dataset is available from 1950 to 2012, while the Autocratic Regimes Dataset is available from 1946 to 2010. Therefore, our choice of data on regimes does not result in a significant loss in coverage. 7 counterparts, multi-party regimes are significantly more likely to experience emergency declarations, with monarchies being the least likely (Hypothesis 1). Democracies use states of emergency for presumably legitimate reasons. In principle, these are temporary derogations from the rule of law grounded in exceptional and temporarily containable measures to restore normal order (Ferejohn & Pasquino, 2004; see also Loevy, 2016). For example, states of emergency are used to deal with natural disasters (e.g., wildfires, tornadoes, etc.). Governors in the United States declare states of emergency as a means to access federal funds and/or mobilize the National Guard. Because of this reality, state-of-emergency declarations occur in the United States more frequently than in other countries. Regarding dictatorships, the literature presumes that, compared to democracies, they experience more derogations from regular constitutional order. The only other study of the determinants of states of emergency in autocracies, however, found few differences between democracies and autocracies: if anything, it was democracies that more frequently responded to political unrest using emergency powers (Bjørnskov & Voigt, 2018a: 111).6 The finding indicates heterogeneity in the use of emergencies. Take Tunisia for example. Before the Arab Spring, the last instance of an emergency declaration was during the bread riots of 1984 under President Habib Bourguiba’s government. Although an autocracy until 2011, President Zine El Abidine Ben Ali’s (1987-2011) powers did not stem from emergency rule after the 1987 coup. South Korea, on the other hand, witnessed twelve garrison and emergency decrees in the 1970s, all under president Park (Greitens, 2016: 161).7 More extreme still is Egypt, which holds the distinction of being the only country in our data to be under a state of emergency for all sixty-five years we have data for (1946-2010).8

[Figure 2 / Figure 3 about here]

Figure 2 provides frequency data on emergency declarations sorted by regions across time. The figure reveals regime-related heterogeneity in the use of emergencies. First, democracies exhibit a more constant probability of experiencing a state of emergency. While military coups have become

6 While the military usually takes control by declaring martial law, civilians more often invoke the state of emergency and the state of siege (Brooker, 2014: 122). 7 See also Domínguez (2011: 593). 8 According to the US State Department, the state of emergency lasted during the entirety of Hosni Mubarak’s tenure as president (1981-2011), ending only in May 2012 (Richard & Clay, 2012: 450). 8

less common since the end of the Cold War, the risk of self-coups has remained (Lührmann & Lindberg, 2019; Svolik, 2015). Thus, the danger for democracies lies in emergency clauses moving some democracies towards autocracy or hybrid regimes (Lührmann & Rooney, 2020). Regarding autocracies, emergencies were pervasive in military dictatorships from the mid- 1960s to the late 1980s and in multiparty regimes since the end of the Cold War. Also, states of emergency were invoked in single-party regimes the most as the Third Wave of democratization accelerated in the mid-1980s. Figure 3 zeroes in geospatially on the post-Cold War period. The top panel in the figure reveals that African countries experiencing states of emergency in the 1990s were primarily military regimes. However, as shown in the bottom panel of Figure 3, multiparty autocracies in Africa were busy enacting emergency measures in the first decade of the century. These autocracies ‘have displaced single-party and military dictatorships as the most common form of autocracy in the world’ (Magaloni, 2008: 733; Schedler, 2013).

Regime type, personalism, and states of emergency The descriptive data confirms the heterogeneity we mentioned at the outset. The fact that states of emergency nonetheless cluster spatially, temporally, and by regime type indicates that they are conditional on other factors such as institutional stability, power struggles, and regime consolidation. Bjørnskov & Voigt (2018a: 111) claim that dictators ‘react to constitutional incentives by calling an SOE [a state of emergency] when the emergency constitution provides particular benefits, and when they do not face a formally independent judiciary or other veto players.’ Contra these authors, we show that incumbents declare emergencies not so much because they can (within their institutional parameters), but because they wish to change these parameters in their favor. Viewed this way, we can explain some of the variation in why regimes invoke emergencies. Monarchies experience so few emergencies because monarchical rule in the modern world is either constitutional or absolute.9 Constitutional monarchies are consolidated democracies, whereas absolute monarchies can be regarded as an extreme case of a personalized regime in which power-

9 According to Wikipedia, absolute monarchs remain in the Kingdom of Eswatini, the Nation of Brunei, the Sultanate of Oman, the State of Qatar, and the Kingdom of Saudi Arabia. ‘The Kingdom of Bahrain and the State of Kuwait’ … [have] ‘representative bodies of some kind, but the monarch retains most of his powers.’ Likewise, the ‘Kingdom of Jordan, Malaysia, the Kingdom of Morocco, and the United Arab Emirates are constitutional monarchies, but their monarchs still retain more substantial powers’ than their European counterparts (see https://en.wikipedia.org/wiki/List_of_current_monarchies). 9

sharing is minimal or non-existent (Menaldo, 2012).10 Our data shows moreover that monarchies experience little mass mobilization and internal war. The stability of monarchies prompts us to ask what role personalism plays in other authoritarian regimes (Magaloni et al., 2013). According to Geddes et al. (2018: 199), it ‘stabilizes authoritarian rule in dictatorships initially led by civilians.’ An important question is whether it does so as well in dictatorships led initially by a military officer or a junta. If so, once consolidated as more personalistic regimes, we should expect dictatorships to invoke emergency declarations less frequently (Hypothesis 2). To further assess whether personalism has such a conditional effect, we derive two additional expectations from, on the one hand, its interaction with military rule and, on the other, its interaction with multiparty rule. Comparatively speaking, the first few years in office are particularly perilous for military dictators (Bueno de Mesquita et al., 2003: 467). Leadership in these autocracies is also more vulnerable to irregular challenges such as executive takeovers and popular uprisings (Svolik, 2012: 162) – a trend we attribute to their lack of success in establishing extra-military institutions such as ruling parties (Magaloni, 2008: 732). The data seems to bear out this causal mechanism because military (and multiparty) regimes experience more internal war. Therefore, states of emergency give militaries time to institutionalize their hold on power to deal with civilian elites and the public. In the case of multi-party regimes, there is the problem of factionalism at the top which occurs because the presence of multiple parties solves the collective action problem of regime opponents (Knutsen & Fjelde, 2013; cited in Knutsen & Nygård, 2015: 663). Multiple parties also increase ‘the bargaining power of the ruling party vis-à-vis the dictator’ (Magaloni, 2008, 717). Therefore, Schedler (2013, 74) refers to these regimes as providing additional ‘spaces of contention’ or ‘arenas of struggle [original emphasis]’ for regimes and their oppositions. Our data indicate that executives in multiparty (and military) regimes have the least constraints placed on them by their legislative and judicial branches compared to democracies and other autocracies. This explains why multiparty dictatorships, which are hybrid regimes, are significantly more likely to undergo autocratization episodes when emergencies are invoked

10 Power is so concentrated in these regimes that in the first type of model we estimate, when interacting regime type and personalism, the interaction term capturing the conditional effect of non-personalism on monarchical regimes identifies no observations in the data. Few polities, in other words, are as personalized in the modern world as absolute monarchies. The only other case of ‘enduring family rule found … in the modern era outside of hereditary monarchies’ is ‘the unprecedented and unmatched case of’ the Kim family dictatorship in North Korea (Song & Wright, 2018: 158). 10

(Hypothesis 3). Once declared, moreover, weak executive constraints also allow multiparty and military regimes to keep states of exception in place longer.

Empirical strategy for modeling states of emergency In this section, we describe the data we use to build our model of the effects of regime type and personalism on the onset and frequency of states of emergency (or SOEs). Because SOE actions are irregular power grabs usually invoked to quell domestic dissent, our empirical approach gauges the explanatory power of variables frequently used to explain the incidence of coups and the severity of state repression. From the ‘standard model’ of political repression (Keith, 2012: 11), which scholars have refined since Poe & Tate (1994) first introduced it (Richards et al., 2015), we derive the following covariates: civil war, regime type11, level of development, economic growth, population size, the size of the armed forces (Albertus & Menaldo, 2012: 152) and personalism (Frantz et al., 2020). Personalism is defined as power accretion vis-à-vis a ruling party and/or the military rather than other branches of government.12 ‘Personalization in authoritarian regimes usually occurs during the first few years after the regime seizes power, when it is still uncertain what the rules of the game will be’ (Frantz, 2018: 49).13 The indicator we use from Geddes et al. (2018) is based on ten ‘observable indicators of personalist power’ (Song & Wright, 2018: 162), such as whether the security apparatus is controlled personally by the regime leader.14 Because the index does not follow a normal distribution and various transformations fail to make it normal, we created a dichotomous variable taking a value of 1 if there is any personalism in the polity and 0 otherwise. From the literature on coups15 and self-coups, in particular chapter 3 in Geddes et al. (2018), we derive the following independent variables: regime duration16, mass mobilization, ethnic

11 In the original model, this referred to the presence of political democracy as measured by the Polity project (Keith, 2012: 83). 12 The dynamic this concept conveys is different from the personalist regime category Geddes (1999) originally introduced which Magaloni et al. (2013) take issue with. 13 See also Meng (2020: 117). 14 See Table 1 in Song & Wright (2018: 163) for definitions of the items used to construct the Personalist Index. 15 We follow Coppedge et al. (2020: 343) in defining coups as ‘overt attempts by the military or other elites within the state apparatus to unseat the sitting head of state using unconstitutional means’ ... ‘A coup attempt is defined as successful if the coup perpetrators seize and hold power for at least seven days’ (Powell & Thyne, 2011: 252). 16 This variable allows us to control for the possibility that the longer a regime has lasted, the more it is likely to survive (Ginsburg & Huq, 2018: 56) since, according to the literature, regimes have different propensities to last (Przeworski et al., 2000). ‘[A]verage tenure among monarchs [from 1789 to the present for example] is nearly 16 years while average tenure among non-monarchs is nearly 5 years’ (Gerring et al., 2021: 609). Also, hybrid regimes ‘lasted only 9 years on 11

fractionalization (Svolik, 2015), total resource (petroleum, coal, natural gas, and metals) income per capita, and the distribution of power based on economic (in)equality in society (Houle, 2018). Extant work has argued that ‘coups breed more coups’ (Haggard & Kaufman, 2016: 315; Croissant et al., 2017: 99; Svolik, 2012: 136-137); we thus add to the analysis a count of the number of coup attempts (whether successful or not) per country since 1950. Finally, we add a count of the number of years since 1946 in which a state of emergency was in force in a country (Bjørnskov & Voigt, 2018a: 114).17

Accounting for heterogeneity: Nonlinear model specifications and estimation techniques To explore regime effects on the propensity to declare a state of emergency, we adopt a nonlinear model specification developed by Woodridge (2011) which flexibly allows heterogeneity to depend on regime-specific characteristics and their time-varying dispersion. This yields the following model specification: Probability( , ) = ( + ), = 1, … , , = 1, … , . (Eq. 1)

𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖 𝐶𝐶𝑖𝑖 𝛷𝛷 𝑋𝑋𝑖𝑖𝑖𝑖β 𝐶𝐶𝑖𝑖 𝑖𝑖( | ,…,𝑁𝑁 𝑡𝑡 ) = ( 𝑇𝑇 | ) where i is a country-level𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 observation𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐷𝐷𝐷𝐷 𝐷𝐷and𝐷𝐷𝐷𝐷𝐷𝐷 𝐷𝐷𝐷𝐷t is𝐷𝐷 the𝐷𝐷𝐷𝐷𝐷𝐷 year𝐶𝐶𝑖𝑖 of𝑋𝑋 𝑖𝑖1each observation𝑋𝑋𝑖𝑖𝑖𝑖 𝐶𝐶𝑖𝑖 (from𝑋𝑋�𝑖𝑖 1952 to 2006). Yit is a dichotomous dependent variable and Xit is a vector of observed explanatory variables. Ci is unobserved country-level heterogeneity. Ф denotes the standard normal cumulative density function. is the country-specific mean generated on samples with complete data on all variables (Schunk,

𝑋𝑋2013�𝑖𝑖 : 68). We also lag measures of regime type, personalism, constraints on the executive, and military size by one year to avoid endogeneity bias. For Eq. 1, we employ a correlated random effects (CRE) probit model for panel data (Woolridge, 2011). A key assumption of the traditional random-effects model is that unit-level heterogeneity is uncorrelated with time-varying covariates.18 Fixed effects, on the other hand, usually leads to an incidental parameters problem in non-linear models. ‘The correlated random-effects model relaxes the assumption of zero correlation between the level 2 error and the level 1 variables’ (Schunck, 2013: 67). This flexibility is allowed by introducing the cluster means of time-varying covariates, which pick up any correlation between these variables and the level 2 (or cluster) error.

average over the period 1800–2000. In contrast, the average autocracy and democracy endured 21 and 23 years, respectively’ (Knutsen & Nygård, 2015). 17 See Online Appendix (OA) Table A.1 for complete information on variable definitions, operationalization, and sources. 18 More formally, ( | ,…, ) = ( ).

𝐷𝐷 𝐶𝐶𝑖𝑖 𝑋𝑋𝑖𝑖1 𝑋𝑋𝑖𝑖𝑖𝑖 𝐷𝐷 𝐶𝐶𝑖𝑖 12

The CRE model also allows for the inclusion of variables that don’t vary within clusters (of which we have none). The quantity of interest to estimate is the average partial effect (or the population average effect) of the deviation scores – or the difference between the cluster mean of a covariate and a particular country-year value. A full battery of yearly dummies on the right-hand side of the regression relaxes the assumption of independence of observations over time within countries. For the second part of the analysis, we estimate logit models of the onset of emergency spells using the time-series-cross-section with a binary dependent variable (BTSCS) analysis technique (Beck et al., 1998). As Beck et al. show, BTSCS data are group duration data and analysis of such data correctly accounts for the lack of independence units in the same cluster are likely to exhibit. This time dependence can be accounted for in a couple of different ways: using a full set of time dummies like in the previous analysis, or cubic smoothing splines. This BTSCS data analysis also requires that we create a variable, ‘spell count’, reflecting the number of years by cluster that have elapsed since the last emergency spell onset or since the country entered the study. To meet this requirement, we adopt the logit model specification suggested by Beck et al. (1998): Probability ( = 1| ) = 1/[1 + ( )] (Eq. 2) − 𝑋𝑋𝑖𝑖𝑖𝑖𝛽𝛽+𝐾𝐾𝑡𝑡−𝑡𝑡0 𝑌𝑌𝑖𝑖𝑖𝑖 𝑋𝑋𝑖𝑖𝑖𝑖= 1, … , , =𝑒𝑒 1, … , . where t0 takes a value of 0 for the year𝑖𝑖 the country𝑁𝑁 𝑡𝑡first enters𝑇𝑇 the database. Otherwise, t0 represents the timing of the previous event. t-t0 is the duration of non-event years between t0 and t. Kt-t0 is therefore a count variable that captures the length of sequential zero values before the current observation. In addition, to capture any unobserved country-level heterogeneity, we specify robust standard errors by country. Moreover, BTSCS analysis requires that we have no missing data on the dependent variable, which we proceed to ensure. The number of observations we lose as a result is small, only 90 out of an original 7,953. Part three of the empirical analysis examines the role emergencies play in autocratization episodes. Following the Episodes of Regime Transformation (ERT) dataset (Edgell et al., 2020), we operationalize autocratization episodes as ‘substantial and sustained decreases’ in the V-Dem electoral democracy 0-1 index (EDI). The EDI is based on Dahl’s (1971) conception of democracy/polyarchy and an episode requires an initial -0.01 decrease in the EDI ‘and a total decrease of at least -0.10 throughout the episode.’

An autocratization episode ends the final year of a negative change less than or equal to the initial decrease (e.g. -0.01), prior to experiencing an annual increase, cumulative

increase, or stasis period. These are defined in the defaults as +0.03, +0.10, and 5 years respectively (Edgell et al., 2020: 18).

Because the variable asks whether an autocratization episode is ongoing during a particular country- year, it is dichotomous and lends itself to BTSCS modeling. A key difference between this and the first two sets of analyses however is that political repression is implied in the definition of an autocratization episode. Also, this repression is allegedly invoked in response to events such as mass mobilization, guerrilla warfare, and terrorist attacks. As such, we follow a three-pronged approach to the analysis of the effect of political characteristics and states of emergency on autocratization episodes. First, we remove from our analysis the variables mass mobilization and civil war. Second, because autocratization episodes sometimes unfold over several years, we use current rather than lagged values to estimate the effect of emergencies on these episodes. Thirdly, since regimes vary in how constrained their executives are, we first estimate models of unconditional and conditional (on personalism) regime effects that omit executive constraints (Models 4 and 5). We then estimate unconditional and conditional (on emergencies) models of the effect of legislative and judicial constraints that exclude the variables regime type and personalism (Models 6 and 7). Our classification of regimes is based on the regime type that exists in a particular country at the end of the year. As Magaloni et al. (2013: 4) point out, ‘this is the more “conventional” approach’ in datasets of political regimes. Regime duration, for example, reflects the number of years the regime has been in power, up to and including the observation year; duration = 0 in the calendar year the regime took power; duration = 1 for the first calendar year in which the regime holds power on January 1. Duration time includes years the regime held power prior to 1946 for independent countries (Geddes et al., 2014).19 To prevent the effect of emergencies from confounding its causes, we thus lag the political characteristics of a country such as its regime type, personalism, the size of its military, and its accumulated number of coups and emergency incidences by one year. Cross-national analyses of regime change usually control for the spatial and temporal diffusion of regimes, especially democracy.20 This is usually done using a measure of the number of democratic regimes in a country’s neighborhood or the world in a particular year. Multiparty

19 See page 1 in the Autocratic Regimes Code Book, available at https://sites.psu.edu/dictators/wp- content/uploads/sites/12570/2016/05/GWF-Codebook.pdf. 20 See Houle et al. (2016) for a recent reconsideration of this literature. 14

autocracies in particular have increased in number since the end of the Cold War because of economic leverage exerted by democracies on polities that were once single-party autocracies (Levitsky & Way, 2010; Meng, 2020; Miller, 2017). One benefit of our approach is that it allows these stimuli to affect countries differently and to vary over time in magnitude.

Empirical findings Table 1 presents the results of our analysis of the incidence (Model 1) and onset (Models 2 and 3) of emergency measures. [Table 1 about here]

As expected, lagged judicial and legislative constraints significantly lessen the incidence of a SOE although they do not significantly delay its declaration. These effects are robust even after controlling for the type of regime in the polity, personalism, and their interaction in the year before the declaration of a SOE. This allows us to conclude that while executive constraints may shorten the length of a particular emergency, they do not prevent executives from declaring them in the first place. We also find that personalism accelerates the frequency of states of emergency and the onset of emergency spells. The most revealing part of the results is the effect of regime types on SOEs and how personalism conditions them. Compared to democracies, multiparty regimes experience significantly more SOEs. This result is in sync with Hypothesis 1 aforementioned. Once personalized, however, they spend significantly less time under emergencies than democracies (Hypothesis 2 is confirmed conditionally). Turning now to the effect of regime type on the onset of a SOE spell (Models 2 and 3), we find that monarchies, for the reasons noted, exert an unconditional and statistically significant delay on this dependent variable. Our model estimates of the magnitude of this delay vary slightly by specification; in addition, the lagged interaction between a military regime and personalism is significant at the 95 percent confidence level in the model with time dummies (Model 2) but insignificant in the model with cubic splines (Model 3). The variation is indicative of the heterogeneity in the data; specifically, the reality that military regimes were more prone to declare emergencies at the height of the Cold War than since (see Figure 2 for the time-dependent, regime- specific distribution of SOEs). [Figure 4 about here]

Still, in a personalized multiparty regime, we uncover results that are consistent with a model predicting less frequent SOEs and delays in spell onsets. We address this expectation graphically for the marginal effect of multiparty (versus military and single party) autocracies on SOEs as regime type interacts with personalism. As Figure 4 indicates, the likelihood of multiparty autocracies experiencing a SOE drops from 11 percent more than democracies to 15 percent less than democracies when they have become personalized. A similar dampening effect unfolds under military dictatorships, although its magnitude is somewhat smaller (17 percent). When addressing the onset of SOEs, we find some evidence (Model 2) that personalism significantly delays the probability of SOE declarations in all three types of autocracies (i.e., military, multiparty, and single party) compared to democracies. Controls included in Table 1 all show anticipated results. We find that mass mobilization, civil wars, and the size of the military apparatus all significantly increase emergency incidence as well as accelerating the onset of spells in the case of the first two variables. Both population size and regime duration decrease this incidence. Scholars expect population size to increase state repression. The negative and statistically significant effect of this variable, however, suggests that larger populations might make it harder for governments to achieve their objectives using SOEs. Finally (and as expected), the relationship between the time that has elapsed since the beginning of the last spell (as indicated by the spell count variable) and the outcome variable is negative and statistically significant. Altogether, the results reveal that most covariates responsible for the incidence of emergencies also explain the time that elapses before a new emergency spell begins.

[Table 2 / Figure 5 about here]

As Table 2 indicates, we do not find that vulnerability to autocratization is significantly higher among authoritarian regimes compared to their democratic counterparts. The non-linear estimates of the interaction terms in Models 4 and 5 show however that personalized multiparty dictatorships are the only polities systematically associated with substantial and sustained autocratization. Figure 5 estimates this conditional marginal effect as a significant increase ranging from 8 to 11 percent with respect to the baseline probability of autocratization. These models also reveal that the number of prior coups exerts a dampening effect on this outcome. Although the literature cited above might lead us to expect that coups predict autocratization, coups in the recent past ‘strongly predict democratization, as they indicate key periods of instability and regime

weakness’ (Miller, 2017: 37). Highlighted in models 6 and 7 are the effects of emergencies on autocratization episodes. As expected, emergencies significantly accelerate the onset of these episodes (Model 6), while the more time passes since the last episode, the more successfully a country can avoid a new one (Models 4-7).

Sensitivity analyses To check the robustness of our findings to sampling issues, we ran a range of sensitivity tests addressing overall model performance and adding relevant contextual variables. Sampling: Cross-national data over long periods are likely to include observations that are not missing at random; this creates systematic bias. Similarly, personalism is a power accretion dynamic taking place at the discretion of a regime’s key stakeholders that is often hidden from public scrutiny. Therefore, it comes as no surprise that 42.7 percent of SOE cases from the V-Dem database do not have values for our personalism indicator. To properly address the risk of sample selection bias, we conducted a panel Jackknife analysis by withdrawing one country from the pooled sample and replacing it with another country iteratively. This methodology helps to identify which sample may drive the statistical association between personalism and states of emergency. Based on our analysis, the most influential country- year observations are the reigns of leaders who came to power through a military coup (e.g., Park Chung-hee and Chun Doo-hwan in South Korea, Ibrahim Babangida in Nigeria, and Pervez Musharraf in Pakistan). These individuals leaped at the opportunity to consolidate their power by declaring an SOE, especially early in their tenure when the rules of the game may be highly uncertain (Frantz, 2018).21 Despite this sampling issue, we find that the dampening effect of personalized multiparty regimes on SOEs persists. Omitted variables: To minimize concerns with omitted variable bias, we include in our benchmark model specification (Model 1, Table 1) a measure for civil war duration as a relevant time-varying variable. As Brambor et al. (2006) recommend, we also added more interaction terms (e.g., for joint effects of civil war duration and regime type) to a nonlinear function of SOEs. Note that we used the duration of civil wars instead of their incidence as a contextual variable. This is because theoretically, the former can better trace various conflict stages such as emergence, escalation, ‘hurting’ stalemate, negotiation, and dispute settlement (Zartman, 1989). We find support

21 See OA Figure A.1 for the full report. 17

for the idea that civil war duration plays a contextual role in determining the frequency of SOEs over conflict stages. See OA Table A.2 for the full report and OA Figure A.2 for the marginal effect plots that capture this contextual dependence. Controlling for this additional contextual effect does not alter our key finding substantively. Model performance: To diagnose logit model performance, we plot ROC (Receiver Operating Characteristic) curves. Post-estimates from the emergency onset models (from Table 1) and the autocratization models (from Table 2) report large percentages of binary outcome observations that are correctly predicted for a positive outcome (i.e. the event occurred): Model 2 (94.16%), Model 3 (94.57%), Model 4 (94.71%), Model 5 (96.21%), Model 6 (94.90%), and Model 7 (95.55%).22 These high success rates are based on the method of using 0.5 as an arbitrary cut-point for predicted probabilities. In so doing, we may overstate the probability that a given observation is a positive outcome based on values of the explanatory variables. In other words, our arbitrary choice of a cut- point can be biased in such a way that any observation with a fitted probability above the cut-point is classified as positive, whereas any observation below the cut-point is classified as negative. For instance, based on the arbitrary cut-point of 0.5, we treat observations with a fitted probability of 0.51 in the same way as observations with a fitted probability of 0.99. To check for how the model distinguishes between positive and negative outcomes as we move this arbitrary cut-point from 0 to 1, we look at the area under the parametric binomial ROC curves.23 The idea is that the greater the area under the ROC curve, the better the model performs at correctly classifying outcomes. OA Figure A.3 compares the performance of multiple models. We find that models with splines where the conditional effect of multiparty regimes subject to personalism is considered (Model 3 from Table 1; Model 5 from Table 2) outperform models with time dummies (Models 2 and 4).

Concluding observations States of emergency are common political events, but little empirical work has been done to understand their duration, relationship to regime types, and factors that account for their frequency. Our study filled this gap in the literature, first with a descriptive analysis of when and where emergencies occur. We then examined the extent to which executives declare emergencies to change

22 To obtain these quantities of interest, we used the Stata estat classification command. This command works with estimation results from BTSCS logit (but not those from CRE probit). 23 We used the Stata roccomp command (available for BTSCS logit) to perform this simulation. 18

the balance of power within a regime, considering the heterogeneity in the data generating process (DGP). Emergencies affect both democracies and nondemocracies alike, but we find that monarchies experience significantly less and multiparty dictatorships significantly more emergencies compared to all other regime types, including democracies. Personalized multiparty regimes, however, witness fewer SOEs and longer spells in between them, while their emergencies are more likely to trigger an autocratization episode. Based on model estimates, this study disregards a ‘one-size-fits-all’ approach to the study of emergencies. The scope of the regime variation is still narrow (and could be biased) due to empirical limitations, especially in how we characterize personalism in hybrid regimes. Future research should explore the nexus between SOEs and the other major form of irregular power transfer, military coups.

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24 Figure 1: Spatial clusters of emergencies by regime type

Figure 2. Trend in state-of-emergency declarations by regime type (1950-2010)

Figure 3. Spatial and temporal distribution of states of emergency, 1990s to 2000s.

Note: Colored dots scaled according to instances of states of emergency.

Figure 4. Conditional marginal effects of personalism interacting with regime types on the probability of SOEs .2

0.11 0.09 Others Others 0.07 .1 Others 0.02 0.02 0.01 0.01 0.01 Others Others 0.01 Others Others Others Others 0

Military Single party Military Multiparty -0.01 Multiparty Single party -0.04 -0.02 -0.02 Single party -0.02 -0.02 -0.02 Military

-.1 -0.08 Onset of SOE Onset of SOE (BTSCS logit w/ splines) (BTSCS logit w/ time dummies)

Multiparty -0.15 Marginal effect of personalism -.2

Instance of SOE (CRE probit w/ time dummies) 95% CIs -.3

Note: For marginal effect calculations, we used the Stata margins command, holding other variables at their means.

Figure 5. Conditional marginal effects of personalism interacting with regime types on the probability of autocratization

.15 .1

Multiparty 0.09 Multiparty 0.08

.05 Military 0.05 Military 0.04

Others Others 0.01 0.002 0 Others -0.003 Others -0.02 Autocratization (BTSCS logit w/ splines) Marginal effect of personalism

-.05 Autocratization (BTSCS logit w/ time dummies)

95% CIs -.1

Table 1. Nonlinear panel regression model estimates of states of emergency

Model [1] Model [2] Model [3] Variables CRE probit BTSCS logit BTSCS logit w/ time dummies w/ time dummies w/ splines

Autocracies (reference: democracies)

Military(t-1) 0.243 -0.482 -0.636

Monarchy(t-1) -0.417 -2.276*** -1.653**

Multiparty(t-1) 1.483*** -0.639 -0.073

Single party(t-1) 0.582 -0.500 -0.235

Personalism(t-1) 1.359*** 1.375* 1.068

Interactions

Military(t-1) × personalism(t-1) -1.210* -1.924** -1.133

Multiparty(t-1) × personalism(t-1) -1.793** -1.650** -1.454*

Single party(t-1) × personalism(t-1) -0.747 -2.042** -1.489

Controls

Judicial constraints(t-1) -2.065*** -0.634 -0.501

Legislative constraints(t-1) -1.797*** -0.448 -0.688 Mass mobilization 0.543*** 0.476*** 0.391*** Civil war 1.459*** 0.777*** 0.613** Ethnic fractionalization index -14.968*** -0.552 -0.448 Log of GDP per capita -0.159 -0.053 -0.159 Economic growth -1.023 -0.674 -0.290 Natural resource income -0.038 0.021 0.020 Political equality -0.206 0.218** 0.136

Number of coups(t-1) -0.054 0.029 0.017

Number of emergencies(t-1) -0.017 0.000 -0.003 Regime duration -0.028*** -0.007 -0.015 Log of population size -2.310*** -0.247* -0.179

Size of the military(t-1) 0.367*** -0.019 0.008

By BTSCS method Spell count -0.043** -0.673***

Spline 1 -0.016*** Spline 2 0.006*** Spline 3 -0.000

Dependent variables (SOEs) Instances Onset Onset

Total observations (no. of countries) 1,890 (68) 2,105 (83) 2,266 (83) Clustered means of the covariates Yes No No Year fixed effects (1952-2006) Yes Yes No Robust standard errors No Yes Yes Pseudo R² 0.167 0.179 rho (Intra-class correlation) 0.859

Notes: One interaction dropped due to multicollinearity. Standard errors and constant not shown. CRE stands for correlated random effects. BTSCS refers to binary time-series-cross-section data analysis. Cubic splines are used to fit a nonlinear model of emergencies. Two-tailed significant tests reported at * p<0.1; ** p<0.05; *** p<0.01.

Table 2. Nonlinear panel regression model estimates of autocratization episodes

Model [4] Model [5] Model [6] Model [7] Variables BTSCS logit BTSCS logit BTSCS logit BTSCS logit w/ time w/ splines w/ time w/ splines dummies dummies

Autocracies (reference: democracies)

Military regime(t-1) -0.716 -0.475

Monarchy(t-1) 0.646 0.951

Multiparty regime(t-1) -1.515 -0.703

Single party regime(t-1) 0.180 0.725

Personalism(t-1) -0.660 -0.264 States of emergency 0.711** 0.316

Judicial constraints(t-1) 1.590*** 0.982*

Legislative constraints(t-1) 0.062 0.605

Interactions

Military regime(t-1) × personalism(t-1) 1.102 0.901

Multiparty regime(t-1) × personalism(t-1) 2.574*** 2.149***

Emergency × judicial constraints(t-1) -1.104* -0.583

Emergency × legislative constraints(t-1) 1.410** 1.550***

Controls Ethnic fractionalization index -0.206 -0.307 0.065 0.106 Log of GDP per capita -0.260 -0.223 -0.494*** -0.514*** Economic growth -1.327 -1.055 -1.244* -0.424 Natural resource income -0.036 -0.030 -0.020 -0.004 Political equality 0.301*** 0.239** -0.137* -0.098

Number of coups(t-1) -0.074** -0.069** -0.010 -0.012

Number of emergencies(t-1) 0.028* 0.008 -0.005 -0.011 Regime duration 0.008 -0.009 -0.008 -0.017*** Log of population size -0.020 -0.018 -0.017 -0.029

Size of the military(t-1) 0.007 -0.014 0.112** 0.080

Dependent variable Autocratization

Using BTSCS method Spell count -0.190*** -0.866*** -0.145** -0.896*** Spline 1 -0.008*** -0.008*** Spline 2 0.004*** 0.004*** Spline 3 -0.001*** -0.001***

Total observations (No. of countries) 2,741 (106) 3,274 (109) 5,662 (143) 5,706 (143) Year fixed effects (1952-2006) Yes No Yes No Wald chi2 163.63*** 311.61*** 398.11*** 686.47*** Pseudo R² 0.274 0.365 0.282 0.373

Notes:

Two interactions dropped due to multicollinearity. Standard errors and constant are not shown. BTSCS refers to binary time-series-cross-section data analysis. Cubic splines are used to fit a nonlinear model of emergencies. Two-tailed significant tests reported at * p<0.1; ** p<0.05; *** p<0.01.

Online appendix Table A.1. Variable definitions, summary statistics, and sources

Variables Definitions Min Max Mean Std. Dev. Sources

States of emergency (SOEs) Instance Declaring a national state of emergency due to a terrorist 0 1 0.32 0.47 V-Dem Dataset Onset attack, an armed conflict/war (domestic), and/or mass 0 1 0.05 0.22 (Version 10) protest/popular uprising. Transformed to a dichotomous (Coppedge et al., value. 1 if a state of emergency is declared, 0 otherwise. 2020).

Autocratization episodes Onset The onset of a gradual decline in democratic qualities. 0 1 0.05 0.22 Following Edgell et al (2020), coded as 1 if the electoral The Episodes of democracy index decreased by 1% and its total decrease Regime was at least 0.1 throughout the episode. Coded as 0 Transformation otherwise. dataset (Edgell et al., 2020). Authoritarian regime types Military Mutually exclusive dichotomous coding was applied for a 0 1 0.18 0.38 The Autocracies of Monarchy given country-year observation. Democracy is used as the 0 1 0.07 0.26 the World dataset Multiparty reference category. 0 1 0.18 0.38 (Magaloni et al., Single Party 0 1 0.19 0.39 2013)

Personalism Power accretion in relation to a ruling party and/or the 0 1 0.84 0.37 Geddes et al. (2018) military rather than other branches of government. Transformed to a dichotomous value. 1 if any personalism, 0 otherwise. Judicial constraints An index of judicial constraints on the executive, with a 0.01 0.99 0.51 0.31 V-Dem Dataset larger value indicating the executive respects the (Version 10) constitution more, is more in compliance with court (Coppedge et al., rulings, and there are more de facto conditions for 2020) judicial independence.

Continued.

Table A.1. Continued.

Variables Definitions Min Max Mean Std. Dev Sources

Legislative constraints An index of legislative constraints on the executive, with 0.00 0.99 0.43 0.35 V-Dem Dataset a larger value indicating the legislature is capable of (Version 10) questioning, investigating, and exercising checks on the (Coppedge et al., executive. 2020) Mass mobilization Continuous measure derived from ordinal (5-level) -3.20 3.32 -0.30 1.29 measure of the mobilization of citizens for mass events such as demonstrations, strikes, and sit-ins.

Ethnic fractionalization index Ethnic fractionalization index from the Historical Index 0.00 0.89 0.42 0.28 Drazanova (2019) of Ethnic Fractionalization Dataset (HIEF)

GDP per capita GDP per capita, logged base 10. 4.90 11.65 843 1.13 V-Dem Dataset Economic growth GDP per capita growth rate. -0.98 3.67 0.03 0.11 (Version 10) Natural resource income The real per capita value of a country’s petroleum, coal, -4.61 11.11 2.53 4.04 (Coppedge et al., natural gas, and metal production, logged. 2020)

Political equality A continuous version of a 5-level measure of power -3.17 3.35 0.44 1.26 distributed by socioeconomic position: the degree with which wealth and income are translated into political power. Converted to logarithmic scale. Number of coups The cumulative number of successful and unsuccessful 0 27 2.47 4.11 coups since 1950 or since the country gained independence if this occurred after 1950. Based on V- Dem’s “e_pt_coup” variable. Continued. Table A.1. Continued.

Variables Definitions Min Max Mean Std. Dev Sources

Number of emergencies The cumulative number of years under a state of 0 65 4.02 8.58 V-Dem Dataset emergency since 1946 or since the country gained (Version 10) independence if this occurred after 1946. (Coppedge et al., 2020)

Civil war Internal or internationalized internal war 0 1 0.16 0.37 UCDP/PRIO Armed Conflict Dataset Version 19.1 (Gleditsch et al., 2002; Pettersson, 2019; Petterson et al., 2019) Regime duration The number of years the regime has been in power, up 1 269 4.03 8.58 Geddes et al. (2018) to and including the observation year Population size Population in millions, logged -1.94 7.22 2.31 1.47 Penn World Table Version 10.0 (Feenstra et al., 2015) Size of the military The natural logarithmic of military personnel in -4.61 8.67 3.61 2.17 Correlates of War thousands National Material Capabilities Dataset Version 5.0 (Singer et al., 1972; Singer, 1987)

Figure A.1. Jackknife analysis of personalism effect (from Model 1, Table 1, main text)

4 p-value <0.05 p-value <0.1 p-value >=0.1 3 2 1 0 The z-score of pr (emergency=1) Countries excluded from the CRE model estimate of personalism (observed years) -1

Iran (51 yrs) USSR (0 yr) Brazil (2 yrs) Chile (14 yrs) Haiti (29 yrs) Laos (29 yrs) Mexico (0 yr) Peru (11 yrs) SyriaTogo (42 yrs)(44 yrs) BeninBolivia (19 yrs)(22 yrs) China (22 yrs)Egypt (55 yrs)Ghana (27 yrs) Liberia (29 yrs) Poland (8 yrs)Russia (11 yrs) Spain (25 yrs) Algeria (31 yrs) Belarus (10 yrs) GambiaGeorgia (38 yrs)(10 yrs)Guinea (46 yrs) Jordan (50 yrs) Lesotho (21Malawi yrs) (28 yrs) Tunisia (24 Ugandayrs) Uruguay (32 yrs) (4 yrs) Zambia (25 yrs) Armenia (10 yrs) Blugaria (15 yrs) Columbia (6 yrs) Hungary (16 yrs) MoroccoNamibia (48 yrs)(13Panama yrs) (15 yrs) PortugalRomania (23 Rwandayrs)(29 yrs)Senegal (42 yrs) (38 yrs)Sri Lanka (9 yrs) VenezuelaVietnam (7 yrs)(10 yrs) Azerbaijan (11 yrs) Paraguay (39 yrs) Swaziland (30 yrs) Zimbabwe (24 yrs) Bangladesh (10 yrs)Bostswana (38 yrs) Guatemala (13 yrs) Ivory CoastKazakhstan (44 yrs)Kyrgyzstan (13 yrs) (13 yrs) Philippines (12 yrs) Uzbekistan Vietnam(13 yrs) North (0 yr) Burkina Faso (27 yrs) Saudi ArabiaSierra (36 Leone yrs) (27 yrs) Turkey (2 yrs: 1960-1)

Central African Republic (32 yrs) NigeriaPakistan (16 yrs: (9 yrs:1977-9, 1985-88, 1986-98) 2002-6) Congo, Democratic Republic (44 yrs) South Africa (15 yrs: 1952-4, 1967-78) South Korea (31 yrs: 1954-60, 1964-87)

Table A.2. Robustness checking to alternative model specifications (civil war duration)

Dependent variable: Instances of states of Model [1] Model [2] Model [3] emergency CRE probit CRE probit CRE probit w/ time w/ time w/ time dummies dummies dummies

Autocracies (reference: Democracy)

Military (t-1) 0.338 -0.362 0.127

Monarchy (t-1) -0.498 -0.540 -0.939

Multiparty (t-1) 1.487** 0.263 1.103*

Single party (t-1) 0.613 0.352 0.323

Personalism (t-1) 1.423** 0.276 1.070 Civil war instance 1.337*** 0.915*** 0.920*** Civil war duration 0.024 -0.040 -0.040

Interactions

Military (t-1) × personalism (t-1) -1.273* -1.057

Multiparty (t-1) × personalism (t-1) -1.821** -1.454*

Single party (t-1) × personalism (t-1) -0.805 -0.387

Military (t-1) × civil war duration 0.440*** 0.450***

Multiparty (t-1) × civil war duration 0.258*** 0.250***

Controls

Legislative constraints (t-1) -1.783*** -0.981* -1.100**

Judicial constraints (t-1) -2.053*** -2.157*** -1.923*** Mass mobilization 0.543*** 0.534*** 0.529*** Ethnic fractionalization index -14.705*** -16.487*** -17.423*** Log of GDP per capita -0.162 -0.184 -0.188 Economic growth -1.029 -1.118 -1.180 Natural resource income -0.039 -0.037 -0.026 Political equality -0.212* -0.184 -0.195

Number of coups (t-1) -0.058 -0.023 -0.024

Number of emergencies (t-1) -0.016 -0.044** -0.049** Regime duration -0.028*** -0.018** -0.019**

Log of population size -2.228*** -1.789** -2.113***

Size of the military (t-1) 0.359*** 0.313*** 0.334***

Number of countries 68 68 68 Clustered means of the covariates Yes Yes Yes Year fixed effects Yes (1952-2006) Yes (1952-2006) Yes (1952- 2006) rho (Intra-class correlation) 0.857 0.856 0.859 Total observations 1,890 1,890 1,890

Notes: Standard errors and constant are not shown. The reference category for personalism is regimes that are not personalistic. CRE stands for correlated random effects. Two-tailed significant tests reported at * p<0.1; ** p<0.05; *** p<0.01.

Figure A.2. Conditional marginal effect of autocracies interacting with civil war duration on the probability of emergency declarations

(a). Maginal effect of military regime (b). Marginal effect of multiparty regime on emergency on emergency .6 .6 80 80

Averaged marginal effect .4 .4 60 60

95% CIs Averaged marginal effect .2 .2 40 40

95% CIs 0 0 Percentage of observations Percentage of observations 20 20 Marginal effect of military regime Marginal effect of multiparty regime -.2 0 0 -.2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Duration of civil war Duration of civil war

Note: For marginal effect calculations, we used the Stata margins command, holding other variables in Model 3, Table A.2 at their means.

Figure A.3. Diagnostic abilities of a binary classifier system using the receiver operating characteristic (ROC) curve

(a). Models of emergency onset (b). Models of autocratization (yes=1, no=0) from Table 1, main text (yes=1, no=0) from Table 2, main text 1.00 1.00 0.75 0.75 0.50 0.50

Model 4 ROC area: 0.8759 0.25

0.25 Model 5 ROC area: 0.8867 Model 2 ROC area: 0.7876 Model 6 ROC area: 0.8699 Sensitivity (the probability of correctly predicting 1)

Model 3 ROC area: 0.7958 Sensitivity (the probability of correctly predicting 1) Model 7 ROC area: 0.8837 Reference Reference 0.00 0.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 1-specificity (the probaility of correctly predicting 0) 1-specificity (the probablity of correctly predicting 0)