Time is Power:
Leader Duration, Elite Reproduction, and Political Stability in
Non-Democracies
Carlos Velasco Rivera∗
Princeton University
[email protected]
Scott F Abramson
Princeton University
[email protected]
November 12, 2013
Abstract
Autocracts often engage in actions aimed at preventing challenges to their rule. Do these actions
benefit their successors and promote the stability of the regime? Researchers face two problems
when trying to answer this question. First, there is no comprehensive data on all potential
actions a ruler may undertake. Second, these actions often reflect as much as they construct
autocratic power. We propose a solution to both problems by looking at leaders of European
non-democracies who ruled and died naturally in office between 500 BCE and 1947 CE. In
particular, we exploit their tenure in office to identify the effect of personal power on a set of
outcomes related to autocratic consolidation. Our empirical analysis shows that leaders who
are in office longer periods of time are themselves less likely to be constrained by parliaments,
more likely to experience hereditary succession, have successors who are less likely to be deposed
and constrained by parliaments, and have regimes - defined as the set of peaceful transitions
following their death - with longer duration.
∗
We thank Michael Barber, Graeme Blair, Carles Boix, Peter Buisseret, Nikhar Gaikwad, Kentaro Hirose, Kosuke
Imai, In Song Kim, Marc Ratkovic, Yuki Shirato, Alex Ruder, Jaquilyn Waddell Boie, Meredith Wilf, and the members
of the Columbia-Princeton-Yale Historical Working Group for helpful comments and suggestions. All mistakes remain
our own responsibility.
...the first and fundamental problem which evidently confronts charismatic domination, if it is to be transformed into a permanent institution, is precisely the question of
the succession to the prophet, hero, teacher, or party leader. It is precisely at that point
that charisma inevitably turns on to the path of statute and tradition
- Max Weber, 1922
1
Introduction
Dictators around the world actively take steps aimed at consolidating their power. Hugo Chavez in
Venezuela jailed judges and stacked the courts with with close allies. Fidel Castro, and his brother
Raul, in Cuba have kept scores of dissidents in prison. Stalin, upon taking office, purged political
enemies. But what are the effects of such actions on the long-term durability of the autocratic
regimes leaders construct? Do the actions of a given leader affect the durability of the regime after
they have left office? This paper provides an answer to these questions.
Researchers face two difficulties in trying to determine the impact of a dictator’s actions on the
consolidation of their regime. First, it is hard to have a comprehensive measure across time and
space of all potential actions a leader can take to achieve this goal. Second, more often than not,
leaders undetake certain actions (such as dissolving parliaments, clamping down on the media, and
killing political adversaries, to name a few) only because they are powerful enough to do so. In other
words, these actions may not have any meaningful independent effect on the durability of a regime
but rather, be an epiphenomenal reflection of the underlying distribution of de facto power. If this
is the case, any estimate of these actions on future regime stability will be confounded.
In this paper we propose a strategy to overcome both of these problems. We focus on all leadership transitions in Europe spanning the period 500 BCE - 1947 CE as recorded in Morby (2002).
While recognizing that this time period encompasses states of various forms, empires, principalities, and territorial states alike, and, moreover, knowing that there certainly exists institutional
variation across these types, we argue these polities all share two central features: the way leaders
exercised and exited power. First, we assume that autocrats of all types will attempt to consolidate
their rule. Second, we assume that in non democracies that there are three ways for leaders to exit
power: (1) They can abdicate peacefully; (2) they can be overthrown; and (3) they can be replaced
1
only after their natural death. These key features allow us that to draw meaningful inferences.
Throughout the paper we focus only on the set leaders who exit power by death, allowing us
to identify an exogenous proxy of a leader’s actions aimed at consolidating power. Since all leaders
seek to strengthen their rule, the longer a dictator is in office the more opportunities they will have
to take actions towards achieving this goal. Furthermore, we argue that this proxy is exogenous
because the health of rulers, particularly in the time period we examine, can be assumed to be
independent of the characteristics of their regimes. In other words, the specific timing of a leader’s
death can be considered “as if” random. If our assumptions hold we can estimate the marginal
effect of an additional year in power on a host of outcomes associated with the consolidation of
non-democratic regimes. At the heart of our analysis is the insight of historian William Chester
Jordan in his discussion of Capetian success. The Capetians, founders of a regime that would
become the paragon of absolutism, “...were successful in part because they survived at all, let alone
for such a long time, and managed to pass on the kingship to their eldest sons (Jordan 2002; p.
54).” The Capetians were able to turn time into power.
We present five main empirical findings consistent with this insight. First, in line with our
assumption that time is a good proxy for the actions leaders take to consolidate their rule, we find
that the longer leaders are in office the less likely they are to call parliaments, institutions designed
primarily to constrain monarchical rule (North and Weingast 1989, Stasavage 2011b, Wright 2008).
Second, we show that the leaders who are longer in office are more likely to have a close relative
(a son or a brother) follow them in power. Third, leaders who are longer in power have successors
that are less likely to be deposed. Fourth, leaders who are longer in office have longer surviving
regimes, defined as the set of peaceful transitions following their rule. Finally, consistent with our
proposed mechanism of intergenerational transfers of power, we show that leaders who are longer
in office have successors who are less likely to be forced into calling parliaments.
Our papers speaks directly to the literature interested in the ways in which personalistic power
becomes institutionalized (Weber 1958; 1968, Blau 1963, Collins 1986). Of particular relevance has
been the question of whether or not charismatic authority can be transformed into more permanent,
bureaucratic or patrimonial, forms (Weber 1946; 1978, Riesebrodt 1999). For us, the key difference
2
between bureaucratic and charismatic authority is that the former is transferable across individuals and the latter is inherent. In our framework, given enough time, a charismatic leaders can
institutionalize authority and pass it on to others. This paper also builds from Huntington’s view
that time in office reflects the degree to which an autocratic regime has become institutionalized
(Huntington 1968). That is, a leader’s tenure in office reflects the probability his regime will both
survive into the next period and successfully transfer authority across generations. Given enough
time autocrats can place allies in key positions, do away with constraining institutions, and build
loyal militaries and police forces to put down possible rebellions and coups. We argue that all of
these efforts will not only make it more difficult for a leader’s adversaries to challenge his rule but
also that of his successors. In other words, time allows leaders to construct power that not only
supports their own rule but which can be transferred across generations. This claim is consistent
with the empirical conclusion drawn by both Geddes (1999) and Hadenius and Teorell (2007) as well
as the game theoretic account of Svolik (2009), all of which show that across formal institutional
arrangements, autocracies of all types - personalist, military, single party, and hybrid regimes alike
- can, given sufficient time, become consolidated against threats to stability.
The rest of the paper is organized as follows. Section 2 proposes a theoretical framework and
outlines a set of testable proposition linking a ruler’s time in power to the consolidation of a political
regime. Section 3 tests these proposition relying on a unique data set of European monarchs who
governed in the period 500 BCE - 1947 CE. Section 4 conducts a set of robustness checks to our
main findings. Section 5 concludes.
2
Theory
How do autocratic regimes become institutionalized? In other words, why do some political regimes
become stable, while others do not? A vast literature has highlighted the interplay of formal
political institutions and regime stability in non-democracies, showing that autocrats may rely on
parliaments (Gandhi and Przeworski 2006; 2007, Svolik 2009, Boix and Svolik 2013), elections (LustOkar 2006, Blaydes 2010), and political parties (Geddes 2006, Magaloni 2008) to solve problems
inherent to autocracies and extend the life of a ruler and the regime. We argue that this literature
3
focuses on a common set of “reformist” strategies that autocrats adopt when they are not powerful
enough and wish to extend their life in office and that of the regime as a whole.
The existing literature, however, ignores what we call the “repressive” strategies autocrats may
adopt when they are powerful enough but have the same goal as autocrats who adopt the “reformist”
agenda, i.e. consolidating their hold to power. Indeed, since at least Machiavelli (1950), scholars
have recognized the nearly all-encompassing motivation autocrats have to do away competitors to
their rule. An autocrat, for instance, instead of investing in establishing a parliament may choose
to use those resources to build a strong repressive apparatus to defeat potential enemies posing a
threat to his rule. Instead of creating a political party, an autocrat may decide to kill all his enemies.
These actions in turn may lead to the consolidation of both his rule and the political regime as a
whole. This section provides a set of propositions postulating the different ways in which a leader’s
actions under a repressive strategy contribute to the consolidations of an autocratic regime.
The rule of England’s King William I illustrates the “repressive” strategy available to autocrats.
William who, following Edward the Confessor’s death without issue seized the English throne by
defeating rival claimant Harold in 1066, was a foreign king in a land where the native nobility was
still relatively powerful and had ample cause to be seditious. However, over time William was capable of reshaping and consolidating the dominant coalition, institutionalizing his rule. Ultimately,
he was able to pass on authority to his preferred and weaker heir, his second son, William II.
He began by placing key allies to fill vacancies within the church hierarchy.1 Key bishoprics
commanding both substantial material and martial resources, had by 1070 become vacated and were
reassigned to close allies. The archbishopric of Canterbury, previously held by the excommunicated
Stigand, was filled by the Benedictine Lanfranc, an ally of the King and his closest advisor, a
man who later would serve to ensure William II’s coronation. The See of York whose previous
bishop had passed away was given to Thomas of Bayeux a Norman and also a close ally of William.
Likewise, when the militarily crucial bishopric of Peterborough was vacated by the death of it’s
Bishop, William filled it with a Norman soldier, Turold, rather than a man with an ecclesiastic
background. The pattern of vacancy and replacement continued so that by the end of his rule of
1
See (Roscoe 1846; ch. 6) and (Freeman 1902; p. 136-147) for a more detailed discussion of William’s reorganization
of the church
4
all the bishoprics in England only two were held by Englishmen, the rest largely by Normans and
other allies of the crown (Bates 1989; p. 106-107).
William similarly reconstituted the coalition of notables upon whose support he relied, replacing
with allies those whose land either by unsuccessful revolt or death without heir became available
to redistribute (Thomas 2008; ch 3.). Even amongst his allies, however, he was strategic in the way
he allocated land and titles, minimizing the possibility of rebellion by giving large but scattered
holding to his close allies, allowing substantial contiguous earldoms only in the borderlands where
they were necessary for defense (Freeman 1902; 155). To further prevent notables from organizing
against his rule William took legal steps, for example, nullifying or preventing marriages between
noble families who in allegiance posed a threat. Like many autocrats he was not above using the
tools of justice to eliminate threats to the regime, imprisoning or executing a number of actual or
perceived conspirators against his rule. Most famously, executing the last English Earl, Waltheof,
in 1076 on likely unfounded charges of treason against the crown (Freeman 1902; p. 170).
A decade following his triumph at Hastings William had wrestled control of the clergy from the
English, largely done away with the native magnates who threatened his rule, and strengthened
the fiscal capacity and strength of the crown. Most famously, he conducted the first modern tax
census, the Domesday survey, recording the holdings and duties of each manor in every shire across
England. Additionally, he continued and strengthened the system of royal justice, having by 1071
replacing each of the English sheriffs, local agents of the crown charged with enforcing the King’s
peace and collecting revenue, with Normans (Morris 1968; p. 35). In August of 1086, less than two
years preceding his death, William was powerful enough to convene all “landowning men of any
account” to Salisbury and force them to proclaim fealty to him.
Having bequeathed his lands in Normandy to his eldest son Robert, William’s chosen successor
in England was his middle son William Rufus. William II, judged by history to be temperamentally
ill suited to rule - “a buffoon with a purpose, a jester (Barlow 1983; p. xi)-” was crowned nearly
instantaneously and with no controversy following his fathers death. The man who crowned him,
his father’s principle advisor, Lanfranc took on the same ministerial role and helped guide him
until his own death two years later. Although lacking in personal gravitas William Rufus was
5
nevertheless capable of both defeating the seditious Earl of Northumbria, Robert de Mowbray, and,
moreover, twice successfully defeating the invasions of Scottish King Malcom, both in large part
because of the material and institutional resources available to him by his father.
The actions taken by William the Conqueror to consolidate his rule are not unlike those of
the twentieth century autocrats take to construct, maintain, and transfer power in contemporary
non-democracies. Take the political life of Korean dictator Kim Il-Sung as a point of comparison.
Kim, who ruled the Democratic People’s Republic of Korea (DPRK) for forty-five years, from 1948
until his death in 1994, built a ruthlessly efficient personalist state apparatus and proved capable
of sustaining hereditary rule across three generations. Kim Il-Sung began his rule in a remarkably
similar position to William the Conquerer. Having come to power as the simultaneous head of
the Workers Party of Korea (WPK) and People’s Army (KPA) he nevertheless faced rivals within
both institutions. Like William, over time, he was able to ruthlessly eliminate and replace these
threats with allies loyal to him and to his son’s succession. Indeed, it was Kim Il Sung’s talents to
accomplish this task that has defined his rule. Biographer Lim Ŭn succinctly characterizes this :
As a military strategist, he was a failure. As a leader he was not qualified to steer economic construction and formal policies of the state. But Kim Il Song had satisfactorily
displayed abilities to purge and repress (Lim 1982; 194)
Still, at the beginning of his reign he was but the head of one of four main factions dominating
the DPRK. His own Guerilla faction, the weakest of all four, faced competition from the Soviet
group of Koreans raised and trained in the USSR, the Domestic group of communist revolutionaries,
and the Yanan faction which fought with Mao and had close ties with the Chinese communists.
Upon ascending to the chairmanship of the WPK Kim immediately began purging his enemies,
playing the various factions off of each other to weaken each group equally in a process of gradual
elimination (Lintner 2005; ch. 4).
Within a decade of the Korean War he had killed or exiled every one of his generals who had
belonged to the Yanan faction and ninety-percent of those who belonged to the Soviet faction.
He was so successful at eliminating threats to his rule that ninety-five percent of all generals who
fought in the Korean war were purged and replaced by those loyal to him.(Lim 1982; p. 190-196).
6
Indeed, by the mid 1960s Kim faced no substantial domestic opposition to his rule (Lintner 2005; p.
74) and by 1982 of the founding twenty-two members of the WPK all but three had been murdered
or exiled by the regime (Lim 1982; p. 194).
The consolidation of political power allowed for the peaceful succession of Kim’s chosen heir,
his son Jong-Il. Described as “critically lacking his fathers charisma” and prior to accession had
only given one singe-line public speech in his career (Lim 1982), Jong-Il began working in the party
early on in life, serving as a deputy as early as 1964. A decade later within party and military
circles he had been recognized as successor. At the Sixth Party Congress in October 1980, the final
meeting of the party leadership until 1997, Kim Jong-Il was named head of party and by 1991 he
was appointed supreme commander of the KPA.
In support of his son, Il-Song placed in several crucial roles close allies. His daughter, Jong-Il’s
sister, was appointed head of light industry. Her husband and his brothers, in turn, were placed
in command of key army units within and surrounding Pyongyang. When Kim Il-Song finally
passed away in his eighty-second year the transition between generations went without incident
because, indeed, “there was no possible successor or opposition (Lintner 2005; p. 103)” - Il-Song
had eliminated them all. Still, upon coming to power Kim Jong Il instigated the same process of
systematically weeding out possible opposition. Within several years of coming to power he had
already reappointed 920 out of 1358 members of the powerful Central Committee, beginning a
process that would lead to the succession of his son Kim Jong-Ŭn upon his death.
Of course, one reason why these leaders were able to carry out such actions is that they were
powerful enough to do so in the first place. However, we contend that even that being the case, rulers
undertake these actions, to prevent potential challenges to their rule. These actions tighten their
hold to power and ultimately led to the consolidation of their regime. As illustrated two accounts
discussed above, we can lay out set of testable propositions about the relationship between the
amount of time an autocrat is in office, the actions he may undertake to consolidate his rule, and
how these affect the overall durability of a political regime.
At the most basic level, a leader requires of time to build and consolidate his rule. An autocrat
needs this resource to invest in favoring certain members of his alliance, to learn about the inten-
7
tions of potential enemies, and to invest in projects such as building up an army and a security
apparatus. Thus, all else equal, leaders who are in power for a longer period of time should have
more opportunity to consolidate their rule.
Leaders also have preferences in terms of the individuals they would like to see as their successors.
In the case of monarchies and other personalist regimes, a ruler will be prefer to have a member
of his family to accede to power following his death. A leader’s ability to see his chosen successor
accede to power though will depend on how tight his own hold of power is. Thus, as a corollary
of the previous proposition, we should expect leaders who are longer periods in office to be more
likely to have their chosen successors to accede to power.
Once a leader passes away his chosen successor inherits the institutional arrangements from his
predecessor. This may prevent potential enemies from coordinating to challenge his rule. A ruler
may also benefit from the material resources he inherited from his predecessor. These may include
resources needed to finance an army and other means to conduct warfare. Thus, all else equal,
and following from the two preceding propositions, successors of leaders who were a longer period
of time in office, should be less likely to experience challenges to their rule and experience a lower
probability of being deposed.
Finally, we expect the actions a leader undertakes to consolidate his rule to have consequences
beyond his immediate successors. While power might be passed on to a given successor, these new
leaders can similarly transmit this accumulated authority thereby having lasting and persistent
effects across multiple generations. Thus, all else equal, the regimes of leaders who were in office
for a longer period of time should enjoy greater political stability.
To summarize, the existing literature examines the impact a certain set of institutions have on
the stability of autocratic regimes. Together, the institutions this literature focuses on (parliaments,
elections, and parties) are part of what can be considered a “reformist” strategy on behalf of
autocrats aimed at stemming potential challenges to their rule. However, we argue that dictators
can resort to “repressive” strategy. Instead of setting up a parliament, they can build a security
apparatus. Instead of allowing the formation of parties they may choose instead their enemies.
The “repressive” strategy is equally important as the “reformist” one. In our view both are aimed
8
the consolidation of an autocratic regime. Our discussion lays out a set of propositions illustrating
the different mechanisms through which political regimes, in particular autocratic ones, become
consolidated. In particular, we make the following claims:
1. All else equal, leaders who are in office longer period of time in office become more powerful
over time.
2. All else equal, leaders who are in office for longer periods of time should be more likely to
select their preferred choice as successors
3. All else equal, successors of leaders who were in power for longer periods should be less likely
to experience challenges to their rule.
4. All else equal, the regimes of leaders who were in power for longer periods of time should
exhibit greater durability than the regimes of leaders who were in office for shorter periods of
time.
In the rest of the paper we test each one of them using a unique data set of European monarchs
who ruled between 500 BCE and 1947 AD.
3
Empirical Analysis
To test the theoretical propositions outlined in the previous section we collect data on leadership
transitions from Morby (2002). For a large number of states throughout history this source includes
the following information: 1) The name of the executive; 2) the dates during which he or she held
power; 3) Whether they were deposed, died in office, or abdicated; and 4) the familial relationship,
if any exists, between leaders of a given country. Outside of Europe and before classical times the
data is incomplete in two ways. First, it reflects a non-random sample of non-European states,
and second, the sources used to construct the dates during which leaders held office are not as
reliable as those used to collect the information on European monarchs. Accordingly, we exclude
non-European data before 500 BCE.
9
We argue that the set of transitions under study allow us to identify exogenous shocks to
institutionalized power. During this period of inquiry there were essentially three ways to leave
power: deposal, abdication, and death. We argue that exit by the death (of natural causes) can
be considered as-if random, thus allowing us to identify the effect of institutionalized power on
our outcomes of interest. In our view, a leader who experiences a sudden death by illness after
serving in office for, say, a few months, will have left lower levels of institutionalized power than
one who experienced the same fate after ruling a country for, say, five years. Moreover, we argue
that the timing of a monarch’s death, particularly in the age before modern medicine, should not
be correlated with variables which also cause hereditary succession and stability. For instance,
the timing of monarch’s death should not be correlated with whether a ruler’s son has a superior
political acumen or whether rulers in a given polity are more likely to experience challenges to their
rule. In the next subsection, we outline the potential pitfalls of this identification strategy and how
we address them.
This section proceeds as follows. First, we show that as a leader’s tenure in office increases
the probability that he is constrained by a parliament declines. That is, as the longer a leader is
in power the less likely he is to be constrained by institutions designed to limit their authority.
Next, we show that a leader’s tenure in office causes both intergenerational transfers of power and
stability. As a leader’s time in office increases so too does the probability that their son or other
close relative will succeed them in power. Additionally, we show that leaders who follow long-lived
leaders are less likely to be deposed. We then assess the relationship between a founder’s tenure
and the long term durability of his regime. We find that longer tenures for founders are associated
with the longer survival of his regime.
3.1
Leader Tenure and the Presence of Parliamentary Constraints
Do leaders become more powerful the longer they stay ino office? The key assumption in our
empirical tests is that a leader’s tenure in office is a good proxy for institutionalized power. In our
view, the longer a ruler is in office, the more opportunities he will have to take step ensuring the
consolidation of his rule. These actions include killing adversaries, placing allies in key positions,
10
acquiring resources to challenge one’s rule, and undermining institutions curbing a ruler’s power
among others.In this section, we provide evidence consistent with this argument.
In particular, we focus on the frequency of parliaments - an institution that first appeared
after 1200 AD composed by nobles, clergy or commoners holding the capacity to constrained the
executive. Since early-modern parliaments served as a forum for various groups in society to limit
the ability of monarchs to carry out their will. Thus, the absence or presence of these institutions
serve as a direct measure of the power held by a leader. Where they met frequently leaders were
less powerful relative to other groups in society. Where they met infrequently leaders were more
powerful.2
To test this claim we rely on a the dataset built by Abramson and Boix (2012) which, for a
subset of the leaders in our data, identifies the annual presence of any body of nobles, clergy, or
commoners that held the capacity to minimally constrain the executive. For the period after 1200,
the era following the development of the first parliaments, the data we construct here includes
twenty countries and 376 unique leaders who died from natural causes. The analysis in this section
show two results. First, we demonstrate that over the course of a leader’s lifetime the probability
of a parliament declines with the length of his tenure in office. That is, our independent variable of
interest is, as expected, correlated with a leader’s power relative to other political actors. Second,
we demonstrate that the longer a given leader is in power, the fewer parliaments his successor will
have to call. In other words, we show that this form of institutionalized power is constructed across
time and then transmitted across generations.
To show that the probability of calling a parliament is declining in leader tenure, we estimate
the following relationship for the subset of leaders who die in office naturally:
Pr(Parliamentit =1|Tenure = t) = f(Tenurei )
(1)
where we are treating the probability of a parliament for leader i in year t of his reign as a smooth
function of t, estimated via loess regression. Figure 1 gives the predicted probabilities for all
2
For the classic work on parliaments as a check to autocratic authority in the early-modern world see North and
Weingast (1989). For more recent work see Stasavage (2011a), Boucoyannis (2005), van Zanden et al. (2010), and
Abramson and Boix (2012).
11
observed values of tenure. We see that, as expected, there is a sharp decline across time in the
probability a parliament is called. This decline is sharpest between the first year of a leader’s reign
and the twentieth. After this point the relationship between additional time in office is relatively
flat. This could be for one of two reasons. First, it could be that by the twentieth year in power
leaders have become as consolidated as they could possibly be. Or, just as likely, this could simply
be a lack of statistical power as we do not have many observations that survive beyond this point,
three quarters of which die in office before the age of twenty-three. Still, it is clear that for most of
0.4
0.3
0.2
0.0
0.1
Probablity of Parliament
0.5
0.6
the data the probability of a parliament is declining in the number of years a leader is in power.
0
10
20
30
40
50
60
70
Time in Office
Figure 1: Loess estimates of the probability a parliament is called across time for leaders
who exit office via a natural death. The x-axis gives the number of years in which a leader has
been in office, zero indicating the year he took office. The blue line on the y-axis gives the predicted
probability a parliament is called in a given year. The grey points are the average probability a
parliament is called for all observations at a given time t. Their circumference is proportional to
the number of leaders who survive up to time t. As with the full sample, the majority of leaders in
the restricted sample for which we observe any parliaments die in office before the twelfth year of
their reign and three quarters before twenty-three years in office.
12
N = 2050
Leader’s Tenure (yrs.)
Son Handover
Brother Handover
Son or Brother Handover
Successor Deposed
Year
Mean
S.D.
Min
25th
75th
Max
17.75
37.4 %
14.29 %
51.59 %
14.44 %
1151 A.D.
14.88
48.3 %
35.00 %
49.9 %
35.16%
501
0
0.0 %
0.0%
0.0%
0.0 %
505 B.C.
5
0.0%
0.0 %
0.0%
0.0 %
939 A.D.
27
100.0%
0.0%
100.0%
0.0%
1482 A.D.
77
100.0%
100.0%
100.0%
100.0%
1947 A.D.
Table 1: Descriptive Statistics (Dynastic Sucession). This table presents descriptive statistics
for the data used in the exploring the relationship between institutionalized power and dynastic
sucessions. For each variable we present the mean, standard devation, min and maximum values,
along with the first and third quartiles of the distribution. The main explanatory variable is a
leader’s time in office (for those who died of natural causes). The outcomes of interest are the
whether the leader handed power over to his son, brother, or either of the two. Year indicates the
year in which a leader first came to office.
3.2
Leader Duration and Intergenerational Transfer of Power
Are long-lasting leaders able to select their most preferred choice as successors and are the chose
successors enjoy more political stability? The next two subsections show that changes in leader
duration has the following effects associated with intergenerational transfers of power and regime
stability, outcomes associated with institutionalized power : (1) the probability of a son serving
as a leader’s successor increases in his tenure; and (2) the probability that a successor is deposed
decreases with institutionalized power. To test these predictions, we exploit the timing of a monarch
death in office. We argue that this is a good proxy of institutionalized power, and since the timing
of death is random we argue that the relationships we estimate are causal.
Table 1 summarizes the data we rely on to conduct these tests. The table presents for each
variable its mean, standard deviation, minimum and maximum values, and first and third quartiles.
The first row of this table presents the summary for leader’s tenure – the main explanatory variable
of interest. The mean tenure is 17.75 with a standard deviation of almost 15 years. The minimum
value is zero and the maximum is 77 years. The next three rows in the table presents the four
outcome of interest: whether a leader’s son is the successor, whether it is his brother, whether is it
either of the two, and whether the successor was deposed. Among all leader’s who died in office, we
13
find that about 37 percent had their son become the successor. In the case of brothers, this figure is
roughly 14 percent. The third outcome (either son or brother is successor) is the sum of the mean
from the second and third rows. The fourth row shows that only close 12 percent of successors were
deposed. Finally, our data spans a significantly long period of time. The first ascent to power in
the sample took place in the year 505 B.C., while the last descent happened in 1964.
To examine relationship between institutionalized power and the probability that a son succeeds
a monarch, we estimate the following probit regression:
Pr(Son =1 | Ti ) = Φ(α + β · Ti + ηt + γf + κc + i )
(2)
where we treat probability that a son succeeds a leader as a function of Ti , the tenure in office
of leader i, and α, a normalizing constant. To account for possible country, time, and family
confounding effects we estimate a series of mixed-effects model where we successively include random
mean zero disturbance terms for country, κc , century, ηt , and family, γf . First, the family specific
disturbance term allows us to account for the unobserved heterogeneity that arises from a group
of leaders that all belong to the same dynastic line. For example, for all of the Tudor kings of
England or Visconti of Milan we can account for the family specific effects. Thus we can control
for the possibility of some effect of being a member of a particular aristocratic group that might
effect both a leader’s longevity and the probability they can appoint their chosen heir. Similarly,
with the century specific disturbance we account for unobserved heterogeneity that may be occur
across time. That is, in case certain historical periods are associated both with greater longevity
of leaders - perhaps because medical technology or incomes have changed - as well as an increase
in intergenerational transfers of power, we can remove this possible confounder.
Last, by controlling for country specific effects we account for possibly unobserved variation in a
given polity’s capacity to produce long lasting leaders and sustain hereditary rule.3 Moreover, to be
assured that we are capturing the effect of duration and not simply an effect related to the existence
of a son to give power to, we estimate the effect of duration on the probability that either a son
3
We re-estimate these logistic regressions treating family, country, and century effects as “fixed.” Comparing these
models to those we present using a generalized Hausman test we fail to reject the null hypothesis that both are
consistent and thus prefer the more efficient mixed-effects models which we present.
14
N = 2050
(1.)
(2.)
(3.)
(4.)
(5.)
(Intercept)
−0.76∗∗∗
(−15.58)
0.03∗∗∗
(14.77)
−0.25∗∗∗
(−5.31)
0.02∗∗∗
(11.52)
−0.66∗∗∗
(−9.52)
0.03∗∗∗
(12.18)
−0.67∗∗∗
(−9.33)
0.03∗∗∗
(12.17)
−0.67∗∗∗
(−9.33)
0.03∗∗∗
(12.17)
Yes
Yes
Yes
Yes
Yes
Yes
−1233.74
−1233.61
−1233.61
Leader Tenure
Random Effects
Country
Century
Family
Log Likelihood
*** p
< 0.001,
** p
−1282.09
−1324.21
< 0.01, * p < 0.05, † p < 0.1
Table 2: Probit Estimates (Probability Son as Successor). This table reports the parameter
estimate from equation 2. We find that across all specifications the probability that a son is the
sucessor increases with a leader’s tenure. The result is robust to using the probability that a son
or a brother is the sucessor as the outcome of interest (column 2), and to the inclusion of country,
century, and family random effects (columns 3-5). Z statistic in parentheses. All models only
identify effects for leaders who die of natural causes.
or brother is appointed as successor. In other words, because not all leaders have sons we estimate
the effect of leader tenure on the probability of being followed by either the son or a brother, the
next closest individual genealogically.
Across all specifications the parameter estimates are nearly identical, indicating that, indeed,
our identification assumptions maintain and, moreover, that we are estimating a causal relationship.
Table 2 summarizes these results. The first column reports the coefficient for the model including
all observations where the leader died of natural causes. The second column examines the effect of
leader tenure on the probability that either a son or brother took was the successor. And columsns
3-5 report parameter estimates when including random effects for country, century, and family.
Across almost all specifications the effect of leader duration is statistically significant and in the
hypothesized direction, allowing us with a high degree of confidence to reject the null hypothesis
of no effect.
The magnitude of these effects are substantial. The estimated effect derived from the baseline
model, manipulating leader duration across its interquartile range, from 5 years to 27 years, in-
15
creases the probability that a son will succeed him by 24.9 percentage points with a ninety-five
percent confidence interval of [21.9, 28.1]4 . The relative risk ratio for probabilities derived from the
same interquartile manipulation yields a value of 1.92 with a ninety five percent confidence interval
of [1.75, 2.12].
To better gauge the size of this effect, in the left-hand panel of Figure 2 we plot the predicted
effects derived from the model presented in the first column with predictors taken over the entire
range of observed data. Again for the full sample, manipulating leader duration from under one
year, the minimum observed duration, to its maximum value, seventy seven years in office, we
see the effects are large, going from a 23.7 % probability of a son following in power to a 92.3 %
probability this occurs. In all, we have shown that, for the subset of leaders who died naturally in
office, the length of their tenure is statistically significant and quite strongly associated with the
probability that they will appoint a son as their successor.
3.3
Leader Duration and Intergenerational Stability
Next, we examine the hypothesis that a leader’s duration in office affects her successor’s likelihood
of being deposed. That is, we explore the notion that more institutionalized leaders are capable
transferring capacity to the subsequent generation. Again, we only include those leaders who die in
office from natural causes. As with the probability of having a son as successor, we again treat the
probability that a leader’s successor is deposed as a function of their tenure in office, Ti . Similarly,
we consider the robustness of the relationship between these two variables to the inclusion of mean
zero random effects for family, century, and country as defined in the previous section. As before,
we estimate this relationship by probit regression.
The results indicate a negative relationship between leader duration and the probability that
their successor is deposed. These results are summarized in Table 3. Across specification the results
are statistically significant at conventional levels, although the models including random effects for
century and family are only significant at the ten percent level. Still, the magnitude of these effects
are, as before, substantial. The interquartile effect, manipulating the duration of the previous leader
4
Confidence intervals are derived from quasi-Bayesian simulation
16
1.0
0.15
LeaderdDuration
Frequency
0.10
0.05
ProbabilitydofdSuccessordDeposal
0.8
0.7
0.6
0.5
0.4
0.3
300
100
0.1
0.00
0.2
ProbabilitydofdSondasdSuccessor
0.9
400
0
10
20
30
40
50
60
70
80
0
LeaderdDuration
0
10
20
30
40
50
60
70
80
Figure 2: Estimated probability that son is successor and that a successor will be
deposed as a function of the leader’s tenure in office. The left-hand side panel in this figure
plots the probability that a leader’s succesor will be his son as a function of his tenure along with
the distribution of leader’s tenure in our sample. We find that tenure is positively associated with
this probability; higher tenure in office leads to an increase in the probability that a leader will have
his son as a successor. The right-hand side panel of the figure plots the probability that a ruler’s
successor is deposed as a function of a leader’s tenure. We find that there is a negative relationship
between these two variables; a longer tenure is associated with a lower probability that a leader’s
successor will be deposed. The predicted probailities are computed used the parameter estimates
from the model reported in column 1 of Table 2 and 3.
from 5 to 27 years in office, decreases the probability of a deposal by 3.32 percentage points with
a ninety-five percent confidence interval of [1.02, 5.66]. This interquartile manipulation produces
a risk ratio of .769, with a ninety-five percent confidence interval of [.629, .917]. In other words, a
leader whose predecessor survived twenty-seven years is going to be approximately a quarter less
likely to be deposed than one whose predecessor survived only five.
17
N = 2050
(1.)
(Intercept)
Leader Tenure
*** p
< 0.001,
** p
(3.)
(4.)
−1.06∗∗∗ −1.14∗∗∗ −1.17∗∗∗ −1.17∗∗∗
(−18.31) (−13.78) (−11.26) (−11.26)
−0.01∗∗ −0.01∗
−0.01†
−0.01†
(−2.86) (−1.98) (−1.82) (−1.82)
Random Effects
Country
Century
Family
Log Likelihood
(2.)
−733.99
Yes
Yes
Yes
Yes
Yes
Yes
−713.79
−706.86
−706.86
< 0.01, * p < 0.05, † p < 0.1
Table 3: Probit Estimates (Probability Successor is Deposed). This table reports the
probit estimates when examining the relationship between leader’s tenure and the probability that
his successor will be deposed. The estimate reported in column (1) shows that there is a negative
relationship between a leader’s tenure and the probability that the successor is deposed. This
result is robust to the inclusion of random country, century, and family fixed effects. Z statistics in
parentheses. All models only identify effects for leaders who die of natural causes.
To better gauge the magnitude of this effect the right hand panel of Figure 2 plots the predicted
change in probability of deposal derived from the first column of Table 3 for all observed values of
leader tenure. Again, the difference between the minimum value and maximum value is quite large.
At the minimum value of less than a year, the predicted probability of deposal is 14.57%, and at the
maximal value, seventy-seven years, the predicted probability is 5.39%. In all, across specifications
we see that there is a substantial negative effect of the a leader’s tenure in office on his successor’s
probability of being deposed, an effect we interpret as the institutionalization of power.
3.4
Institutional Transmission of Power
Is a leader’s time in office a good proxy for the degree of power transferred across generations? The
hypothesized mechanism driving our results is that institutionalized autocratic power is transferable
across generations. That is, a given leaders actions aimed at destroying opposition to his rule should
affect the probability that his successor can rule unconstrained. In this section we show that the
longer a leader is in power the lower the probability that his successor will face parliamentary
18
constraint.
We accomplish this in three ways, each for only the subset of leaders who die naturally. First,
we simply estimate the relationship between a leader’s tenure and the fraction of years in which
his successor faced a parliament. This relationship is estimated via OLS and, because this quantity
is bounded between zero and one, Tobit regression to account for this two-way censoring. These
results are summarized in Table 4. Column 1 presents the results with no controls. Columns two
through four successively introduce fixed effects for country, century, and family. We see that, across
specifications, the relationship between a leader’s tenure and the fraction of years their successor
faces a parliamentary constraint is negative and statistically significant.
These effects, plotted in Figure 3 are substantial. For a leader who survives less than a year in
office there is eighty-eight percent of her successor’s years in office are predicted to have parliaments
called. For leaders who survive longer, the decline in parliamentary frequency is sharp; for leaders
who survive beyond thirty-three years in office the fraction of their successor’s reign in which
parliaments are predicted to meet is statistically indistinguishable from zero.
Since not all successors last the same time in office, we examine the effect of a leader tenure on
the probability a parliament meets in the first year of their successor’s reign. Estimated via probit
regression these effects are summarized in Table 5. As with the previous results, we see a negative
effect of leader tenure on the existence of a parliament. To gauge the magnitude of this relationship
Figure 4 plots the predicted probabilities derived from the unrestricted model. For leaders who
lasted less than a full year in office the predicted probability of their successor calling a parliament
in their first year in power is just slightly above 75%. For those who follow a leader who lasted
thirty years this probability declines to 40%. For the leader who lived 71 years, the maximum
observed value in this sample, the predicted probability of his successor calling a parliament is
approximately 7%.
The results presented in this section suggest that during the period of time when parliaments
existed as checks to the power of monarchs, for the subset of leaders who died of natural causes,
the duration of a leader’s time in office, is correlated with a decline in the probability he will face
a parliamentary constraint. Moreover, we have shown that for the class of leaders for whom we
19
OLS
N =376
(1.)
Intercept
Tenure
R2
(2.)
(3.)
(4.)
66.83∗∗∗
(3.28)
−1.10∗∗∗
(0.14)
82.19∗∗∗
(5.47)
−0.26∗∗
(0.10)
84.66∗∗∗
(7.54)
−0.28∗∗
(0.10)
65.25∗∗∗
(11.27)
−0.26∗∗
(0.09)
0.15
0.70
0.71
0.83
88.88∗∗∗
(7.14)
−2.17∗∗∗
(0.217)
95.37∗∗∗
(9.60)
−0.41∗
(0.17)
100.06∗∗∗
(14.11)
−0.41∗
(0.17)
65.03∗∗∗
(16.44)
−0.30∗
(0.13)
Tobit
Intercept
Tenure
Log Likelihood
Country Effects
Century Effects
Family Effects
*** p
< 0.001,
** p
−362.00
−159.53
Yes
−151.88
Yes
Yes
−64.96
Yes
Yes
Yes
< 0.01, * p < 0.05, † p < 0.1
Table 4: The Effect of Leader Tenure on Successor Parliamentary Frequency (OLS).
This table captures the relationship between leader tenure and the fraction of years their successor
calls a parliament. The upper panel gives OLS estimates and because this fraction is bounded
between 0 and 1 the lower panel provides Tobit estimates that account for this two-way censoring.
All coefficients are multiplied by 100, yielding the effect of a year in power on the percentage of
time in office his successor calls a parliament. The intercept gives the probability a parliament is
called if a leader survived in office less than a year. Standard errors in parentheses.
20
1.0
0.5
FractionPofPSuccessor'sPReignPWithPParliament
0.0
0
10
20
30
40
50
60
70
LeaderPTenure
Figure 3: The Effect of Leader Tenure on Successor Parliamentary Frequency (Tobit).
. For leaders who exit office via a natural death this figure provides tobit estimates of the fraction
of years a leader’s successor calls parliament across time. The x-axis gives the number of years in
which a leader has been in office, zero indicating the year he took office. The grey points are the
average fraction of successor parliaments for a given leader who had tenure, t. Their circumference
is proportional to the number of leaders who survive up to time t. The figures shows that there
is a negative relationship between a leader’s tenure and the share of years his successor will see
parliaments meet.
have argued their tenure is effectively random, their duration in office causes their successors to
face fewer constraints to their authority. Both of these results are consistent with the claim that
time in office is a good proxy for institutionalized power.
21
Probit
(1.)
0.64∗∗∗
(0.11)
−0.03∗∗∗
(0.00)
Intercept
Tenure
Log Likelihood −237.22
N
376
Country Effects
Century Effects
Family Effects
*** p
< 0.001,
** p
(2.)
(3.)
(4.)
0.09
(0.27)
−0.02∗
(0.01)
0.09
(0.27)
−0.02∗
(0.01)
0.03
(0.27)
−0.02∗
(0.01)
−179.60
376
Yes
−179.60
376
Yes
Yes
−175.47
376
Yes
Yes
Yes
< 0.01, * p < 0.05, · p < 0.1
Table 5: Parliamentary Meeting in Successor’s First Year. This table gives the parameter
estimates from probit regressions capturing the effect of leader tenure on the probability that a
parliament meets in the first year of his successor’s reign. Columns two through four add in country,
century, and family random effects. The results reported in the table show that a leader’s tenure
has a negative effect on the probabilty of parliament meeting. The effect is robust to the inclusion
of country, century, and family random effects. Standard errors are included in parentheses.
3.5
Leader Duration and Regime Stability
Finally, do intergenertational transfers of power have an impact on political stability beyond one
generation? To answer this question, we take as the unit of analysis a regime, defined as the total
number of peaceful transitions following the death of a founder, i.e. the first ruler of a country or
one who is in power following a deposal and dies of natural causes. During the time period at hand,
we argue that peaceful transitions lead to turnover of power between individuals from the same
coalition. Thus, in line with the theoretical model presented above we may expect, that greater
institutional power leads not only to a lower probability of the immediate succesor being deposed,
but also to translate in a longer durability of regimes.
Table 4 summarizes the data we rely on to examine the relationship between a founder’s tenure
and regime stability. For each variable we report the mean, standard deviation, minimum and
maximum values, and the first and third quartile of the distribution. The first row of the table
summarizes founder’s tenure – the main explanatory variable in this subsection. In the data we
analyze there are 327 founders out of close to 2500 power transitions. The variable founder’s tenure
22
1.0
0.8
0.6
0.4
Pr(Successor Parliament in Yearr 1)
0.2
0.0
0
10
20
30
40
50
60
70
Leader Tenure
Figure 4: Parliamentary Meeting in Successor’s First Year. Predicted probabilities of a
successor having to call a parliament in the first year of his reign. Estimates are derived from the
model presented in Column 1 of Table 5. The grey points are the mean number of parliaments for
the set of leaders who die naturally t years into their rule. Their circumference is proportional to
the number of leaders who survive up to time t.
measures how long they were in power. The mean and standard deviation for this variable are 18
and 14 years, respectively. The minimum and maximum values are 0 and 50 respectively. Our
main outcome of interest is a regime’s duration. This outcome is defined for the set of peaceful
transitions following the rule of a founder and measures the time until the first deposal. The mean
regime duration is close to 120 years. The standard deviation is 155 years, while the minimum and
maximum values are 0 and 898 years respectively. As in the the previous subsections, our analysis
covers a significantly large period of time: from 498 B.C. to 1964 A.D. In addition, we find that
approximately 17 percent of all regimes did not experience a deposal.
Figure 3 examines the relationship between regime duration and founder’s tenure. To examine
this relationship, we classify regime duration according to whether a founder’s tenure was: 1) no
greater than 10 years; (2) between 10 and 20 years; (3) between 20 and 30 years; (4) between 30
and 40 years; and (5) more than 40 years. The figure presents a set of violin plots depicting the
23
N = 327
Founder’s Tenure (yrs.)
Regime Duration (yrs.)
Year
Censored
Mean
S.D.
Min
25th
75th
Max
18.31
119.60
1056 A.D.
17.13 %
14.63
154.75
509
37.73 %
0.0
0.0
505 B.C.
0.0 %
6.0
14.25
865 A.D.
0.0 %
30.0
173
1332 A.D.
0.0 %
57.0
898
1947 A.D.
100.0 %
Table 6: Descriptive Statistics (Founder’s Tenure and Regime Duration). This table
presents descriptive statistics for the data used in the exploring the relationship between institutionalized power and regime duration. For each variable the table reports the mean, standard
deviation, minimum and maximum value, along with the first and third quartiles of the distribution. The main explanatory variable of interest is founder’s tenure and the main outcome is regime
duration. The table also describes the distribution for the year of a founder’s ascent to power and
the number of right-censore observations (i.e. those whose regime did not experience a deposal).
distribution of regime duration (x-axis) across these five categories for founder’s tenure (denoted by
the letter T along the y-axis). As we can see from the figure, there is a positive correlation between
regime duration and founder’s tenure. We observe, for example, that for higher level of founder’s
tenure the distributions shift to the right.
Given our definition of regimes it seems natural to conduct our empirical analysis within a
survival analysis framework. Under this framework the focus is on the “time to death” of a unit
of analysis. Here, we focus on exploring the relationship between a founder’s time in power –
which proxies institutionalized power – and the durability of his regime. In particular, we are
interested in determining the effect a founder’s time in power on the hazard rate of a regime –
i.e. the instantaneous rate at which regimes experience a violent removal of power conditional
on having survived up to a given point. To limit parametric assumptions, we adopt the Cox
proportional hazard model, estimating the following relationship between a regime’s rate of failure
and a founder’s tenure:
λ(t)i = λ(t)0 × exp(Ti · β + ηt + γf + κc )
(3)
where λi , the hazard rate, is treated as a function of λ0 , the non-parametric baseline hazard, and
Ti the regime founder’s duration. We estimate the model in a mixed effects framework variously
24
40 < T ≤ 50 yrs.
●
30 < T ≤ 40 yrs.
●
20 < T ≤ 30 yrs.
●
10 < T ≤ 20 yrs.
●
T ≤ 10 yrs.
●
0
200
400
Regime Duration (Years)
600
800
Figure 5: Regime Duration by Founder’s Time in Power. This figure shows the distribution
of regime duration by a founder’s time in power. Regime duration (x-axis) was classified into five
bins according to the founder’s tenure (T), which is displayed along the y-axis. The five bins for
the data are for founders with tenure: (1) no greater than 10 years; (2) between 10 and 20 years;
(3) between 20 and 30 years; (4) between 30 and 40 years; and (5) with more than 40 years. The
different violin plots show that there is a positive correlation between a founder’s tenure and regime
survival. As the founder’s tenure increases, we see that the median for regime duration increases.
The plots also show that shifts to the right, though the variance, as the width of the boxplots show,
also increases.
including century, country, and family random effects.5 . 7 presents the estimates of β, the parameter capturing the relationship between the hazard and founder tenure, under different model
specifications. Column 1 shows the results under the baseline model, where founder’s time in power
is the only covariate explaining regime duration. Columns 2-4 assesses the robustness of founder’s
tenure when including random effects for country, century, and family. Across models we find that
5
We use the Breslow method to deal with ties.
25
N = 327
(1.)
Founder’s Tenure
(2.)
−0.013∗∗ −0.01∗
(−3.06) (−2.21)
Random Effects
Country
Century
Family
*** p
< 0.001,
** p
Yes
(3.)
(4.)
−0.012∗ −0.014∗
(−2.36) (−2.59)
Yes
Yes
Yes
Yes
Yes
< 0.01, * p < 0.05, † p < 0.1
Table 7: Parameter Estimates from Cox Proportional Hazard Model. The table reports
the estimates for the effect of a founder’s tenure in power on the regime hazard rate under different
model specifications. Column (1) presents the results under our baseline model. Columns (2)-(4)
present the results when we include random effects for country where the regime is located, family
to which the founder belongs, and century under which the founder governed. We find that a
founder’s time in power has a negative effect on a regime’s hazard rate. This result is robust across
specifications. Z statistics in parentheses.
the founder’s tenure has a negative effect on a regime’s hazard rate. That is, the longer a founder
rules, the lower the hazard is for his regime. This effect is significant at the 1 percent level and is
robust to the inclusion of country, century, and family random effects.
The definition of our unit of analysis allows us to estimate more intuitive and fine-grained
quantities of interest. We focus on the impact that a founder’s tenure has on two: a regime’s
survival function (defining the probability of regime surviving beyond a time period, conditioning
on have existed up to that point) and its expected time to failure (the probability of experiencing
a deposal).6
We are interested in how the effect of a founder’s tenure varies in relation to different changes
6
Formally, these quantities are defined as follows:
S(t | X) = S0 (t)exp(Xi β)
E[Ti | X] =
∞
Z
S(t | X)dt
0
where S(t | X) is the survival function conditional on covariates X and S0 (t) is the baseline survival function at time
t. Note that the the expected time to failure E[Ti | X] is fully characterized by the survival function. Also notice
that since the survival function depends on parameter β, the expected time to failure of a regime is also going to be
affected by this parameter.
26
in the duration of his rule and baseline levels for the years which he ruled. 6 presents the results for
this analysis. The first row shows the effects of increasing a founder’s tenure by 25, 35, and 55 years
from a baseline tenure of 5 years. We find that the founder’s tenure effect on the survival function
increases with the magnitude of the change in his tenure. For instance, increasing a founder’s tenure
by 25 years leads to an increase in the probability of his regime surviving beyond two hundred years
(conditional on not having experienced a deposal up to that point) of about 10 percentage point.
The effect on this probability is about 25 percentage points when the magnitude of change in the
founder’s tenure is 55 years. We find that this pattern (i.e. a higher effect of founder’s tenure on
the survival function) holds across baseline levels in the founder’s rule. Further, 6 shows that there
are diminishing returns to founder’s tenure for regime durability. For example, a 25 year increase in
a founder’s tenure leads to a 10 percentage point increase in the probability of his regime surviving
beyond two hundred years for a baseline of 5 years in power; the effect is almost half for the 15-year
baseline.
Finally, we consider the the effects of a founder’s tenure on the expected time to a deposal. We
estimate the effect of a founder’s length of time in power across three baseline levels in the time he
has ruled: 5, 10, and 15 years. In addition, for each baseline level in the time a founder has been
in power, we consider different changes in the amount he stays in office (changes from a minimum
of 10 to a maximum of 40 years). Figure 4 presents the results for this analysis. Two patterns are
apparent from this figure. First, the founder’s tenure effect on the expected time to deposal in a
regime increases at a constant rate with the time a ruler is in power. For instance, increasing the
founder’s tenure by 10 from a baseline of 5 years leads to a 25-year increase in the expected time
to deposal in regime. This effect increases to about 100 years when with a 35-year increase in the
founder’s tenure. Second, the effect of tenure in the expected time to deposal is constant across
baseline level of a founder’s time in power. For example, across baseline levels of tenure, increasing
the founder’s time in power by 10 years leads to a 25-year increase in a regime’s expected time to
deposal.
27
4
Robustness Checks
To be assured that we estimating a causal relationship between leader duration and our outcomes
of interest we conduct a set of robustness checks. In doing so, we are addressing three potential
pitfalls to our identification strategy. First, leader who die older in office may be more likely to
have a son old enough to take over power. Second, more physically vigorous leaders might be more
capable of transferring authority across time and also of living longer. Under either of these two
scenarios, our analysis might be capturing the effect of physical vigor and/or fitness to rule and not
institutional power. To address this concern we limit our analysis to those leaders who survived
in office less than ten years, eliminating the leaders who lasted the longest in office, which are the
observations most likely to confound our estimates.
Finally, one may argue that a monarch’s timing of death is correlated with the probability that
a ruler experiences a violent deposal. This could explain, for example, a lower rate of deposal for
successors of monarchs who died in office after a long period compared to the one for monarch
who died in office only after a short period. To address this concern we limit our analysis to the
case where we can be most sure that the probability of a monarch experiencing a violent deposal
is the same. We identify the set of observations by comparing the probabilities of deposal given a
monarch’s time in power and select those for which we cannot conclude are significantly different.
If there is confounding of the type described above we would expect our parameter estimates to
differ from those derived from the unrestricted sample. However, since we are severely restricting
the data, discarding between almost a quarter and two-thirds of it, we should expect the restricted
model to be much less precisely estimated. In other words, we have a possibly biased but more
efficiently estimated effect in the full sample and an inefficient but unbiased estimate in the restricted
mode. To see if our estimates change, we compare the restricted sample estimates to the full sample
using a generalized Hausman test.
We use the probability of deposal conditional on the time a monarch has been in power to
determine the point at which we truncate the sample from below. To do so, we fit the KaplanMeier survival curve. This curve represents the probability of a leader staying in power beyond a
given year conditional on having stayed in power up to that point. Figure 8 displays the survival
28
Restricted Tenure
Leader Tenure
Hausman χ2
p value
N
*** p
< 0.001,
** p
(1.)
(2.)
Son as Successor
< 10
>5
0.071∗∗
(0.020)
33.662
0.000
685
(3.)
(4.)
Successor Deposed
<10
>5
0.024∗∗∗ −0.031
(0.002)
(0.021)
17.024
5.513
0.000
0.064
1623
685
−0.003
(0.003)
4.210
0.122
1623
(5.)
(6.)
Regime Duration
<10
>5
−0.059
(0.037)
1.608
0.205
122
−0.007
(0.005)
3.560
0.059
250
< 0.01, * p < 0.05, † p < 0.1
Table 8: Robustness Checks For Baseline Model. This table reports the parameter estimates
and standard errors for the analysis restricting the minimum tenure and maximum tenure to five
and ten years, respectively. Columns 1 and 2 present parameter estimates for the effect of leader’s
tenure on the probability of dynastic succession. Columns 3 and 4 present the estimates for the effect
of leader’s tenure on the probability that a successor is deposed. Finally, columns 5 and 6 present
the estimates for the effect of a founder’s tenure on regime durability. The estimates reported in
each column are compared to those derived from the unrestricted data using a generalized Hausman
test. The results from the Hausman test are reported in the table along with p-values under the
null that the parameters are the same across the restricted and unrestricted samples.
function using the information for all power transitions in our data. The figure shows, for example,
that the probability of staying in power beyond 20 years conditional on having ruled until that point
is about 80 percent. The most striking pattern in the figure is that survival curve stays relatively
flat for values in leader duration above 5 years. Conducting a test of the difference in the estimated
hazard rate at each failure time, we find no statistically significant difference at conventional levels
between each failure time after 5 years.7 Thus, we use five years as our minimum duration bound
in our attempt to address the existence of different degrees of “safety” across regimes.
In general, the results from the restricted models confirm our conclusion that leader tenure
leads to both an increased probability of having a son as a successor and to political stability.
For example, we see in columns one and two of Table 8 that, as before, the relationship between
leader tenure and the probability of having a son is positive and statistically significant. However,
a Hausman test comparing these estimates to those derived from the unrestricted data shows that,
indeed, are statistically different. Nevertheless, the results from this exercise still indicate that as
7
The figure reporting the results from this test is included in the appendix.
29
before there is a positive effect of leader tenure on hereditary succession.
In Columns 3 and 4 we similarly restrict our data, now examining the effect of leader tenure
on the probability that a ruler’s successor is deposed. Again, consistent with the theoritical model
presente in the paper, the parameter estimates are negative. However, they are not statistically
significant. Still, when we compare these restricted estimates to those from the models with the
unrestricted state we see that they are indistinguishable from each other at conventional levels. In
other words, when we restrict the data in order to limit the likelihood of bias, we see that these
inefficient restricted sample produce estimates that are statistically no different from those derived
from our unrestricted sample.
Last, in Columns 5 and 6 we conduct the same exercise, though now examining the effect of
a founder’s tenure on the amount of time until the regime he founded experienced a deposal. As
in the analysis of the unrestricted data the effect is in the prediction direction, however, in the
restricted analysis the result becomes statistically insignificant. However, when we compare the
models where we limit the data to those estimated with the unrestricted dataset we once more find
that there is no statistically significant difference between the parameters derived from the full and
restricted samples.
In this section we have restricted our analysis to subsamples where the upper-limit of a founder
tenure is not greater than ten years and where the lower limit is five years. The purpose of
restricting the analysis to this cross-section of the data is to avoid the presence of two potential
confounders in the analysis: the latent threat of deposal facing a regime and/or a founder’s genetic
composition. When we restrict the analysis to these subsamples we find a positive relationship
between a leader’s duration and the probability that a the first successor is a close relative. Under
the survival analysis framework, we also find a positive correlation between a founder’s tenure and
regime durability (measured by the survival function and expected time to a deposal). As discussed
in the appendix under this analysis we not only exploit the randomness arising from a founder’s
death from natural causes, but also avoid the presence of confounders. Thus, we are confident in
having estimates for the causal effects of a leader tenure (which proxies institutionalized power) on
different quantities of interest related to the consolidation of power.
30
5
Conclusion
Do leaders benefit from actions taken by their predecessors? This paper provides a positive answer
to this question. We focused on the set of European rulers who ruled and died in office in the period
spanning the years 500 BCE and 1947 AD. This, we argue, allowed us to use the length of time of
their rule as a proxy for the degree to which they are able to institutionalize power. Further, this
strategy allowed us to use the specific timing of their death as exogenous shocks in the degree of
institutionalization in a particular regime.
Our findings show that the actions a particular leader may carry out during his rule, have
implications for the overall stability the regime his successor inherits. We find that leaders who are
able to govern for longer period of time are more likely to experience hereditary successions, less
likely to have deposed successors, and more likely to enjoy more durable regimes. Furthermore,
consistent with our claim that the length of time is a good proxy for the degree to which a ruler
has consolidated his hold on power, we find that probability a leader calls parliament decreases
with the number of years he has been in office. Finally, to provide further evidence that power
is transferred across rulers, we find that rulers who succeed leaders who were in power for longer
periods call parliaments a smaller fraction of their total time in office.
Though our analysis is limited to non-democracies in Europe during a particular time period,
we believe our findings have important lessons for autocracies in the contemporary world. The
fundamental problem of retaining power in modern autocracies is the same as in previous eras.
Contemporary autocrats still have to prevent enemies from coordinating to overthrown them, keep
allies content by distributing spoils among them, and are hesitant to allow the establishment of
institutions constraining their rule. Thus, in our view, Raul Castro in Cuba, Nicolas Maduro in
Venezuela, and Kim Jung-un in North Korea are secure in power, in large part because of the efforts
their predecessors took to consolidate their rule.
More generally, our results illustrate the Samuel Huntington’s insight in relation to the consolidation of political regimes. In his view, longevity is the best indicator that an organization has
become “institutionalized” (Huntington 1968). One of the observable implications, is that the older
an organization is, the more likely it is to exist through any future specified time period (p. 13).
31
We show exactly this. Similarly, institutionalization can alternatively be measured by what he calls
generational age. That is, the ability of the first generation of leadership to pass on power to a
second. This paper demonstrates that longevity in power causes this second measure of institutionalization, the ability to transfer power to a second generation. In total, the actions of long-lasting
rulers lead not only strengthen their hold on power, but also contribute to the consolidation of the
regime as a whole.
32
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34
Appendix
35
Change in Founder's Tenure
200
400
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30
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200
Baseline Founder's Tenure
10 years
0
5 years
Difference in Survival Curves (Percentage Points)
55 years
40
35 years
40
25 years
0
Regime Duration (Years)
Figure 6: Founder Duration Effect on Conditional Probability of Regime Survival. This
figure shows the average treatment effect of institutionalization on the conditional probability of
regime survival for different change magnitudes and baseline levels in a founder’s tenure. Two main
patterns are at play in the figure. First, given an initial value of a founder’s tenure, the effect of
institutionalization on the probability of regime survival is increasing in the magnitude of a change
in a ruler’s tenure. For example, increasing a founder’s time in power by 25 years conditional on
have ruled for five years, leads to an increase of about 10 percentage points on the probability that
his regime will survive for longer than 100 years conditional on having survived up to that point in
time. For the same representative founder, the effect is about 25 percentage points for a change of
55 years in a founder’s tenure. Second, the magnitude of these effects is smaller for higher baseline
levels of a founder’s tenure. Increasing a founder’s time in power by 25 years when the baseline is
5 years, leads to an increase in the probability of regime survival about 10 percentage points; this
effect is almost half when the founder’s baseline tenure is 15 years.
36
Baseline Founder's Tenure
10
15
20
25
30
35
40
250
200
0
50
100
150
200
0
50
100
150
200
150
100
50
0
ATE on Expected Time to Deposal
15 years
250
10 years
250
5 years
10
15
20
25
30
35
40
10
15
20
25
30
35
40
Change in Founder's Tenure
Figure 7: Founder Duration and Expected Time to Failure. This figure shows the effect of
institutionalization on the expected time to the first violent transition of power in a regime. Two
patterns emerge from these three panels. First, the expected time to failure of a regime increases
with the change magnitude of a founder’s tenure. For instance, increasing a monarch’s tenure by
10 years from a baseline of 5 years leads to an increase of about 25 years in the average duration
of a regime. In contrast, increasing a monarch’s tenure by 40 years from the same baseline leads to
an increase in the average duration of the regime of about 120 years. Second, the marginal effect
of a founder’s tenure is constant. In other words, changes in the founder’s tenure leads have similar
effects on the average duration of the regime irrespective of the founder’s baseline time in power.
37
0.6
0.4
0.0
0.2
Survival Probability
0.8
1.0
Kaplan-Meir Survival Curve
0
20
40
60
80
Leader Tenure
Figure 8: Kaplan-Meier Survival Function. This figure plots the Kaplan-Meier Survival function based on all power transitions in Europe after 500 B.C.. This function determines the probability of a leader statying in power beyond a given year conditional on not having been deposed up
to that point. For example, that the probability of staying in power beyond 20 years conditional
on having ruled until that point is about 80 percent. The most striking pattern in the figure is that
the survival functions stays relatively constant for values in leader duration above 5 years.
38
Difference In Kaplan-Meir Hazard Estimates
Failure Time 2
α < 0.1
55
48
45.5
44.5
43.5
42
40.5
39.5
38.5
37.5
36
34.5
33.5
32
30.5
29.5
28.5
27.5
26.5
25.5
24.5
23.5
22.5
21.5
20.5
19.5
18.5
17.5
16.5
15.5
14.5
13.5
12.5
11.5
10.5
9.5
8.5
7.5
6.5
5.5
4.5
3.5
2.5
1.5
0.5
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.010.010.01 0 0 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.010.01 0 0
0.010.010.01 0 0.01 0 0 0 0 0 0 -0.01 0 0 0 -0.01 0 0 0 0 0 0 -0.01 0 0 0 0 -0.01-0.01 0 0 -0.01-0.01 0 -0.01 0 0 0 0 -0.01 0 0.01 0 -0.01 0
0.01 0 0 0 0 0 0 -0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01 0 0 -0.01-0.01-0.01
0.01 0 0 0 0 0 0 0 0 0 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01 0 0 0 -0.01-0.01
0.020.010.010.010.010.010.01 0 0 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.010.01 0 0
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0.01 0 0 0 0 0 0 0 0 0 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 0 -0.01-0.01 0 -0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01 0 -0.01 0 -0.01-0.01-0.01-0.01 0 0 0 -0.01-0.01
0.020.010.010.010.010.010.010.010.010.010.01 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0 0.01 0 0 0 0 0 0.01 0 0 0 0 0.010.010.01 0 0
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0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.01 0 0 0 0 -0.01-0.01 0 0 -0.01-0.01 0 -0.01 0 0 0 0 0 0 0.01 0 0 0
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0.020.010.010.010.010.010.01 0 0 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.010.010.01 0 0.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0 0.01 0 0 0 0 0.010.010.01 0 0
0.010.010.010.010.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.01-0.01 0 0 0 0 0 -0.01 0 0 0 0 0 0 0.01 0 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.010.010.01 0 0.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.010.01 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.01 0 0 0
0.020.010.010.010.010.010.01 0 0 0 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0.010.010.01 0 0
0.010.010.01 0 0.01 0 0 0 0 0 0 -0.01 0 0 0 -0.01 0 0 0 0 0 0 -0.01 0 0 -0.01 0 -0.01-0.01 0 0 -0.01-0.01 0 -0.01 0 0 0 0 -0.01 0 0 0 -0.01 0
0.010.010.010.010.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.01-0.01 0 0 0 0 0 -0.01 0 0 0 0 0 0 0.01 0 0 0
0.010.010.01 0 0.01 0 0 0 0 0 0 0 0 0 0 -0.01 0 0 0 0 0 0 -0.01 0 0 0 0 -0.01-0.01 0 0 -0.01-0.01 0 -0.01 0 0 0 0 0 0 0.01 0 0 0
0.010.010.010.010.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.01-0.01 0 0 0 0 0 -0.01 0 0 0 0 0 0 0.01 0 0 0
0.01 0 0 0 0 0 0 0 0 0 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01 0 0 0 -0.01-0.01
0.010.010.01 0 0.01 0 0 0 0 0 0 -0.01 0 0 0 -0.01 0 0 0 0 0 0 -0.01 0 0 -0.01 0 -0.01-0.01 0 0 -0.01-0.01 0 -0.01 0 0 0 0 -0.01 0 0 0 -0.01 0
0.01 0 0 0 0 -0.01 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01 0 0 -0.01-0.01-0.01
0.01 0 0 0 0 0 0 -0.01 0 -0.01 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01 0 0 0 -0.01-0.01
0.01 0 0 0 0 -0.01 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01 0 0 -0.01-0.01-0.01
0.01 0 0 0 0 -0.01 0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01 0 -0.01-0.01-0.01-0.01 0 0 -0.01-0.01-0.01
0 -0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.01-0.02-0.02-0.02-0.02-0.02-0.02-0.02-0.02-0.02-0.02-0.01-0.02-0.02-0.02-0.02-0.02-0.02-0.02-0.01-0.02-0.02-0.02-0.02-0.02-0.01-0.02-0.02-0.02-0.02-0.01-0.01-0.01-0.02-0.02
0.5
3.5
6.5
9.5 12.5
16.5
20.5
24.5
28.5
32
36
39.5
44.5
α < 0.05
55
Failure Time 1
Figure 9: Difference in Survival Probabilities. This figure plots on the difference in estimated
Kaplan-Meir hazard rates. Differences shaded purple are significant at the ten percent level. Those
shaded red are significant at the five percent level. Those shaded blue are statistically indistinguishable from zero. We see that nearly all differences past five years are statistically indistinguishable
from zero.
39