States of Emergency: In Whose Interest Are They Invoked?

Abstract

Arno Tausch’s illustrious career spanned five very prolific decades. The topics he explored include political stability, militarization, terrorism, and political violence, to name a few. In this essay, we honor his legacy with research that investigates why governments declare a state of emergency. Scholars see in these events instances of political instability. We argue that emergencies can create potential opportunities for executives to reshape the balance of power in their favor. However, the opportunity dynamics they open, either through extra-constitutional means (e.g., a coup or auto-coup) or autocratization, vary by regime type and are contingent on the personalization of executive power within a regime. In tending to this heterogeneity in regimes’ invocations of emergencies, we follow Arno Tausch’s exemplary use of quantitative analysis to answer complicated questions. Arno Tausch relied on both deductive and inductive methodologies. We first use an inductive approach to uncover meaningful patterns. We then conduct a nonlinear analysis of state-of-emergency (SOE) episodes from 1950 to 2010. Our findings indicate that multiparty dictatorships produce significantly more SOEs compared to democracies. However, they witness fewer SOEs in the presence of more powerful executives. The latter are also more likely to trigger an autocratization episode.

Variable Definitions, Summary Statistics, and Sources

Variables	Definitions	Min	Max	Mean	Std. Dev.	Source
State of emergency
Instances	Declaring a national state of emergency due to a terrorist attack, an armed conflict/war (domestically), and/or mass protest/popular uprising. 1 if a state of emergency is declared, 0 otherwise. Transformed to a dichotomous value.	0	1	0.32	0.47	V-Dem Dataset (Ver. 10) (Coppedge et al., 2020). The Episodes of Regime Transformation dataset (Edgell et al., 2020).
Onsets		0	1	0.05	0.22
Autocratization Episode
Onsets	The onset of a gradual decline in democratic qualities. Following Edgell et al. (2020), the onset of autocratization episodes was coded as 1 if the electoral democracy index decreased by 1% and its total decrease was at least 0.1 throughout the episode. Coded as 0 otherwise.	0	1	0.05	0.22
Authoritarian regime types
Military	Mutually exclusive dichotomous coding was applied for a given country-year observation. Democracy is used as the reference category.	0	1	0.18	0.38	The Autocracies of the World dataset (Magaloni et al., 2013)
Monarchy		0	1	0.07	0.26
Multiparty		0	1	0.18	0.38
Single Party		0	1	0.19	0.39
Personalism	Power accretion in relation to a ruling party and/or the military rather than other branches of government. Transformed to a dichotomous value. 1 if any personalism, 0 otherwise.	0	1	0.84	0.37	Geddes et al. (2018)
Judicial constraints	An index of judicial constraints on the executive, with a larger value indicating the executive is more respectful of the constitution, more in compliance with court rulings, and there are more de facto conditions for judicial independence.	0.01	0.99	0.51	0.31	V-Dem Dataset (Ver. 10) (Coppedge et al., 2020)
Legislative constraints	An index of legislative constraints on the executive, with a larger value indicating the legislature is capable of questioning, investigating, and exercising checks on the executive.	0.00	0.99	0.43	0.35
Mass mobilization	Continuous measure derived from ordinal (5-level) measure of the mobilization of citizens for mass events including demonstrations, strikes, and sit-ins.	−3.20	3.32	−0.30	1.29	V-Dem Dataset (Ver. 10) (Coppedge et al., 2020)
Ethnic fractionalization index	Ethnic fractionalization index from the Historical Index of Ethnic Fractionalization Dataset (HIEF)	0.00	0.89	0.42	0.28	Drazanova (2019)
GDP per capita	GDP per capita logged base 10.	4.90	11.65	843	1.13	V-Dem Dataset (Ver. 10) (Coppedge et al., 2020) V-Dem Dataset (Ver. 10) (Coppedge et al., 2020) V-Dem Dataset (Ver. 10) (Coppedge et al., 2020) UCDP/PRIO Armed Conflict Dataset Version 19.1 (Gleditsch et al., 2002; Pettersson, 2019; Pettersson et al., 2019)
Economic growth	GDP per capita growth rate	−0.98	3.67	0.03	0.11
Natural resource income	The real per capita value of a country’s petroleum, coal, natural gas, and metal production, logged.	−4.61	11.11	2.53	4.04
Political equality	A continuous version of a 5-level measure of power distributed by socioeconomic position: the degree with which wealth and income are translated into political power. Converted to a logarithmic scale.	−3.17	3.35	0.44	1.26
Number of coups	The cumulative number of successful and unsuccessful coups.	0	27	2.47	4.11
Number of emergencies	The cumulative number of years under a state of emergency.	0	65	4.02	8.58
Civil war	Internal or internationalized internal war	0	1	0.16	0.37
Regime duration	The number of years the regime has been in power, up to and including the observation year	1	269	4.03	8.58	Geddes et al., 2014
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 the thousands	−4.61	8.67	3.61	2.17	Correlates of War National Material Capabilities Dataset (Version 5.0) (Singer et al., 1972; Singer, 1987)