"Civilizations" and Political-Institutional Paths: A Sequence Analysis of the MaxRange2 Data Set,...

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“Civilizations” and Political-Institutional Paths: A Sequence Analysis

of the MaxRange2 Data Set, 1789 – 2013*

(Draft, not for quoting without permission of the authors.)

Max Rånge and Mikael Sandberg [email protected] & [email protected]

University of Halmstad, Sweden https://sites.google.com/site/maxrangedat

Abstract

In what sequences have nations changed institutionally in history and does that order matter for later democratization? If so, are there historical-institutional pathways of “civilizations”?

These previously neglected research problems are addressed in this paper on the basis of a new, unique, and enormous data set tracking all political institutions and systems in the

world monthly since 1789. The aim is both empirical and theoretical: to take steps toward an

understanding of the sequential aspects of political-institutional evolution. Results visualize sequences at regime level that show few signs of path dependency. They also show that

democracy may emerge in all types of regimes, though at varying paces. Separating religious-majority nations, Muslim systems are less affected by democracy diffusion than

other religious-majority nations. Muslim political systems also exhibit larger regime type unpredictability. Taken together with estimates of GDP per capita, majority religions explain

a minor share of discrepancies between regime types: wealth of nations is more important than majority religion on a general, regime type diversity level. However, specifications of

institutional details will have to be made in future research in this new area of historical political-institutional study.

Presented at the American Political Science Association Annual Meeting 2014 in Washington D.C., August 28-31, 2014

* We wish to thank Halmstad University, HOS (School of Social and Health Sciences), and CESAM (Centre for

Social Analysis) for supporting this project.

mailto:[email protected]

mailto:[email protected]

https://sites.google.com/site/maxrangedat

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Introduction

In what sequences have nations changed institutionally in history and does that order matter

for later democratization? If so, are there historical-institutional pathways of “civilizations”?

These previously neglected research problems are addressed in this paper on the basis on a

new, unique, and enormous data set tracking all political institutions and systems in the

world monthly since 1789: MaxRange2, created by one of the authors of this paper, Max

Rånge. The purpose of this first overview, an initial analysis of the regime type dimensions

and institutional sequences of this new data set, is to assess whether the evolution of

political institutions is “path dependent”—that is, whether democratization can be inferred

as primarily an outcome of previous institutional experience, and whether there are

“civilizations”—in term of majority religions— forging pathways for groups of nations. The

aims are both empirical—to introduce the new MaxRange2 data set on political institutions

since 1789—and theoretical—to take steps toward an understanding of the sequential

aspects of political-institutional evolution.1 In addition, we introduce the sequence analysis

developed for the study of life-history data on the individual level to the nation-level analysis

of institutional sequences.

Method

We apply a sequence analysis technique developed for life-cycle study of individuals over the

course of schooling, further education, work life, marriage, and so on. More specifically, we

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apply some of the features of the toolbox TraMineR in R for analyzing and visualizing

categorical state sequences (Gabadinho 2011). A similar technique for sequence analysis is

also offered in Stata (Brzinsky-Fay et al. 2006). The primary objective of these techniques is

to extract workable information from sequential data sets—i.e., to summarize, sort, group,

and compare sequences. The resulting groups and sequences can then be used in classical

inference of explanatory models, so that sequences and groups of sequences can be used as

explanans and explanandum in causal modeling. A common approach in sequences analyses

for categorizing patterns, also used in this paper, consists of computing pairwise distances by

means of alignment algorithms, such as optimal matching (Abbott and Forrest 1986; Abbott

and Tsay 2000). The resulting groups or clusters can then be related to hypothesized

covariates by means of logistic regressions or classification trees (Gabadinho 2011). More

recently, Elzinga and Liefbroer (2007) and Widmer and Ritschards (2009) have suggested a

complementary approach to focus on longitudinal diversity and complexity in the sequences.

Complex techniques are the analysis of transversal characteristics of data (wave-like

dynamics), suggested by Billari (2001). Such wave-like evolution can be compared between

groups and clusters, something which may give insights into the historical dynamics of

various groups of institutions. This is particularly interesting for political scientists in

analyzing longitudinal democratization data since we have several theories and previous

results of waves and diffusion of democracy (Huntington 1991).

The New MaxRange Time Series Institutional Data from 1789

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Though there are some shorter time-series data sets on political institutions, such as the

Freedom House, the Economist Intelligence Unit (EUI), the Institutional and Elections Project

(IAEP), and the Adam Przeworski Democracy and Dictatorship data sets (Przeworski 1991),

there have only been three data sets that go as far back as the early 19th century: Polity IV

(from 1800), the Boix, Miller, and Rosato Political Regimes data (BMR, from 1800) (Boix et al.

2012) , and the Vanhanen Polyarchy data set (from 1810) (Vanhanen 2003). The two last

data sets are interesting but very limited in variable structure: both relate to Dahl’s

definition of democracy as based on contestation and participation (Dahl 1971). Vanhanen

defines a democracy index by combining the share of party representatives not belonging to

the largest party in the elected parliament and participation in the elections (Vanhanen

1997). In the BMR data set that runs to 2007, democracy versus non-democracy is given as a

binary interpretation on the basis of the same distinction. This means that neither

Vanhanen’s Polyarchy nor the BMR give indications of political regime institutions in its

details and variety, but rather provides us with an index of democracy. These two databases

are also no longer updated.

Polity IV data, on the other hand, covers all nation states with a population of more than

500,000 inhabitants starting from 1800 (Jaggers and Gurr 1995; Gurr 1974; Marshall and

Jaggers 2002, 2010; Eckstein and Gurr 1975). In this continuously updated data set, a

complex structure of innovative measurements of institutions is measured. In fact, even

experienced researchers argue that the Polity IV data structure is complicated and

problematic (Hadenius and Teorell 2005). We argue that it would be much simpler and

scientifically useful if traditional definitions are used. If needed, a complex structure can

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later be invented on the basis of quite formal, well-known, and uncontested definitions and

operationalizations.

Not only is Polity IV obsolete in concepts and measurements, but it is also exhausted

empirically. Since it has been used in most studies of democratization studies, there is very

little left to squeeze out of it today. In our recent efforts, Lindenfors, Jansson, and Sandberg

used Polity IV data in the analysis of transitions times between autocracy and democracy

(Lindenfors et al. 2011). Jansson, Lindenfors, and Sandberg found that transition to

democracy duration (up to 12 years) increased the likelihood of survival of the resulting

democratic regime (Jansson et al. 2013). Sandberg and Lundberg (2012) discovered certain

institutional path-dependencies using Polity IV data in a principal component analysis. But

during our analyses, we have increasingly realized that new, more detailed and informative

data are needed to advance the studies of institutional evolution on a world scale.

Our research here has the competitive empirical advantage of sole access to an extremely

promising new data set on political institutions in all countries of the world since at least

1789, on a monthly basis, and since 1600 on a yearly one. This data set, MaxRange (and its

new version MaxRange2), created by Max Rånge, consists of variables of political regimes

coded on a 1-1000 categorical (and not necessarily linear) scale. Codes for binary variables

on all the political institutions underlying the categorization of the variable political regimes

have partly been created in their initial form (with Mikael Sandberg). The yearly data set has

54,127 country-year cases so far, and more have been added for the years back to 1600. The

monthly data set, so far stretching back to 1789, has 12 times more cases (649,524), which

make it by far the biggest and most comprehensive political regime data set in the world.

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MaxRange can be merged with other data sets, such as Polity IV (1800- ). Since the previous

version of MaxRange, limited by its 100-degree scale, has now been updated into a 1000-

degree scale of political systems, we refer to this later version as MaxRange2.

MaxRange2 thus has several advantages compared to other available data:

1. A monthly time-series from 1789 for a longer time than any other data set, making in

particular the study of transitions more detailed and reliable, since they normally

occur on more detailed time-scales than years (however, this is not the subject here);

2. Yearly data available from 1600, i.e. at least 200 years longer than any other

comparable time series data set;

3. A formal and “normal” classification of democratic and non-democratic regimes in

categories on the basis of non-abstract definitions like: “monarchy,”

“parliamentarism,” “Head of State elected,” etc., lacking in previous time series data.

4. The 1000-degree scale is more detailed than any other institutional data set. The

scale defines both the classifications described above and is used for dummy variable

creation, so that singular institutions are coded in a way that creates up to 1000

different systems’ unique combination of these classifications.

In sum, the data set is not only a huge accomplishment in itself; it offers the opportunities to

resolve fundamental issues of institutional evolution and in that sense to revolutionize our

institutional analysis.

In the coding of political regimes, political systems, and institutions, MaxRange focuses on:

(1) the institutional structure, (2) the strength of the executive, (3) normal vs. interim status

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of the regime (particularly useful in transition studies), (4) the Head of State position, (5) the

concentration of powers to the executive, (6) the Head of Government position, a number of

institutional dummies indicating presence or absence of a number of formal institutions, and

summarizing all previous dimensions, finally (7) a simplified executive strength variable.

By the variable (1) institutional structure, various forms of executive powers and systems are

defined as formal institutions or complexes of institutions, such as parliamentarism,

presidentialism, semi-presidentialism, interim, military, colonial structure (see appendix and

our descriptive article, forthcoming). By the variable (2) executive strength, MaxRange

provides the degree to which political system executives have constitutional powers:

dominating, absolute, or weak executive powers. In the variable (3) normal vs. interim

systems, regimes are evaluated in the relation to their degree of “normality” in contrast to

being in an interim, therefore unstable condition. In cases where institutional constructs are

of interim type, some important classifications are made, such as military junta or martial

law institutions. These categorical values make possible more detailed transition studies

since they are unique monthly data.

The (4) Head of State variable indicates whether the nation-state is a republic, a monarchy,

or has any unified head of state at all. In the (5) executive concentration variable, we find

values of the executive powers in terms of whether they are concentrated, separated, or

undefined in this respect. Similarly, in the (6) Head of Government variable there are values

indicating who is fulfilling that function; a president, prime minister, monarch, or any other

defined head of the executive. In the last variable (7) simplified strength, MaxRange provides

a simplified summary of the degree to which executive powers are either decentralized,

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centralized, or balanced as an account of the overall character of the state (s ee the code

book in appendix).

The resulting political institutional scale positions each political system each month since

1789, or year since 1600, on a 1-1000 scale, where the degrees from 760-1000 are defined

democratic. In this paper however, since transitions are not considered in monthly detail,

only yearly data from 1789 are used. First, we may compare MaxRange2 data with Polity IV

in terms of number of democracies.

Figure 1 around here.

As is seen in the figure, MaxRange2 differs from Polity IV data with respect to the

measurement of democracy mainly in that it is a longer data set (from 1789 rather than

1800 in this case), and that more nations are included since Polity IV only includes nations

with populations greater than 500,000. MaxRange also includes several other smaller

political units listed in the appendix, namely those that proclaimed themselves independent.

However, in addition to that, MaxRange2 is more inclusive than is Polity IV in the

measurement of democracy (value 6 or above on the institutionalized democracy variable,

the value indicated as threshold for democracy on the Polity home page).

In this initial analysis of regime type data, values of the MaxRange2 institutional variable 1-

1000 are first grouped into regime types in order to present data and major patterns in a

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more comprehensible introduction to the data set. In the creation of the regime type

simplified data set, the following operationalization was made (see table 1).

Table 1 around here.

Table 1 describes operational definitions of regime types in the MaxRange2 data set’s

regime and institutional variable with a value scale 1-1000. Absolutism is defined as having

MaxRange2 values from 5-75 (for further description, see the codebook in the appendix).

Examples are Albania 1789 to 1911 and Brunei 1986 to 2013. Anarchy is defined as the

second regime type, with values from 80-145 and 170-195 on the MaxRange2 scale.

Examples are Bhutan 1789 to 1884 and Syria 2012-2013. A third regime type,

Totalitarianism, is defined by several values on the MaxRange2 scale given in table 1.

Examples are Afghanistan 1789-1917 and Tajikistan 1992-2013. The fourth regime type,

Military, is defined by values from 290-295, 340-345, and 470. Examples are Haiti 1804-1805

and Egypt 2013. Like totalitarianism, the Authoritarian Regime Type is defined at a larger

number of MaxRange2 levels. Examples are France in 1824-1828 and Turkmenistan in 2008-

2013. Finally, democracy is defined as 790-805 and 815-845. Examples are Ireland 1789-

1831 and 1921-2013; and Switzerland 1803-2013. Added to that is an “Other” category. The

regime type definitions are preliminary and so far primarily made for purposes of initial

exploration and descriptive visualization of the rich data material and its analysis by means

of TraMineR.

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Survey of the Field

Institutional change and political trajectories have been studied in several social science

disciplines, and in fact before these disciplines emerged, in the classic texts by Aristotle, de

Tocqueville, Marx, and other social philosophers. Interestingly, in Politics, Aristotle had

already presented a model over how certain types of regimes could change into others, such

as aristocracies into oligarchies, and constitutional regimes into democracies. Modern

sociological contributions to historical comparative analysis include Barrington Moore’s

study of the social origins of dictatorship and democracy (Moore 1966), and Theda Skocpol’s

and Charles Tilly’s contributions to the historical explanations of revolutions and democracy.

Moore saw the roots of totalitarianism and democracy in the social organization of the

agrarian systems of nations and the way they industrialized. While Skocpol included

international competitive pressures in her explanation of revolutions (Skocpol 1979), Tilly

placed the most focus on indigenous factors such as regime change toward democracy in

interaction with popular contention (Tilly 1992, 2003).

Among political scientists, we find historically comparative analyses in Lipset and Rokkan

(Lipset and Rokkan 1967), who proposed roots and “junctures” of our political and party

systems in the Reformation, the “democratic revolutions,” and the industrial revolution. In

that sense, they were perhaps the pioneering political scientists of institutional path

dependency, even before the term was coined by economists like Arthur and David (Arthur

1994; David 1985). In classics by Schumpeter (1942), Dahl (1971), Linz and Stepan (1996),

and others (e.g., Vanhanen 1997), we do find narratives of institutional dynamics. Non-

democratic institutions are normally defined with reference to Huntington (1991), Linz and

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Stepan (1996), Diamond (2002), O’Donell and Schmitter (1986), Epstein et al. (2006), and

others (e.g., Hadenius and Teorell 2007). In modern political science, Collier and Collier

(2002), Pierson (2004), and Putnam (1992) have suggested institutional path dependencies

on the basis of theory and case studies (rather than time-series data).

In this paper, we instead consider the determinants of democracy and other regime types in

the perspective of institutional evolution. We therefore ask why institutions —and their

resulting political systems—change by probing sequence interpretations of path dependence

in our new data. Our reasons are found in the fact that we, in our previous modeling and

studies of democratization internationally based on the heretofore dominant Polity IV data,

have noticed the compatibility of the path dependency concept with evolutionary and

political culture studies (Åberg and Sandberg 2002; Sandberg 2011; Jansson et al. 2013;

Sandberg and Lundberg 2012; Sandberg 2003/04; Lindenfors et al. 2011; Sandberg 2000).

The social vs. natural science divide appears in the analysis of institutions. Natural science

perspectives help us understand why and how institutions emerge (Young 2001; Bowles

2004). North’s concept of institutions as “rules of the game” or, more specifically, the

“humanly devised constraints that structure political, economic and social interaction”(North

1990) has been extremely influential but offers a limited explanation of why, how, and in

what order they were humanly devised. Ostrom adopted an evolutionary concept of

institutions (Ostrom 1990) by means of game theory, thereby focusing on institutions as

equilibria, which is not easily translated into statistical modeling, whatever the qualities of

her work in other respects. In fact, political science has a few but important contributions to

non-evolutionary, institutional theory (for these titles, see March and Olsen 1989, 1996;

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Pierson 2004; Peters 2012), while also being greatly influenced by institutional economics,

some of which are in fact evolutionary (Veblen 1912; North 1990; Young 2001; Bowles 2004;

Hodgson 2000). Greif (2006), Aoki (2001), and Acemoglu and Robinson (2009) have made

important recent contributions to institutional analysis. Again, this is made without

longitudinal and quantitative rigor specifically on the emergence and diffusion of

institutional innovation.

As concept arising from evolutionary studies of innovation, “path dependence” as a social

phenomenon is associated with evolutionary economists, perhaps primarily David and

Arthur (David 1985; Arthur 1994) 2. In Pierson’s Politics in Time (2004), path dependence is

defined as “social processes that exhibit positive feedback and thus generate branching

patterns of historical development” (p. 21). Here, we do not investigate whether there is

feedback, only if there are actual patterns of historical development among political

institutions that support the thesis that institutional history matters for current institutions

and that there might be branching processes or multiple pathways of institutional evolution.

As Lipset and Rokkan considered path dependence—without naming it so—in political

systems (Lipset and Rokkan 1967), we first look at regime type level path dependences, and

whether pathways of regimes can be considered. Is the later evolution of democracy, for

instance, only the case in countries that historically have sequences of specific other non-

democratic regimes? Are there differences among nations with different religious majorities,

as suggested in Huntington’s Clash of Civilizations hypothesis (Huntington 1993) and in some

later scholarship (Potrafke 2012, 2013; Charron 2010)? Or do we instead see a diffusion

pattern, so that democracy spreads in all types of regimes (Coppedge 2001; Gleditsch and

Ward 2006; O'Loughlin et al. 1998; Sandberg 2011; Starr 1991; Wejnert 2005; Elkink 2011),

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irrespective of the historical and original regime types and of the majority religion of the

nations?

Regime Type Sequences

We may now overview the historical evolution of political institutions and regime types

among nations since 1789. Using the sequence approach on MaxRange2 political-

institutional data, a sorted sequence distribution of regime type data is first presented in full

in figure 2.

Figure 2 around here

First, in the upper diagram of this figure, we see the stacked order of sequences in world

political regime types 1789-2013. In this first, grey-scale figure (with Absolutism in dark and

democracy in light grey), all yearly institutional states are described in full, i.e., without a

regime type grouping of variable values. In the next figure, the same data are grouped into

regime types from absolutism to democracy, and coded by color. In the bottom layer of this

colored diagram, we have diminishing portion of absolutism, with a similarly diminishing

layer of despotism on top. The thin layer of colonial states increases in number until the

1960s, where it disappears. The totalitarian layer, prevalent since 1789, continues to grow

until 1989, where it decreases dramatically, though without disappearing. Authoritarianism

also grows in number until the mid 1960s, where it starts to vanish. In the aftermath of the

1989 implosion of totalitarianism, it again grows in number, however. Democracy, however,

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steadily grows in number of nations after its low tide around 1840, and in particular from the

early 1990s.

Looking at the lower diagram, we instead see backwards from various regime types, which

previous institutional states and sequences nations have historically had. In the upper part of

that lower diagram, we see the democratic cascade, beginning in the early 1800s but in

particularly diffusing from the 1860s and after the world wars. The conclusion we can draw,

considering the multitude of institutional states and sequences before democratization, is

that there is no obvious particular regime type predecessor to democracy.

Below the democracy diffusion in the figure, we see the (green) authoritarian group of

countries and from which regime types it evolved. Again we cannot say directly what type of

regimes nations have before they become authoritarian. We do see a mix of absolutist,

anarchist, despotic, colonial, and totalitarian precursors to authoritarianism. The same is

more or less true of still totalitarian nations; we can note a number of different precursors to

it, while of course democracy seems rare. But we need more detailed scrutiny of the degree

to which background factors may influence sequences and democracy. In figure 3 below, the

last diagram of figure 2 is divided into blocks of sequences in accordance with their regime

type status the first year (1789). Since there are 9 regime types, we have 9 blocks of

sequences sorted in the order of the regime types in 2013.


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In our view, the figure 3 falsifies the institutional path dependence hypothesis, if

investigated at crude regime type level. As is seen in the figure, all nations, whatever regime

type they had in 1789, exhibit some and a growing number of democracies over time to the

present. Diffusion of democracy occurs among all previously non-democratic regime types.

In the first diagram 1 in figure 3, the 31 nations with absolutism in 1789 are shown to

overcome their absolutism, in some layers becoming democratic already in the early 1900s,

transforming into totalitarian and authoritarian, but after 1989 increasingly being

democracies. Among the 2 anarchies in 1789, we see later colonial states, with patchy

transition periods of authoritarianism, totalitarianism, and democracy. Among the 90

nations that were despotic in 1789, almost all are now democracies. The transition started

already in the early 1800s with colonial and authoritarian transitions. Increasingly, in

particular after World War I, we see both democratic examples as well as totalitarian, but

again, after 1989, approximately half of them become democratic. All 6 nations that were

colonies in 1789 are democracies now. Of the three totalitarian nations in 1789, two are now

democratic, while one is still totalitarian. Among the three authoritarian nations in 1789, all

are democracies today. Among the six democracies in 1789, only four are democracies

today, however. Even in the other regime type category, we see democracy diffusing in the

post-war era. On the whole, the pattern is clear: democracy diffuses on regime type level in

all nations irrespectively of their prior regimes.

Modal states for all sequences (Appendix 2) verifies the transitions to democracy in all types

of regimes since 1789. All regime types except anarchy exhibit democracy in 2013.

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Finally, we can calculate the Shannon entropy indicator, called the entropy index (Billari

2001). It equals 0 when all cases are in the same state (it is thus easy to predict in which

state an individual nation is located). It is maximum when the cases are equally distributed

between the states, in this case the regime type states 1-9 (it is thus hard to predict in which

state an individual nation is located). Given the entropy index evolution in the MaxRange2

data set (figure 4), we see that predictability increases in an accelerating pace in the post-

Second World War era for all the regime type groups as of 1789 except absolutism and the

“other” category, in particular after 1989, as more democracy dominates among world

political regime types. Only among the nations that had absolutism or an “other” regime

type in 1789 can we see an increase in unpredictability. Absolutism seems thus to be an

unpredictable state that can lead to any other type of regime.

Religion and Regime Sequences

Thus far, we noted that democracy could diffuse in more or less all previous regimes. But our

second question is whether diffusion of democracy occurs as easy in all types of nations. Are

there differences among nations with different religious majorities, as could be inferred

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from Huntington’s Clash of Civilizations hypothesis (Huntington 1993) and in accordance

with some later empirical results (Potrafke 2012, 2013; Charron 2010)?

We have very few available background variables for nation states prior to 1789, but one we

do have is the kind of religion that dominated in nations. In this case, we use Laporta et. al

data (1999), where the majority religion is given for 1980.3 In classic quantitative studies of

democracy, Lipset correlated wealth with democracy. Maddison has managed to make an

estimation of the wealth in terms of GDP per capita for a large number of nations in 1820

(2007).

Investigating the patterns of religious nation group sequences of institutions, we will

consider four groups: Catholic, Muslim, Protestant, and Other denomination majority

nations, in accordance with available data. We therefore first study Catholic political systems

in relation the rest (figure 5).


The Catholic group of 63 countries, as seen in figure 5, includes several of the oldest

democracies, and also many of the second and third wave democracies. The backgrounds for

these democracies are mainly authoritarian but also despotic. Looking at the nations 28-63

in the figure, we see that authoritarianism is likely before early democratization, while

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despotism is more common among the late democratizers. Some of the despotic nations

have a period of being a colony before turning democratic.

In the Muslim group of 45 countries, as seen in diagram 2 in figure 5, we have only a few late

democratizers today (nations 35-41) and only patches of democracies of the first, second,

and third wave. Particularly few democracies seem to emerge from absolutism, but some

periods of democracy arise out of colonies and authoritarian rule. The post-1989

authoritarian and totalitarian Muslim nations form two distinct cascades, visible around

nations 16 and 32.

The Protestant nations are difficult to distinguish from the Catholic, as is seen in the last

diagram in figure 5. The same typical pattern described for Catholic countries holds also for

the Protestant ones. Both democratize increasingly after the world wars, both have the

(green) authoritarian predecessor states, both only to a minor extent (5-10 nations or so) are

authoritarian today, and are otherwise democratic.

In Appendix 3, we can use modal values to distinguish more clearly what is typical to each

group. The Catholic nations typically pass through despotism and a couple of decades of

authoritarianism in the late 19th century and increasingly grow democratic after World War

II (with a reversal in the early 1970s). Muslim nations rather pass through absolutism and

despotism, before they, after World War II, shift to a number of non-democratic regime

types, including totalitarianism, authoritarianism, and even despotism. Protestant nations

typically suffer from despotism until the end of World War II, when the most common state

is increasingly democracy. We can indeed find typical traits of civilizations, or at least

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considerable differences among nations with different religious-majority groups, even if we

from the institutional data used so far cannot in detail give an indication of exactly why or by

means of exactly what institutions.

Differences between the three religious-majority group regimes are also obvious when

presenting the mean time spent from 1789-2013 in various regime types in each group. In

the appendix, we see that among Catholic-majority countries, time spent as democracy is

less than both in despotism and authoritarianism. Among Muslim-majority systems, time

spent in despotism, absolutism, totalitarianism, and authoritarianism exceeds the time spent

in democracy (and as mentioned, the democracy value includes those countries with above

790 on the MaxRange2 scale, i.e., both limited and qualified democracies). Among the

Protestant-majority nations, only mean time spent under despotism is longer than the time

spent under democracy.


Finally, entropy of the three groups of nations reveals interesting variety. In the Catholic-

majority nations, entropy decreases drastically, indicating an accelerating predictability—in

this case, further democratization. The same is true for the smaller group of Protestant-

majority nations. In Muslim-majority nations, however, we notice an ominous increase in

entropy, making them less predictable in their future evolution.

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Finally, can the religious-majority factor contribute in statistically significant ways to the

explanation of further institutional evolution? TraMineR offers tools for analyzing

discrepancies of sequences. The logic is that the sum of squares can be expressed in terms of

distances between pairs, used in the optimal matching process, which makes it possible to

estimate dispersion, which is, in turn, the basis for analysis of variance (Studer et al. 2011).4

Table 2 around here


In the first of the four sliding R squared analyses in figure 7, we see the contribution of being

a Catholic-majority nation on the variance in discrepancies in regime types. The overall

pseudo R squared is significant but very low. However, the influence of religion on

discrepancy varies greatly over time. Pseudo R squared stretches from around 0.005 three

times from the 1950s to the 1970s, while it peaks twice with 0.05 in the 1990s. Previously in

history, Catholic majority reached levels around the same value in early 1800s and around

1890.

Looking at the sliding R squared for Muslim influence on discrepancy, we see a drastic

increase since the early 1970s, i.e., in the third wave, from around 0.025 to 0.15. The overall

pseudo R squared is only 0.04. Previously in history, there is one peak between the World

Wars of 0.05, i.e., at the same level as for the Catholic-majority nations at least three times

in history. So the Muslim effect is a third wave phenomenon.

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The Protestant-majority factor increased its importance for the R squared of discrepancy up

to more than 0.05 in the late 1970s to the early 1980s, with some additional minor peaks

after that. Since Protestant countries are relatively few, it means that a relatively small

number of Catholic countries democratized around that time. But apart from that peak,

there are no important periods in history for Protestant-majority reduction in shared

discrepancy variance.

The other denomination factor has an extremely weak pseudo R squared and peaks of 0.025

or smaller.

Finally, we generalize the previous approach for multiple covariates. We measure the

additional contribution of each covariate when we account for all other covariates.

Significance is assessed again through permutation tests. Results are presented in table 2.

Table 3 around here.

The multiple factor analysis reveals the extremely limited contributions of religious majority

to the explanation of regime type sequence discrepancies. Religious majorities contribute

with few percentages, while the factor GDP per capita (as measured by Maddison for 1820),

explains 0.24 of the discrepancy in regime type states. Religion provides extremely little

explanatory power by itself, as compared by the proxy for early levels of wealth in the time

series under investigation which explains over ten times more. Economic wealth seems a

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much more important factor for long term political-institutional pathways than do the

religious majorities of nations. The somewhat paradoxical conclusion means that sequences

of institutions do not matter for later democratization, but majority religion does, namely as

an obstacle if Muslim. Why this is the case with only the Muslim-majority nations, we do not

yet know in detail. Simultaneously, using the religious majorities as explanations for overall

institutional discrepancy helps us only to a limited extent. Rather, it is wealth in terms of

GDP per capita that can explain at least a quarter of the discrepancy found in institutions

using MaxRange2 data.

Discussion

Aristotle pioneered the sequence analysis of political regime types. He noted (in Politics,

Book four, Part II), that kingly rule, aristocracy, and constitutional government, the “true

forms,” may lead to three corresponding “perversions”: tyranny, oligarchy, and democracy.

Later in Book four, Part VI, he also considered several forms of democracy on the basis of

their origin. Interestingly, no political scientist since seems to have made an empirically

based analysis of which types of political system we actually have had—and in what order or

sequence—in the world, and how these sequences may be explained. One reason is of

course a lack of data. The data set dominant today, Polity IV, has yearly institutional

variables since 1800, but they are conceptualized in a way that makes them more or less

impossible to use for a sequence analysis (Lundberg and Sandberg 2012).

23

Thus far, we have not found any previous attempt to follow up this study of Aristotelian

themes. As we know, MaxRange2 data is also the largest data set in the world on political

systems, and we are confident in presenting nothing less than the first ever comprehensive

sequence analysis of all political institutions and political regimes in the world since 1789. As

the data structure is new—a huge nominal to ordinal scale time series for over 200 nations

over 224 years—new techniques for analysis are also required. In our project and this paper,

relying on the MaxRange2 institutional data set used, stretching as far back as 1789 for all

nations in the world, we can investigate sequences or pathways in world system states over

the previous two centuries with respect to the major political institutions.

MaxRange2 data, as mentioned, uses a 1000-degree scale of political systems or regimes, of

which approximately 175 are actually used in the empirical material of all nations in the

world since 1789. Sequences of 175 states are not used in this analysis, since that would

produce a hopelessly complex but fascinating fabric of institutional states (see the upper

diagram in figure 2). Instead, groups of institutional states have been created (defined in

appendix 3 and described in the middle and lower diagrams in figure 2), namely a categorical

scale of 9 different types: (1). Absolutism, (2) Anarchy, (3) Despotism, (4) Colony, (5)

Totalitarianism, (6) Military, (7) Authoritarianism, (8) Democracy, and finally (9) Other. Using

this simplification, we may first consider regime type path dependencies, in particular

whether democracy is more or less connected with previous experiences of certain other

regime types. Obviously, later analyses may be elaborated with more or less detailed

categorizations.

24

We first consider the sequences and notice that democracy may diffuse from all types of

previous regimes. We then also notice that Muslim-majority nations, for reasons unknown in

detail, are typically reluctantly immune to democracy. For this reason, one might be

surprised that the discrepancy of institutional setups over time in sequences to a large

extent cannot be explained by religion in nations. Instead, as a general rule, an economic

factor such as wealth among nations plays a much more important role in affecting nations

in their regime type variety. But we must distinguish the role of majority religion in

understanding receptivity for democracy diffusion separately from the statistical explanation

of general discrepancy among regime types. Religion is important for the former, but not for

the latter, for which instead wealth is a much more critical factor. However, the specific

details of these causal mechanisms are not yet known. We wish to use further specifications

at sub-regime institutional level in our future analyses of MaxRange2 data with the purpose

of reaching a richer explanation of these mechanisms.

25

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Data

MaxRange



32

Figure 1. Democracy among Political Systems in the World: A Comparison of MaxRange2 and Polity IV

33

Figure 2. The Historical Landscapes of Political Institutions, MaxRange2, 1789-2013

Note: MaxRange2 Institutional (in grey, total institutional scale 1-1000) and Regime Type Sequence Indexes: in color, by 1-9 Regime Types and by End State Order).

34

Figure 3. Regime Type Sequences in End State Order, by Regime Types in 1789

35

Figure 4. Entropies for Regime Types as of 1789

Note: Regime Types 1-9 defined in the Figure 3 legend.

36

Figure 5. “Civilizations” and Regime Type Sequences

Note: Catholic, Muslim, Protestant, and Other Denomination Majority Nations (Right Column=1) vs Non-Catholic, Non-Muslim, Non-Protestant, and Non-Other Denominations (Left Column=0)

37

Figure 6. Entropy in Catholic, Muslim, and Protestant Majority Systems vs. Non-Catholic, Non-Muslim, and Non-Protestant

Note: Catholic, Muslim, and Protestant Majority Systems (blue dashed=1) vs. Non-Catholic, Non-Muslim, and Non-Protestant (red=0)

38

Figure 7. Sliding Pseudo R Squared influence of Catholic (purple), Muslim (dark red), and Protestant Majority (green) in a Nation on Regime Type Discrepancy, 1789-2013

39

Table 1. MaxRange2 and Regime Types

MaxRange2 values Regime Types

Examples of Country-Year Cases

5 thru 75 Absolutism Albania 1789-1911, Brunei 1986-2013

150 thru 155 Anarchy Bhutan 1789-1884, Syria 2012-1013

80 thru 145, 170 thru 195 Despotism Algeria 1789-1947, Uzbekistan 2004-2013

350 thru 355, 230 thru 235=4 Colonial Barbados 1789-1961, Zambia 1924-1950

250, 360 thru 365, 320 thru 335, 260 thru 265, 240 thru 245, 200 thru 225, 390 thru 405, 300 thru 305, 270 thru 285, 160 thru 165

Totalitarian Afghanistan 1789-1917, Tajikistan 1992-2013

290 thru 295, 340 thru 345, 470 Military Haiti 1804-1805, Egypt 2013

310 thru 315, 370 thru 375, 380 thru 385, 410 thru 415, 420 thru 425, 430 thru 435, 440 thru 445, 455, 460 thru 465, 480 thru 485, 490 thru 495, 500 thru 505, 510 thru 525, 530 thru 565, 570 thru 575, 590 thru 605, 620 thru 645, 660 thru 715, 740 thru 755, 780 thru 785

Authoritarian France 1824-1828, Turkmenistan 2008-2013

790 thru 805, 815 thru 845, 870 thru 895, 905 thru 1000 Democracy Ireland 1789-1831, 1921-2013; Switzerland 1803-2013

Else Other

40

Table 2. ANOVA Test Values of Religious Majority on Regime Type Discrepancy

Catholic Muslim Protestant Other Denomination

Pseudo R2 0.029** 0,039** 0.015* 0.032*

Note: ** = sign. at 0.01 level, *=sig. At 0.05 level. For interpretation of pseudo R Squared, see Studer et al. (2011).

41

Table 3. Multi-Factor Discrepancy Analysis of GDP per capita, Catholic, Muslim, Protestant and Other Denomination Majority contributions to explained discrepancy in Regime Types.

Variable Pseudo F Pseudo R2 P value GDP per capital (1820) 1.1611457 0.235492471 0.016**

Catholic majority 0.6087257 0.003011120 0.692

Muslim majority 3.3356224 0.016499976 0.011** Protestant majority 1.6936994 0.008378047 0.133

Other denomination 0.5554048 0.002747363 0.751 Total 1.5709051 0.347049318 0.001**

Note: **p values sig. at 0.01 level. For interpretation of pseudo R2 see Studer et al. (2011).

42

Appendix 1. Mean Time Spent in Regime Types: Catholic, Muslim, and Protestant Majority Systems (right column) vs. the Non-Catholic, Non-Muslim, and Non-Protestant Majority Systems (left column)

43

Appendix 2. Regime Type Modal Value and Sequence by End State

44

Appendix 3. Modal State Sequences of Catholic, Muslim, and Protestant Nations (Right Column=1) vs. Non-Catholic, Non-Muslim, and Non-Protestant Nations (Left Column=0), 1789-2013

0

Sta

te fre

q. (n

=134)

Modal state sequence (0 occurrences, freq=0%)

X1790 X1820 X1850 X1880 X1910 X1940 X1970 X2000

0

0.25

.5

0.75

1

1

Sta

te fre

q. (n

=45)

Modal state sequence (0 occurrences, freq=0%)

X1790 X1820 X1850 X1880 X1910 X1940 X1970 X2000

0

0.25

.5

0.75

1

Absolutism

Anarchy

Despotism

Colonial

Totalitarian

Military

Authoritarian

Democracy

Other

45

Appendix 4. Code book MaxRange2 (see landscape table next pages)

46

Endnotes 1 By political systems, we mean the fundamental arrangements of political institutions

adopted by nations. Often, types of political systems are categorized as different regime

types. For example, political systems belonging to the regime type “democracy” may have

presidentialism or parliamentarian political systems, depending on the institution regulating

how cabinets or governments are being formed.

2 David (2007) defines a path-dependent stochastic system as “one possessing an asymptotic

distribution that evolves as a consequence (function) of the process's own history.” We have

a simpler operational definition of institutional path dependence, namely a statistically

significant and substantial contribution from previous institutions on the explanation of

variance or event histories in survival or emergence of specific later institutions.

3 We assume rather crudely that the same religion was in majority in 1789.

4 Applying this procedure, an estimate of the empirical distribution of F under independence

and compute the F value associated with a random permutation which randomly reassigns

each covariate profile to one of the observed sequence, and to repeat this step R (in this

case 5,000) times. The test can be performed comparing the groups of nations with various

religious majorities historically in order to assess the association. Homogeneity of the

difference of within group discrepancy is made on the basis of a generalization of the

Bartlett Test and Significance assessed through permutation tests (Studer et al. 2011).

MaxRange2

value

Demo- cracy level

Democracy

Auto- cracy

Institutional structure

Executive strength

Normalal vs. Interim

Head of State Executive Concentration

Head of Government

Simplified Strength

Election of Head of State

1000 QD 1 0 Parliamentarism Constitutional Normal Republic

Concentrated Executive Prime- Ministerial Decentralized

Indirectly

995 QD 1 0 Parliamentarism Constitutional Normal Monarchy

Concentrated Executive

Prime- Ministerial

Decentralized

Undefined

990 QD 1 0 Presidential- Parliamentarian

Constitutional Normal Republic

Separated Executive

Prime- Ministerial

Decentralized

Directly

985 QD 1 0 Divided Executive


Separated Executive

Prime- Ministerial

Decentralized

Directly



Separated Executive

Prime- Ministerial

Decentralized

Directly

975 QD 1 0 Semi- Presidential


Prime- Ministerial

Decentralized

Directly



Presidential

Decentralized

Directly

965 QD 1 0 Parliamentarian- Presidential

Significant Normal Republic

Presidential

Balanced

Directly



Presidential

Balanced

Directly



Prime- Ministerial

Decentralized

Indirectly



Presidential

Decentralized

Indirectly



Presidential

Balanced

Indirectly



Presidential

Balanced

Indirectly

935 QD 1 0 Parliamentarian Constitutional Normal Undefined Undefined

Prime- Ministerial

Decentralized

Undefined

930 QD 1 0 Presidential Constitutional Normal Republic Undefined

Presidential

Decentralized

Undefined

925 QD 1 0 Accountable

Presidential

Constitutional Normal

Republic

Presidential

Decentralized

Directly

920 QD 1 0 Accountable Presidential


Presidential

Decentralized

Directly

915 QD 1 0 Presidential Constitutional Normal Republic

Presidential

Decentralized

Directly



Presidential

Decentralized

Directly

905 QD 1 0 Parliamentarian Constitutional Government Acting Undefined


Prime- Ministerial Undefined

Undefined

900 Int 1 0 Interim Parliamentarian

Undefined Government Extra- Parliament Undefined


Prime- Ministerial

Undefined

Undefined


Presidential

Decentralized

Directly



Presidential

Decentralized

Directly

885 QD 1 0 Council Parlimentarian


Prime- Ministerial Decentralized

Indirectly

880 QD 1 0 Council Parliamentarian



Indirectly

875 QD 1 0 Constitutional Executive



Indirectly

870 QD 1 0 Constitutional Executive



Indirectly

865 Int 1 0 Parliamentarian Constitutional Parliamentarism obsolete election Undefined

Undefined Prime- Ministerial Undefined Undefined

860 Int 1 0 Parliamentarian Constitutional Parliamentarism illegitimate election Undefined

Undefined Interim Undefined Undefined

855 Int 1 0 Interim Undefined Interim Post- election Undefined


850 Int 1 0 Interim Undefined Interim Post- election Undefined


845 ED 1 0 Parliamentarian Constitutional Normal Undefined



Undefined

840 ED 1 0 Presidential- Parliamentarian



Undefined

835 ED 1 0 Monarchical Parliamentarism

Constitutional Normal Monarchy Prime- Ministerial

Decentralized

Undefined

830 ED 1 0 Monarchical Parliamentarian

Constitutional Normal Monarchy

Concentrated Executive Monarchy

Decentralized

Undefined

825 ED 1 0 Divided Executive

Constitutional Normal Republic Prime- Ministerial

Decentralized

Undefined

820 ED 1 0 Semi- Presidential


Presidential

Decentralized

Undefined

815 ED 1 0 President Constitutional Government Acting Republic


Presidential

Decentralized

Undefined

810 Int 1 0 Interim Undefined Government Extra-Parliament Republic


Presidential

Decentralized

Undefined

805 ED 1 0 Parliamentarian- Presidential


Presidential

Decentralized

Undefined

800 ED 1 0 Presidential Constitutional Normal Republic


Presidential

Decentralized

Undefined

795 ED 1 0 Parliamentarian- Presidential


Presidential

Balanced

Undefined

790 ED 1 0 President Significant Normal Republic


Presidential

Balanced

Undefined

785 LD 0 0 Semi- Parliamentarian

Constitutional Normal Undefined

Prime- Ministerial

Decentralized

Undefined

780 LD 0 0 Undefined Constitutional Normal Undefined

Prime- Ministerial

Decentralized

Undefined

775 Int 0 0 Interim Undefined Interim Elected Assembly Undefined

Presidential

Undefined Undefined

770 Int 0 0 Interim Undefined Interim Opposition grand coalition Undefined


Presidential

Undefined Undefined

765 Int 0 0 Interim Undefined Interim Parliament Plural Undefined

Undefined Interim Decentralized

Undefined

760 Int 0 0 Interim Undefined Interim Parliament Dominated Undefined

Undefined Interim Decentralized

Undefined

755 LD 0 0 Monarchical- Parliamentarian

Significant Normal Monarchy Monarchical

Balanced

Undefined


Significant Normal Monarchy

Concentrated Executive Monarchical

Balanced

Undefined

745 LD 0 0 Parliammentarian-Presidential

Constitutional Interim Presidential Republic

Presidential

Decentralized

Undefined

740 LD 0 0 President Constitutional Interim Acting President Republic


Presidential

Decentralized

Undefined

735 Int 0 0 Interim Undefined Interim coalition

New Regime – Old Regime Undefined

Undefined

Interim

Undefined Undefined

730 Int 0 0 Interim Undefined Interim Pre- election coalition Constitutional Undefined


725 Int 0 0 Interim Undefined Interim coalition Undefined


720 Int 0 0 Interim Undefined Interim New Undefined


Regime Constitutional or Pre- election

715 LD 0 0 Parliamentarian Constitutional Normalal Undefined


Prime- Ministerial

Decentralized

Undefined

710 LD 0 0 Presidential- Parliamentarian


Prime- Ministerial

Decentralized

Undefined


Constitutional Normal Monarchy

Prime- Ministerial

Decentralized

Undefined


Significant Normal Monarchy


Balanced

Undefined

695 LD 0 0 Divided Executive

Constitutional Normal Republic Prime- Ministerial

Decentralized

Undefined

690 LD 0 0 Sem- Presidential


Presidential

Decentralized

Undefined

685 LD 0 0 Parliamentarian- Presidential


Presidential

Decentralized

Undefined

680 LD 0 0 President Constitutional Normal Republic


Presidential

Decentralized

Undefined

675 LD 0 0 Parliamentarian Dominating Normal Undefined


Prime- Ministerial Centralized

Undefined

670 LD 0 0 Parliamentarian Dominating Normal Undefined


Prime- Ministerial

Centralized Undefined

665 LD 0 0 President Dominating Normal Republic


Presidential




Presidential


655 Int 0 0 Interim Undefined Interim New Regime Undefined


Interim

Undefined Undefined

650 Int 0 0 Interim Undefined Interim Old Regime Constitutional Undefined

Undefined

Interim

Undefined Undefined

645 LD 0 0 Monarchical Constitutional Normal Monarchy Monarchical

Decentralized

Undefined

640 LD 0 0 Monarchical Constitutional Normal Monarchy


Decentralized

Undefined



Presidential

Balanced

Undefined

630 LD 0 0 President Significant Normal Republic


Presidential

Balanced

Undefined

625 LD 0 0 Parliamentarian Overwhelminging

Normal Undefined

Concentrated Executive Prime- Ministerial


620 LD 0 0 Parliamentarian Overwhelming Normal Undefined


Prime-Minnisterial


615 Int 0 0 Interim Undefined Interim Old Regime Pre- Election Undefined

Concentrated Executive Interim

Undefined Undefined

610 Int 0 0 Interim Undefined Interim Old Regime Reform Undefined

Concentrated Executive Interim

Undefined Undefined

605 LD 0 0 President Overwhelming Normal Republic


Presidential


600 LD 0 0 President Overwhelming Normal Republic


Presidential


595 LD 0 0 Colony Undefined Normal Undefined

Concentrated Executive Colonial

Undefined Undefined

590 LD 0 0 Colony Undefined Normal Undefined


Undefined Undefined

585 Int 0 0 Interim Undefined Interim dem open to ref opp

pos Undefined

Undefined

Interim

Undefined Undefined

580 Int 0 0 Interim Undefined Interim dem ref open to reform Undefined

Undefined

Interim

Undefined Undefined

575 False Authoritarianism

0 0 Parliament Constitutional Normal

Undefined Prime- Ministerial

Undefined Undefined

570 FA 0 0 Presidential Constitutional Normal Republic


Presidential

Decentralized

Undefined

565 LD 0 0 Monarchical Constitutional Normal Monarchy Monarchical

Decentralized

Undefined

560 LD 0 0 Monarchical Constitutional Normal Monarchy


Decentralized

Undefined


Significant/ Dominant

Normal Republic

Presidential


550 LD 0 0 President Significant/ Dominant

Normal Republic


Presidential


545 LD 0 0 Parliament Dominate Normal Undefined


Prime- Ministerial


540 LD 0 0 President Dominate Normal Republic


Prime- Ministerial


535 LD 0 0 Monarchical Significant/ Dominant

Normal Monarchy Monarchical

Balanced

Undefined

530 LD 0 0 Monarchical Significant/ Dominant

Normal Monarchy


Balanced

Undefined

525 Semi-Auth

0 0 Parliament Constitutional Normal Undefined


Prime- Ministerial

Decentralized

Undefined

520 SA 0 0 Presidential- Parliamentarian



Prime- Ministerial

Decentralized

Undefined

515 SA 0 0 Divided Executive


Prime- Ministerial

Decentralized

Undefined

510 SA 0 0 Semi- Presidential


Presidential

Decentralized

Undefined

505 FA 0 0 Parliamentarian Constitutional Normal Undefined

Prime- Ministerial

Undefined Undefined

500 FA 0 0 Presidential Constitutional Normal Republic


Presidential

Undefined Undefined

495 SA 0 0 Parliamentarian- Presidential


Presidential

Decentralized

Undefined

490 SA 0 0 Presidential Constitutional Normal Republic


Presidential

Decentralized

Undefined

485 De Facto Authoritarian

0 0 Parliamentarian Constitutional Normal

Undefined Concentrated Executive

Prime- Ministerial

Decentralized

Undefined

480 DFA 0 0 Parliamentarian Constitutional Normal Undefined



Undefined

475 Int 0 0 Military Undefined Military-civil coalition Undefined

Undefined Prime- Ministerial Undefined Undefined

470 Milit 0 0 Military Undefined Military civil domination, Constitutional Undefined

Undefined

Military

Undefined Undefined

465 SA 0 0 Monarchical Dominating Normal Undefined Undefined Undefined Centralized Undefined


Dominating Normal Undefined

Undefined Undefined Centralized

Undefined

455 LD 0 0 Military Constitutional Normal Undefined

Concentrated Executive Military

Decentralized

Undefined

450 SA 0 0 Military Constitutional Normal Undefined


Decentralized

Undefined

445 SA 0 0 Monarchical Significant Normal Monarchy Monarchical

Balanced

Undefined

440 SA 0 0 Monarchical Significant Normal Monarchy


Balanced

Undefined

435 DFA 0 0 Parliamentarian- Presidential


Presidential

Decentralized

Undefined

430 DFA 0 0 President Constitutional Normal Republic


Presidential

Decentralized

Undefined

425 SA 0 0 Monarchical Significant Normal Monarchy Monarchical

Balanced

Undefined

420 SA 0 0 Monarchical Significant Normal

Monarchy

Concentrated

Executive Monarchical

Balanced

Undefined


Dominating Normal Republic

Presidential




Presidential


405 A 0 1 Parliament Constitutional Normal

Republic


Undefined

400 A 0 1 Party Constitutional Normal Republic


Secratary General

Decentralized

Undefined

395 A 0 1 Parliamentarian- Presidential


Presidential

Decentralized

Undefined

390 A 0 1 President Constitutional Normal Republic


Presidential

Decentralized

Undefined



Presidential

Balanced

Undefined

380 SA 0 1 President Significant Normal Republic


Presidential

Balanced

Undefined

375 LD* 0 1 Divided Executive Significant /Dominating

Normal Republic

Prime- Ministerial


370 LD* 0 1 Semi-Presidential Significant/ Dominating

Normal Republic


Presidential


365 HA 0 1 Monarchical Dominating Normal Monarchy Monarchical Centralized Undefined

360 HA 0 1 Monarchical Dominating Normal Monarchy



355 Colo 0 1 Parliament Weak Normal Undefined

Prime- Ministerial

Undefined Undefined

350 Colo 0 1 Parliament Weak Normal Undefined

Prime- Ministerial

Undefined Undefined

345 Military 0 1 Military Significant Normal Undefined


Balanced

Undefined

340 Milit 0 1 Military Significant Normal Undefined


Balanced

Undefined

335 HA 0 1 Semi- Presidential


Prime- Ministerial


330 HA 0 1 Parliamen Dominating Normal Republic


Prime- Ministerial


325 HA 0 1 Parliamentarian- Presidentialidential


Presidential


320 HA 0 1 President Dominating Normal Republic


Presidential


315 DFA 0 1 Parliamentarian- Presidential

Sign Normal Republic

Presidential

Balanced

Undefined

310 DFA 0 1 President Sign Normal Republic


Presidential

Balanced

Undefined

305 A 0 1 Parliament Constitutional Normal Republic

Prime- Ministerial

Decentralized

Undefined

300 A 0 1 President or Party undefined



Presidential

Decentralized

Undefined

295 Milit 0 1 Military Undefined Normal Undefined Undefined Military Undefined Undefined

290 Milit 0 1 Military Undefined Interim military civil, domin Undefined

Undefined

Military

Undefined Undefined

285 A 0 1 Parliamentarian- Presidential

Sign Normal Republic

Presidential

Balanced

Undefined

280 A 0 1 Presidential Sign Normal Republic


Presidential

Balanced

Undefined

275 A 0 1 Monarchical Significant/ Dominating



270 A 0 1 Monarchical Significant/

Dominating

Normal

Monarchy

Concentrated

Executive Monarchical


265 HA 0 1 Parliamentarian- Presidential

Overwhelming Normal Republic

Presidential


260 HA 0 1 Presidential Overwhelminging

Normal Republic


Presidential


255 Auth 0 1 Monarchical Weak or constitutional

Normal Monarchy

Estates Generale Monarchical

Decentralized

Undefined

250 A 0 1 Monarchical Weak or Constitutional

Normal Monarchy Monarchcial

Decentralized

Undefined

245 HA 0 1 Parliamentarian- Presidential


Presidential


240 HA 0 1 Presidential Dominating Normal Republic


Presidential


235 Colony 0 1 Colonial Overwhelminging

Normal Undefined


Undefined Undefined

230 Colony 0 1 Colonial Overwhelminging

Normal Undefined


Undefined Undefined

225 HierarchicalAuthoritarian

0 1 Parliamential Dominating Normal

Undefined Concentrated Executive

Prime- Ministerial


220 HA 0 1 Presidential or Monarchical

Dominating Normal Undefined

Concentrated Executive Undefined


215 HA 0 1 Party Based Government


General Secretary


210 HA 0 1 Party Based Government



General Secretary


205 HA 0 1 Party-Based Government


General Secretary


200 HA 0 1 Party-Based Government



General Secretary


195 Desp 0 1 Parliamentarian- Presidential


Presidential


190 Desp 0 1 President Dominating Normal Republic Concentrated Presidential Centralized Undefined

Executive



Presidential


180 Desp 0 1 Presidentialident Overwhelming Normal Republic


Presidential


175 Desp 0 1 Monarchical Overwhelminging



170 Desp 0 1 Monarchical Overwhelminging

Normal Monarchy



165 A 0 1 Monarchical Constitutional or Significant

Normal

Monarchy Monarchical Balanced

Undefined

160 Authorita

rianism

0 1 Monarchical Constitut or

Significant

Normal

Monarchy Monarchical

Balanced

Undefined

155 Anarchical

0 1 Undef* Undefined Disputed governance Undefined

Undefined Undefined Undefined Undefined

150 Anarchical

0 1 Undef* Undefined Semi- Anarchical Undefined

Undefined Undefined Undefined Undefined

145 Despotism

0 1 Party Overwhelming Normal Republic Secretary General


140 Desp 0 1 Party Overwhelming Normal Republic

Concentrated Executive Secretary General


135 Desp 0 1 Semi-pres Overwhelming Normal Undefined Prime- Ministerial Centralized Undefined

130 Desp 0 1 Parliamen Overwhelming Normal Undefined

Concentrated Executive Prime- Ministerial




Presidential


120 Desp 0 1 President Overwhelming Normal Republic


Presidential


115 Desp 0 1 Military Overwhelming Normal Monarchy



110 Desp 0 1 Military Overwhelming Normal Undefined



105 Desp 0 1 Colonial Overwhelming Normal Undefined


Colonial


100 Desp 0 1 Colonial Absolute Normal Undefined


Colonial


95 Desp 0 1 Undef* Overwhelming Martial law institutions Undefined

Concentrated Executive Undefined


90 Desp 0 1 Undef* Absolute Martial law suspended institutions Undefined


Undefined Centralized Undefined

85 Desp 0 1 Undef* Absolute Suspended parliament Undefined


80 Desp 0 1 Undef* Absolute Suspended parliament Undefined



75 Absolutism

0 1 Military Absolute Normal Undefined Military


70 Absol 0 1 Military Absolute Normal Undefined



65 Absol 0 1 Military Absolute Occupational military rule Undefined



60 Absol 0 1 Military Absolute Military Junta Undefined



55 Absol 0 1 Parliamentarian- Presidential

Absolute* Normal Republic

Presidential


50 Absol 0 1 President Absolute* Normal Republic


Presidential


45 Absol 0 1 Monarchical Absolute Normal Monarchy

Concentrated Executive Monarcical


40 Aab 0 1 Monarchical Absolute or Overwhelming

Normal Monarchy



35 Absol 0 1 Parliamentarian- Presidential

Absolute or Overwhelming

Normal Republic

Presidential


30 Absol 0 1 Presidential or Party

Absolute or Overwhelming

Normal Republic


Presidential


25 Absol 0 1 Monarchical Absolute Normal Monarchy Monarchical Centralized Undefined

20 Absol 0 1 Monarchical Absolute Normal Monarchy



15 Absol 0 1 Sem- Presidential

Absolute Normal Republic

Prime- Ministerial


10 Absol 0 1 Parliamentarian* Absolute Normal Republic


Presidential


"Civilizations" and Political-Institutional Paths: A Sequence Analysis of the MaxRange2 Data Set,...

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