Centralized Institutions and Sudden Change
JARED RUBINy
California State University, Fullerton
First Draft: July 2009
This Draft: March 2010
Abstract
Why do sudden and massive social, economic, and political changes occur when and where they
do? Are there institutional preconditions that encourage such changes when present and discourage such
changes when absent? In this paper, I employ a general model which suggests that such changes are more
likely to occur in regimes with centralized coercive power - those with the ability to impose more than one
type of sanction (economic, legal, political, social, or religious). On the one hand, centralized authorities
are often better able to suppress subversive actions by the citizenry when external shocks are small or do
not affect many, as citizens have little incentive to incur numerous types of sanctions. However, citizens in
such regimes are also more likely to lie about their internal preferences (e.g., falsely declare loyalty to an
oppressive government), and authorities have less incentive to accommodate dissent than in decentralized
regimes. Yet, this entails that large shocks which encourage some individuals to transgress the authorities'
dictates may trigger a cascade - as more individuals change actions, social pressures encourage even more
to change, and a new, substantively different outcome emerges. The model is applied to the occurrence and
severity of protests that followed austerity measures taken in developing nations since the 1970s as well as
the Iranian uprising of summer 2009.
I am extremely grateful to Avner Greif, Kristin Kleinjans, and Timur Kuran for their extremely helpful insights. Comments received
at seminars at Cal State Fullerton were also quite helpful. All errors are mine.
y E-mail:
jrubin@fullerton.edu; Address: Department of Economics, California State University, 800 N. State College Blvd., Fuller-
ton, CA 92834; Tel: (657) 278-2387; Fax: (657) 278-3097; http://faculty.fullerton.edu/jrubin/
1
Introduction
Economists are well aware that small events may act as a spark that leads to a signi cant change in equilibrium outcomes. In some instances such events act as a signal in the decision process, leading to social
learning phenomena such as herd behavior and informational cascades that encourage individuals to ignore
their private information and follow the actions of others (Banerjee 1992, 1993; Bikhchandani, Hirshleifer,
and Welch 1992, 1998).1 In other cases, small events encourage some individuals to publicly reveal their previously suppressed, privately held preferences, which leads to information revelation and changes in status
or reputation bestowed on certain actions, in turn resulting in starkly different equilibria (Kuran 1989, 1995a,
1995b, 1998; Bernheim 1994; Kuran and Sunstein 1999; Kuran and Sandholm 2008).
These works contribute signi cantly to our understanding of social behavior and help explain why massive social change can occur. However, this literature lacks an account of the broad institutional and macroeconomic settings under which massive social change arises - why do we see such change under certain
circumstances but not others?2
This paper provides such an account, suggesting that massive economic, social, or political change may
be one consequence of an institutional arrangement in which one type of authority (economic, legal, political,
social, or religious) has multi-lateral coercive power. I formulate a model with three types of agents: a group
of citizens and two different institutional authorities. The latter players can be thought of as religious authorities, political gures, social icons, or legal authorities. One of the authorities is a "centralized" authority, with
the degree of centralization being the level of the cost imposed on the other authority for transgressing the
central authority's dictates. This speci cation re ects the idea that political authorities often depend on religious authorities for legitimacy, economic authorities are often subject to the whims of political authorities,
and the like.
1 These
2 For
phenomena have also been studied in the experimental laboratory. See Anderson and Holt (1997), Çelen and Kariv (2004).
example, Banerjee (1992, 1993) provides a basic set of circumstances that lead to herd behavior, but provides no macroeconomic
or institutional conditions under which such behavior is more likely to arise. Similarly, Bikhchandani, Hirshleifer, and Welch (1992,
1998) do not incorporate such conditions into their study of information cascades. Kuran (1989) emphasizes that a pre-condition for
revolutionary activity can be a substantial amount of private opposition (and public support) for a regime, but does not specify why such
conditions arise. Kuran (1995a) does note that preference falsi cation is less likely to happen in democracy than in autocracy because
the former has fewer means of restricting expression, but he does not provide an explanation for why this is so. Bernheim (1994) hints
at a cultural explanation, arguing that this phenomenon is more probable in societies that place more emphasis on social status.
1
The model analyzes the interactions between citizens and the authorities under the situation where the
preferences of the latter are not aligned with those of the former. Citizens derive intrinsic utility from their
own actions and have sanctions imposed on them for diverging from the actions of the two institutional
authorities. These "actions" could represent any number of phenomena in which the desires of some citizens
diverge from those of institutional authorities, such as speaking or writing freely, having more than one child,
or practicing minority religions. Citizens also derive disutility when their actions differ from endogenously
determined social norms, which may arise from the importance individuals place on social identity (Akerlof
and Kranton 2000, 2005), social custom and reputation (Akerlof 1980; Romer 1984; Kuran 1989, 1998;
Kuran and Sunstein 1999), or status and conformity (Fershtman and Weiss 1993; Bernheim 1994; Fershtman,
Murphy, and Weiss 1996; Akerlof 1997; Kuran and Sandholm 2008).
Unlike works on preference falsi cation (especially Kuran [1987, 1989, 1995a, 1998]), I do not assume
that citizens' optimal actions are constant over time in the absence of social sanctions or preference updating.
Instead, the institutional authorities respond to the actions of the citizenry, which in turn changes citizens'
optimal actions. These dynamics entail the result that highly-centralized authorities - those with coercive
power over more than one type of sanction - are more insulated from pressures for change when exogenous
shocks are small. In this case, there is less incentive for citizens in centralized economies to violate the
dictates of the authorities, as they incur more than one type of sanction from doing so. In turn, actions remain
stable as long as shocks are suf ciently small.
However, this logic also entails that citizens are more likely to choose actions further away from their
intrinsic optimum (or bliss point) in centralized economies. Moreover, centralized authorities have less incentive to accommodate the actions of the citizenry than decentralized authorities, since decentralized authorities
can respond to their actions without fear of censure from a central authority. These two features entail that
a cascade or bandwagon effect may result in centralized regimes when a shock arises that is large enough
to encourage some citizens to transgress the authorities' dictates. In other words, when a small portion of
society transgresses the law, the publicly revealed actions of these citizens changes the social norm so that
more citizens (who choose actions diverging from their intrinsic optimum) are encouraged to transgress the
law, which encourages even more to evade the law, and so on. This social effect, in turn, encourages the
authorities to signi cantly update their dictates, resulting in massive change from the previous equilibrium.
This is not the case in less centralized economies, as authorities are more likely to accommodate the actions
of the citizenry, and preferences are therefore less likely to be falsi ed.
2
One implication of this thesis is that institutional authorities with multi-lateral coercive power may seem
insulated from upheaval when in fact they are quite vulnerable. Because of this underlying vulnerability,
authorities in such societies often do anything to suppress large shocks from occurring, such as suppressing
and controlling media and harshly combating dissent. Such tactics work to suppress small shocks. However,
this also works to further push the actions of the citizenry away from their preferences, which may cause a
larger cascade and thus unintentionally sow the seeds of the authority's demise.3
This paper relates to the large economic, sociological, and political science literature in revolutions.4
The present paper is not meant to accept or deny the validity of any of these arguments, but instead offers
a complementary hypothesis. I do not account for the organizations or leadership that is often instrumental
to revolutionary activity, but instead provide a mapping from broad institutional structure to massive social,
political, and economic change.
Historical examples of massive and unexpected changes occurring in centralized regimes include the
Iranian Revolution of 1979, the Bolshevik Revolution of 1917, the Taiping Rebellion in China (1850-1864),
and the Protestant Reformation. In this paper, I employ the insights of the model to analyze the numerous
austerity protests which occurred in the developing world since the 1970s as well as the Iranian uprising of
summer 2009. An econometric analysis of these protests from 1976-1992 suggests that protests were more
severe in decentralized economies if they followed small shocks (proxied for by indices of IMF involvement), but were more severe in centralized economies if they followed large shocks. Moreover, an analytical
narrative of the events following the disputed Iranian election of June 2009 suggests that the severity of the
protests following the "shock" of the election results was a direct consequence of the institutional situation in
which political authorities (especially President Ahmadinejad) depended on religious authorities (especially
Supreme Leader Ayatollah Ali Khamenei) for legitimacy, giving the latter a great deal of control over all
types of sanctions - religious, political, social, legal, and economic.
3 For
more on the broad effects of the centralization of coercive power throughout world history, see Greif (2005). Iyigun (2009) argues
that there is a positive connection between monotheism and the length and bredth of dynastic power, as monotheisitc religions have
general been complementary to centralized government (due to high xed costs of starting a monotheistic religion). This argument is
quite consistent with the one made in the present paper.
4 The
mechanism underlying revolutionary activity in the present paper is closest to Kuran (1989, 1995a, 1995b), who analyzes the
implications of public revelations of private preferences. For overviews of the social analysis of revolutions, see Tanter and Midlarsky
(1967), Shugart (1989), and Goldstone (1994, 2001).
3
2
The Model
2.1
Setup
Consider an economy in which all players are in nitely lived and actions are common knowledge. The
players engage in a dynamic game with perfect information of previous actions. There are N + 2 players
(where N is large): N heterogeneous citizens and two institutional (social, political, economic, legal, or
religious) authorities, I1 and I2 .5
Citizens are conformists and derive utility in three ways: from choosing actions close to their intrinsic
"bliss point", conforming to endogenously determined social norms, and conforming to the actions of the
institutional authorities.6 The model analyzes situations in which the preferences of some citizens differ
exogenously from those of the authorities, so actions could represent varying levels of freedom of speech,
press, or religion, publicly expressed dissatisfaction with the government or religious authorities, or public
opinion on social issues.
Each period (denoted with subscript t) begins with every citizen j choosing some observable action,
+
B
aj;t 2 R+
j . They have unobserved types, aj 2 Rj , which represent their exogenously determined intrinsic
bliss points. Their intrinsic utility is decreasing in the distance between their action and their type.7 After
citizens act, authorities I1 and I2 choose observable actions i1;t 2 R+ and i2;t 2 R+ . The endogenization
of this choice differs from previous papers focusing on institutional focal points, such as Kuran (1987, 1989,
1995a, 1998), which assume that such points are exogenously given.8 I1 and I2 have unobserved types,
+
B
+
iB
1 2 R and i2 2 R , and their utility is decreasing in the distance between their action and their type.
Citizens face three costs. Two of these costs are a function of the distance between the citizen's action
and the actions of the two authorities. These costs are increasing in the size of the violation (as in Romer
5 The inclusion of only two authorities allows for tractability.
The intuition underlying the main results of the model holds in more realistic
situations including numerous types of authorities.
6 The
speci cation in the present model assumes that individuals derive utility from conforming. Gints (2003) suggests that "pro-social"
behavior may be biologically determined, as humans improved their biological " tness" by internalizing cultural norms. See also Greif
(2009, 2010). A richer model would include a portion of non-conformists and would arrive at similar results as long as this portion is
not too large or heavily concentrated in the central portion of the type distribution.
7 This
formulation is similar to those in Bernheim (1994) and Kuran (1987, 1995a, 1998). Like my model, these models show that many
individuals will conform to single actions despite heterogeneous underlying preferences.
8 Greif
(2009, 2010), however, proposes a model in which "moral authorities" choose norms in a manner similar to the one in this model.
4
[1984], Iannaccone [1988], Bernheim [1994], Kuran [1987, 1995a], Akerlof [1997], Kuran and Sandholm
[2008], Rubin [2009], and many others) and represent the costs associated with breaking a religious dictate,
breaking a law, violating a political norm, and the like, depending on the type of authority in question.
The third cost is a function of the distance between their action and the average action of the citizenry,
P
at = N1 j aj;t . This "social cost" captures the in uence of social norms which may arise from the importance individuals place on social identity, social custom, reputation, status, or conformity. Assume that N is
large enough that each citizen has a trivial effect on at , so they view at as exogenously given. The salient
features of the norm are that it is endogenously determined and it is not affected by the actions of any one
citizen. The norm evolves over time whenever citizens change their actions.9
Each citizen j maximizes the following utility function in each period:
Uj;t =
b aj;t
aB
j
2
c (aj;t
i1;t
2
1)
d (aj;t
i2;t
2
1)
e (aj;t
at
2
1)
,
(1)
where b, c, d, and e are weighting parameters greater than zero.
The primary exogenous parameter of concern in the model, , denotes the degree to which authority I1
derives utility by conforming to the action of I2 . Although there are certainly endogenous elements to
in reality - for example, the degree to which one authority depends on the other could be a function of the
degree to which the citizens abide by the dictates of the latter - endogenizing this key variable unnecessarily
complicates the model. This paper concentrates on massive equilibrium changes over a short period and
how such changes arise quite rapidly. Broader institutional change which endogenously effect the level of
centralization likely follows in the long run due to the interactions described in the model, but should not be
affected in the short run by the rapid, massive change which is at the heart of this model.10
measures the degree to which I2 has multi-lateral coercive power over the citizenry, as higher levels of
indicate that I2 has greater in uence over the sanctions imposed by I1 . Centralization of coercive power
may arise from political authorities depending on religious authorities for legitimacy, economic authorities
9 The
social cost accounts for numerous utility speci cations, such as one in which citizens derive utility from minimizing the sum of
the difference between their actions and each of the citizenry. Alternatively, social costs could be modeled as resulting from divergence
from the mode action of the citizenry. I do not employ this speci cation because it is less tractable but it would not alter the intuition
espoused in the model.
10 Moreover,
Rubin (2009) shows in a similar model that under a basic set of circumstances, endogenizing the degree of centralization
merely exacerbates the effects seen under an exogenous parameterization, as the feedback between players is more enhanced.
5
depending on political or legal authorities for long-term viability, and the like.11 The centralization parameter
enters I1 's utility as a scalar that affects the utility I1 receives from choosing an action different from I2 . At
= 0, there is no such cost, at
= 1, there is an in nite cost (if i1;t 6= i2;t
1 ),
and at
2 (0; 1), there
is a positive cost that is increasing in . While it is possible (in reality) that I2 may also face costs from not
conforming to I1 , the assumption of unidirectional centralization of coercive power allows for tractability. It
follows from this setup that at
= 1, the two authorities are ostensibly the same actor: power over numerous
dimensions is centralized in one authority (I2 ). At large , I2 has signi cant but not unlimited power over
varying types of sanctions.
I1 faces a public support cost which is increasing in the distance between its action and the average action
of the citizenry. This speci cation accounts for numerous factors underlying the relationship between institutional authorities and the citizenry. For one, the authority's legitimacy is undermined when its dictates are
transgressed by society. Moreover, transgression by the citizenry signals that the dictate decreases ef ciency,
which would in turn decrease the utility of the authority if it is in a position to extract rents from the citizenry
(Rubin 2009). The upshot of this speci cation is that institutional laws are endogenously determined.
I1 also derives utility from minimizing the distance between its action and its own internal bliss point
(iB
1 ), and it maximizes the following utility function in each period:
Vt1 =
f i1;t
iB
1
2
g (i1;t
i2;t
2
1)
h (i1;t
2
at ) ;
(2)
where f , g, and h are weighting parameters greater than zero.
I2 derives utility in a similar manner as I1 , except that it is not concerned with conforming to the action
of the other authority. It maximizes the following utility function in each period:
Vt2 =
j i2;t
iB
2
2
k (i2;t
2
at ) ,
(3)
where j, and k are weighting parameters greater than zero. Since the model is intended to shed light on the
consequences of centralization of institutional authority, I assume that I2 is more concerned with conforming
to its bliss point than the mean action of the citizenry relative to I1 . In other words, assume that jh > kf . I
11 For
more on the role of the interaction between political and religious institutions in the Islamic and Christian worlds, see Lewis (1974,
1993, 1995), Razi (1987), Greif (2002), Kuran (2005), Rubin (2009), Coşgel, Miceli, and Ahmed (2009), Coşgel and Miceli (2009), and
Coşgel, Miceli, and Rubin (2010a, 2010b). For more on the centralization of coercive power, see Greif (2005) and Karaman (2009).
6
also assume that both institutional authorities derive more utility from actions close to their bliss points than
from actions close to the mean action of the citizenry. That is, j > k and f > h. Dropping these assumptions
does not dramatically change the results but does entail that they do not hold for a small (and unrealistic) part
of the parameter set.
2.2
Institutions and Massive Social Change
In this section I study behavior in order to derive a link between the level of centralization of coercive power
( ) and equilibrium actions following a shock that affects the utility which some citizens receive from conforming to their bliss point. I denote equilibrium actions with a superscript * and employ the following
de nitions:
De nition 1 A Nash equilibrium is stable in period t if aj;t = aj;t
1
8 j, i1;t = i1;t
De nition 2 A shock occurs between periods 0 and 1, where a portion
derive from choosing actions close to their bliss point multiplied by
1,
and i2;t = i2;t
1.
of the citizenry has the utility they
> 1.12
Assume that a stable equilibrium exists in period zero and that a0 < i1;0 and a0 < i2;0 .13 Assume without
loss of generality that these actions arise from the relationship between the bliss points of the players, where
P B
1
B
B
j aj < i1 < i2 . That is, the bliss point of the central authority is further away from the mean bliss
N
point of the citizenry than is the bliss point of the non-central authority. The setup entails that the shock
encourages some of the citizens to transgress the authorities' actions (instead of the less interesting case in
which shocks encourage citizens to choose actions closer to those of the authorities).
The shock occurs in between periods zero and one. The shock encourages some citizens to choose actions
closer to their bliss points, which in turn changes the social norm. The endogenous change in the social norm
encourages more citizens to change their actions in following periods. The economy eventually reaches a
new stable equilibrium in some period t , which is de ned as the rst period in which a stable equilibrium
emerges.
12 This
de nition of shock is overly strict in order to allow for a more straight-forward analysis. For example, similar qualitative results
emerge if the shock affects citizens differently.
13 Existence
is straight-forward to prove, but the proof is omitted here for the sake of conciseness. An existence proof is available upon
request.
7
I begin the analysis by studying equilibrium actions in a stable equilibrium in period 0. Understanding
the actions taken in this period is essential for understanding the effects of the shock in relation to the level
of centralization ( ). Do citizens initially choose actions close to their bliss point? Do the institutional
authorities choose actions close to their bliss points or do they choose actions close to the average action of
the citizens? More importantly, how do the answers to these questions change over different values of the
centralization parameter ( ) and what do these differences entail regarding the relationship between the effect
of the shock and the level of centralization of coercive power?
The rst order conditions for each of the players (in period t) give the following conditions:
aj;t =
baB
j + ci1;t 1 + di2;t 1 + eat
b+c+d+e
i1;t =
1
(4)
;
f iB
1 + gi2;t 1 + hat
;
f + g+h
(5)
jiB
2 + kat
:
j+k
(6)
i2;t =
The steady-state solution in period 0 is derived by employing the equation a0 =
state condition at = at
1.
This gives the following:
ci1;0 + di2;0 + ea0 + Nb
1 X
a0 =
aj;0 =
N j
b+c+d+e
c gjiB
=) a0 =
2
cf iB
1 + j+k
f + g+h
b+
+
djiB
2
j+k
gj
cf + cj+k
f + g+h
+
+
b
N
P
j
P
j
aB
j
:
1
N
P
j
aj;0 and the steady
aB
j
(7)
dj
j+k
Next, consider the interactions between the players. Institutional laws and social pressures encourage
most citizens to choose actions different than their bliss point. Indeed, the two types of pressures interact
with each other. When institutional sanctions are severe (c and d are large), this encourages citizens to choose
actions in favor of the authorities instead of their bliss points, which in turn pushes the social norm closer to
the authority's actions. The latter reinforces the former, as social pressures induce citizens to want to appear
to optimize by choosing certain actions even though they would choose actions closer to their bliss point in
the absence of social pressures. Since citizens only see the actions, not the bliss points, of other citizens,
even a small portion of citizens choosing actions close to the dictates of the authorities can encourage other
citizens to choose such actions, because they are concerned with deviations from the norm at . This pushes
8
the norm away from citizens' bliss points, which further encourages actions that deviate from bliss points.
This phenomenon reinforces itself, leading to equilibria, under some parameter sets, where the entire society
chooses actions close to those of the authorities instead of near their own bliss points, even when citizens do
not derive much utility from following the authorities (c and d are small) but derive signi cant disutility from
transgressing the social norm (e is large). This entails that equilibria can be fragile: it may take only a few
citizens changing actions to undermine the process described above. When some agents change their actions
after the shock and choose actions close to their bliss points, other citizens will be encouraged to follow suit,
and a bandwagon effect ensues.
This process where individuals publicly act in a way that con icts with their private preferences is related
to preference falsi cation, a phenomenon that has been studied in great detail by Timur Kuran (1987, 1989,
1995a, 1995b). Kuran (1995a, p. 3) de nes preference falsi cation as "the act of misrepresenting one's genuine wants under perceived social pressures". In the context of the model, preference falsi cation encourages
the bandwagon effect described above. When citizens falsify their preferences, they choose actions which
differ from their bliss point. Part of the reason they do so is that perceived social pressures encourage them
to choose actions close to the social norm - an outcome that is exacerbated when institutional sanctions are
severe (c and d are large). Thus, when a shock causes some citizens to move closer to their bliss points, there
is more for others to gain on the margin from drastically changing their actions to ones closer to their bliss
points, as the social norm moves in this direction. The incentive to change actions in such a manner is greater
where preference falsi cation is greater, since the marginal bene t of choosing actions closer to one's bliss
point is greater. Thus, a bandwagon ensues when citizens suf ciently falsify their preferences.
Yet, despite the importance of preference falsi cation on equilibrium outcomes, the literature gives no
comprehensive account for the conditions under which it is likely to exist.14 In particular, do certain types of
institutional structures encourage preference falsi cation? Is there any link between the degree of institutional
centralization ( ) and preference falsi cation?
There are many means through which preference falsi cation may be manifested. For example, when
social pressures are signi cant, it is possible that the social norm will differ signi cantly from the actions
of the authorities and the citizens. That is, an anarchic focal point could emerge in which citizens' true
14 For
example, Kuran (1989) emphasizes that revolutionary activity is likely to occur when there exists signi cant preference falsi cation
in which citizens publicly support a regime but privately despise it, but he does not specify under which conditions preference falsi cation
arises.
9
preferences lies somewhere between those of the authorities and their actions. For the remainder of this
paper, however, I will concentrate on the types of equilibria that are more common empirically - namely,
those in which citizens falsify their preferences in favor of the institution. For example, this could account
for individuals falsely declaring loyalty to a government, uncomfortably adopting a hideous fashion trend, or
grudgingly abiding by a religious dictate.
Consider the relationship between centralization ( ) and the degree of preference falsi cation in period 0.
In highly centralized economies, authority I1 chooses actions closer to the centralized authority, I2 , all else
being equal. This encourages the citizenry to choose actions closer to I2 , in turn increasing the difference
between the citizens' actions and their bliss points and hence increasing the degree of preference falsi cation
in the economy.15
Having established a theoretical link between preference falsi cation and centralization, consider the
effect of the shock on the actions of the citizens in the subsequent stable equilibrium (in period t ). The
utility of citizens affected by the shock is:
S
Uj;t
=
b aj;t
aB
j
2
c (aj;t
i1;t
2
1)
d (aj;t
i2;t
2
1)
e (aj;t
at
2
1)
(8)
:
Thus, the equilibrium action in period t (and all periods hence) for those affected by the shock is:
aj;t =
baB
j + ci1;t + di2;t + eat
:
b+c+d+e
(9)
To solve for the steady state solution in period t , employ the equation
at =
where
P
P
j;N S
j;S
1 @
N
X
at =
aj;t +
j;S
= b + c + d + e and
+
b
N
1
aj;t A ;
(10)
portion of the population affected by the shock and
portion of the population not affected by the shock.
1), this entails that
= b(
(ci1;t + di2;t + eat ) +
X
j;N S
aj;t is the sum of the actions of the
aj;t is the sum of the actions of the 1
Denoting
15 This
0
P
j;S
aB
j
+
logic is formalized in a proof that is available upon request.
10
(1
) (ci1;t + di2;t + eat ) +
b
N
P
j;N S
aB
j
c gjiB
(
+ (1
) )
2
cf iB
1 + j+k
f + g+h
+
djiB
2
j+k
=) at =
+(
+ (1
!
+
b
N
) ) b+
P
j;S
aB
j +
gj
cf + cj+k
f + g+h
+
( + )b
N
dj
j+k
P
j;N S
aB
j
(11)
:
For the rest of the analysis, assume that the average bliss point of a citizen who receives a shock is lower
P
P
1
B
than the average of those who do not, or 1N j;S aB
j < (1
j;N S aj . This merely entails that those
)N
who receive a shock are on average further away from the actions of the centralized authority.16 I also assume
for simplicity that adding one more citizen to the group who receives a shock does not change the average
P
bliss point of this group. In other words, 1N j;S aB
j is constant. Finally, I assume that all citizens place
suf cient weight on the costs associated with breaking the dictates of the authorities that these costs are a
salient part of their decision-making calculus.
De ne the effect of the shock as the difference in mean stable equilibrium actions:
is,
= at
a0 . That
is a measure of the change brought about by the shock. The central question of this paper is, "how does
the institutional centralization of coercive power ( ) affect the degree of change ( ) brought upon by some
shock?"
There are there possible answers to this question, depending on the parameters in question. The different
parts of the parameter space which give rise to different equilibrium outcomes are delineated below.
B
Case 1 The bliss point of the central authority, iB
2 , differs greatly from that of the non-central authority, i1 .
In this case, an increase in centralization ( ) always entails a greater change in equilibrium outcomes
( ), regardless of the size of the shock. This is because the non-central authority, I1 , is encouraged to
choose actions close to those chosen by the central authority, I2 , whose preferences differ greatly from those
of the citizenry. Thus, citizens choose actions further away from their bliss points, all else being equal,
entailing that when citizens with the most to gain decrease their actions after the shock, others have greater
incentive to also decrease their actions, since their marginal return is larger. These actions encourage both
authorities to decrease their actions, and this process continues until the old equilibrium unravels and a new
stable equilibrium emerges. This outcome - a shock leading to a substantial change in equilibria - is similar
16 Removing
this assumption affects some equilibrium outcomes, but does not affect the fundamental intuition, and is thus employed for
simplicity. Indeed, to the extent that the authorities can steer the shock to certain portions of the population, those that choose actions
differing from the wishes of the authorities are likely those whom they would choose, so as to prevent a critical mass of opponents from
forming.
11
to outcomes in the herding and informational cascades literature, but for fundamentally different reasons.
Those works suggest that signi cant changes in equilibrium outcomes occur because individuals cease using
private information and instead follow common-knowledge signals (Banerjee 1992, 1993; Bikhchandani,
Hirshleifer, and Welch 1992, 1998). In this model, which is closer to Kuran (1987, 1989, 1995a, 1995b,
1998) and Bernheim (1994), shocks encourage actions which change social norms to a suf cient degree that
cascades occur despite citizens taking into account their private information or types.17
Case 2 The bliss point of the central authority, iB
2 , does not differ greatly from that of the non-central authority, iB
1 , and the shock is not systemic ( is small).
In this case, a cascade never emerges and centralized authorities are better able to suppress dissent even when the magnitude of the shock, , is large. This occurs when
is small enough that the change in
actions by the citizens affected by the shock does not suf ciently change the norm so that a cascade forms.
Moreover, unlike in Case 1, citizens do not falsify their preferences to as signi cant of a degree, since the
central authority chooses actions closer to the mean action of the citizenry when iB
2 is small. This further
decreases the size of the parameter set over which a cascade emerges.
Case 3 The bliss point of the central authority, iB
2 , does not differ greatly from that of the non-central authority, iB
1 , and the shock is systemic ( is large).
In this case, the level of change ( ) is increasing in the degree of centralization only when the shock size
( ) is suf ciently large. The intuition underlying this result is that if the shock is suf ciently small, the social
norm does not change dramatically and so a cascade never forms. In the absence of a cascade, decentralized
institutional authorities (I1 ) are more responsive to changes in the mean action of the citizenry, as they face
lower costs from differing from the dictates of the central authority for responding. On the other hand, a
suf ciently large shock encourages a cascade, and the process described in Case 1 follows whereby the level
of social change ( ) is increasing in the degree of centralization ( ).
The intuition underlying these three cases is formalized in Proposition 1. The proof is in Appendix A.
17 This
concept is also similar to the concept of "availability cascades" analyzed by Kuran and Sunstein (1999), in which expressed
perceptions help form the perceptions of others, which in turn makes these perceptions increasingly likely through their availability in
public discourse. Unlike this work, however, I am not concerned with information revelation (such as expanding the scope of public
discourse) beyond that of one's type.
12
Proposition 1 There are three possible outcomes relating the level of change ( ) and the level of centralization ( ). In the
iB
2 plane, the parameter set over which each case emerges is demonstrated graphically
in Figure 1. These outcomes are:
Case 1)
is increasing in . This occurs when iB
2 is suf ciently large.
Case 2)
is decreasing in . This occurs when iB
2 and
Case 3) 9 some
such that
is decreasing in
. This occurs when iB
2 is suf ciently small and
>
Moreover, the threshold level
are suf ciently small.
when
<
and
is increasing in
when
is suf ciently large.
in Case 3 is decreasing in
and iB
2 .
[FIGURE 1 HERE]
Case 3 is shown graphically in Figure 2. This gure shows the effect of the shock ( ) on the the level of
social change ( ) for economies with two different initial levels of institutional centralization (
where
H
>
L
H
and
L
,
).
[FIGURE 2 HERE]
2.3
Discussion
Proposition 1 formalizes numerous aspects of the relationship between the centralization of coercive power,
preferences, and massive equilibrium change. Yet it also begs as many questions as it answers. Such questions include, but are not limited to: "Under what real world conditions are the three cases espoused above
realized?" "What type of actions are represented in each of the cases?"
Case 1 emerges when the desires (bliss points) of the centralized authority are not aligned with those
of the other authorities and the citizenry. Taking such actions thus bene ts the authority at the expense of
everyone else. Hence, Case 1 does not represent most types of economic actions - although the authority
may have different distributional desires than the citizens, both groups can be better off when ef ciency
is maximized.18 If the action in question is an economic one, such as taxation, upholding property rights,
providing social services, or printing money, the bliss point of the central authority will diverge from the
citizenry, but likely not by enough for Case to 1 emerge. On the other hand, certain types of political or
18 Case
1 could represent an economic action if the "centralized authority" is a group of powerful special interests acting collectively. Such
special interests often have the ability to dole out political and economic sanctions - sometimes through extra-legal means - and thus
could play the role that the central authority plays in the model.
13
social actions may be represented in Case 1, especially actions that are designed to keep the central authority
in power. Examples of these types of actions include suppression of free speech, religion, or the media,
election rigging, or the formation of secret police forces. The type of shocks which may be associated with
such examples include the public outing of brutal state tactics, an obviously rigged election, or the capturing
of a popular opposition gure. In any of these examples, Case 1 predicts that increased centralization of
coercive power always leads to greater change after a shock occurs, as the citizenry was privately opposed to
the authority's actions before the shock, and thus a cascade is more likely to arise.
In Cases 2 and 3, however, the bliss point of the centralized authority does not differ substantially from
those of the other authorities and the citizenry. In other words, the actions in question are the those in
which the preferences of the institutional authorities are broadly aligned with those of the citizenry and both
can bene t from their implementation. Such characteristics are often found in economic policies, such as
subsidies on essential goods, trade restrictions, or setting interest rates. Case 2 arises when shocks are not
systemic; this may occur, for example, when an economic shock affects one industry. In this case, the shock
affects a small enough portion of the population that no cascade emerges, and centralized authorities are
better able to suppress dissent. On the other hand, Case 3 arises when shocks are systemic. Such shocks
occur when macroeconomic variables such as in ation, interest rates, or government debt suddenly change.
In such a case, centralized authorities are better able to suppress dissent when the shock level is small (for
example, when in ation rises by a small amount), but are more susceptible to massive changes when shock
levels are large (for example, in periods of hyperin ation).
The three cases detailed in Proposition 1 thus represent different types of shocks. A qualitative representation is shown in Figure 3.
[FIGURE 3 HERE]
Since this paper is concerned with massive economic, political, and social change, I concentrate on Cases
1 and 3. The intuition espoused above offers two explanations for why apparently tranquil, centralized societies can quickly undergo massive changes. One explanation, which is also offered by Kuran (1989, 1995a,
1995b), is that preference falsi cation can encourage a latent bandwagon to unfold after a shock. That is,
an economic, political, or social shock may move the equilibrium social norm by enough to encourage all
citizens, even those who are not directly affected by the shock, to choose drastically different actions.
The other explanation, which is novel to this paper, sheds light on the potential effects of economic shocks
(Case 3). It suggests that a high degree of centralization of coercive power discourages marginal changes to
14
equilibrium actions after small economic shocks. Citizens have less incentive to transgress the dictates of the
authorities, as they incur two institutional costs from doing so, and fear of multi-lateral punishment discourages citizens from "pushing the envelope" after a small shock. The same citizens may be more encouraged
to "push the envelope" in less centralized societies, however, as they face less cost from doing so and the
authorities (especially I1 ) react by changing the actions to a greater extent. On the other hand, when larger
economic shocks materialize, greater equilibrium changes are more likely to result in highly centralized
economies. This occurs because citizens falsify their preferences to a greater extent in such economies, and
thus large shocks encourage some citizens to change their actions, in turn making it more likely that a cascade
away from the authority's dictate will arise.
Kuran (1995a, 1995b) does suggest that unanticipated regime change is more likely to occur in politically
repressive countries. His hypothesis coincides to some degree with the one made in the present paper, though
it is not clear that Kuran's hypothesis holds when other types of freedoms (religious, economic, legal) exist
in politically repressive regimes. The essential difference between the present hypothesis and Kuran's is that
I stress the interdependence of institutions that are able to impose different types of sanctions. This leads to a
similar conclusion as Kuran, as such institutional structures are often found in politically repressive regimes.
This analysis also helps underscore an important feature underlying political legitimacy: legitimacy exists
not only when people believe in the legitimacy of the government, but when they believe that other people
will act in a manner that supports the legitimacy of the government. This is in line with Greif (2006, p. 13840), who argues that a set of institutionalized rules (in this case, the legitimacy of the government) is con ned
to those that are reproduced, and not refuted, by their implied behaviors. That is, equilibrium actions require
that individuals correctly anticipate each others actions regardless of the beliefs underlying these actions. In
turn, this creates a self-enforcing belief in the beliefs of others. There is a subtle but important difference
between this notion of legitimacy and a more conventional, Weberian notion espoused by Razi (1987, 1990),
who views legitimacy as being dependent on "the extent to which the relevant portion of the population
perceives that the regime is behaving [legitimately]" according to an established set of norms.19 Instead, I
suggest (in line with Kuran [1995]) that when a centralized authority acts "illegitimately" (in terms of the
model, it increases i2 ), it only loses legitimacy when citizens see others acting in a way that undermines
legitimacy. Otherwise, citizens may continue to falsify their preferences, and the government will still be
19 See
Greif (2010) for a similar de nition.
15
viewed as legitimate.
The model also sheds light on the connection between institutional centralization and revolutions. Numerous de nitions for revolution exist in scholarly works.20 For example, Goldstone (2001) de nes revolution
as "an effort to transform the political institutions and the justi cations for political authority in a society, accompanied by formal or informal mass mobilization and non-institutionalized actions that undermine existing
authorities." Kuran (1989, 1995a) broadly de nes a revolution as a discontinuous change in public opinion or
social order and Davies (1962) de nes revolutions as "violent civil disturbances that cause the displacement
of one ruling group by another that has a broader popular basis for support."
The model suggests that revolutions may occur after an economic shock only when the shock is suf ciently large; on the other hand, smaller shocks are more easily repressed in centralized regimes. "Shock" is
a somewhat ambiguous term, but the literature on revolutions provides numerous types of shocks that could
precipitate revolutions, such as sharp reversals in economic fortunes (Davies 1962; Tanter and Midlarsky
1967), rapid economic growth (Olson 1963), defeat in war, or sustained population growth (Goldstone 2001).
The analysis also indicates that social pressures may assist organizations in overcoming free-riding problems potentially associated with revolutionary activity. Tullock (1971) argues that revolutions are public
goods, and that the individually-rational action is to never participate in revolutions. Tullock's model, however, fails to incorporate utility derived from social interaction.21 The present model derives a starkly different result largely due to the incorporation of social costs, suggesting that once a cascade begins, social costs
avoided by joining the revolution may be greater than the costs (that are avoided by free-riders) imposed by
one of the authorities.22
Finally, consider the implications of the model for centralized regimes. Holding the degree of centraliza-
20 There
is a large literature merely attempting to de ne the term revolution. I have no desire to enter this debate, summarized nicely by
Goldstone (2001), and instead note that revolutions are an extreme form of massive equilibrium change.
21 On
the other hand, Olson (1971) recognizes that social pressures may work to overcome free-riding problems in small groups, but these
sanctions cannot work in larger groups. As Kuran (1995a) points out, Olson's analysis has dif culty accounting for collective action
when public expression of preferences is costly.
22 This
intuition is supported by Goldstone (2001), who argues that, unlike Tullock (1971), revolutionary actors do not act, or even think
of themselves, as acting alone. Instead, social ties - established by preexisting networks - play an instrumental role in recruitment. In a
similar vein, Goldstone (1994) suggests that the group, not the individual, is the relevant unit of analysis when studying revolution. This
insight is not necessarily contradictory to the one presented in the present paper. Indeed, in many cases the existence of such groups is
the reason that social costs and cascades originate.
16
tion ( ) constant, which parameters affect the likelihood of revolution in such regimes? Proposition 1 suggests
that
(the shock level at which equilibrium change is greater in the centralized regime for all
>
) is
decreasing in iB
2 and . Thus, a straight-forward way for centralized authorities to prevent revolutions is to
attempt to decrease . This also pushes the economy closer to Case 2, where centralized authorities can more
easily suppress dissent. This often takes the form of the authority attempting to do everything possible to
ensure that if an economic shock occurs, it is spread to as small of a portion of the population as possible.
This provides a reason why centralized, authoritarian governments often impose restrictions on domestic and
international media. Public dissatisfaction expressed in protests or other types of grievances is more likely to
result in a bandwagon the more that people know that others are willing to act in a way that coincides with
their own intrinsic beliefs. Hence, media suppression is more likely to exist in economies where coercive
power is centralized, since anti-government bandwagons of public opinion are more likely to form as a result
of a systemic shock in such economies.
The following testable predictions arise from the model:
Economies where coercive power is centralized are more insulated from change resulting from small
economic shocks (small ) than decentralized economies. However, massive changes in equilibrium
outcomes are more likely to emerge in centralized economies, as bandwagons are more likely to form
upon a larger economic shock (large ).
Centralized authorities are more likely to suppress dissent if shocks are not spread to much of the
population (small ), while decentralized authorities are more likely to marginally change their actions
in accordance with changing social norms. On the other hand, centralized authorities will not be able
to suppress dissent when shocks are felt by larger portions of the population (large ), and massive
equilibrium changes will result.
Revolutions or massive equilibrium changes are more likely to occur in centralized economies regardless of the size of the shock when the preferences of the authority (iB
2 ) diverges greatly from those of
the citizenry (aB
j ). This may occur when the shock is political or social in nature; examples include
governments that represent the interests of a small group of oligarchs or corporations at the expense of
the mass of the population.
17
3
Austerity Riots and Centralization
A primary implication of the model is that countries with centralized institutions are more insulated from
change when economic shocks are small but are more susceptible to sudden, massive changes when economic
shocks are large (Case 3). In this section, I test this hypothesis by analyzing the severity of austerity protests
in the developing world between 1976 and 1992. Such protests were common in the developing world
beginning in the mid-1970s in reaction to measures employed - almost always as a condition of IMF aid - to
combat in ation and government debt. I test the relationship between a series of "IMF pressure" variables
and severity of protests over differing degrees of institutional centralization to shed light on the relationships
espoused in the model. Although the data cannot speak to the microeconomic mechanisms highlighted in
the model (namely, those related to internal and expressed preferences), it can shed light on the connections
between macroeconomic events and changes in publicly expressed opinions.
Modern austerity protests began in the mid-1970s, with the rst one occurring in Peru in 1976. The
protests were sparked by austerity measures which were almost always imposed by the IMF as a condition of
assistance. The stated aims of these measures were freeing up markets and cutting government spending in
order to reduce government debt and curb massive in ation. These market-based measures, known by some
as “shock treatment”, included currency devaluation, broad reduction of spending on the public sector, privatization of state-owned corporations, cuts in public subsidies for food and basic necessities, wage restraints,
higher interest rates, and elimination of protectionism (Walton and Seddon 1994).23
These policies sparked protests in many places where they were imposed. Such protests were de ned
by Walton and Seddon (1994) as “large scale collective actions including political demonstrations, general
strikes, and riots, which are animated by grievances over state policies of economic liberalization implemented in response to the debt crisis and market reforms urged by international agencies.” The international
agency most associated with these protests is the IMF, and hence Joseph Stiglitz has named them “IMF riots”.
The distributional implications of these policies are clear – most policies negatively affected the urban
poor, at least in the short run (Walton and Ragin 1990; Walton and Seddon 1994). The protests were primarily
urban in nature, often following a rise in a price for a speci c good or an elimination of a subsidy. In some
cases, the result was relegated to one city and remained non-violent, such as organized strikes planned in
Ecuador and Bolivia (Walton and Ragin 1990). On the other extreme, protests turned into deadly riots which
23 For
a scathing review of these policies over the last half-century, see Klein (2007).
18
spread throughout the country, as was the case of the Venezuelan protest of 1989, where a week of rioting
spread from Caracas to 16 other cities.
What determines the differences in severity of these protests? This topic has received some attention
from sociologists, who have proposed a wide range of explanations. Walton and Seddon (1994) and Walton
and Ragin (1990) provide statistical evidence that overurbanization plays a key role in both the presence and
severity of the riots. They suggest that the linkage between the two lies in the development of organizational
infrastructure capable of mobilizing political action. Walton and Ragin (1990) also suggest that IMF pressure
(which they proxy for with four different indicators to create an index of IMF involvement) signi cantly
affects protest severity but in ation and debt do not.24
In this section, I analyze how changes in IMF involvement affect the likelihood of severe protest in a country. When IMF pressure is suf cient, pressure to liberalize markets is likely to ensue, and protests may follow.
The model suggests that when IMF pressure (the "shock" in the model) is small, centralized economies will
better be able to suppress protests, but marginal changes will occur in decentralized economies. However,
signi cant IMF pressure is more likely to precipitate massive changes in centralized economies. In other
words, the following prediction arises from the model:
Prediction 1: When there is a small amount of IMF pressure, austerity protests will on average be less
severe in countries with more centralized institutions, all else being equal. However, when there is a large
amount of IMF pressure, austerity protests will on average be more severe in countries with more centralized
institutions.
3.1
Data
In order to limit statistical dif culties arising from analyzing a biased subset of riots, I have gathered data
on austerity protests covering the same years as Walton and Seddon (1994): 1976-1992. The former date
denotes the onset of the rst modern austerity protest while the latter represents the time in which Walton and
Seddon went to press.
As noted by Walton and Ragin (1990), obtaining a complete list of debtor countries that experienced
international pressure to implement austerity measures can only be formulated indirectly, as the exact terms
24 On the other hand, Auvinen (1997)
nds that poor economic performance (indicated by high in ation and large debt service) is associated
with political demonstrations, riots, and strikes.
19
negotiated between the IMF and debtor countries is kept secret. To this end, I employ three measures identied by Walter and Ragin as indicative of IMF pressure: 1) the country employed the "extended fund facility"
(EFF), generally reserved for countries suffering a signi cant imbalance of payments relating to structural
maladjustments in production and trade (IMF [various]), in a given year between 1976 and 1992;25 2) the
country's ratio of IMF funds used to its IMF quota exceeded 125% in a given year between 1976 and 1992;
3) the country rescheduled or renegotiated its debt in a given year between 1976 and 1992.26 70 countries
satis ed one of these three criteria between 1976 and 1992.27 I form a panel which is restricted to years in
which one of these criteria were satis ed in the country in question within one year (either before or after).
A list of these countries and the years employed in the data is listed in Table 1.
[TABLE 1 HERE]
40 countries in the data set experienced austerity protests between 1976 and 1992. A Lexis-Nexis search
of news reports of the 70 countries listed in Table 1 (as well as any listed in Walton and Seddon [1994]
that were not in Table 1) from 1976-1992 produced 116 separate instances of austerity protest. Protests and
riots were only documented if they resulted from austerity measures or IMF pressure - other types of antigovernment protests or strikes are not included in the data. Planned general strikes and small, non-violent,
sector-speci c strikes are also not included in the data (even if they resulted from austerity measures), as this
paper is intended to investigate shocks that affect the population as a whole (high ).28
25 The
IMF de nes the extended fund facility as "An IMF lending facility established in 1974 to assist member countries in overcoming
balance of payments problems that stem largely from structural problems and require a longer period of adjustment than is possible under
a Stand-By Arrangement. A member requesting an Extended Arrangement outlines its objectives and policies for the whole period of
the arrangement (typically three years) and presents a detailed statement each year of the policies and measures it plans to pursue over
the next 12 months." (IMF)
26 Walter
and Ragin split the last category into two: debt rescheduling and debt renegotiation. My reading of the data suggests that the
line between these two is often blurred, so I have lumped them together. Data for EFF and IMF quota comes from various IMF Annual
Reports; data for IMF funds used comes from the World Development Indicators database; data for debt rescheduling and restructuring
comes from clubdeparis.org, Kuhn and Guzmàn (1990), and Dillon et al. (1985).
27 Some
countries were omitted from the dataset due to lack of IMF or control data. These include:Angola, Barbados, Burma, Dominica,
Equatorial Guinea, Grenada, Liberia, Somalia, Uganda, Western Samoa, Yemen, and Yugoslavia. Iran and South Korea did not satisfy
any of these conditions but are included in the data since there was an austerity protest in each country.
28 All
results are broadly robust to inclusion of general strikes and industry-speci c protests as severity 1 protests. These results are not
included but are available upon request.
20
I subjectively coded each of these protests by severity using the following criterion. An instance was
scored 1 if it were a small, con ned (to one or two cities) protest or general strike, 2 if it were either prolonged but con ned or widespread but not prolonged, with minimal deaths resulting, and 3 if the protest
were prolonged, contained widespread riots, and resulted in many deaths. Only protests carrying all three
of these characteristics were coded level 3. An example of a protest coded 3 occurred in Algeria in October
1988, when 159 protesters were killed in a few major Algerian cities over the span of week and thousands
were injured and arrested. While coding these protests is an admittedly subjective process, a reading of the
articles reporting on the protests generally showed that most protests/riots easily t into one of these three
categories. The differences between protests coded 1 and other protests are especially stark. The difference
between protests coded 2 and 3 are less obvious and thus more subjective, but I control for this in the analysis
by lumping these two types of protests together. The interested reader can reference Appendix D, which
provides a description of how the protest data were collected as well as brief summary of the salient features
of each of the 116 protests. I also create an alternative index, noted in this table, for protests in which the
severity level was not obvious. All results are robust to this alternative speci cation. Tables 2 through 4 and
Figure 4 show the data and summary statistics of these protests by country, year, and continent.
[TABLES 2, 3, AND 4 AND FIGURE 4 HERE]
Numerous proxies are available for the degree of “centralization” of coercive power. The intuition outlined in the model indicates that a good proxy is one that accounts for one authority's ability to affect numerous types of sanctions. One such variable is spelled out in the Polity IV data set (Marshall and Jaggers 2008):
constraint on the executive. This variable is de ned as:
The extent of institutionalized constraints on the decision-making powers of chief executives,
whether individuals or collectivities. Such limitations may be imposed by any "accountability
groups." In Western democracies these are usually legislatures. Other kinds of accountability
groups are the ruling party in a one-party state; councils of nobles or powerful advisors in monarchies; the military in coup-prone polities; and in many states a strong, independent judiciary.
This variable ranges from 1 to 7, with 1 equaling “Unlimited Authority” (no regular limitations on the
executive's actions) and 7 equaling “Executive Parity or Subordination” (accountability groups have effective
authority equal to or greater than the executive in most areas of activity). This variable provides an ideal proxy
21
for the degree of institutional centralization, as spelled out in the model, because it measures the degree to
which political authorities can extend multifarious sanctions, especially judicial ones.
Another mechanism through which the centralization of coercive power provides the conditions for
protest is that more centralized regimes are better able to suppress individual freedoms - in particular, political freedom. In this light, another, less direct proxy for the "centralization" of coercive power is a degree
of political freedom variable provided by Freedom House (2009). This variable ranges from 1 to 7, with 1
equaling "most free" and 7 equaling "least free". Although this is a less direct measure of institutional centralization than the constraint on the executive variable, the model suggests that similar patterns should be
seen when either of these variables are employed as a proxy for centralization.
Other controls are employed to account for phenomena which economists and sociologists consider as
salient factors associated with protest activity. These include the urbanization rate, per capita GDP, population, and a measure of religious fractionalization.29 The religious fractionalization index is constructed like a
Her ndahl index, equaling the sum of the proportion of each religion in the country squared. The summary
statistics of all controls are reported in Table 5.
[TABLE 5 HERE]
3.2
3.2.1
Analysis
Testing for the Presence of Protest
The data provide a chance to test the model's primary prediction: countries with centralized coercive authorities should have smaller changes in public opinion when shocks are small, but larger changes when shocks
are large. Yet, before we can test how shocks (proxied with IMF variables) affect the severity of protests, we
must establish whether the proxies are predictors of protest. In particular, the model suggests the testing of
the following equation, where Protest is a dummy equaling 1 if there is a protest in a given year and country,
29 Data
on urbanization, GDP, and population comes from World Bank (various). The measure of religious fractionalization is derived
from data found in Barrett, Kurian, and Johnson (2001).
22
i is the country, t is the year, and X is the set of control variables:30
Protestit =
0 + 1 Shockit + 2 Centralization
Proxyit + 3 Shockit * Centralization Proxyit + Xit +"it (12)
There are 3 different IMF variables that proxy for a shock: use of IMF funds (divided by IMF quota),
use of EFF funds (divided by IMF quota), and the number of restructurings and reschedulings. The rst two
variables are de ated by the country's IMF quota, which is based on its standing in the world's economy.
Walton and Ragin (1990) note that all three of these variables are good predictors of austerity protests, with
the last one being the strongest predictor.31 I also create variables for the greatest single-year level of the
three IMF proxies over the previous two years. These latter variables may be more suitable, since the chain of
events from IMF pressure to implementation of austerity programs to protests can take months to matriculate
and are thus sometimes realized over multiple years.
For the centralization proxy, the Polity IV "constraint on the executive" and the Freedom House "Political
Freedom" variables are available. Since these variables range from 1-7, it is not clear exactly how to interpret
the regression coef cients. For example, is the difference between 1 and 2 the same as the difference between
5 and 6? To overcome this problem, I create three dummy variables: i) equals one if the constraint on the
executive variable is 1 (least constraint) and 0 if it equals 2-7; ii) equals one if the constraint on the executive
variable is 1 or 2 and 0 if it equals 3-7; iii) equals one if the political freedom variable is 6 or 7 (least free)
and 0 if it equals 1-5.32 In all of these variables, the centralization proxy is greater when the dummy equals
one. Robustness checks using different cutoff points for the dummy variables (e.g. equaling one if constraint
30 The
controls employed in these regressions are broadly consistent with those pointed out by Auvinen (1997) as ones that traditionally
have received attention in the economic and sociology literature related to political con ict. They include urbanization rate, real per
capita GDP, population, religious fractionalization, and the number of previous protests. In ation and government debt are not included
as controls because they frequently lead to IMF involvement.
31 Walton
and Ragin formulate an "IMF pressure index" which is the summation of the z-scores of all 4 IMF indicators. I do not do this
for two reasons: 1) I nd, like Walton and Ragin, that the number of restructurings and reschedulings is by far the best predictor; 2) the
sum of the z-scores is dominated by the restructuring variable, which is not normally distributed, and hence converting it to a z-score is
erroneous. I have analyzed the regressions reported in this paper using Walton and Ragin's z-score variable and the results are similar though not as strong - as the ones reported here. These results are available upon request.
32 I
also created a dummy equaling one if the political freedom variable 7 (least free) and 0 if it equals 1-6, but many of the regressions had
missing values and I have not included these results, which are available upon request.
23
is less than 4) provide weaker but broadly similar results. These are not reported because their interpretation
is much less straight-forward - the model makes clear predictions about the differences between severely
centralized authorities and not severely centralized authorities, but there may be ambiguities (especially if
the relationship between centralization and protests is non-linear) in the differences between moderately
centralized authorities and other authorities.
Equation (12) does not provide a test of the model's hypothesis - instead, it provides a test of how well
the IMF variables proxy for a "shock" in the context of the model. The model indicates that the coef cient on
the IMF "shock" variable,
1,
should be positive (and statistically signi cant). If it is not, then the variable
in question is not a predictor of protests in general, and thus should shed little light on how institutional
centralization affects the severity of protests.
Yet, not all "shocks" are alike - some are weak (i.e., leading to little austerity) and some are strong. Thus,
the interaction between the shock and the centralization proxy can also tell us something about the strength
of the shock. For one, we would expect a "strong" shock to increase the probability of protest regardless of
the degree of centralization. Hence, the coef cient on the interaction term,
3,
will be small and insigni cant
in regressions using "strong" IMF variables. However, if the shock is "weak" - that is, one that leads to few
or small austerity measures being taken - the intuition of the model suggests that more centralized economies
will experience smaller shocks, since authorities are better able to suppress dissent. That is, the coef cient on
the interaction term,
3,
will be negative and signi cant in regressions using "weak" IMF variables. In other
words, the regression model in equation (12) provides us with the following insights:
1. An IMF pressure variable is not a good predictor of protests if
1
is statistically insigni cant. If
1
is
statistically insigni cant, then:
2. An IMF pressure variable is a "weak" predictor of protests if
3. An IMF pressure variable is a "strong" predictor of protests if
3
is negative and statistically signi cant
3
is statistically insigni cant
The above delineation of weak versus strong proxies is not meant to support any prediction of the model.
Indeed, the model is used to de ne whether a shock is weak or strong - thus, the "strength" of the IMF
pressure proxy is determined by de nition, not empirics. However, these de nitions are useful later for testing
the affects of centralization on protest severity given that a protest occurs. Since protests of all severities are
lumped together in equation (12), the delineation between weak and strong proxies should only be viewed as
one that allows for cleaner testing in the following section of the main prediction of the model.
24
A logit model is used to test this regression. It is possible that some of the years with zero protests are
censored. If international news reports did not pick up on an austerity protest, then it is not included in
the data. However, where this is true it likely works against the hypotheses presented in this paper, as more
centralized governments are generally more able to suppress news coverage of protests. Hence, any censoring
will make the IMF proxies appear weaker than they actually are.33
The regression results are reported in Table 6. The only results reported are the coef cients on the "Shock
Variable" ( 1 ) and the interaction term ( 3 ). All other coef cients and regression statistics are not reported,
but are available by request. All regressions also include continent and year dummies.34
[TABLE 6 HERE]
The results in Table 6 indicate that the "use of IMF funds" variable is not a good predictor of protests,
but the "EFF" and "restructuring/rescheduling" variables are good predictors. That is, the coef cient on the
IMF Pressure Index,
1,
is insigni cant in all but one of the regressions where "use of IMF funds" is the
shock proxy but is always signi cant when the EFF and restructuring variables are used as proxies. However,
the EFF variable appears to be a "weak" predictor, as in nearly every regression the interaction term,
3,
is
negative and signi cant. As noted above, the interpretation of this result is that an increase in these variables
have a positive effect on the probability of prediction when centralization (or freedom) is suf ciently small,
but in highly centralized (or unfree) countries, the effect is small or even negative. The model suggests that
this is what we would expect to see when shocks are small - marginal changes may result in decentralized
economies, but centralized authorities will be able to suppress the effects of such a shock.
On the other hand, the number of restructurings or reschedulings appears to be a "strong" predictor of
protests, a result also found in Walton and Ragin (1990). The coef cient on the proxy ( 1 ) is positive and
highly signi cant in all regressions, but the coef cient on the interaction term ( 3 ) is highly insigni cant in
all regressions. This indicates that an increase in the number of restructurings or reschedulings has a positive
affect on the probability of protest regardless of the degree of centralization. Indeed, the terms of IMF
33 Alternatively,
I have tested all regressions in this paper using a Tobit model with the lower limit censored at 0. All results are broadly
the same. These results are available upon request. For a particularly relevant discussion on the use of Tobit and interpreting regression
coef cients, see Roncek (1992).
34 Including
country dummies would be ideal, but there is too little variation within countries over time for this to be a feasible approach,
as all results are dependent on a small number of observations.
25
"conditionality" (policy prescriptions that are agreed to by all parties) are generally agreed to in return for the
renegotiation or rescheduling of debt repayment (Walton and Ragin 1990), so it should not be surprising that
this variable is the "strongest" predictor of protests.
It is also possible that the EFF and restructuring proxies have independent effects (or close to independent)
on the probability of protest. Indeed, the correlation between the two variables is 0.142. The two variables are
not independent, but it is possible that regressions 6.1-6.3 suffer from omitted variable bias if both of these
proxies are not controlled for in the same regression. Indeed, of the 274 observations (year and country) where
the rescheduling variable is positive, only 47 (17.2%) also have positive EFF variables. On the other hand,
only 47 of the 133 observations where EFF variable is positive (35.3%) also have a positive rescheduling
variable. This entails that not accounting for both of these "shock" proxies in the same regression may lead
to biased results. In other words, a more appropriate regression model is represented by equation (13):
Protestit =
0
+
1A EFFit
1B Reschedulingit
+
3A EFFit * Centralization Proxyit +
+
2 Centralization
Proxyit +
(13)
3B Reschedulingit * Centralization Proxyit + Xit + "it
Table 7 reports the results of this regression. It appears that both the EFF and Rescheduling proxies
remain good predictors of protests, as
1A
and
1B
are positive and signi cant in almost all regressions.
The EFF variable remains in most regressions as a "weak predictor", as
3A
is negative in all regressions
and signi cant at the 11% level in 4 of the 6 regressions. The rescheduling proxy also remains as a "strong
predictor", as
3B
is highly insigni cant in all regressions.
[TABLE 7 HERE]
3.2.2
Relating "Centralization" and Protest Severity
The analysis in the previous section suggested that the EFF variable is a weak predictor of protests while
the number of restructurings or reschedulings is a strong predictor. On the one hand, the terms "weak" and
"strong" are merely artifacts of the model. On the other hand, delineating these variables by their "strength"
allows for the testing of model's primary prediction. This is, namely, that small shocks will result in more
change in decentralized economies, as centralized authorities are more able to suppress small, marginal
changes in actions. However, larger shocks will lead to larger changes in centralized economies. In terms of
the variables in question, the following prediction emerges:
26
Prediction 2: All else being equal, a change to a more centralized economy:
i) has no signi cant effect (or possibly a slight negative effect) on the probability of a more severe
protest occurring when the EFF and restructuring variables are small (no shock)
ii) has a negative (and possibly signi cant) effect on the probability of a more severe protest occurring when the EFF variable is large but the restructuring variable is small (weak shock)
iii) has a positive and signi cant effect on the probability of a more severe protest occurring when
the restructuring variable is large (strong shock)
Prediction 2 suggests that there is a relationship between the "strength" of the shock and the severity of
protests. Thus, an appropriate test should consider the determinants of protest severity given that a protest
occurred. Including non-protests years is akin to comparing apples with oranges; and since the previous
regressions showed that the EFF and rescheduling proxies are good predictors of protest activity, it is not
necessary to include non-protest years in a regression aimed at shedding light on the determinants of protest
severity. Not including non-protest years also has the property that we can test the different predictions
relating to "weak" and "strong" shocks. If non-protest years are included, "weak" and "strong" shocks will
have certain properties arise via de nition rather than empirics, as these terms are de ned in the context of
regressions analyzing the determinants of protest activity (0 or 1).
Testing the regression model in equation (14) sheds light on the above prediction. The dependent variable,
Protest Severityit , equals one if the most severe protest in a given year and country equals 2 or 3 and equals
zero if the most severe protest equals one. Unfortunately, there are too few observations of protests of severity
3 to test the model using a dependent variable which equals one only if the protest is of severity 3. This should
not detract from the results, however, as the most signi cant differences in protest severity are between those
coded 1, which are generally con ned and short, and 2 and 3, which are more widespread, long, and violent.
Protest Severityit =
0
+
1A EFFit
3A EFFit * Centralization Proxyit +
Prediction 2 suggests that
2
+
1B Reschedulingit
+
2 Centralization
Proxyit +
(14)
3B Reschedulingit * Centralization Proxyit + Xit + "it
is insigni cantly different from zero, as this variable can be interpreted as
the effect of increased centralization when both the EFF and rescheduling variables are zero, and thus there
is no or little shock. If anything, this coef cient should be negative, since protests resulting from unobserved
shocks (that do not show up in the EFF or rescheduling variables) should be more easily suppressed in
27
centralized economies. It also suggests that
3A
is negative and possibly signi cant,35 as it can be interpreted
as the additional effect of increased centralization when there is an increase in the EFF variable (a "weak
shock") but the rescheduling variable is zero. Finally, the model suggests that
3B
is positive and signi cantly
different from zero, as this coef cient can be interpreted as the additional effect of increased centralization
when there is an increase in the number of reschedulings (a "strong" shock). These predictions are broadly
con rmed in Table 8, which reports the results of logit regressions represented in equation (14):
[TABLE 8 HERE]
These regressions broadly support the predictions of the model. The coef cient on the interaction term
of EFF and centralization variables,
3A ,
is negative in all speci cations, although it is never statistically
signi cant. On the other hand, the coef cient on the interaction term of the rescheduling centralization
variables,
3B ,
is positive in all regressions and signi cant in four of them. It is highly signi cant in all three
regressions where the rescheduling variable is considered over a two year span, which is a more theoretically
appealing metric since the time between the onset of IMF pressure and protest often extends over the span
of one year. Finally, the coef cient on the centralization proxy term,
2,
is highly insigni cant in four of the
regressions, though it is signi cant in the other two. As noted above, the negative coef cient in the bottom
panel of regression 8.2 is consistent with the model, as protests arising from smaller, unobserved shocks
should be more easily suppressed in a centralized economy. On the other hand, the positive and highly
signi cant coef cient on
2
in the middle panel of regression 8.1 does not support the model's predictions;
however, this coef cient is highly insigni cant when a more appropriate metric - the "shock" over 2 years
(regressions 8.2) - is considered.
Yet, although the sign of the coef cients broadly accord with the predictions of the model, the coef cients
are dif cult to interpret. As Norton, Wang, and Ai (2004) point out, the cross-partial derivative of two
continuous terms that are interacted is not the coef cient on the interaction term, as it is in OLS. Instead,
we are concerned with the marginal effects, which can be calculated at different levels of "shock" (EFF and
rescheduling). That is, the following equation provides the marginal effects:
35 The
model does not necessarily indicate that the interaction effect will be signi cant when the shock is "weak" because a "weak" shock
may not spur massive protests in either centralized or decentralized economies. It does suggest, however, that the point estimate should
be negative.
28
4F (u)
= F (u; CP = 1)
4CP
F (u; CP = 0) .
where F (u) is the logit cumulative distribution function and "CP" is the centralization proxy. This
equation provides the change in probability of protest severity equaling 2 or 3 (versus 1) when there is a
change from low to high centralization. The marginal effects of three panels of regression 8.2, taken at the
mean of all control variables, are reported in Tables 9A-9C.36 These tables report the marginal effects at
different levels of EFF and restructuring.37
[TABLES 9A, 9B, and 9C HERE]
The results reported in Tables 9A-9C are broadly consistent with the logic laid out above. Columns
9A.1, 9B.1, and 9C.1 report the marginal effects when the restructuring variable equals 0. As expected, as
the EFF variable (a "weak" shock) becomes larger, an increase in the centralization parameter decreases the
probability of a more severe protest occurring. These effects are highly signi cant in 9A.1 and 9C.1, where
EFF borrowing of 5 times greater than the IMF quota can entail a decrease in probability of severe protest by
80% when switching from a decentralized to centralized economy. As noted before, the intuition underlying
this result is that when only weak shocks are present, centralized authorities will be better able to suppress
revolt. Moreover, it is only when the EFF variable is high enough that citizens in decentralized economies
may have incentive to protest; that is, when both shock variables are small, there is nothing to spur protest,
and there should be no difference between centralized and decentralized economies.
On the other hand, Columns 9A.3, 9B.3, and 9C. 3 report the marginal effects when the restructuring
variable equals 2.38 The marginal effects are always highly positive (between 0.878 and 0.935) and highly
signi cant when there are two restructurings and no EFF use. Although the signi cance goes away at higher
levels of EFF in Columns 9A.3 and 9C.3, it remains for all levels of EFF in Column 9B.3. These marginal
36 The
continent and year dummies are not reported at the means. The reported results are for Central America and 1985. The broad
patterns seen in Tables 9A-9C are robust to all continents and years.
37 Marginal
38 The
effects are derived using the nlcom procedure in Stata.
only irregularity in Tables 9A-9C is that the marginal effects in Column 9A.2 are negative and signi cant when the EFF variable
equals 4 or 5 and the restructuring variable equals 1. The negative signi cance of this variable is likely due to the omission of the
triple interaction term centralization*EFF*restructuring, which is expected to be positive. I omitted this variable because the EFF and
restructuring were too infrequently both greater than zero in order for such a variable to impact the analysis in a meaningful way.
29
effects indeed support the theory that "strong" shocks lead to an increased probability of more severe protests
happening in centralized economies.
Zambia provides an instructive case relating centralization, EFF use, and restructuring. Between 19811984, Zambia borrowed signi cantly from the EFF (3.78 times the quota) while having the lowest possible
constraint on the executive and a "bad" score (5) on political rights. Although Zambia also had loans restructured in two of these years, the model suggests that these relatively weak shocks would precipitate little
protest activity - and indeed, no protests occurred in these years. Following these years, Zambia restructured
or rescheduled loans in three years - 1986, 1990, and 1992. In 1986 and 1990 the constraint on the executive
was still as small as possible, while by 1992 a regime change greatly increased the constraints as well as political freedoms. The model suggests that these stronger shocks should lead to greater protest activity when
there is more centralization (1986 and 1990) but not when the economy is more decentralized (1992). In fact,
in 1986 there was a protest of severity 2 and in 1990 there was a protest of severity 3. However, when greater
constraints on executive authority and greater political rights emerged by 1992, a debt restructuring occurred
without protest.39
One possible point of contention in this analysis is that the "constraint on the executive" and "freedom"
variables are merely proxying for democracy. Indeed, both variables are positively correlated with the Polity
IV democracy index, which ranks countries from 0 (no democracy) to 10 (highly democratic). To test this,
I run robustness checks replacing the constraint on the executive and freedom variables with three different
democracy dummies - one equaling one only if the index equals 0, the second equaling one if the index equals
0 or 1, and the third equaling one if the index equals 0, 1, or 2. The results are reported in Appendix E.
Table A.1 suggests that after controlling for the presence of democracy, the number of restructurings or
reschedulings is still a strong predictor of protest, but EFF use and IMF fund use are not. This is similar
to the above results, except that EFF use is no longer a signi cant predictor of protest. Employing the
same methodology as above, the determinants of protest severity are then tested for using the rescheduling
variables as a proxy for the "shock" and are reported in Table A.2. The only clear pattern that holds in these
regressions is that that severe protests are less likely to occur in undemocratic economies, at least when the
number of restructurings in one year is controlled for. However, this result disappears when the number of
39 Of
course, there are examples that contradict the results of the model and analysis. Venezuela provides one case - a severe protest
(coded 3) occurred in 1989 after a restructuring and the use of the EFF in spite of political freedoms being relatively widespread and the
presence of signi cant constraints on the executive.
30
restructurings over 2 years is employed as the "shock" proxy. Interestingly, in 5 of the 6 regressions, the
coef cient on the interaction term is not signi cant. This result emerges because these regressions analyze
the effects of severe lack of democracy (0-2 on a 10 point scale) or severe constraints on the executive (0-1 on
a 7 point scale). Although there is a correlation between democracy and the constraint and freedom variables,
there is substantial variation amongst severely undemocratic countries with respect to constraints placed on
the executive and political freedoms. The regressions reported in Table A.2 suggest that the constraint and
freedom variables are indeed picking up something that the democracy variables are not - indicating that the
constraint and freedom variables are not merely proxies for the presence of democracy.
4
Massive Protest and Change in Iran
“When one person alone enacts, executes, and judges the law, there will be problems.”
–Ayatollah Javadi Amoli, June 2009
The Iranian uprising of summer 2009 typi es the dynamics explored in the model. Massive protests
erupted in Tehran following the presidential election on June 12, which the Iranian government claimed was
won by incumbent president Mahmoud Ahmadinejad by a surprising 63-34 percent margin over moderate
challenger Mir Hussein Moussavi. Most analysts considered these results to be fraudulent, as later inspections
found that more votes were cast than there were eligible voters in numerous provinces.
The scale of these protests was enormous, with up to half a million people taking to the streets in daily
uprisings which lasted for weeks after the election results were announced. These protests occurred despite
waves of arrests and violent efforts to repress dissent – efforts revealed to the world in gruesome form in a
widely circulated video of a young woman, Neda Agha-Soltan, dying as a result of a bullet that was likely
shot by a pro-government militant.
The most surprising aspect of the revolt was that it occurred despite condemnation from the Iranian
Supreme Leader, Ayatollah Ali Khamenei. To many Iranians, the edicts of the Supreme Leader are literally
the law and are rarely questioned in public, his powers being absolute (Keller 2009; Wright 2009). The
Supreme Leader's right to rule is based on the concept of velayat-e faqih (guardianship of religious jurists),
espoused by the leader of the 1979 Revolution leader Ayatollah Ruhollah Khomeini. Theoretically, this
ensures that legislation and bills are in accordance with Islamic principles and laws, even though the Supreme
Leaders since the Revolution have frequently evaded Islamic principles in order to propagate their own power
(Tamadonfar 2001). Under this regime, the faqih is not accountable to the public, but political obedience is
31
viewed as a religious duty (Tezcür and Azadarmaki 2008). The role of the faqih in Iranian politics is codi ed
in the Iranian Constitution, with numerous articles establishing supreme, centralized power for religious
authorities:
The powers of government in the Islamic Republic are vested in the legislature, the judiciary, and
the executive powers, functioning under the supervision of the absolute religious Leader and the
Leadership of the Ummah (italics mine)
The Constitution provides for the establishment of leadership by a holy person possessing the
necessary quali cations and recognized as leader by the people.
The protests were a massive change from pre-election actions, where the word of the Supreme Leader was
considered and codi ed as immutable. In spite of condemnation by Khamenei, the protests were supported
by parts of nearly every segment of Iranian society. What made the scale of this uprising so large?
The model sheds a great deal of light on the suddenness and scope of the Iranian uprising. The “centralized authorities”, the Supreme Leader and the religious elite, had a great deal of power over numerous types
of sanctions, as codi ed by the Iranian Constitution and practiced by their control of the military, command
over patronage resources, and veto power over elected representatives. These institutional rules manifested
themselves in a way that the central religious authorities dictated religious, political, economic, legal, and social sanctions for anyone disobeying their dictates – not only for the citizenry, but also for political authorities
such as Ahmadinejad.
This entailed rampant preference falsi cation prior to the elections. Indeed, a 2003 survey by Tezcür
and Azadarmaki (2008) of Tehrani views of the government revealed signi cant dissatisfaction: 53% of
respondents said that the political system is rarely or never responsible, 45% said that the state completely
fails, and 68% said that they always or occasionally face unfair treatment by the state.
The model predicts that the centralization of authority would allow religious authorities to easily suppress
dissent following a small economic shock which affects a small portion of the population (Case 2). This was
indeed the case over the previous decade, as the central government ruthlessly suppressed student revolts in
1999 and 2003. The government tolerated these uprisings for only a few days before violently suppressing
dissent – Basij vigilantes were sent to campuses and ung students from windows, bloodied many with bricks
or chains, and jailed many (MacFarquhar 2009).
Yet, the post-election uprisings were much more widespread, cutting across all age groups and economic
classes. This was in large part due to the nature of a national election, which is more important to the broader
32
population than the somewhat narrow concerns of the students. This sentiment was noted by one of the many
protesters, who suggested that “the student protests failed because people left the students alone. This time
they cannot defeat us because everybody has joined.” (Fathi 2009b). Even four weeks after the election results
were announced protesters lined the streets, some noting that “we are in this together” (Slackman 2009).
The widespread nature of the uprising thus conforms with the model's prediction that when shocks are
large and/or systemic, massive changes in equilibrium actions will result in centralized regimes. The model's
intuition also helps explain why the Ahmadinejad government was so concerned with preventing media coverage of the protests. In the weeks after the election, the government banned coverage of the demonstrations,
arrested journalists, threatened bloggers, cut off text messaging services, and attempted to shut down social
networking websites (Fathi 2009b).40 Less media coverage meant that less Iranians were aware of the extent
of the newly-expressed, anti-authoritarian actions, hence lowering the portion of the citizens affected by the
"shock" ( ).
Even before the uprising, the OpenNet Initiative (opennet.net) noted that speech was heavily regulated in
Iran and its technical ltering system was among the most extensive in the world. In fact, the Constitution
requires that “[the media] must strictly refrain from diffusion and propagation of destructive and anti-Islamic
practices.” This makes sense in the context of the model, as the centralized nature of the government meant
that it was in its interest to suppress information that may encourage people with alternative desires to express
their opinion.
Moreover, Khamenei portrayed the unrest as the work of outsiders, especially the U.S. and Britain, claiming that agents were attempting to stage a “velvet revolution” (Fathi 2009a).41 This also makes sense within
the context of the model, as the government has an interest in claiming that the protests were not truly representative of the private preferences of Iranians. If the government were to succeed in this portrayal, it would
push social norms back to where they were before the election.
The suddenness and scale of the Iranian protests are thus attributable to two features: the centralization
of Iranian authority and the widespread affect of the seemingly fraudulent election results. The centralization
of authority entailed a situation in which preferences were falsi ed to provide the appearance of conformity
40 The
regime was not completely successful in their attempt to suppress coverage of the uprising; word of the protests was broadcast to
the outside world largely through Twitter and Facebook.
41 Blaming
outsiders is a common tactic – for example, the Shah of Iran attributed the 1979 Revolution to a Communist conspiracy (Time
1978).
33
with the central authority, even though private opinion was vastly different. Before the election, very few
individuals were willing to transgress the authority's dictates, as doing so entailed manifold sanctions. However, the “shock” of the election results provided incentive for enough individuals to incur the costs associated
with acting in accordance with their private preferences. In turn, a cascade emerged, whereby the social pressures associated with transgressing the religious authorities were loosened, and the social norm endogenously
shifted to one in which protest was acceptable. As one analyst, speaking on condition of anonymity in an
Los Angeles Times article, noted: “Public respect for [Khamenei] has been signi cantly damaged. Opposing
him is no longer the same as opposing God” (Daragahi 2009). Unlike the student protests, whose grievances
affected a narrow swath of the population, the fraudulent nature of the election results affected all parts of the
population. In terms of the model, the signi cance of the shock ( ) and its ubiquitous nature ( ) were both
large enough to encourage massive social change.
New York Times columnist Roger Cohen echoed this analysis, suggesting that “the loss of trust by millions
of Iranians who'd been prepared to tolerate a system they disliked, provided they had a small margin of
freedom, constitutes the core political earthquake in Iran” (Cohen 2009a). This is also likely the reason
why the authoritarian, centralized governments of China, Burma, and Cuba selectively censored news of the
Iranian uprising (Cha 2009). These centralized governments are similarly susceptible to cascades which arise
from “shocks”, and the Iranian uprising may have served as such a shock.
This analysis helps underscore an important feature underlying political legitimacy: legitimacy exists not
only when people believe in the legitimacy of the government, but when they believe that other people will
act in a manner that supports the legitimacy of the government. Indeed, the Iranian movement of summer
2009 suggests that legitimacy can unravel when people's beliefs about what others believe changes. There is
a subtle but important difference between this notion of legitimacy and a more conventional, Weberian notion
espoused by Razi (1987, 1990), who views legitimacy as being dependent on "the extent to which the relevant
portion of the population perceives that the regime is behaving [legitimately]" according to an established set
of norms. If this were true, then a revolt should have happened as early as the 1980s, when Khomeini made
statements undermining the norms which legitimized the theocracy (he contended that laws that violate the
Islamic law are binding only if they serve the interest of the governing elite), or when Khamenei, who was
not a leading religious scholar, was chosen to succeed Khomeini, implicitly lifting the political order over the
legitimizing religious order (Roy 1999; Tamadonfar 2001). Instead, I suggest (in line with Kuran [1995]) that
when a government acts "illegitimately" (in terms of the model, it increases i2 ), it only loses legitimacy when
34
citizens see others acting in a way that undermines legitimacy. Otherwise, citizens may continue to falsify
their preferences, and the government will still be viewed as legitimate.
There is signi cant evidence that the legitimacy accorded to the Supreme Leader was undermined by the
uprising. Top religious leaders, largely encouraged by former president Ayatollah Ali Akbar Hashemi Rafsanjani, considered restructuring the Supreme Leader structure to a more decentralized one, openly accused
Khamenei of partisanship, met to discuss replacing Khamenei, and called the disputed presidential election
and the new government illegitimate. The most senior cleric in Iran, Grand Ayatollah Hossein Ali Montazeri,
went as far as to issue a fatwa calling Khamenei illegitimate (Sahimi 2009). Even after Ahmadinejad was
sworn in as president, a group of Iranian clerics issued an anonymous letter calling Khamenei a dictator and
demanding his removal (Worth and Fathi 2009). It is dif cult to imagine a scenario in which such a “stunning
breaking of taboos” (MacLeod 2009) would have occurred before the uprising, as the absolute, legitimate
power wielded by Khamenei would have made such actions extremely costly.
This analysis also suggests a reason why truly democratic elections may be incompatible with centralized
authority. Two of the Iranian system's key sources of legitimacy were the principle of clerical guidance and
the popular mandate derived from democratic elections (MacLeod 2009). As one conservative Iranian cleric
noted, “the legitimacy of the election comes from the supreme leader's approval, but the level of acceptance
comes from votes” (Cohen 2009b). However, when citizens privately hold preferences that contrast with the
political leadership, private elections are one way for individuals to manifest these preferences, as doing so
does not entail any of the sanctions that would normally be incurred by a public transgression. Moreover, most
citizens have an interest, however slight, in the outcome of nationwide elections. Hence, elections provide a
rare chance for the ubiquitous breaking of norms without consequence – a situation that may result in massive
changes to equilibrium outcomes. This is why centralized regimes that attempt a façade of democracy may
abandon secret ballots or harass voters, as Robert Mugabe did in the 2008 Zimbabwe presidential election, or
they may fabricate the results to such an extent that the belief of a democratic system never really emerges, as
was the case in Saddam Hussein's Iraq (where Hussein won two different elections with 99.96% and 100%
of the vote). The former strategy maintains the costs imposed by transgressing (not voting for) the authority,
thus decreasing the likelihood of a shock, while the latter strategy entails that the election “results” are not a
shock.
Although the Iranian protests did not lead to a regime change - Ahmadinejad ended up being sworn in
by Khamenei for a second term - they did fundamentally alter Iranian political and religious institutions and
35
the legitimizing relationship between the two. The Ahmadinejad wing was able to maintain power because it
controlled the military, not because it was legitimized by religious authorities. This encouraged Ahmadinejad
and his Basij supporters to enact a more militant, authoritarian regime – legitimized by force, not religion.
We can only speculate what this means for the political future of Iran, but history – and the model – suggest
that an even larger movement may not be too far off.
5
Conclusion
This paper analyzes the institutional roots of massive social, economic, and political changes. It employs
a simple economic model which suggests that authorities in centralized economies are relatively insulated
from small economic shocks but may be susceptible to massive changes if economic shocks are larger. This
result arises because citizens in centralized economies are more likely to falsify their preferences, as they
face numerous costs from choosing actions near their intrinsic optima, and centralized authorities have less
incentive to accommodate citizens than decentralized authorities. This entails that small economic shocks
are unlikely to upset the equilibrium outcome, as individuals are unwilling to incur the numerous sanctions
associated with transgressing the centralized authority's dictates in response to a small shock. However, a
large shock may encourage some citizens to incur these costs, which can result in a cascade whereby social
norms, and eventually institutional dictates, dramatically change.
This hypothesis is tested using data from austerity protests in 1976-1992. I use various measures of
IMF involvement to proxy for a "shock", as such involvement often directly led to austerity measures that
contributed to existence and severity of the protests. A regression analysis supports the model's hypothesis,
suggesting that after an increase in a "weaker" IMF variable (use of the Extended Fund Facility), protests are
more severe in decentralized economies. Yet, an increase in a "stronger" IMF variable (the number of debt
restructurings or renegotiations) led to more severe protests in more centralized economies.
This logic is also employed to shed light on aspects of the Iranian protests of summer 2009. It suggests
that the scale of the uprising can be attributed to the centralization of coercive power and the widespread affect
of the election results. The latter entailed that the "shock" was widespread throughout the population, whereas
the former entailed that falsi cation of beliefs about the government was rampant before the elections. These
two phenomena combined to form a cascade of opinion after the election results were announced, resulting
in a massive change in publicly expressed opinion.
The model and the insights drawn from the Iranian case also helps shed new light on the underpinnings
36
of political legitimacy. Namely, it suggests that legitimacy is an equilibrium outcome only when individuals
believe that other people will act in a manner that supports the legitimacy of the government. Legitimacy can
thus be fragile in centralized regimes, as high levels of private dissatisfaction entail that a shock can lead to a
signi cant change in people's beliefs about what others believe.
The model applies to many other historical and contemporary phenomena. For example, the fall of
communism in the Soviet Union, due in part to the multi-faceted sanctions imposed by the Communist Party,
may in part be explained by the logic spelled out in this model. Similarly, the centralization of authority
in Tsarist Russia may have led to the incredible scale of the Bolshevik Revolution (while also explaining
the central government's ability to suppress numerous smaller outbreaks in the nineteenth century), and the
interdependence between the Chinese bureaucracy and Qing rulers may help explain the scale of the Taiping
Rebellion (1850-1864), which followed after massive de ation systemically affected much of the Chinese
countryside. Moreover, the model may have implications for the future of centralized regimes such as those
in Iran, China, Burma (Myanmar), North Korea, and Zimbabwe.42
This paper is not meant to suggest that a general framework exists for predicting revolutions or massive
social change.43 The amount of variables necessary for a revolution to occur likely renders this an impossible
task. This paper, does, however, suggest that one ubiquitous set of conditions - those associated with the
centralization of coercive power - are conducive to massive changes in equilibrium outcomes. In a sense, then,
the conclusion is much different from Kuran (1995), who argues that revolutions are completely unpredictable
based on structural conditions. Instead, this paper argues - using a framework similar to Kuran's - that certain
institutional conditions are more suitable for a revolution or massive equilibrium change than others.
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43 Nor
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43
A
A.1
Proofs
Proof of Proposition 1
In order to determine the effect of dependence on the level of social change ( ), rst determine the sign of
the derivative
. Note that:
@
@
@
@
gj
f + j+k
g+h
Denoting
@a0
=
@
= b + c f+
0
cg @
jiB
2
j+k (f +
g+h)
dj
j+k ,
+
@a0
@
=
@at
:
@
@a0
@ :
start by solving for
1
gjiB
2
j+k
f iB
1 +
A
(f + g+h)2
j
j+k (f +
cg
gjiB
gj
(f + j+k
)
g+h)
f iB + j+k2
c f1+ g+h
(f + g+h)2
B
iB
1 + jhi2
kf iB
1
=
(jh
=
+
=
iB
1
Next, solve for
@at
=
@
kf )
jhcf
f + g+h
iB
2
1
N
b jh iB
2
P
j
aB
j
@at
@ ,
B cg [ b (
B
B
B
B
B
B
B
cg
@
jkf
j+k
iB
1 +
(j+k)(f + g+h)2
cg
denoting
+ (1
j
(f + g+h)( j+k
)
[
b(
P
j
aB
j
+
gj
cf + cj+k
g+h
0
@
1)) ]
6
6
4
1) + (
44
b
P
+ (1
aB
j
B
j aj
!
iB
1
i
kf iB
1
j;S
P
1
N
j
dj
j+k
dj
+ j+k
, and
aB
j +
)b(
=
jiB
2
j+k
(f + g+h)
i
aB
j
+ (1
f iB
1 +
gjiB
2
j+k
(f + g+h)2
c gjiB
2
cf iB
1 + j+k
f + g+h
N
j
iB
2
2
gj
cf + cj+k
f + g+h
2
+d
aB
j
= b + f+
)b(
gj
(f + j+k
)
(f + g+h)2
1
N
b+
= b + c + d + e,
1) + (
j
2
h
B
iB
1 [b + c + d] + b jh i2
c g
f + g+h
b
N
+
P
P
2
P
1
N
kf iB
1
+
djiB
2
j+k
2
(j+k)(f + g+h)2
cg
h
jf iB
2
+
(j+k)(f + g+h)2
cg
0
f iB + j+k2
c f1+ g+h
(j+k)(f + g+h)2
cg
jf iB
2
+
b
N
2
gjiB
jf iB
2
+
djiB
2
j+k
+
djiB
2
j+k
( +b(
1))b
N
2
1)) ]
!
P
)b(
1
A
3
+
j;N S
(A3)
aB
j
7
7
5
1):
1
C
C
C
C
C
C
C
C
A
!
0
=
0
=
B
B
@
(jh
kf )
"
B
iB
1 + jhi2
c gjiB
2
cf iB
1 + j+k
(j+k)(f + g+h)2
cg
b(
1) jf
iB
2
iB
1
B
B
B
B
@
jf iB
2
b
b(
h
iB
1 [
jh
+ b(
2
[
=b+
+
jf iB
2
dj
j+k ,
b(
and
>
B
B
B
B
@
b
b(
0
B
=) @
>
=
h
+ b(
2
b(
1) + (
1
N
2
b(
0
B
B
B
B
B
B
2B
B
B
B
B
B b(
@
jh
1)
jf
1) +
+
2
2
iB
1
[
# C
C
A
B
a
j;N S j
2
1)) ]
kf iB
1
+
P
B
j;S aj
+b(
N
+
iB
1 [
P
2
)b(
1)) ]
b(
P
B
j;N S aj
1)) (b + c + d)]
i
P
1
B
iB
+
1
j;S aj
N
i
P
1
B
kf (1
) iB
a
1
j
j;N
S
N
gj
f + j+k
g+h
1)) b + c f +
2
+
1
C
C
C
C
A
dj
j+k
= b + c + d + e,
1)):
1
N
kf iB
1
2
b(
1) +
B
j;S aj
1
N
1)
)b(
)b(
B
j aj
CB
A@
2
N
1
1))b P
( +b(
N
jhiB
2
+ (1
P
10
2
]
)b(
1) + (
P
[
aB
j
b(
b jh
1) +
P
1
N
1) +
1
N
iB
2
(
B
j aj
iB
2
e)]
P
]
iB
1
P
B
j aj
e)] +
3
1
B
B
j;S aj
N
6 jh i2
7
b 4
5+
P
1
B
kf
iB
a
1
j;S j
N
2
P
1
B
) iB
2
j;N S aj
N
6 jh (1
+ b(
1)) 4
P
1
B
kf (1
) iB
1
j;N S aj
N
45
i
B
j;S aj
kf (1
jf
h
P
(
iB
1
kf
j;N S
(j+k)(f + g+h)2
cg
iB
2
+ (1
+ (1
) iB
2
1)) jh (1
b (
1
N
iB
2
aB
j +
kf )
)b(
jf iB
2
1) +
> 0 when (again denoting denoting
@E
@
+ (1
h
2 2
j;S
b (jh
(j+k)(f + g+h)2
cg
0
b P
(b + c + d) + b
h
e) + b jh iB
2
iB
1 (
N
1) + (
kf iB
1
Combining these two, this means that
gj
cf + cj+k
f + g+h
+
b(
1) + ( + (1
P
1
B
kf
j;S aj
N
P
1
B
) iB
2
j;N S aj
N
1)) jh (1
(j+k)(f + g+h)2
cg( +(1
)b(
1))
iB
1
kf iB
1 [
b(
iB
2
h
j+k
!
b(
jhiB
2
(j+k)(f + g+h)
cg
0
[
iB
2
+
djiB
2
+
f + g+h
jf
B
@
=
jf iB
2
) iB
1
i
+
1
N
2
(
1
P
j;N S
aB
j
e) +
kf
iB
1
1
C
C
C
C
C
C
C
C
3 C
C
C
7 C
5 A
1
N
P
B
j aj
C
C
C
C
i A
1
C
i A
1
C
A
=)
P B
1
B
j aj
N
6 jh i2
B
B
B
i1 (
e) + b 4
1) ( b (
1) + 2 ) @jf i2
P B
1
kf iB
1
j aj
N
2
3 1
P
1
B
B
j;S aj
N
6 jh i2
7 C
B
B
jf i2
(
1) +
i1
4
5+ C
P
C
1
B
C
kf
iB
a
1
j;S j
N
C
2
3
C
P
C
1
B
B
jh
(1
)
i
a
C
2
j
j;N S
N
6
7
C
( + b(
1)) 4
5
C
P
1
B
C
kf (1
) iB
a
1
j;N S j
N
C
A
P
P
1
1
B
B
jh iB
kf iB
2
1
j aj
j aj
N
N
b(
0
>
b
B
B
B
B
B
B
2B
B
B
B
B
B
@
=)
(
b(
B jf iB
B
2
B
B
2B
B
B
B
@
>
2
>
2
b(
iB
1
@ jf iB
2
@
@
B
) @jf iB
2
2
iB
1 (
2
B
6 jh i2
e) + b 4
kf iB
1
1
N
P
1
N
B
j aj
P B
j aj
3
P
1
B
B
j;S aj
N
6 jh i2
+(
b (1
)) 4
P
1
aB
kf
iB
1
N
3j;S j
2
P
1
B
) iB
2
j;N S aj
N
7
6 jh (1
b4
5
P
1
B
B
a
kf (1
) i1
j;N S j
N
1) + 2
0
Hence, if (A4) holds,
0
1) + 2
0
=)
2
0
iB
1 e+(
2
0
jf iB
2
iB
1 (
P
e) +
B 2
3
B
B
B
B
a
B 6 jh i2
j j
N
7
@ b4
5
P
B
kf
iB
1
j aj
N
2 0
1
0
X
1
A kf @
b) 4jh @ iB
aB
2
j
N
j;S
iB
1
7C
5A
31
7C
5A
1
7 C
5+ C
C
C
C
C
C
C
A
1
C
C
C
C
A
31
(A4)
131
1 X B A5A
aj
N
j;S
> 0. The rest of the proof shows that under some conditions (A4) is always
true, and under other conditions, (A4) is only true when
suf ciently large when 1) (A4) does not hold at
left-hand side of (A4) with respect to
is suf ciently large. (A4) is only true when
! 1; 2) (A4) holds at
is
! 1; 3) the derivative of the
is greater than the derivative of the right hand side with respect to .
On the other hand, if 2 and 3 hold but (A4) is also true at
46
! 1, then (A4) always holds. Start by analyzing
(A4) at
! 1, where
= b + c + d + e,
0
jf iB
2
2
B
B
B
B
B b (2
B
B
B
B
B
B
B (
@
Next, (A4) holds at
> (1
2
)
2
(1
B
B
@ jf i2
jf iB
2
iB
1
2
)
2
b
iB
2
)
0
(1
jf
iB
1 (2 (
+
e)
P
1
N
B
6 jh i2
)4
kf iB
1
2
B
6 jh i2
b) 4
kf iB
1
+ 2 (1
=)
B
B
B
B
B b (2
B
=) B
B
B
B
B
B (
@
gj
cf + cj+k
f + g+h
1
N
aB
j
P
j
P
1
N
and
)+
j
1
N
dj
j+k ,
aB
j
j;S
aB
j
P
j;S
aB
j
=
+ (1
)b(
1):
1
C
C
C
7 C
5 C
C
C>0
C
3 C
C
C
7 C
5 A
3
(A5)
! 1 if:
2
0
=b+
6 jh
)4
kf iB
1
2
B
6 jh i2
b) 4
kf iB
1
1
N
e)
P
)
)+
1
N
B
j aj
B
j;S aj
P
j;S
aB
j
e)
1
N
j;S
P
N
1
N
2
)
0
P
j
aB
j
3
P
B
j aj
P B
j aj
jf iB
2
7C
5A
e)] >
7
5
N
(A6)
31
B
j;S aj
P
B
j;S aj
(
3
N
3 C
C
C
7 C
5 A
P
1
N
aB
j
1
e) +
B
j aj
N
aB
j
j;S
1
C
C
C
7 C
5 C
C
C>
C
3 C (1
C
C
7 C
5 A
3
P
1
N
iB
1 (
P
)+
B
6 jh i2
) 4
kf
iB
1
(2
P
P
(2 (
B
6 jh i2
b) 4
kf
iB
1
2
B
j aj
1
N
1
N
iB
1 e+(
)
jf iB
2
B 2
B
B
B
B 6 jh i2
@ b4
kf
iB
1
2
B
6 jh i2
b) 4
kf
iB
1
2
)
iB
1 [(1
2
+ (1
(2 (
iB
2
(
0
7
5
iB
1 (
B 2
B
B
B
B 6 jh i2
@ b4
kf iB
1
1
N
e) +
P
1
N
B
j aj
P
j
aB
j
1
3 C
C
C
7 C
5 A
(A7)
Note that the left-hand side of (A7) is equal to the left-hand side of (A5). Finally, check whether the derivative
of the left-hand side of (A4) with respect to
is greater than the derivative of the right hand side with respect
47
to . This is true when:
2
> (1
+ 2 (1
)
2
)
(1
0
B
B
@ jf i2
)
iB
1 e+(
This condition is the same one as in (A6). Hence,
greater than 0 only at suf ciently large
(A7) and
2
0
jf iB
2
iB
1 (
B 2
B
B
B
B 6 jh i2
@ b4
kf
iB
1
2
B
6 jh i2
b) 4
kf
iB
1
N
e) +
P
B
j aj
N
1
N
P
j
aB
j
P
1
N
B
j;S aj
P
B
j;S aj
1
3 C
C
C
7 C
5 A
(A8)
31
7C
5A
is always greater than 0 if (A5) and (A7) hold, but is
@
@
if (A7) holds but (A5) does not. Denote
as the left-hand side of
as the right-hand side of (A7) : Hence, we have the following three cases:
Case 1:
@
@
is always greater than 0. This holds if
Case 2:
@
@
is always less than 0. This holds if
Case 3: There exists some
such that
@
@
> 0.
.
<
< 0 when
and
>
. This holds if
iB
2 plane. The left-hand side of (A5),
, is constant in
<
> 0 when
@
@
2 ( ; 0).
Next, determine where these cases fall in the
. To see how
changes in iB
2 , take the derivative:
@
=
@iB
2
jf (2 (
e)
This must be positive since jf > jh and 2 (
e)
)
jh (
2b)
2b. Thus, an increase in iB
2 increases the
>
parameter set over which Case 1 emerges. Case 1 will thus always emerge if
B
B
B
B
iB
2 . By assumption, the lowest value of i2 = i1 . At i2 = i1 ,
= (jh
This is de nitely negative if
(
0
B b (2
kf ) @
(
b) > b (2
)
b)
iB
2
iB
2
) ()
> 0 at the minimum value of
equals:
1
N
P
B
j aj
1
N
P
B
j;S aj
1
C
A
> 2b. For the remainder of the analysis, assume
that this condition holds. In words, this simply means that citizens care suf ciently about sanctions imposed
by the authorities relative to their own utility, as discussed in the body of the text. With this assumption,
B
< 0 at iB
2 = i1 , and thus there must exist some i where
48
0 and Case 1 emerges when iB
2
i and
< 0 when < i . i is de ned as follows:
2
6 jf (2 (
iB
1 4
kf (
i =
e)
)
2b)
jf (2 (
2
3
7
5 + (jh
e)
)
6 b (2
kf ) 4
(
)
b)
jh (
1
N
1
N
2b)
When iB
2 < i , either Case 2 or Case 3 is possible, depending on the value of
<
. Since
@
@iB
2
> 0 and
@
@iB
2
B
j aj
P
j;S
aB
j
3
7
5
(A9)
. Case 2 emerges when
< 0, it must be that the parameter set over which Case 2 emerges is weakly
decreasing in iB
2 . It is also clear that
! 0 as
! 0 and
1 as
!
iB
2 < i , Case 2 emerges when
! 0 and Case 3 emerges when
means that there must be some
for every value of iB
2 (< i ) where
and
P
! 1. This means that when
! 1. Moreover, since
<
@
@
< 0, this
entails that Case 2 emerges
entails that Case 3 emerges. Finally, the line dividing the space between Cases 2 and 3 must be
concave since
@2
@
2
> 0.
Finally, if Case 3 is the relevant one, it is clear that any change in a parameter which increases the
likelihood of Case 1 emerging decreases
Case 2 emerging increases
. Thus,
, while a change in a parameter which increases the likelihood of
is decreasing in
49
and iB
2 .
B
Tables
Table 1: IMF Pressure by Country and Year
Algeria
Argentina
Bangladesh
Benin
Bolivia
Brazil
Bulgaria
Burkina Faso
Cameroon
Central African Republic
Chad
Chile
Congo (Dem. Rep.)
Congo (Rep.)
Costa Rica
Côte d'Ivoire
Dominican Republic
Ecuador
Egypt
El Salvador
Ethiopia
Gabon
Gambia, The
Ghana
Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hungary
India
Iran
Jamaica
Jordan
Kenya
Korea (South)
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mexico
Morocco
Mozambique
Nepal
Nicaragua
Niger
Nigeria
Pakistan
Panama
Papua New Guinea
Peru
Philippines
Poland
Romania
Senegal
Sierra Leone
Sri Lanka
Sudan
Tanzania
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Uruguay
Venezuela
Zambia
Zimbabwe
X = IMF Pressure within one year
1981 1982 1983 1984 1985
1976
1977
1978
1979
1980
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
1986
1987
1988
1989
1990
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
1991
X
X
X
X
X
X
X
X
X
X
1992
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
50
X
X
X
X
X
X
Total
2
12
17
5
14
11
3
3
16
12
3
17
16
15
13
13
15
12
17
11
8
16
16
11
5
8
5
16
12
15
3
13
0
17
5
17
0
14
15
12
16
12
17
16
10
3
15
11
11
17
12
5
17
17
13
17
16
17
17
17
17
13
15
4
7
13
17
12
17
11
Table 2: Protests And Severity by Country
Country
Algeria
Argentina
Bangladesh
Bolivia
Brazil
Bulgaria
Chile
Congo (Dem. Rep.)
Côte d'Ivoire
Dominican Republic
Ecuador
Egypt
El Salvador
Gabon
Ghana
Guatemala
Haiti
Honduras
India
Iran
Jamaica
Jordan
Mexico
Morocco
Mozambique
Nicaragua
Niger
Nigeria
Panama
Peru
Philippines
Poland
Romania
Senegal
Sierra Leone
South Korea
Sudan
Tunisia
Venezuela
Zambia
Total
Protests
Indexed 1
0
0
1
5
3
1
1
1
0
4
3
0
2
1
1
0
0
1
6
1
2
0
4
0
1
1
0
0
2
3
3
3
3
1
0
0
2
1
2
2
61
Protests
Indexed 2
1
1
0
2
5
0
3
0
0
2
1
0
0
0
0
1
1
0
0
0
2
0
0
1
0
0
1
3
2
9
0
0
0
0
1
0
3
0
2
1
42
51
Protests
Indexed 3
1
0
0
0
0
0
0
1
1
1
0
1
0
0
0
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
0
2
0
0
1
0
0
1
1
13
Total
Protests
2
1
1
7
8
1
4
2
1
7
4
1
2
1
1
1
1
1
6
1
4
1
4
3
1
1
1
3
4
12
3
3
5
1
1
1
5
1
5
4
116
Table 3: Protests and Severity by Year
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
Total
Protests
Indexed 1
0
0
1
0
0
1
0
7
7
6
7
5
2
8
4
6
7
61
Protests
Indexed 2
1
2
2
1
1
0
3
5
3
5
5
2
2
1
6
2
1
42
Protests
Indexed 3
0
1
0
0
1
1
0
0
2
0
0
0
1
3
2
2
0
13
Number of
Protests
1
3
3
1
2
2
3
12
12
11
12
7
5
12
12
10
8
116
Table 4: Protests and Severity by Continent
Region
Africa
Asia
Central America
Europe
Middle East
South America
Total
Protests
Indexed 1
9
10
16
7
2
17
61
Protests
Indexed 2
9
0
8
0
2
23
42
52
Protests
Indexed 3
3
1
1
2
5
1
13
Total
Protests
21
11
25
9
9
41
116
Table 5: Summary Statistics, IMF and Centralization Variables
Observations
Mean
Variance
Only Including Years Used in Regressions
IMF and Centralization Variables
Use of Funds/Quota (if > 1.25)
Extended Fund Facility/Quota
# of reschedulings/restructurings
Constraint on Executive (1-7)
Political Rights (1-7)
Economic and Demographic Controls
Urbanization Rate (%)
Real GDP (per capita, $K, 1987 USD)
Population (millions)
Religious Fractionalization
Use of Funds/Quota (if > 1.25)
Extended Fund Facility/Quota
# of reschedulings/restructurings
Constraint on Executive (1-7)
Political Rights (1-7)
Economic and Demographic Controls
Urbanization Rate
Real GDP (per capita, $K, 1987 USD)
Population (millions)
Religious Fractionalization
216
133
274
807
807
0.58
0.51
0.37
3.40
4.45
1.076
1.614
0.289
4.761
3.608
807
803
807
807
39.71
0.96
31.58
0.71
389.964
0.844
8448.2
0.047
40
19
43
81
81
Protest Years Only
1.04
0.81
0.59
4.09
3.63
1.383
2.454
0.394
5.430
3.111
81
81
81
81
51.34
1.15
60.95
0.81
341.627
0.713
25468.7
0.030
**Years used in regressions are ones within one year of one of the IMF va ria bles being positive
53
Table 6: Logit Regression, IMF Pressure Index and the Presence of Protests
DEPENDENT VARIABLE: Protest Presence (0 or 1)
(6.1)
(6.2)
(6.3)
Centralization and/or Coercive Power Variables
Constraint on Executive Constraint on Executive
Political Rights Dummy
Dummy 1
Dummy 2
IMF Pressure Indices
Use of Funds/Quota (if > 1.25) (δ1 )
Interaction with Centralization Variable (δ3)
Extended Fund Facility/Quota (δ 1 )
Interaction with Centralization Variable (δ3)
# of reschedulings/restructurings (δ 1)
Interaction with Centralization Variable (δ3)
Use of Funds/Quota (if > 1.25), over 2 years (δ1 )
Interaction with Centralization Variable (δ3)
Extended Fund Facility/Quota, over 2 years (δ 1)
Interaction with Centralization Variable (δ3)
# of reschedulings/restructurings, over 2 years (δ1 )
Interaction with Centralization Variable (δ3)
Economic and Demographic Controls
Continent Dummies
Year Dummies
0.245*
[0.129]
-0.994***
[0.319]
0.135
[0.145]
-0.194
[0.232]
0.12
[0.134]
-0.234
[0.249]
0.290**
[0.114]
-0.911*
[0.541]
0.325***
[0.125]
-0.444*
[0.235]
0.214**
[0.108]
-0.63
[0.542]
0.550**
[0.259]
0.047
[0.484]
0.570**
[0.279]
-0.020
[0.443]
0.595**
[0.263]
-0.071
[0.469]
0.190
[0.127]
-0.761**
[0.298]
0.070
[0.143]
-0.042
[0.222]
0.087
[0.131]
-0.168
[0.243]
0.312***
[0.105]
-1.020*
[0.525]
0.305***
[0.117]
-0.339*
[0.196]
0.220**
[0.099]
-0.705
[0.522]
0.584**
[0.267]
-0.190
[0.454]
0.558**
[0.283]
-0.114
[0.430]
0.574**
[0.267]
-0.093
[0.451]
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Constraint on Executive dummy 1 uses Polity IV data: constraint of 1 = 1; constraint of 2-7 = 0; 1 is greatest constraint, 7 is least constraint
Constraint on Executive dummy 2 uses Polity IV data: constraint of 1-2 = 1; constraint of 3-7 = 0
Political Rights dummy uses Freedom House data: rights of 6-7 = 1; rights of 1-5 = 0; 1 is most free, 7 is least free
Control variables not reported are the number of previous protests, urbanization rate, real per capita GDP, population, and a religious fractionalization index
Standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1
54
Table 7: Logit Regression, IMF Pressure Index and the Presence of Protests
DEPENDENT VARIABLE: Protest Presence (0 or 1)
(7.1)
(7.2)
(7.3)
Centralization and/or Coercive Power Variables
Constraint on Executive Constraint on Executive
Political Rights Dummy
Dummy 1
Dummy 2
IMF Pressure Indices
Extended Fund Facility/Quota (δ 1A)
Interaction with Centralization Variable (δ3A )
# of reschedulings/restructurings (δ 1B )
Interaction with Centralization Variable (δ3B )
Extended Fund Facility/Quota, over 2 years (δ 1A )
Interaction with Centralization Variable (δ3A )
# of reschedulings/restructurings, over 2 years (δ1B)
Interaction with Centralization Variable (δ3B )
Economic and Demographic Controls
Continent Dummies
Year Dummies
0.279**
[0.116]
-0.865
[0.530]
0.492*
[0.265]
0.126
[0.487]
0.312**
[0.127]
-0.434*
[0.236]
0.509*
[0.286]
0.068
[0.448]
0.197*
[0.110]
-0.576
[0.532]
0.553**
[0.268]
-0.023
[0.472]
0.292***
[0.107]
-1.040*
[0.548]
0.496*
[0.274]
-0.017
[0.467]
0.279**
[0.119]
-0.316
[0.197]
0.457
[0.292]
0.003
[0.436]
0.196*
[0.101]
-0.72
[0.543]
0.504*
[0.274]
0.025
[0.460]
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Constraint on Executive dummy 1 uses Polity IV data: constraint of 1 = 1; constraint of 2-7 = 0; 1 is greatest constraint, 7 is least constraint
Constraint on Executive dummy 2 uses Polity IV data: constraint of 1-2 = 1; constraint of 3-7 = 0
Political Rights dummy uses Freedom House data: rights of 6-7 = 1; rights of 1-5 = 0; 1 is most free, 7 is least free
Control variables not reported are the number of previous protests, urbanization rate, real per capita GDP, population, and a religious fractionalization index
Standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1
55
Table 8: Logit Regression, Centralization and the Severity of Protests
DEPENDENT VARIABLE: Most Severe Protest in a Given Year (1 or 2/3)
(8.1)
Centralization and/or Coercive Power Variables
Constraint on Executive Dummy 1 (η 2)
Constraint*EFF/Quota (η3A )
Constraint*# of reschedulings (η 3B )
Constraint on Executive Dummy 2 (η 2)
Constraint*EFF/Quota (η3A )
Constraint*# of reschedulings (η 3B )
Political Rights Dummy (η2 )
Political Rights*EFF/Quota (η3A)
Political Rights*# of reschedulings (η3B)
Economic and Demographic Controls
Continent Dummies
Year Dummies
(8.2)
Centralization and/or Coercive Power Variables
0.234
[1.025]
-1.004
[0.769]
2.794*
[1.637]
Constraint on Executive Dummy 1 (η 2)
1.676*
[0.948]
-0.264
[0.421]
0.795
[1.150]
Constraint on Executive Dummy 2 (η 2)
-0.142
[0.998]
-0.490
[0.667]
1.688
[1.208]
Political Rights Dummy (η2 )
Yes
Yes
Yes
Constraint*EFF/Quota, 2 years (η 3A )
Constraint*# of reschedulings, 2 years (η3B)
Constraint*EFF/Quota, 2 years (η 3A )
Constraint*# of reschedulings, 2 years (η3B)
Political Rights*EFF/Quota, 2 years (η3A)
Political Rights*# of reschedulings, 2 years (η 3B )
Economic and Demographic Controls
Continent Dummies
Year Dummies
-0.173
[1.185]
-2.273
[2.033]
3.521**
[1.462]
0.109
[1.226]
-0.411
[0.396]
3.310**
[1.287]
-2.482*
[1.362]
-1.738
[1.582]
4.352***
[1.419]
Yes
Yes
Yes
Constraint on Executive dummy 1 uses Polity IV data: constraint of 1 = 1; constraint of 2-7 = 0; 1 is greatest constraint, 7 is least constraint
Constraint on Executive dummy 2 uses Polity IV data: constraint of 1-2 = 1; constraint of 3-7 = 0
Political Rights dummy uses Freedom House data: rights of 6-7 = 1; rights of 1-5 = 0; 1 is most free, 7 is least free
Control variables not reported are the number of previous protests, urbanization rate, real per capita GDP, population, and a religious fractionalization index
Standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1
56
Table 9A: Marginal Effects of Centralization Parameter, Regression 8.2
Marginal Effect: ΔPr(Protest Severity = 2 or 3) /ΔCentralization / Coercive Power Variable
(9A.1)
(9A.2)
(9A.3)
Centralization / Coercive Power Variable = Constraint on Executive Dummy 1
# of reschedulings, over
2 years = 0
# of reschedulings, over
2 years = 1
# of reschedulings, over
2 years = 2
EFF/Quota, over 2 years = 0
-0.036
[0.249]
0.678***
[0.208]
0.935***
[0.067]
EFF/Quota, over 2 years = 1.25
-0.406
[0.272]
0.092
[0.461]
0.739*
[0.399]
EFF/Quota, over 2 years = 1.5
-0.446
[0.273]
-0.010
[0.439]
0.650
[0.571]
EFF/Quota, over 2 years = 2
-0.514*
[0.276]
-0.160
[0.328]
0.420
[0.902]
EFF/Quota, over 2 years = 2.5
-0.573**
[0.276]
-0.256
[0.245]
0.183
[0.928]
EFF/Quota, over 2 years = 3
-0.627**
[0.273]
-0.326
[0.229]
0.006
[0.662]
EFF/Quota, over 2 years = 4
-0.722***
[0.257]
-0.441*
[0.268]
-0.171
[0.261]
EFF/Quota, over 2 years = 5
-0.800***
[0.229]
-0.551*
[0.306]
-0.269
[0.273]
Standard errors in brackets
All control variables reported at the mean, except for the continent dummy (C. America) and year (1985)
*** p<0.01, ** p<0.05, * p<0.1
57
Table 9B: Marginal Effects of Centralization Parameter, Regression 8.2
Marginal Effect: ΔPr(Protest Severity = 2 or 3) /ΔCentralization / Coercive Power Variable
(9B.1)
(9B.2)
(9B.3)
Centralization / Coercive Power Variable = Constraint on Executive Dummy 2
# of reschedulings, over
2 years = 0
# of reschedulings, over
2 years = 1
# of reschedulings, over
2 years = 2
EFF/Quota, over 2 years = 0
0.019
[0.214]
0.623***
[0.208]
0.923***
[0.098]
EFF/Quota, over 2 years = 1.25
-0.082
[0.249]
0.585***
[0.210]
0.911***
[0.097]
EFF/Quota, over 2 years = 1.5
-0.105
[0.259]
0.575***
[0.215]
0.908***
[0.097]
EFF/Quota, over 2 years = 2
-0.153
[0.279]
0.552**
[0.227]
0.900***
[0.099]
EFF/Quota, over 2 years = 2.5
-0.204
[0.300]
0.524**
[0.244]
0.891***
[0.102]
EFF/Quota, over 2 years = 3
-0.256
[0.322]
0.493*
[0.264]
0.879***
[0.107]
EFF/Quota, over 2 years = 4
-0.358
[0.366]
0.417
[0.315]
0.848***
[0.126]
EFF/Quota, over 2 years = 5
-0.358
[0.412]
0.325
[0.380]
0.804***
[0.164]
Standard errors in brackets
All control variables reported at the mean, except for the continent dummy (C. America) and year (1985)
*** p<0.01, ** p<0.05, * p<0.1
58
Table 9C: Marginal Effects of Centralization Parameter, Regression 8.2
Marginal Effect: ΔPr(Protest Severity = 2 or 3) /ΔCentralization / Coercive Power Variable
(9C.1)
(9C.2)
(9C.3)
Centralization / Coercive Power Variable = Political Rights Dummy
# of reschedulings, over
2 years = 0
# of reschedulings, over
2 years = 1
# of reschedulings, over
2 years = 2
EFF/Quota, over 2 years = 0
-0.353
[0.239]
0.314
[0.231]
0.878***
[0.169]
EFF/Quota, over 2 years = 1.25
-0.481*
[0.259
-0.031
[0.180]
0.550
[0.534]
EFF/Quota, over 2 years = 1.5
-0.500*
[0.262]
-0.067
[0.166]
0.457
[0.612]
EFF/Quota, over 2 years = 2
-0.537**
[0.267]
-0.119
[0.145]
0.280
[0.653]
EFF/Quota, over 2 years = 2.5
-0.571**
[0.273]
-0.156
[0.145]
0.144
[0.541]
EFF/Quota, over 2 years = 3
-0.604**
[0.279]
-0.184
[0.160]
0.057
[0.373]
EFF/Quota, over 2 years = 4
-0.666**
[0.287]
-0.236
[0.208]
-0.023
[0.140]
EFF/Quota, over 2 years = 5
-0.723**
[0.290]
-0.289
[0.267]
-0.054
[0.090]
Standard errors in brackets
All control variables reported at the mean, except for the continent dummy (C. America) and year (1985)
*** p<0.01, ** p<0.05, * p<0.1
59
C
Figures
Figure 1: Three Cases,
i2 plane
Figure 2: Case 3, Graphically
60
Figure 3: Three Cases, Qualitatively
Figure 4: Total Protests by Year
61
D
Protest Data Description
I rst identi ed all countries that either satis ed at least one of the three "IMF pressure" variables noted in
the text or were identi ed by Walton and Seddon (1994) as having protests in this period. For all of these
countries, I searched all articles in Lexis-Nexis Academic that arose from the following search string: (IMF
OR austerity) AND (riot OR protest) AND [country]. This method produced 116 instances of protest, as
opposed to 146 instances found by Walton and Seddon. The difference is likely a result of coding of small
protests – I did not count events such as general strikes (unless violence broke out) or small, sector-speci c
strikes; it is not clear where Walton and Seddon's cutoff point occurs. However, there do not appear to be any
inconsistencies in the counting of large protests.
As noted in Section 3.1, protests and riots were only documented if they resulted from austerity measures
or IMF pressure. In the case of protests, a speci c aim of those protesting had to be some sort of austerity
measure, such as higher prices or reduced wages. If riots broke out, they are only counted in the data set if
they were a result of some austerity measure. Thus, a government coup would not be part of the universe of
observations, unless it were directly the result of austerity measures.
As noted, I subjectively coded each of these protests by their severity, using the following criterion. An
instance was scored (1) if it was a small, con ned (to one or two cities) protest or general strike, (2) if it
was either prolonged but con ned or widespread but not prolonged, with minimal deaths resulting, and (3)
if the protest was prolonged, contained widespread riots, and resulted in many deaths. The severity of some
protests was not obvious. For these protests, I created an "alternate index", listed below. The index does not
take a value if a protest cannot reasonably be considered of a different severity than is given in the severity
index. The alternate takes on a different severity value if it could reasonably be considered a different severity
than the one I originally coded. All protests are listed below with a brief description and severity level.
62
Africa
Country
Month
Year
Description
Congo (Dem. February
1989
Rep.)
September 1991
Student protests after gas price increase; 1000 involved in fights
with police
30 dead; precipitated regime change
Côte d'Ivoire
February
1990
Gabon
Ghana
Severity Alternate
Index Index?
1
--
3
2
Protests against wage cuts; lasted two months until austerity
dropped; relatively non-violent
3
2
December 1989
Series of strikes against austerity; national conference called
1
--
November 1978
80 strikes after austerity measures
1
--
Mozambique January
1990
Waves of strikes and protests over austerity
1
--
Niger
February
1990
10 students killed, 50 wounded in protest against austerity
2
--
Nigeria
April
May
May
1988
1989
1992
6 students killed in riot over increased gas prices
Two weeksof rioting against austerity, 30 killed
23-100 dead in riots in many cities
2
2
2
1
---
Senegal
March
1988
Urban riots over austerity measures
1
--
Sierra Leone
July
1980
4 days of riots after gas price hike; 2 dead
2
1
March
1981
--
1982
2
--
March
1985
2
--
October
1987
Protests in northern towns over inflation, shortages
4 days of student protests over austerity; thousands involved, 28
deaths
3 days of rioting following food price hikes; 5 deaths, 2000
arrested
Student protests over food and fuel price hikes, 10,000 involved,
one death
Widespread demonstrations after price hikes
1
January
Sudan
December 1987
December 1986
Zambia
January
July
June
1989
1989
1990
15 dead, 1000 arrested in widespread riot over hike in price of
cornmeal; price hike rescinded
61 arrested after change in food subsidies
Youth food riots after food price hike
30 dead, hundreds injured in weekend riot
2
1
1
--
2
--
1
1
3
--2
Asia
Month
Year
Bangladesh
June
1986
Thousands protest in Dhaka over rising cost of living
1
--
India
February
September
November
June
September
November
1986
1991
1991
1992
1992
1992
Thousands protest price hikes
Thousands march in New Delhi to protest austerity
Millions strike against economic policies
Millions strike against economic policies
Protests in New Delhi after oil price hike
1,200 arrested, 50 injured in protest over rising prices
1
1
1
1
1
1
-------
Philippines
November 1984
July
1991
January
1992
8,000 protest against IMF in Manila
Violent transport driver strike of economic policies
Protest against rise in electricity prices
1
1
1
----
South Korea
May
Series of strikes and protests over austerity; 300 dead
3
2
1980
Description
Severity Alternate
Index Index?
Country
63
Central America/Caribbean
Country
Month
Year
April
1984
July
August
November
January
March
August
El Salvador
January
February
Guatemala
Description
Severity Alternate
Index Index?
3
--
1984
1984
1984
1985
1988
1990
Protests over entire month throughout D.R. against austerity; 120
dead, 6,000 arrested
25,000 protest resumption of IMF talks
Students protest price hikes; 1 dead
Hundreds protest milk price hikes
Waves of protests after price hikes; hundreds arrested
Two weeks of protests over economic policies; 4 dead
2 day strike over price increases; 8 dead; 1000 arrested
1
1
1
1
2
2
----1
--
1986
1986
8,000 protest over austerity measures
50,000 protest over austerity measures
1
1
---
September 1985
Protests over bus fare increase; 1 dead, 466 arrested
2
1
Haiti
June
1984
Violence in 5 towns over 3 months over austerity; 3 dead
2
1
Honduras
May
1984
20,000 protest against austerity in Tegucigalpa
1
--
Jamaica
January
January
April
March
1979
1985
1985
1986
3 days of rioting over gas price increase; 7 dead
Nationwide riots over gas price increase; 10 dead
Protests in several towns over austerity
Student, opposition protests over austerity
2
2
1
1
-----
Mexico
February
June
October
October
1983
1983
1983
1985
Thousands protest in Mexico City against austerity
Thousands of students protest against austerity
Thousands protest in Acapulco against austerity
7,000-10,000 marched against austerity
1
1
1
1
-----
Nicaragua
October
1990
Protests throughout Managua over austerity
1
--
Panama
November
July
March
June
1984
1985
1986
1991
150,000 marched in protest over austerity
Violent 2-day general strike over austerity
Week-long strike, riot against austerity; one death
Students, teachers riot over austerity in Panama City
2
1
2
1
1
-1
--
Dominican
Republic
Europe
Year
Description
Severity Alternate
Index Index?
Country
Month
Bulgaria
November 1990
20,000 protest austerity plan
1
--
Poland
January
April
May
1990
1992
1992
Street protests in Warsaw over austerity
30,000 protest plans for austerity measures
80 protests throughout country over IMF plans
1
1
1
----
Romania
November
December
December
April
September
1987
1987
1989
1991
1991
Thousands of workers riot over wage cuts
Student and worker protests throughout country
Hundreds dead in widespread riots
15,000 demonstrate in Bucharest
Miners, protesters battle police for 3 days; 3 dead
1
1
3
1
3
----2
64
Middle East/North Africa
Country
Month
Year
November 1986
Algeria
2
1
3
--
1977
Riots throughout country over food price increase; 79 dead
3
--
August
1991
Sporadic protests over bus fare increases
1
--
April
1989
Riots for a week throughout country over austerity; 8 dead
3
2
July
1981
2
1984
General strike after price increases; 66 killed
Riots in half dozen cities after food price hikes; 240 killed over
four days
33 killed, 210 arrested in two days of riots over economic
hardships worsened by austerity
3
January
3
2
2
--
Riots in main towns after bread price doubled
1
--
1988
Egypt
January
Iran
Jordan
December 1990
Tunisia
Severity Alternate
Index Index?
3 days of student rioting in Constantine; 4 deaths; 186 jailed
Followed increase in food prices; over a week of riots; 159 dead,
154 wounded; riots across country
October
Morocco
Description
January
1984
South America
Month
Year
Argentina
March
1982
6 wounded, 400 arrested in austerity protest
2
1
Bolivia
March
September
January
August
April
November
November
1983
1985
1986
1986
1987
1987
1989
Strike by public workers against austerity; 2 dead
Month-long strike; state of siege declared; thousands arrested
Waves of protests against austerity measures
Widespread protests; state of siege declared
10,000 demonstrate against austerity
10,000 demonstrate against austerity
Teachers strike, 700 arrested, state of siege declared
1
2
1
2
1
1
1
---1
----
May
July
September
October
1983
1983
1983
1985
Riots in Sao Paulo and Rio de Janeiro over austerity
Workers around the country strike against austerity
Poor riots in Rio de Janeiro; austerity behind price rise
Thousands protest non-repayment of government debt
Thousands riot in Brasilia against austerity; looting and
vandalizing
Violent demonstrations in Rio de Janeiro after increase in bus
fares; 100 buses set on fire; military called in
Nationwide protest over wage freeze
Miners rampage in Peixoto; 2 dead, many injured; protest over
austerity measures
2
2
1
1
1
1
---
2
1
Labor protests and riots; 4 dead, hundreds arrested
Riot police clash with protesters; 200 arrested
3 days of protests; 40,000 in Santiago; more protests elsewhere
over austerity; 6 dead
Riot police break up anti-austerity protest in Santiago; 70 arrested,
3 bombs dismantled
Brazil
Chile
Ecuador
Peru
November 1986
July
1987
March
1989
March
1990
June
August
1983
1983
October
1983
July
1985
October
1982
January
1985
January
1986
September 1992
July
1976
June
July
May
May
March
September
March
November
September
August
July
1977
1977
1978
1978
1983
1983
1984
1984
1988
1990
1991
Description
Severity Alternate
Index Index?
Country
3 days of demonstrations against austerity; state of emergency
called
Street protests, bus driver strike over raise in fuel prices
Protests over prices in front of U.S. Embassy
Protests in 3 cities over austerity; 3 explosions
Demonstrations against tax and price increases; civil rights
suspended
Students demonstrate against price rise in 5 cities
Students, workers protest price hikes; 2 dead; 12 cities
Food price riots due to austerity; 12 dead
Youths revolt throughout Peru; 3 dead
24-hour strike against austerity; minimal violence
24-hour strike against austerity; 2 dead; 100 arrested
Workers and students strike in 6 towns; 1 dead
Student protest over austertiy in Lima; 250 arrested
Riots against austerity; looting and windows smashed
Week-long riots over food price hikes; 3 dead
All-out strike national strike; 2 dead
65
2
1
1
--
2
--
2
1
1
--
2
--
2
1
2
1
1
1
1
----
2
1
2
2
2
2
1
2
2
1
1
2
2
1
----1
1
---1
E
Robustness Checks
Table A.1: Democracy and the Presence of Protests
DEPENDENT VARIABLE: Protest Presence (0 or 1)
(A1.1)
(A1.2)
(A1.3)
Centralization and/or Coercive Power Variables
Democracy 0
Democracy 1
Democracy 2
-0.068
[0.162]
0.212
[0.221]
-0.051
[0.157]
0.199
[0.224]
-0.087
[0.148]
0.335
[0.228]
0.127
[0.165]
0.100
[0.203]
0.202
[0.139]
-0.031
[0.185]
0.14
[0.135]
0.078
[0.188]
0.753**
[0.320]
-0.348
[0.438]
0.828***
[0.289]
-0.712
[0.456]
0.763***
[0.285]
-0.534
[0.458]
-0.043
[0.154]
0.145
[0.214]
-0.028
[0.150]
0.135
[0.219]
-0.065
[0.142]
0.259
[0.222]
0.126
[0.141]
0.108
[0.180]
0.161
[0.125]
0.045
[0.168]
0.12
[0.122]
0.129
[0.170]
0.521*
[0.301]
0.003
[0.428]
0.512*
[0.268]
-0.034
[0.458]
0.512*
[0.267]
0.035
[0.466]
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
IMF Pressure Indices
Use of Funds/Quota (if > 1.25) (δ1 )
Interaction with Centralization Variable (δ3)
Extended Fund Facility/Quota (δ 1 )
Interaction with Centralization Variable (δ3)
# of reschedulings/restructurings (δ 1)
Interaction with Centralization Variable (δ3)
Use of Funds/Quota (if > 1.25), over 2 years (δ1 )
Interaction with Centralization Variable (δ3)
Extended Fund Facility/Quota, over 2 years (δ 1)
Interaction with Centralization Variable (δ3)
# of reschedulings/restructurings, over 2 years (δ1 )
Interaction with Centralization Variable (δ3)
Economic and Demographic Controls
Continent Dummies
Year Dummies
All democracy dummies use Polity IV data : democ racy 0 = 1 if Polity variable = 0; democracy 1 = 1 if Polity variable = 0 or 1; democracy 2 = 1 if Polity variable = 0, 1,
or 2
Control variables not reported are the number of previous protests, urbanization rate, real per capita GDP, population, and a religious fractionalization index
Standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1
66
Table A.2: Democracy and the Severity of Protests
DEPENDENT VARIABLE: Most Severe Protest in a Given Year (1 or 2/3)
(A2.1)
Centralization and/or Coercive Power Variables
Democracy 0 (η2 )
-2.204**
[1.045]
-0.258
[1.020]
Democracy 0*# of reschedulings (η3B)
Democracy 0 (η2 )
Democracy 0*# of reschedulings, 2 years (η 3B )
Democracy 1 (η2 )
-0.144
[1.281]
-2.854**
[1.251]
-2.255**
[0.994]
1.484
[0.926]
Democracy 1*# of reschedulings (η3B)
Democracy 1 (η2 )
Democracy 1*# of reschedulings, 2 years (η 3B )
Democracy 2 (η2 )
-0.51
[1.175]
-0.791
[0.945]
-2.248**
[1.037]
1.23
[0.913]
Democracy 2*# of reschedulings (η3B)
Democracy 2 (η2 )
Democracy 2*# of reschedulings, 2 years (η 3B )
Economic and Demographic Controls
Continent Dummies
Year Dummies
-0.674
[1.211]
-0.756
[0.949]
Yes
Yes
Yes
All democracy dummies use Polity IV data: democracy 0 = 1 if Polity variable = 0; democracy 1 = 1 if
Polity variable = 0 or 1; democra cy 2 = 1 if Polity variable = 0, 1, or 2
Control variables not reported are the number of previous protests, urbanization rate, real per capita GDP,
population, and a religious fractionalization index
Standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1
67