Download International Dimensions of Democratization

International dimensions of democratization…
Draft version, comments welcome
Kristian S. Gleditsch
Faculty of Social Sciences, University of Glasgow
Adam Smith Building S210
Glasgow G12 8RT, Scotland, UK
E-mail: [email protected]
Abstract: Most research on democracy treats each nation state as completely independent, thus ignoring
some of the central aspects of interdependence and interaction across borders in World Politics. This
paper demonstrates that international aspects such as the political structures in proximate states and the
regional threats that states face strongly influence the likelihood that a country will be democratic and
experience transitions. The distribution of authority characteristics is not independent between countries,
but displays strong evidence of patterns of diffusion. I show that the most commonly emphasized
domestic processes within countries cannot by themselves account for the observed variation in
distribution of political democracy over time and space. The regional context in which countries are
located and their prior history provide important elements for explaining transitions and changes in the
distribution of authority structures over time. Przeworski and Limongi's (1997:159) claim that “democracy
appears exogenously as a deus ex machina”overlooks these patterns, and is thus incorrect.
…
I am grateful for comments from Brian A’ Hearn, Matthew Baum, Renske Doorenspleet, Håvard Hegre, Yi Feng,
Erik Gartzke, Nils Petter Gleditsch, Zeev Maoz, Solomon Polacheck, Bruce M. Russett, and in particular Michael D.
Ward. This research was supported in part by a grant from the Research Council of Norway #128560/530. An earlier
version of this paper was presented at the annual meeting of the Peace Science Society, Ann Arbor, MI, 8-10
October 1999.
Democracy and democratization in time and space
Theories accounting for how political structures vary over time and across space have a long tradition.
The best-known effort is probably Lipset’s (1960) “social requisites” hypothesis, which states that a
society’s level of development influences its prospects for democracy. Other perspectives hold that
certain cultural aspects, norms, or values favor the emergence and sustainment of democratic rule (e.g.,
Almond and Verba 1963, Muller and Selligson 1994, Putnam 1993). Still others emphasize distributional
aspects such material inequality or the relative strength of groups or classes (e.g., Muller 1988, 1995,
Rueschemeyer, Stephens and Stephens 1992, Vanhanen 1990). Theories of transitions to democracy more
specifically stress factors such as the timing of national development, negotiated pacts between elites, and
“critical junctures” or forms of path dependence in political development (e.g., Bollen 1979, Casper and
Taylor 1996, Moore 1973, O'Donnell, Schmitter and Whitehead 1986, Przeworski 1991, Rueschemeyer et
al. 1992).
These explanations differ considerably from each other, and may suggest quite different
observable implications for variation in authority structures (see, e.g., Vanhanen 1990 for a
comprehensive review). Despite their differences, however, these schools are in one sense all “similar” in
that they relate a country’s prospects for democratization or sustaining democratic rule to various
domestic economic, societal and political factors. By focusing exclusively on domestic processes, these
theories effectively treat the distribution of democracy as if this were independent among countries.1
Similarly, most research interested in the consequences of political democracy tends to treat the
distribution of authority structures and changes in these as stemming from purely domestic processes.
I argue that the distribution of democracy is not independent among countries and that
international factors and processes influence the likelihood that a country will become or remain
democratic. The international context thus contains central aspects of democratization processes. The first
part of this paper discusses some international contexts of democratization. The second part shows
empirically that the distribution of democracy and democratization cluster spatially, suggesting an
international dimension to democratization over time. The third part demonstrates more systematically
that this clustering cannot be attributed to other domestic factors affecting authority structures, and
evaluates the relative importance of internal and external dimensions of democratization. Though
Przeworski and Limongi (1997) are correct in concluding that transitions to democracy cannot be
1
Studies on effects of external dependence or how position in the so-called “World-System” influence prospects for
democratic rule provide a possible exception (e.g., Bollen 1983, Wallerstein 1979). Other doubts about these
theories aside (see e.g., Weede 1996), these usually consider dependence between national economies rather than
political structures, and rarely draw explicit linkages between World-System position and authority structures.
Those that do almost exclusively hypothesize that external factors affect domestic conditions which in turn influence
a country’s authority structures or propensity for democracy. Somewhat ironically, the domestic processes by which
external dependence is held to impede democratization are actually often very similar to those highlighted by the
social requisites school.
1
predicted from wealth alone, their claim that democratization is random is patently incorrect, as there is a
systematic relationship between transitions and regional context.
International contexts of democratization
Though most work on democratization emphasizes domestic causal relationships that are presumed to be
constant over time, it is widely recognized that the global distribution of democracy varies considerably
over time. Figure 1 displays the distribution of democracy in the international system since 1800, as
measured by the proportion of democratic polities and the mean level of institutionalized democracy on
the composite scale in the POLITY 98 data.2
-2
-4
Mean level of democracy
0
0.4
0.3
0.2
0.0
-6
0.1
Proportion of democracies
2
0.5
Proportion of democracies
Mean level of democracy
1800
1850
1900
1950
2000
Year
Figure 1: The global distribution of democracy, 1800-1996
The extent of political democracy in the world indeed seems greater at the present than at any
previous period in history (e.g., Huntington 1991). Though the mean and median values resemble the socalled “three waves” of democracy, other measures of central tendency yield quite different images of the
2
This scale ranges from a low of –10 to a high of 10 in degree of institutionalized democracy (Gurr, Jaggers and
Moore 1989). I here treat the scale as if it were continuous, though it is strictly speaking ordinal rather than interval
(Gleditsch and Ward 1997). The threshold for “democracy” is here set to a score of six or above.
2
evolution of political democracy in the world. The mode is the minimum value for most years well
throughout the middle of the 19th Century, and fluctuates between high and low values in the 20th Century
after World War I. Only in the latter part of the 19th and the earliest part of 20th Century are the modal
values close to the mean. That the mode oscillates between the extremes of the POLITY 98 scale reflects
a bimodal distribution, but also indicates that there is considerable difference in “typical countries” over
time.
As the domestic and economic conditions that typically are assigned causal importance tend to
change relatively slowly over time, it seems questionable whether the observed variation in the
distribution of democracy can be attributed to changes in these factors alone. At a minimum, the
relationship between such domestic attributes and authority structures does not seem structurally stable
over time.3
The recent changes toward greater political democracy commonly known as the “third wave” of
democracy have sensitized researchers to that trends in democratization may reflect influences from a
changing global environment rather than similar processes operating independently or in a parallel
fashion within each case (e.g., Huntington 1991). In a remarkable change of emphasis, Whitehead (1996)
even claims that of all the democracies existing at the beginning of the 1990s, only in Sweden,
Switzerland, and the United Kingdom did political democracy evolve independently of international
events. Though recent work focuses almost exclusively on the diffusion of democratic authority
structures, changes at the global level have not been consistently toward greater democracy. Most
notably, the breakdowns of fragile democracies in the inter-war period and in the aftermath of
decolonialization suggest that a spread of autocracy prevailed for much of the 20th century (see also
O'Loughlin et al. 1998).
Merely attributing democratization or autocratization to some “international context,” however,
explains little without clarifying the relevant context and some hypothesized mechanisms that influence
the emergence of democracy. Many look to changes at the global level itself, and attribute variation in the
global distribution of democracy over time to changes in largely autonomous belief structures and the
relative status of political ideologies (e.g., Fukuyama 1992), or the changing nature of hegemony and the
position of the United States on democracy (e.g., Robinson 1996). These studies tend to remain rather
descriptive, and rarely provide any explicit hypotheses as to why the balance of ideologies or hegemony
change in such a way as to favor democracy.4
3
Some of the differences stem from new states entering the international system rather than changes in countries in
the system. But while some of the major breakpoints in Figure 1 are associated with changes in the composition of
the system — notably the two world wars and the process of decolonialization — the variation in the mode of
democracy indicates that there is no simple relationship between system size and the global distribution of
democracy.
4
Marxist and World-Systems analysts such as Robinson (1996) essentially assert that whatever changes occur are
due to the changing nature of hegemony — thereby directly assuming what is to be proven. US presidential
administrations also seem to differ considerably in the emphasis on democracy in foreign policy. Though the Carter
administration, at least initially, placed greater emphasis on democracy and human rights, these features were much
less prominent under Reagan and Bush when the “third wave” picked up speed.
3
In one of the few empirical studies of global level influences, Ray (1995b) evaluates how much
of the total variance in transitions to democracy can be attributed to system-level and national attributes
respectively. His analysis indicates that the global share seems relatively modest, and Ray surmises that
the global influences on democracy may have been overstated.
Such largely negative findings could also be due to the all-encompassing nature of the notion of
global context. Global-level influences on democratization are often presented in a fashion so general as
to render a systematic assessment difficult. Assuming that everything is related to everything else makes
empirical analysis nearly intractable, and assuming universal global influences might be as grossly
inaccurate as fully identical processes operating independently within each country. Invoking global
context furthermore implicitly assumes that external influences on regime change and political authority
structures at any point in time are essentially similar or consistent across all countries.
By contrast, I submit that regions or local environments provide a more reasonable context of
diffusion and container of primary external factors and interactions influencing changes in political
authority structures. Geography and distance induce dependence and affectedness, and shape incentives
and behavior among neighboring countries. Spatially grounded measures that incorporate influences from
neighboring states can help address implications of interdependence for international conflict and
cooperation (Gleditsch 1999, Gleditsch and Ward 2000, Shin and Ward 1999), and provide a promising
avenue to examine international dimensions of democratization.
Diffusion and dependence at the local level can have dramatically different implications for
entities within the global system, depending upon the specific composition of neighboring states or their
regional context. A stable share of countries that by some criterion are judged to be “democracies” at the
global level may mask considerable variation at the local level. Local differences such as countries within
highly autocratic regions becoming more autocratic while polities in more democratic contexts experience
transitions to democracy could wash out in the aggregate. Though such changes go in different directions,
they can nonetheless be seen as qualitatively similar processes that induce similarity in authority
structures within regions or geographical clusters that differ markedly between each other.
Towards a theory of diffusion
What factors underlie such local diffusion and dependence in authority structures? Recent literature on
democratization argues that democracy emerges as an outcome of enduring social conflict when no single
actor possesses sufficient resources to impose itself upon others (e.g., Olson 1993, Przeworski 1988,
Vanhanen 1990). Institutionalizing methods for sharing power and establishing political and civil rights
become rational options when social actors are unable or unlikely to have their unrestricted preferences
prevail. There is no inherent reason why such struggles over influence and applicable resources should
“stop at water’s edge,” or be fully confined within the boundaries of individual countries. The balance of
power or the resources and influence that given social groups can mobilize can be altered decisively not
4
only by domestic factors and processes, but through external events and opportunities for assistance from
“outside” actors as well.
External influences that alter the balance of power at the domestic scene may be located at a
variety of types of levels and actors. Many non-governmental social and political networks are clearly
transnational in nature and operate across national borders. Though such groups may rarely alone
undermine ruling coalitions or influence policies directly, they can exert critical indirect effects through
altering the existing distribution of resources or influence that groups within states can muster. Many
political movements associated with ethnic groups such as the Irish Republican Army and the Kurdish
Labor Party (PKK) have drawn heavily upon resources mobilized in diasporas. History exhibits several
examples of international networks actively trying to change the course of events in other countries,
spanning from peace activists to commercial interests, armed insurgencies, and revolutionary varieties of
Islam (Deutsch 1954, Keck and Sikkink 1999, Randle 1991, Smith, Pagnucco and Lopez 1998). States or
domestic coalitions in power will often take an active interest in events occurring in neighboring countries
and try to influence outcomes according to their preferences. Foreign policies are shaped by demands
from transnational actors (e.g., Davis and Moore 1997), and neighboring states may supply important
resources to actors that can affect the outcomes of political struggles at the margin.
Such coalitions between external and internal actors are likely to exert the most dramatic effects
on authority structures when there are shifts in the coalitions that hold power in neighboring entities, as
these tend to be associated with large changes in the resources or means of influence that actors can
mobilize. Many studies invoke variations on Schelling’s (1971) “tipping model” to show how merely
small changes in external context can suffice to yield cascades of individual participation, thereby
generating a critical mass in political contestation (e.g., Kuran 1989, 1991, Lohmann 1994). Such
processes are often held to have played out in the fall of socialism in Eastern Europe, where the initial
political changes in Poland and Hungary changed the relative influence of actors and constraints on
feasible actions which spurred subsequent changes in Czechoslovakia and East Germany.
Diffusion processes may also induce regime changes other than transitions to democracy. Ghana
can be seen as prototypical for the development of governance in many post-colonial African societies.
After the relatively democratic constitution at the time of independence in 1958, president Nkrumah
became increasingly dictatorial and severely limited political opposition. The one-party system
introduced in 1964 quickly spread to many neighboring states. More recently, regime changes and
insurgencies in Central Africa display similar diffusion processes. Many regard the Ugandan support as
critical for the 1994 RPF takeover in Rwanda. The changes in Rwanda again boosted Kabila’s armed
uprising that toppled Mobutu in neighboring Zaïre. Many regime changes involving major political
reorientations may not show up in measures of authority structures, however, as changes in the leadership
or ideological orientation of autocracies do not necessarily yield changes in institutional structures per se.
If diffusion processes operate between states operate more generally, we should see a serial
clustering geographically both in the distribution of authority structures as well as changes in such
5
attributes. I will later show that we indeed find such clustering in authority structures, and that transitions
and survival for regimes co-vary dramatically with the regional context.
Democratization and the diffusion of conflict and peace
Though transnational integration might provide a facilitating condition for many forms of diffusion
processes, some forms are clearly possible even in the absence of significant integration. In particular, the
diffusion of conflict and insecurity within regions may severely constrain prospects for democratic rule.
Many have elevated the so-called “democratic peace”, or the empirical finding that pairs of
democracies seem to have a lower likelihood of conflict than other combinations of polities, to an
“empirical law” of international relations (e.g., Levy 1989, Russett 1993). In this research tradition, the
distribution of authority structures in the international system is taken as exogenous or given when
comparing the conflict proneness of pairs of countries with different authority structures. However, the
likelihood that a country is democratic or experiences transitions may itself be related to its propensity for
conflict and peace. Stated differently, democracy cannot be considered fully exogenous if countries that
are less likely to experience conflict or enjoy a more secure peace have better prospects for democracy in
the first place.
Many proponents and opponents of the democratic peace concede that the likelihood of
transitions or sustaining democracy in part may be related to peace itself, but disagree about whether such
broader linkages revolve around positive feedback between democracy and peace or reverse causal
linkages from secure peace to democracy. In the first instance, linkages from democracy to peace
reinforce the democratic or broader liberal peace proposition. Under the latter view, the notion of
democracy as a path to peace confuses the direction of causality and the substantive implications of the
association between the two.
Many proponents of the democratic peace suggest feedback mechanisms between peace and
democracy, where democracy first makes peace more likely and the improved prospects for peace
subsequently enhances the prospects for sustaining democracy (see, e.g., Russett 1998). In addition, some
of the domestic socio-economic factors seen as conducive to democratization and democratic
consolidation such as economic performance, wealth, and trade may conceivably influence the propensity
for peace and conflict (e.g., Hegre 1999, Rosecrance 1986). Many critics assert as a self-evident
proposition that peace is a prerequisite for preserving democracy. Some surmise that the absence of war
between democracies could merely stem from that democracy is likely to break down under the threat of
war and thus rarely survives until war breaks out (e.g., Layne 1994). Others hypothesize that times of war
will be associated with a decline in democracy, as efforts to wage war can be incompatible with civil
liberties or induce at least temporary restrictions in democracies (e.g., Gates, Knutsen and Moses 1996).
Similarly, Mansfield and Snyder (1998) hold that young democracies with fragile institutions are at
greater risk for reversals when involved in or at risk of conflict.
6
Numerous cases where stability and lack of belligerence go together with autocratic rule attest to
that peace cannot be sufficient for democracy. Nor can peace or absence of conflict involvement be
strictly necessary for democracy, since many democracies participate in interstate wars and sustained
conflict and yet remain democracies. Of course, this does not imply that the no linkages may exist from
conflict and peace to democracy, and few probably hold that merit of hypotheses on the potential effects
of peace on democratization should be judged by whether such deterministic regularities obtain. These
examples do, however, indicate that assertions that democracy is incompatible with conflict in their
general form are not evidently correct, and that such hypotheses require emendation to be tested
empirically in any meaningful manner.
Thompson (1996) set forward a more general war-making/state-making perspective on
democratization that clarifies some possible linkages. According to Thompson, the political systems that
eventually emerge historically have been strongly shaped by rulers’need to obtain resources and mobilize
for military efforts. Sustained perpetual rivalry and threats to vital security tend to foster authoritarianism
as power becomes more centralized. A situation of relative regional peace, by contrast, may facilitate the
initial emergence of political pluralism as internal political processes can unfold within some degree of
insulation from external demands or threats. Thompson argues that contemporary and historical “zones of
peace” emerged when previously dominant states were forced to abandon ambitions of regional
hegemony. In Norway, for example, efforts to push for full independence from Sweden were put on hold
in the late 19th century over concerns over increased Russification in Finland, and it is not incidental that
the 1905 independence coincides with the defeat of the perceived threat in the Russo-Japanese war.
Barzel and Kiser (1997) set forward an alternative interpretation of the role of war and security in
the emergence of voting institutions in medieval Europe. Warfare has in some cases moved states towards
democratization through rulers’need to cede political rights to subjects in exchange for the ability to
extract tax revenue (e.g., Kiser and Barzel 1991). Though many theories relate the emergence of
democracy and political rights to weak rulers, Barzel and Kiser hold the security of rule as essential for
the possibility of contracting between rulers and the ruled. The less secure a ruler, the lower the potential
level of cooperative contracting, and the lower the rate of development of voting institutions. In addition
to lower internal threats, the relative geographical isolation and protection from external threat may in
part explain why voting institutions were more developed and durable in England than in France.5 While
these linkages are quite different from those of Thompson (1996), the observable implications for
linkages between regional peace and democracy are largely similar.
From this perspective, it is less surprising that the presence or absence of war among states
correlates with the extent of their political democracy. The idea of democracy as a path to peace amounts
to “putting the cart before the horse” if a situation of relatively secure peace or absence of regional threat
was an important prerequisite for the emergence of democratic political systems. Though this “regional
5
If regional threats do impede democracy, there is certainly something to be said for “splendid isolation.” Such
linkages may in part underlie the relationship between democracy and insularity some emphasize (e.g., Anckar and
Anckar 1995).
7
peace precedes democratization” hypothesis does not necessarily disprove the democratic peace, it
suggests that constraints on initial democratization and their relationship to international interactions may
have been disregarded in previous work. Such omissions make it difficult to evaluate the implications of
existing findings and leave an unstable foundation for inferring policy implications.
Thompson and Barzel and Kieser illustrate their arguments by various historical cases, but
linkages from war and peace to democracy have rarely been examined in any systematic manner. Some
studies examining whether wars exert some effect on the prospects for democracy find that conflict
involvement does not seem to decrease democracy (e.g., McLaughlin 1996, Mousseau and Shi 1999).
However, not all incidents of “war” and “peace” as the absence of war at some given point in time are
qualitatively equivalent, and correlating general war involvement with democracy can miss out on central
ways by which conflict may influence democracy.
First, researchers have examined whether general participation in conflict is associated with
democratization and autocratization, though the relevant variable is the extent of threats to security in a
country’s regional context. The “cart before the horse” hypothesis suggest that persistent insecurity over
time can inhibit the emergence of democracy rather than that participation in conflict is incompatible with
democracy in and of itself. Existing efforts to test linkages from conflict to prospects for democracy
focus on a state’s general “belligerence”, and have not sought to identify the locus of conflict involvement
or to what extent conflicts pose threats to a state’s vital security. Participation in UN peacekeeping forces
outside the regional context or remote colonial wars that do not expose the core territory to risks, for
example, should not be expected to lead to a breakdown of democracy in developed societies.
Second, effects of conflict on the prospects for democracy are not necessarily one-time events
immediately following outbreaks of war, but hinge upon more enduring forms of insecurity and threats
that do not necessarily break out into open warfare or conflict all the time. Rather than just the peaks of
conflict resulting in wars, we need to consider the broader array of hostile interactions in a country’s
regional context over time. The history of interaction shapes the perceived insecurity that may affect
long-term prospects for democracy through risk of recurrence and diffusion of conflict. Though the extent
of real and perceived “threats” over time is a latent or not directly observable factor, measures based on
interaction across time and space can provide more valid indicators than data on single events.
To assess the element of threat, I thus distinguish between local conflict and conflict involvement
elsewhere in the international system. If the hypotheses linking conflict and peace to democracy have
some merit, we should observe a relationship between democracy and the stability of peace in a country
i’s regional context.
8
Empirical analysis
Clustering of democracy and democratization
If the idea of regional diffusion of authority structures has some merit, we should to observe a positive
relationship between the composition of authority structures in the proximate regional context and a
country’s own domestic authority structures. Democracy and democratization should cluster in disparate
and qualitatively different zones over time and space. We can explore the empirical record more
systematically through descriptive statistics of the extent of clustering and variation across space.
Conceptually, the extent of spatial clustering in data on geographically situated units can be
studied at either a global or a local level. Attributes are clustered globally if values on the particular
variable are distributed in some geographic pattern deviating systematically from a random distribution or
what one would expect from chance. By contrast, local measures of spatial clustering identify whether
specific regions cluster or exhibit strong similarity in attributes or behavior. Stated differently, global
indicators of spatial clustering indicate whether aspects are non-randomly distributed in space, whereas
local indicators indicate which regions or locales display high or low values respectively.
The most common statistic for assessing the extent of global clustering in some variable x for a
cross-section of geographical units is the so-called Moran’s (1950) I coefficient. This is defined as
I=
n
∑∑
i
~
w
j ij
∑∑
i
j
~ ( xi − x )( x j − x )
w
ij
∑ i ( xi − x ) 2
(1)
~
~ denotes an element [i, j ] of a row-standardized matrix W
that acquires a non-zero value if
where w
ij
units i and j are contiguous. 6 The value of the Moran’s I statistic indicates the similarity of xi for each
unit i with its J neighboring entities x j . However, the coefficient is not bounded by ±1 , and the expected
value is E ( I ) =
−1
rather than 0. The estimated standard error of the Moran’s I statistic,
N− 1
af c h af
V I = E σ 2I − E I , allows testing whether the observed levels of global clustering in a given
2
sample differ significantly from the null hypothesis of no spatial clustering.7
6
~
~
W by convention is set to zero so that countries are
The rows of W each sum up to one. Note that the diagonal of
not considered contiguous with themselves.
7
b gturns out to be somewhat cumbersome to express and depends upon
The expected value of the variance E σ
2
I
sampling assumptions. Cliff and Ord (1973:15) develop the expected value of the variance of Moran’s I under a
b g is b g
) b
g
n S1 − nS 2 + 3S 0
2
normality assumption and a randomization assumption. Under normality, E σ
b g turns out to be
randomization E σ
2
I
b
g
n n − 3n + 3 S 1 − nS 2 + 3 S 0 − E ( x i − x
2
2
4
2
9
while under
S0 n − 1
I
2
2
n − n S 1 − 2 nS 2 + 6 S 0
S 0 (n − 1)(n − 2 )(n − 3)
2
2
2
2
, where
To assess whether the distribution of political authority structures cluster geographically in a nonrandom fashion in the international system, I calculate Moran’s I coefficients for the levels in the
composite institutionalized democracy scale in the POLITY 98 data set for each year in the period 187596, as well as changes on this scale over the prior ten years. I generate contiguity data from a new data set
on the minimum distances between polities, and consider states contiguous if they are less than 950
kilometers apart from each other.8 I plot the value of the Moran’s I coefficients over time in Figure 2.
Significant values are indicated by filled circles, and blank circles indicate cases where the null
hypothesis of no spatial dependence cannot be rejected at p <.05. The expected value of Moran’s I is
plotted as a solid line.9
-0.2
0.2
0.6
Moran's I (950 km)
1.0
Moran's I, level of democracy
1880
1900
1920
1940
1960
1980
2000
1960
1980
2000
Year
-0.2
0.2
0.6
Moran's I (950 km)
1.0
Moran's I, change in democracy
1880
1900
1920
1940
Year
Figure 2: Moran’s I for clustering of institutionalized democracy and changes in democracy, 1875-1996
S0 =
∑ ∑
n
n
i=1
j=1
a
f
wi , j + w j , i , S 1 =
1
2
∑ ∑
n
n
i =1
j =1
a
w i , j + w j ,i
f
2
, S =
2
∑ ∑
n
n
i =1
j =1
a
~ + w
~
w
i,j
j ,i
f , and w
2
i, j
denotes an element
[i, j ] in a binary connectivity matrix W , all the other variables as defined above.
8
This data set allows varying the cut-off criteria for what is to be considered “relevant” distances, and has been
adjusted to incorporate border changes since 1875. It is described in greater detail in Gleditsch and Ward (1999a). A
950-km cut-off criterion has a range of inclusion corresponding quite closely to the notion of geographical regions. I
have used cut-off criteria for relevance set to 950, 475, and 50 kilometers respectively to generate binary contiguity
matrices. For considerations of space, only results based on the 950 km threshold are shown here. The results at
other distance threshold do not yield substantively different conclusions and are available on request.
9
The expected value of Moran’s I depends upon sample size, but the change from one year to another is relatively
small and the flat nature of the line suggests that comparisons across time are not too problematic here. Though the
null hypothesis here obviously is spatial independence (i.e., that observations are not correlated across space), the
interpretation of tests can be ambiguous since potential patterns of spatial dependence may lack a known
representation. In this sense, we only fail to reject a null hypothesis of no-spatial dependence for some specific
hypothesized pattern, and cannot conclusively rule out other patterns of cross-unit dependence (Anselin 1988).
10
The upper row in Figure 2 displays the extent of global clustering in institutionalized democracy.
These results strongly suggest that institutionalized democracy is distributed non-randomly in space. With
the exception of a few years (notably during World War II), the values of Moran’s I indicate quite
consistent, significant spatial clustering in the observed data from the end of the 19th century. In other
words, the likelihood of observing whether a given country will be democratic or not differs according to
its regional context or the properties of neighboring countries.
The lower row indicates whether the changes in authority structures over a ten-year period appear
to be distributed geographically in a non-random fashion. Changes are quite infrequent, and for most
years, the Moran’s I coefficients do not achieve statistical significance. From the late 1970s and on,
however, the null hypothesis of no spatial clustering is rejected for most years. Even at the aggregate level
of the international system, we thus find evidence of a geographical clustering in political changes and
transitions during the “third wave” of democratization.
The Moran’s I indicates geographic clustering in the aggregate, but does not by itself indicate
where democracies and autocracies cluster. The so-called Gi* statistic is a common indicator of local
clustering or whether values of a given variable x are similar for entities around some location i (Ord and
Getis 1995). This is defined as
Gi* =
∑
j
wij x j −
∑
$ x n∑ wij2 −
σ
j
i
( wij + wii ) x
∑ iw ij / (n − 1)
2
,
(2)
where w ij denotes element [i, j ] in a binary contiguity matrix W and x j denotes an observation at
location j.10 The values of the Gi* statistic can be interpreted as standardized Z-scores. A positive value of
Gi* at a particular location implies spatial clustering of high values around that location, while a negative
value indicates a spatial grouping of low values.
10
Since the interest here is in to what extent observations are similar to their neighbors, the diagonal entries in W
are all set to one. An alternative statistic Gi , which does not include the values of i itself, indicates to what extent
values surrounding location i are similar values. See Ord and Getis (1995) and Gleditsch (1999: ch. 4) for further
details.
11
Values of G*i Democracy (1992)
(<-3)
(-3,-1.96)
(-1.96,-1.65)
(-1.65,1.65)
(1.65,1.96)
(1.96,3)
(>3)
No Data
Map 1: Values of Gi* for level of democracy, 1992
The annual Gi* statistics for the years 1875-1996 reveal several discernible zones or clusters of
states with similar authority structures within the international system. Map 1 displays the localized
clustering in democracy in the international system in 1992, as seen in the values Gi* at a 950-km
threshold. Shades of green denote zones with significant positive spatial clustering or strong clustering of
high levels of democracy. By contrast, shades of orange indicate significant spatial clustering of low
values of democracy or zones of autocracies. There is significant positive clustering of democracy
throughout most of Europe, and a similar, though less strong, clustering in democracy in the Western
Hemisphere. Conversely, these zones of democracy are matched by a belt of strong clustering in
authoritarianism, stretching from Central and East Africa through the Middle East.
Can we find similar evidence of clustering for changes in authority structures as well? Map 2
indicates the extent of clustering for changes in authority structures over the prior decade as assessed by
the Gi* statistic. The significant clustering in the changes toward greater democracy both in the Southern
Cone and Eastern Europe leads some support to that the “third wave” of democratization in part unfolded
regionally.
12
G*i change in democracy, 1982-92
(<-3)
(-3, -1.96)
(-1.96, -1.65)
(-1.65, 1.65)
(1.65, 1.96)
(1.96, 3)
(>3)
No Data
Map 2: Values of Gi* for changes in level of democracy over prior 10 years, 1992
Internal and external dimensions of democracy
Though many suggest international dimensions to democratization, relatively few empirical analyses have
examined systematically the importance of regional diffusion. Existing efforts to analyze empirically the
diffusion of democracy and authority structures across borders (e.g., O'Loughlin et al. 1998, Starr 1991)
largely disregard the potential influences that domestic attributes and processes may exert.
Merely reporting an aggregate relationship between a country’s authority structures and the
composition in its regional context does not in itself provide convincing evidence that diffusion processes
operate. There is in all likelihood comparable non-random geographical clustering among the principal
social and economic conditions hypothesized to influence democracy. As such, what analyses that ignore
domestic aspects altogether attribute to diffusion may simply stem from geographical clustering in
omitted domestic attributes that influence the propensity for democracy. 11 To say something about the
relative effects of external dimensions of democracy, these must be assessed while simultaneously
considering some of the plausible confounding domestic factors. While I cannot consider all theories
relating democracy to domestic processes here, the primary alternative hypothesis is obviously the “social
11
Gleditsch (1996) compared the influences of diffusion and socio-economic development on democracy as
reflected by the Freedom House data more systematically. This study encompassed a relatively limited sample of
13
requisites” school or that democracy is related to a country’s level of socio-economic development (e.g.,
Lipset 1960, 1994).
Much of the existing comparative research on the relationship of democracy to domestic
attributes and processes exhibits various other problems. Studies have relied on largely cross-sectional
designs where the variance is predominantly between countries. However, researchers are interested in
the likely implications of changes in the independent variables over time, and often proceed to make
inferences about prospects for democratization. Though rarely stated explicitly, many assume that the
temporal dynamics simply are mirror images of the differences between countries. In principle, however,
there is no logical connection between the two empirical domains, and it can easily be shown that
inferences about temporal dynamics based on cross-sectional variation may be misleading if the two are
not identical (e.g., Brunner and Liepelt 1972, Smith 1995).12 The basic point that the dynamics of
democratization cannot be directly inferred from levels or the distribution of democracy at a given point
often appears to have been overlooked.13
Many studies pool time-series for several countries, but fail to address the problems that the
structure of the data poses for analysis (Arat 1988, Gonick and Rosh 1988).14 Given the inherent temporal
ordering of annual observations for some country i, it would be tenuous at best to treat these observations
as independent. As authority structures rarely change from one year to the other, these observations are
likely to be highly correlated over time. Most of the variance in the pooled data is thus still between
countries rather than over time. Though there is considerable empirical evidence indicating a strong
association between a country’s development, it is less clear whether the causal effect most commonly
inferred — that economic development will yield changes toward greater democracy over time — obtains
as strongly as many assume.
To assess the relative importance of external and internal factors, I examine how the distribution
of democracy varies across time and space according to both domestic social requisites as well as external
factors in the surrounding regional context. I measure a society’s economic wealth or “social requisites”
countries, however, and suffered from problems of non-systematic coding of “proximity” or “closeness” between
entities, based upon subjective assessments of each individual case.
12
Some critics like Tilly (1984) hold that cross-sectional designs are based on an underlying assumption that history
follows general sequences or similar paths. This assertion, however, is highly questionable. Since the values on right
hand side variables can both decrease and increase, there is no form of “evolution” assumed by the design alone. If
the processes over time and space are similar, the variation across units in a cross-section might be more
informative about long-run behavior than relatively short individual unit time-series.
13
Though various more historically grounded macro-comparative studies examine changes in democracy over time
(e.g., Moore 1973), many researchers rely on often rather controversial historical interpretations as unambiguous
and indisputable “evidence” (see Goldthorpe 1991, Lustick 1996). Others criticize the extreme determinism, where
class constellations and events several decades, if not centuries, earlier are held to fully determine current regime
structures (e.g., Przeworski and Limongi 1997).
14
Burkhart and Lewis-Beck (1994) try to remedy some of these shortcomings, but understates the extent of
permanence over time in the data by purging the serial correlation of observations over time before the analysis and
(see Beck and Katz 1996, Gleditsch 1996). Since regimes rarely change from one year to another and the serial
correlation approaches 1, purging the autocorrelation essentially leaves a regression of covariates on the first
differences of the annual observations where any change would have to be attributed to the covariates by
construction.
14
c h
by the natural logarithm of its GDP per capita in purchasing power parities at time t (denoted ln Pi ,t
symbolically), using data from the Penn World Tables (Summers and Heston 1991).15 I assess the
regional context of democracy by the average of the authority structures in the region surrounding country
i using a 950-km threshold (denoted DiR,t ). I use the clustering of years without local conflict involvement
τ in the polities within 950-km of each country i as a proxy indicator of the extent of threat or stability of
peace (denoted Gi*,tτ ). More specifically, I regress a country i’s level of democracy at time t (denoted
Di ,t ) on the natural logarithm of its GDP per capita in purchasing power parities at time t, the regional
average authority structures at time t, and the stability of peace in the regional context. To prevent serial
correlated residuals from inducing biased estimates and statistical overconfidence, 16 I include the first lag
of the dependent variable or country i’s level of democracy in the preceding year (i.e., Di ,t − 1 ) on the righthand side. This parameter can also be interpreted as a measure of the persistence over time in the
dependent variable. Symbolically, we have the regression equation
af
Di ,t = α 0 + β1 Di ,t − 1 + β2 ln( Pi ,t ) + β3 DiR,t + β4 Gi*,t τ + µ i ,t ,
(3)
all the variables as defined above.
15
Many suggest that differences in GDP capita probably matter comparatively more at lower levels of per capita
income (e.g., Burkhart and Lewis-Beck 1994, Jackman 1973). As a linear relationship seems implausible, I use a
logarithmic transformation of GDP per capita. The natural logarithm is obviously a somewhat arbitrary
transformation, and other types of non-linear functional forms are of course possible (see, e.g., Jackman 1973 for a
more extended discussion). Some include an additional a square term of GDP per capita (e.g., Przeworski and
Limongi 1997). However, though effects may decrease in magnitude at higher per capita income, this specification
seems theoretically inappropriate as there is no clear reason why additional income above some threshold should be
associated with a lower level of democracy.
16
Serial dependence or autocorrelation implies that the error terms for different observations are correlated rather
than independent of each other. The variance-covariance matrix V of the coefficient estimates for a regression
c h
−1
c h c h
−1
X T σ 2 Ω X X T X . Generally stated, an error structure exhibits autocorrelation
whenever Ω is not the identity matrix I , and the expected value of the off-diagonal entries ω ij are not all 0.
Generalized least squares (GLS) techniques model the correlation-structure of Ω explicitly by some hypothesized
$ , and can be shown to yield consistent estimates and various desirable asymptotic properties (e.g., Hsiao
estimate Ω
1986). Since the “true” structure of Ω is generally not known, researchers must resort to some hypothesized
T
model is given by X X
estimate and it is commonly assumed that error structures follow either AR(1), MA(1), or ARMA (1,1) processes. In
an AR(1) structure, the correlation between two residuals ε t , ε t − j reflects a common correlation coefficient ρ < 1
c
as well as their temporal distance so that cor ε t , ε t −
h = ρ . The serial correlation is always larger than zero, but
j
j
becomes increasingly smaller and negligible for longer temporal distances. An MA(1) structure implies that the
error term incorporates the values of prior residuals ε t− 1 in addition to the stochastic component µ t , so that
ε t = µ t − λµ t − 1 . Analysts typically estimate ρ from the residuals of a regression, and then purge the
autoregressive component from the model until the estimated residuals appear to be white noise. However, the
properties of GLS hold only asymptotically and may not obtain in an actual limited sample situation. Beck and Katz
(1996) provide Monte Carlo simulation results indicating that feasible GLS estimates are not generally more
efficient than regular OLS regression, with some minor corrections to control for autocorrelation and
heteroskedasticity, in applied settings with the data properties common in social science. More importantly, purging
the serial correlation might be an adequate statistical solution, but coefficient estimates that fail to reflect the
stickiness or autoregressive structure can easily be analytically misleading in that we are likely to exaggerate the
substantive implications of changes in the right-hand side variables on the dependent variable.
15
As the model probably will not fit all countries equally well and some may display larger
variance in the error terms than others, the pooled nature of the data is also likely to invalidate the
assumption of homoskedasticity or constant variance of the expected values of the error term between
observations.17 Table 1 thus reports heteroskedastic consistent standard errors for estimating Equation 3
based on MacKinnon and White’s (1985) jack-knife estimator.
Table 1: Regression of level of democracy on regional context, wealth, and conflict
LEVEL OF DEMOCRACY
INDEPENDENT VARIABLES
COEFFICIENT
ESTIMATE
STANDARD
ERROR
-1.122
.045
Democracy at time t-1
.960
.005
GDP per capita, natural log
.147
.001
Regional context of democracy
.0258
.007
Regional clustering of years at peace
.040
.018
Constant
F4,4095 = 22990, N = 4096
As reflected in the .96 coefficient estimate in Table 1, there is a near deterministic relationship
between level of democracy in the previous and current year. Given the high persistence in authority
structures over time, variation in the explanatory variables is unlikely to be associated with dramatic
effects on the level of democracy, at least in the short term. The high degree of permanence indicated here
cannot be dismissed merely as an idiosyncratic feature of the POLITY data, but should be seen as an
actual feature of authority structures.18 Of course, dichotomous indicators of “democracies” and “nonHeteroskedasticity means that the entries on the diagonal matrix Ω in the variance covariance matrix of the
coefficient estimates V are not identical but vary in size. Heteroskedasticity does not bias coefficient estimates, but
the standard error estimates will no longer be efficient. White (1980) show that a so-called “sandwich estimator”
Sw , or a diagonal matrix with the squared estimated residuals of the regression σ$ i2 on the diagonal, under quite
17
general conditions yield a consistent estimate of σ Ω . MacKinnon and White (1985) suggest an alternative
2
σ$ i2
estimator known as the “jack-knife” S mw . This weights the diagonal entries
by the influence each
1− h$i2
observation exerts on the coefficient estimates, as measured by the diagonal elements of the so-called “hat” matrix
c h X . Davidson and MacKinnon (1993) suggest that the jack-knife estimator S
given by H = X X X
T
−1
T
better than the original White estimator
mw
performs
Sw in small samples.
18
This estimate corresponds almost perfectly to a similar analysis (Gleditsch 1996) using the Freedom House data
on democracy, with a different panel and a distinct set of right hand side variables in the model.
16
democracies” would tend to display even greater persistence over time than the more disaggregated 21point POLITY institutionalized democracy scale.
The coefficient estimate for the other exogenous variables are all in the hypothesized direction
and statistically significant at conventional levels. As in previous research, there is a positive association
between the natural log of a country’s GDP per capita and its level of democracy. However, if we
consider the coefficient estimate relative to the metric of the variable, the actual implied differences
following variation in wealth are relatively limited. The difference between the GDP per capita of
developing countries and the values corresponding to developed OECD countries in Western
Europe/North America19 actually translates to a relatively marginal predicted difference, less than a full
point on the institutionalized democracy scale.20
Attributes such as GDP per capita or economic income are furthermore relatively static or display
changes only slowly over time. Consider a developing society with a GDP per capita of about $2,000 —
roughly equivalent to Spain in the 1950s or Guatemala in the 1970s. If we assume that this experiences a
sustained real growth rate of about 7.5% annually, even over a period of 20 years, the propensity for
democracy predicted by the model increase only marginally as its GDP per capita reaches approximately
$8,500. Many previous studies may thus have overstated the impact of changes in levels of development.
Level of democracy cannot be directly predicted on the basis of level of wealth alone. At the same time,
democratization or sustaining democracy need not be impossible in developing societies. Other recent
analysis relying on somewhat different approaches similarly find limited effects of changes in
development on democracy over time (e.g, Londregan and Poole 1996, Przeworski and Limongi 1997).21
Some may blame the measure of wealth for the seemingly weak association. Development is
often used in a much broader sense than the monetary value of the economic output, including
factors such as economic diversification, levels of education, and at times even civil liberties and the
status of women. Some argue that a proper test of the development to democracy hypothesis should
include various other indicators as alternatives to GDP per capita or additional variables (e.g.,
Gasiorowski 1988). However, proposed composite indices of “development” typically lack a coherent
theoretical foundation, and are often not comparable over time. Adding a series of alternate variables
in the same regression would induce massive multicollinearity and unstable inferences. Finally, most
other social and economic aspects hypothesized to be important for democracy such as material
19
GDP per capita values in PPP vary between $221 and $31,969 in the sample, all figures in 1985 constant dollars.
The higher value limit, however, is somewhat misleading, since the distribution is highly skewed. The largest values
stem from a few rather unrepresentative countries. Notably, some of the oil producing countries in the Middle East
display very high GDP per capita, though these do not necessarily correspond to many of the aspects attributed to
“developed societies.” By comparison, GPD per capita in 1990 for the United States is about $18,000, or a natural
log of about 9.8.
20
The difference between results using the raw GDP per capita figures and logged figures is relatively marginal, and
do not change the qualitative conclusions here. The overall fit as measured by an F-test, however, is somewhat
better for the regression using the logged figures.
21
An econometric study by Barro (1996), arguing that development causes democracy, examines a cross-section
with average growth rates and may understate the persistence in authority structures.
17
inequality (e.g., Muller 1995) are likely to display even less change over time than GDP per capita.
As such, it is unlikely that these would display a stronger association with authority structures over
time.22
The coefficient estimate for the regional context of democracy may seem relatively small as well.
However, since the metric of the variable is restricted to a range from -10 to 10 and the distribution
covers the entire span of possible values between the extremes, the predicted difference over reasonable
ranges are at least as large as the plausible effects of variation in GDP per capita. The susceptibility to
change and feasibility of variation over a short time period is obviously also greater for the regional
context of democracy than most social and economic attributes. This indicates that the regional context of
democracy regional can be substantively more important than economic development, in particular as
regional shocks or changes elsewhere in a region diffuse.
Finally, Table 1 indicates some evidence of a positive relationship between a country’s levels of
democracy and the geographical clustering in peace within in its regional context, as measured by
bg
Gi*,t τ . Countries located in more peaceful regions tend to display more democratic authority structures
than countries in regions where threats to security are more pronounced. By distinguishing between types
of conflict involvement and considering impacts of conflict involvement beyond outbreaks, we find
empirical evidence for a negative relationship between conflict or insecurity and democracy. While the
measure of regional conflict is not devoid of problems, this supports that authority structures and conflict
may be related more broadly than just the seeming peace between democracies.
Levels of significance aside, as with economic development, the relationship between conflict,
peace, and the prospects for democracy does not appear as strong as some have surmised. Based upon the
aggregate data, values between -3 and 5 can be considered a feasible range of variation. The predicted
differences in authority structures for countries located in highly conflictual as opposed to relatively
peaceful regional contexts are still relatively limited. Many developing societies where linkages from
conflict and insecurity to authority structures presumably may apply also fall out of the sample due to
missing GDP per capita data.23
Some hypothetical thought experiments can help illustrate what these coefficient estimates imply
with respect to how international and domestic conditions influence the likelihood that countries will
become democratic over time. If we assume that the continuous values reflect some form of latent
propensity for a country to be democratic (with values above 6 indicating a democracy), we can examine
22
It may be contended that any such changes in the level of democracy are expected to be gradual, and therefore
should be reflected in changes in past values of authority as well. A longitudinal study by Lichbach (1984),
however, concludes that such gradual transitions were not the rule in the evolution of European polities.
23
Excluded observations from developing countries include Afghanistan, Albania, Cambodia, Cuba, Lebanon,
Libya, North Korea, South Vietnam, Vietnam/North Vietnam, Yemen (after unification) and South Yemen. None of
the post-Soviet republics — Estonia, Georgia, Kazakhstan, Kirgizhia, Latvia, Lithuania, Moldova, Tajikistan,
Turkmenistan, Ukraine, Uzbekistan — or the former states of the Yugoslav republics — Bosnia, Croatia,
Macedonia, Slovenia — are included here, as these states all become independent at the very end of the sample
period. Finally, New Zealand drops out of the analysis as it has no neighboring entities within the 950 kilometer
distance span.
18
how changes in changes in the right-hand side variables over time influence the likelihood that a country
will be democratic. For a country starting at a GDP per capita of about $2,000 with a high annual growth
rate of 5%, keeping the other values at 0 (which is roughly similar to the aggregate means), the predicted
values on the continuous scale do not reach the threshold for “democracy” even over a forty-year period.
Consider then a different scenario where a country has the same growth rate, but during the first ten years
is located in a zone of protracted conflict and polities with low levels of political democracy. Following
some external shock, the composition of authority structures in neighboring countries as well as the extent
of threats to security in the regional context gradually moves or “co-evolves” from the high to the low end
and remains there at the end of the period. The estimated propensity for a country experiencing such
5
0
-5
-10
Propensity for democracy
10
changes towards a more democratic and peaceful regional context are shown in Figure 3.
1950
1960
1970
1980
Year
Figure 3: Predicted levels under transition experiment
As can be seen, the predicted propensity declines at first, reflecting a regional context with
protracted conflict and perpetuating autocracy. Following the change in the regional context after the first
ten initial years, however, the estimated propensity increases quite dramatically. Towards the end of the
period it eventually reaches the threshold value of six for “democracy” indicated by the dashed line.
Though this hypothetical thought experiment admittedly is somewhat arbitrary and assuming different
starting values and changes over time would of course yield different predictions, it indicates that social
19
and economic processes within states are unlikely to alone induce changes in the distribution of
democracy in the absence of changes in the regional context. International influences and regional context
can exert very important influences on a country’s authority structures. Rather than exogenous factors or
nuisances, such differences and changes in the regional context seem central aspects of democratization
processes. Such factors can both work independently and support or contravene other domestic processes.
Though we cannot conclusively predict exogenous shocks or what aspects regional contexts will acquire,
we can make better informed predictions about democratization if we know something about how these
aspects change.
A Markov process specification of transitions
Though the above results tell us something about how authority structures vary according to regional and
domestic attributes, these findings do not necessarily translate directly into evidence on changes in
authority structures. A plausible model must take into account that relationships to levels of democracy
may not be equivalent to effects on changes or transitions. Gleditsch and Ward (1997) suggested that
changes in authority structures could be analyzed as a Markov chain process of transition between
different states over time,24 and demonstrated the relative lack of variance by showing that the observed
unconditional probabilities of transitions in the POLITY III data were rather low. Figure 4 similarly
displays the estimated first-order transition probabilities for the 21-point institutionalized democracy scale
with the updated POLITY 98 data. The rows of the matrix, or the values on the left axis of the figure,
indicate state at the previous time period t − 1, while the columns or values on the right hand axis indicate
state at the next time period t . As can be seen from the height of the diagonal, almost all observations
remain at the same state, and the on-diagonal transition (im)probabilities are never lower than .86 for any
single state on the scale.
24
A Markov chain specifies the probability distribution of some discrete variable
af
af
yi (t ) at time t as a function of the
state of observation i at previous time periods and a J × J matrix P t of the probabilities of transition between the
various J possible states that yi (t ) may acquire. All the entries of P t in each row, indicating transitions from a
given state at some time period t to the next, must sum to unity. A Markov chain is said to be first-order Markov if
the transition probabilities depend only on the state at the preceding time period yi (t − 1) and are independent of
the state at previous T time periods yi (t − 2), yi (t − 3),..., yi (t − T ) . Finally, a Markov chain is said to be
stationary if the transition probabilities do not depend on time t (see, e.g., Amemyia 1985: 412-7, Harary, Norman
and Cartright 1965:371-7).
20
1
ability
Transition prob
0.6 0.8
0 0.2 0.4
-1
0
-5
De
m
oc
ra 0
cy
at
tim
et
-1
10
5
0
5
-5
10
t
time
t
a
cy
ocra
m
e
D
-10
Figure 4: Observed first-order Markov transition probabilities
Though not readily apparent from Figure 4, “anocracies” that combine features of both autocracies and
democracies tend to have somewhat higher transition values. If we take the expectation of the first-order
matrix several years ahead or power, we find greater concentrations at the extremes of the
institutionalized democracy scale. This yields some support to Eckstein and Gurr’s (1975) “congruence
theory”, which holds that incongruous anocratic polities are less stable and tend to become either more
consistently autocratic or democratic over time. The expected values estimated from the first-order
transition probabilities over a time span of thirty years are displayed in Figure 5.
21
1
ability
Transition prob
0.6 0.8
0 0.2 0.4
-1
0
-5
De
m
0
oc
ra
cy
at
tim
et
10
5
5
-5
10
0
t+30
time
t
a
cy
ocra
Dem
-10
Figure 5: Estimated first-order Markov transition probabilities at time t+30
Figure 5 clearly indicates two main peaks of concentration or stability in the density of the
expected transition probabilities, located at the extreme poles of autocracy and democracy. The
probabilities that polities will remain at these two stages over a period of thirty years are .52 and .81
respectively. In the middle ranges, the likelihood of observing no change over three decades is generally
less than .2. As such, even the seemingly static relationships of the estimated first-order transition
probabilities indicate differences in the long-term expected stability for polities over time, as well as some
cues as to how polities with different combinations of authority structures are likely to evolve. “Perfectly”
consistent autocratic and democratic polities tend to remain coherent over time, whereas anocracies with
elements of both are likely to become either more autocratic or more democratic over time. The
asymmetric shape of the surface of the off-diagonal entries reflects the secular trend towards greater
democracy over the 18th and 19th century. More autocratic polities have experienced changes toward
democracy than constrained polities experiencing converse changes or transitions to more autocratic
forms of governance.
The unconditional first-order Markov model above can be extended to derive conditional
transition probabilities given some set of covariates of interest. Przeworski and Limongi (1997, especially
178-183) use a Markov chain to estimate the conditional likelihood of transitions between regime types
given a country’s GDP per capita or “level of development.” Their results indicate little effect of
differences in level of development, and Przeworski and Limongi (1997) insist that any seeming
association between development and democracy stems from differences in how economic performance
influence the survival of different types of regimes. Growth or increasing development enhances the
22
survivability of democracies, and performance is relatively more important for the survival of
democracies than autocracies. Autocracies display a “bell shaped” curvilinear pattern of instability, and
are most likely to be replaced at “moderate” levels of performance. Since regimes in their view are
endogenous to performance, development and democracy may appear to be associated, but transitions to
democracy are entirely unrelated to economic performance. 25 In fact, Przeworski and Limongi (1997)
claim that there are no systematic causes of transitions to democracy, and that regime change is fully
exogenous and simply emerges as a “deus ex machina” out of the whims of history.
Though the effects of economic wealth may be more negligible than many have surmised,
Przeworski and Limongi’s claim that transitions are random are highly inconsistent with the notion of
international contexts of democratization and diffusion between countries. In the following section, I
demonstrate that this claim is incorrect, as the regional context strongly influences the likelihood that
countries will experience transitions.
A Markov model of transitions and regional context
By estimating the probability of transitions conditional on the regional context of democracy, we can
gauge the importance of spatial diffusion and directly evaluate Przeworski and Limongi’s (1997) claim
about the randomness of transitions. Przeworski and Limongi (1997) reiterate the defense in Alvarez et al.
(1996) of dichotomous indicators of political democracy. Despite some skepticism over the
appropriateness of dichotomous indicators (e.g., Bollen 1993, Bollen and Jackman 1989), I here
dichotomize democracy as a value of six or above on the POLITY 98 democracy scale to facilitate
af
comparison. The binary variable Ri t indicates the state or regime type of country i at time t. At any
given time period, this can be either autocratic or democratic. For simplicity, I hereafter denote the two
possible states for a country i at time t simply as Ai,t and Di ,t , where one condition must be true and the
other by implication false. The probability that a given regime Ri is autocratic at time t can be denoted
c
h
respectively p Ri ( t ) = Ai ,t . Since there are only two mutually exclusive outcomes or regime types for a
c
h
c
h
state at any point in time t, we must by implication have that p Ri ( t ) = Di ,t = 1 − p Ri ( t ) = Ai ,t .
Let p jk (t ) be the probability of being at state k at time t given that an entity was at state j at time
af
t − 1. We can denote the set of transition probabilities in P t as paa (t ), pad ( t ), pda ( t ), pdd (t ) . These
indicate the probabilities of a country remaining autocratic from one year to the other, a transition to
democracy, a transition to autocracy, and remaining democratic respectively. In a first-order Markov
25
Many scholars argue that economic performance could be endogenous to regime type in the sense that aspects of
democratic institutions such as the rule of law may have important consequences facilitating economic growth (e.g.,
Alesina and Perotti 1994, Clague et al. 1996, Scully 1992, Wittman 1989). However, Przeworski and Limongi
(1993) strongly reject that regime type consistently affects economic growth. Though some find evidence of a
positive relationship between democracy and growth, these results do not appear robust to alternative specifications
(de Haan and Siermann 1995, Levine and Renelt 1992).
23
process with no exogenous variables where the state Ri (t ) at time t only depends upon the state in the
a f
preceding period Ri t − 1 and the transition probabilities or survival rates p jj between states are constant
a f
over time, the expected probabilities of observing either state are given by E Ri t + 1 = P( t ) × Ri (t ) .
Since each column contains all the possible ways a democracy or autocracy can be observed at time t and
the possible transitions and survival probabilities must sum to one across each row, the probability of
observing an autocracy given previous state can be written as
c
h
c h
= p$ c A h+ p$ 1 − p$ c A h
= p$ + b p$ − p$ gp$ c A h,
E Ai ,t R i ( t − 1) = p$ aa p$ Ai ,t − 1 + p$ da p$ Di ,t − 1
i ,t − 1
da
i ,t − 1
da
aa
(4)
i ,t − 1
da
where the probabilities in the last expression can be estimated from the observed data.
Amemyia (1985: 414-5) shows that stationary first order Markov chain models conditional on
some set of exogenous variables X can be parameterized in a fashion similar to limited dependent
variables models as Pjk (t ) = F X t β for some function where
K
∑
k =1
F jk = 1. If diffusion mechanism operate
between countries and the regional context influences the likelihood of transitions, the conditional
probabilities p jj would differ according to the extent of democracy in the spatial context, DiR,t − 1 , as
measured by the composition of regimes in a country i’s J neighboring countries at time t-1. We can
estimate the conditional probability of observing an autocracy at time t given spatial context at time t-1 by
a regression without an intercept term with either a logit or a probit link while we condition on the regime
status of country i in the previous year t-1. 26 The probabilities of observing a democracy and transitions
can then be derived from these estimates as shown above.
I thus estimate the equation
c h
e
j
p$ Ai ,t = Φ β1 DiR,t − 1 + α 1 DiR,t − 1 × Ai ,t− 1 + µ i ,t ,
(5)
where Φ is the cumulative density function of the standardized normal distribution. The results of the
probit model for the conditional transition probabilities in Equation 5 are displayed in Table 2.
26
Whether logit or probit is appropriate depends on whether the error term is expected to follow a logistic or normal
distribution. The two will generally yield identical results (see, e.g., Maddala 1983). I use probit here to facilitate
comparison with Przeworski and Limongi (1997).
24
Table 2: Probit of regime type by spatial context, 1876-1996
CONDITIONING VARIABLES
COEFFICIENT
ESTIMATE
STANDARD
ERROR
Spatial context of democracy t− 1 (i.e., β1 )
-.1326
.0062
Spatial context of democracy t− 1
× Regime t− 1 (i.e., α 1 )
-.0563
.0085
Residual deviance27: 10372.65, N=9286
As can be seen from the negative coefficient estimate β1 , the likelihood that a country will be
autocratic declines quite dramatically if the spatial context is more democratic. The negative sign of
coefficient α 1 indicates that the effect of the regional context is relatively greater for countries that were
autocracies in the previous year. This suggests that a more autocratic regional context decreases the
probability of a transition to democracy even further for autocracies. However, since β1 is greater than
α 1 , this effect is reversed when the regional or spatial context is more democratic, and autocracies are
much less likely to remain autocracies if the level of democracy is high in adjacent countries or its
regional context. Both democracies and autocracies are considerably less likely to survive in a regional
context characterized by authority structures that differ from those of the country itself. Substantively,
though the odds of transitions in the aggregate may seem generally low, there is a clear tendency for
transitions towards states that are more similar to the spatial context.
27
Note that conventional goodness of fit measures based on residual deviance to null deviance are meaningless in
this setting, since that model does not include an intercept and there is no clear null alternative for the estimated
model.
25
-5
0
5
0.8
0.4
10
-10
-5
0
5
Autocracy persists
Democracy endures
0
5
0.4
0.0
0.8
0.4
-5
10
0.8
Spatial context of democracy
Survival rate from t-1 to t
Spatial context of democracy
0.0
-10
0.0
Probability of transition t-1 to t
0.8
0.4
-10
Survival rate from t-1 to t
Autocratization
0.0
Probability of transition t-1 to t
Democratization
10
-10
Spatial context of democracy
-5
0
5
10
Spatial context of democracy
Figure 6: Estimated survival and transition probabilities given spatial context of democracy
What is the magnitude of predicted effects with respect to the likelihood of survival and
transitions? Figure 6 shows the implied predicted likelihood of regime survival and change from the
above coefficients. As can be seen, the estimated probabilities co-vary closely with the values of the
spatial context. In fact, the estimated values approach 0s and 1s. These results suggest an almost
deterministic relationship between spatial context and the likelihood that a country will endure or
experience regime changes that resemble the composition of authority structures in its regional context.
Autocracies are almost certain to endure in highly autocratic regional interaction contexts, and the
predicted likelihood of observing a democracy among other autocratic states virtually approaches zero.
However, the estimated probabilities that an autocracy will endure if located in a highly democratic
regional context are negligible. Conversely, changes to democracy become more likely as the regional
context of authority structures that a country is located in becomes more democratic. By the same token,
democracies are predicted to break down if the regional context becomes more autocratic.
These results are broadly consistent with many transitions both in the recent “third wave of
democracy” as well as the “second wave of autocracy” after decolonialization. In fact, it is hard to find
clear examples where polities persist in stark contrast to their surrounding entities or regional context.
Many of the examples that come to mind are countries that have somehow isolated themselves from
26
influences from their regional context, either geographically or through self-sufficiency, size, or the
pervasive influence from external actors. The fact that Cuba is an island may in part account for the
stability of the regime, while India and the superpower “overlay” in the case of Israel and its Middle
Eastern neighbors might provide examples of the latter (e.g., Buzan 1991, Väyrynen 1984).
Table 3: Observed versus predicted regime status from Equation 4, 1876-1996
OBSERVED
PREDICTED REGIME STATUS
REGIME
STATUS
Autocracy
Democracy
Autocracy
4815
862
Democracy
1664
1944
The predicted effects are undoubtedly strong, and one might wonder how credible they are.
Large-scale transitions between “democracy” and “non-democracy” are obviously very rare events. Since
the above result is based on a relatively small number of transitions and might be sensitive to the choice
of cut-off point for “democracy”, the results may not be stable or robust to alternative specifications. The
“fit” of the model in terms of predictive ability can be evaluated by comparing predicted and observed
regime states in Table 3, which indicates a reasonable correspondence. The overall percentage of
observations that are classified correctly is 73%. However, the validity of the overall classification as a
measure of prediction is somewhat limited by the preponderance of autocracies. Similarly, there is no
clear null model to evaluate the increase in predictive ability from. Perhaps more interestingly, we also
find that the proportion of democracies predicted correctly by the model is about 54%.
These results provide strong support for that the regional context of authority structures exerts
strong effects on regime changes. Knowing a country’s location and the characteristics of surrounding
entities does yield some predictive power, and there is a marked tendency for cases to change in ways
similar to their regional context over time. Given such evidence of dependence and diffusion between
countries, the claim that regime change is entirely random and exogenous seems incorrect or at least
overly strong. Like Monet’s water lilies that look like random dots up close and only acquire shape with a
change of perspective, the randomness of democratization may lie in the eye of the beholder.
Is there an equilibrium probability of democracy given wealth?
How do these results compare with Przeworski and Limongi (1997), and what are the implications of
regional context and diffusion for their inferences? Do the claims about the existence of stable
equilibrium probabilities of democracy given economic wealth hold up when the regional context is taken
27
into account? And finally, do the conditional probabilities of transitions given regional context results
differ if we consider potential influences of economic wealth as well?
Przeworski and Limongi’s (1997) analysis is somewhat opaque and not fully documented. Based
on the description that they “estimate transitions probabilities on level [ of per capita income ] ... plus its
square” (1997:160), I infer that they estimated a probit model specifying the functional relationship
between GDP per capita (presumably in constant figures) and democracy as a second order quadratic
term, i.e.,
c h
e
j e
j
p$ Ai ,t = Φ β1 Pi ,t − 1 + β2 Pi 2,t − 1 + α 1 Pi ,t − 1 × Ai ,t− 1 + α 2 Pi 2,t − 1 × Ai ,t− 1 + µ i ,t ,
(6)
all the terms as defined above. The squared term in the functional relationship is justified by the “nonlinearity of observed patterns” in transitions.28
The Przeworski and Limongi (1997) study differs markedly from the present study in terms of the
data, functional form, time period, and countries in the sample. Przeworski and Limongi (1997) define
their population only loosely, and include a number of micro-states that I do not include in the population
of independent states.29 The effective dates of many countries’independence in their study differ
considerably from commonly recognized dates. The Penn World Tables constrain the Przeworski and
Limongi sample to the 1950-90 period, while data on regional context are available for virtually every
country year in the 1875-1996 period.30 As data on economic wealth are missing from a large number of
countries, the aggregate transition probabilities may conceivably look quite different in the two studies
given differences in the composition of the samples.
Most importantly, Przeworski and Limongi (1997) use a dichotomous indicator of democracy in
the Alvarez et al. (1996) data set (hereafter ACLP),31 and this displays some noticeable differences with
the binary indicator of democracies and autocracies based on the institutionalized democracy scale in
POLITY 98. Alvarez et al. (1996) report very high aggregate correlations between their binary indicator
and other measures of democracy, including the POLITY data. Since the institutionalized democracy
28
This specification presumes that the coefficient estimate of GDP per capita is negative while that the square of
GDP per capita income is positive, or increases the likelihood of observing an autocracy. Many dispute whether the
relationship between development and democracy is linear, as additional development above some level probably
matters less. However, this particular functional form is non-monotonic, and substantively it is not clear why one
should expect the likelihood of democracy to decrease with greater wealth beyond some threshold. Przeworski and
Limongi seem to defend the specification on the basis of the fit to the data. Yet, they also exclude six (unnamed)
countries without other justification than that these “derive more than half their income from oil revenue”
(Przeworski and Limongi 1997:159).
29
For a discussion of some of the problems involved in identifying the population of states in the international
system and establishing dates of national independence, see Gleditsch and Ward (1999b).
30
Estimates of the transitions probabilities given regional context based on a sample restricted to the post 1945
period are nearly identical to the 1875-1996 estimates, and do not change the substantive interpretations notably.
These results are available on request. Note also that the temporal difference should be irrelevant under the
assumption that the transition probabilities P t are constant and do not change over time.
af
31
I am grateful to José Antonio Cheibub for providing me with a copy of the Alvarez, Cheibub, Limongi and
Przeworski data set.
28
scale in POLITY is heavily bimodal, it might be assumed that, if dichotomized, the two measures should
be identical or at least very similar. This, however, turns out not to be the case.
Of all the country-years for which the two data sets overlap,32 the percentage of observations in
agreement between the Alvarez et al. regime indicator and the dichotomous democracy is about 92%.
Some of the discrepancies, however, seem substantial. Botswana is never considered a democracy in the
ACLP data, despite being assigned a perfect score of 10 on the institutionalized democracy scale in the
POLITY data since 1966. Similar examples include Gambia 1965-93 and Malaysia 1957-68, neither of
which is ever considered a “democracy” in ACLP. In addition, several country-years with low scores in
POLITY are considered “democracies” in the ACLP data, including Argentina 1950-61 (ranging between
–9 and –1 in POLITY), the Dominican Republic 1966-77 (-3 in POLITY), and Guatemala 1950-54, 195862, 1968-81, 1986-90 (never above 2 in POLITY).
Alvarez et al. (1996:10-3) discuss the case of Botswana at some length in the context of what
they call “type II” errors in classifying democracies. According to Alvarez et al., some systems in which
chief executives are elected in seemingly free multi-party elections may not be genuine democracies,
since it is not clear whether the ruling party actually would cede power to the opposition if it were to lose
an election. On the basis of this, they only classify regimes in which there has been a peaceful transfer of
power as “democracies.” Periods prior to a turnover of political power — such as Norway until 1965 and
Japan for most of the postwar period — are then “backcoded” retroactively as democracy-years.
This approach has numerous problems. Several polities commonly considered “democracies” are
classified as “autocracies” by this operationalization.33 Most fundamentally, turnovers or transfer of
power are not a defining property of political democracy, but should rather be seen as a consequence of
democracy being in place. Though a turnover criterion has often been used as a “litmus test” for
classifying democracies (e.g., Huntington 1991, Ray 1995a), turnovers in themselves do not appear in any
common definitions of democracy. We may be interested in the “stability” of democracy to opposition
victories, but the consequences of democracy should be distinguished from definitions of the concept
itself. Measures that are systematically associated with observable components that are not part of the
definition of democracy display a particular form of non-random measurement bias or a “method factor”
32
Alvarez et al (1996:3) describe their data set as “a classification of political regimes as democracies and
dictatorships for a set of 141 countries between 1950 or the year of independence,” but provide no definition of
system membership or states in the international system. Upon closer inspection, their data seem to classify only
country-years for which data is available in the Penn World Tables. For example, West Germany is in their data
from 1950, while East Germany appear only after 1970. Various developing and socialist states such as Afghanistan,
Albania, Cambodia, Cuba, Lebanon, Libya, North Korea, South Yemen, and Vietnam are simply not in the ACLP
data at all. In so far as the likelihood of observing data for a given country thus may be correlated with aspects of
interest in other studies (e.g., conflict), this data set contains inherent sample selection biases which make it less
useful for general purposes.
33
The Alvarez et al. (1996) data set includes an indicator of observations excluded by “possible type II error.” They
suggest that people may include these cases as “democracies” if they disagree with the retroactive coding rule.
However, this includes numerous cases of country-years which would not qualify as democracies on other criteria,
such as Mexico (at least prior to the 1990s).
29
(see Bollen 1993).34 This is analogous to the problems with operational definitions of democracy that
incorporating stability in the measure itself (see Bollen and Jackman 1989). If we make stability a
defining property of democracies, we exclude ourselves from studying the stability of democratic
regimes. Since many of the polities commonly considered “democracies” do not meet the turnover
criterion and are never classified as democracies, we can by construction not observe a breakdown of
democracy or transition to democracy in these countries in the ACLP data. However, these countries
would seem to provide pertinent information about the survival of democracies by the criteria set forward
in Przeworski and Limongi (1997).
From the above, it is clear that the two data sets have incommensurate definitions of democracy.
Furthermore, the two data sets are coded differently with respect to the timing of transitions in the annual
data. POLITY will generally indicate the regime that was in place throughout the larger part of the year,35
while ACLP explicitly codes transition year according to the characteristics of the regimes that emerge in
that year. Accordingly, it is less surprising that the degree of agreement on “transitions” between the two
is not particularly high. As can be seen in Table 4, only about 40% of the transitions in the ACLP data set
can be retrieved from the POLITY data, and only slightly more than half the transitions in POLITY
appear in the ACLP data set.
Table 4: Share of transition years for POLITY and ACLP data
ACLP DATA SET
POLITY
No transition
Transition
No transition
4197
55
Transition
34
37
If the agreement between the two data sets on transitions between states is so low out the outset,
then predicting transitions from one year to the other may obviously vary considerably if other
conditioning variables vary as well. Ignoring other differences, however, I am able to essentially replicate
the results reported by Przeworski and Limongi estimating Equation 6 with the POLITY binary
democracy data.36 These results are displayed in Table 5.
34
Bollen (1993) and Bollen and Paxton (1999) examine some systematic measurement biases in various subjective
measures of liberal democracy.
35
An accompanying project labeled POLITY IIID (e.g., McLaughlin et al. 1998) contains the actual timing of
changes in the POLITY data.
36
Przeworski and Limongi (1997:159) state that they exclude “six countries that derive at least half of their income
from oil revenues,” but do not identify the countries. Though no theoretical justification is provided as to why these
cases are not pertinent to the study, I exclude some of the wealthy oil producing countries in the Middle East —
Bahrain, Kuwait, Saudi Arabia, and United Arab Emirates — to emulate their restriction.
30
Table 5: Probit results for transitions by GDP per capita and its square, 1950-1990
EQUATION 4
PARAMETER
COEFFICIENT
ESTIMATE
STANDARD
ERROR
β1
-.00090
.00006
α1
.00226
.00008
β2
.00000005
.000000004
α2
-.00000001
.000000005
Residual deviance 1274.7, N=4188
Przeworski and Limongi hold that their results indicate that Huntington (1968:43) “was correct
with regard to dictatorships: they exhibit a ‘bell-shaped pattern of instability’(Przeworski and Limongi
1997:160)”. They use these results to derive conditional “equilibrium probabilities” or proportions of
democracies and autocracies that the distribution will converge to and remain stable, “whatever the initial
distributions (1997:180),” in the absence of exogenous disturbances. However, they provide no
theoretical rationale for why there should be such stable “equilibrium probabilities” over time. These
claims seem to treat the empirical data as an immaculate arbiter with almost extreme reverence. Even if
such “equilibrium probabilities” of democracy and autocracy exist, can these be corroborated as “inherent
features” of data in the absence of a theory? In the following, I show that Przeworski and Limongi’s
empirical results depend upon a seemingly arbitrary parameterization and do not hold up under other
plausible alternatives.
The non-linear functional specification of a square term seems insufficiently justified on
theoretical terms and largely arbitrary. There is no explanation for why the “equilibrium probability” for
democracies should decline at higher levels of economic wealth, and the natural log of GPD per capita
provides a more reasonable non-linear specification that preserves monotoncity. This suggests an
alternative model for the likelihood of observing an autocracy given economic per capita income and
regime state in the previous year specified as
c h
e j
e j
p$ Ai ,t = Φ β1 ln Pi ,t − 1 + α 1 ln Pi ,t − 1 × Ai ,t− 1 + µ i ,t ,
all the terms as previously defined. The estimated results for Equation 7 are displayed in Table 6.
31
(7)
Table 6: Probit results for natural log of GDP per capita, 1950-1990
CONDITIONING VARIABLES
b g
GDP per capita t− 1 β1
b g
GDP per capita t− 1 × Regime t− 1 α 1
COEFFICIENT
ESTIMATE
STANDARD
ERROR
-.2635
.0103
.5694
.0138
Residual deviance: 621.837, N=4188
As can be seen from Table 6., the simpler model in Equation 7 has a smaller residual deviance,
despite having two fewer restrictions or a smaller loss in degrees of freedom than Przeworski and
Limongi’s more complicated model in Equation 6. The alternative specification is in this sense clearly
both less restrictive and more consistent with the data than the specification Przeworski and Limongi
propose.
Przeworski and Limongi’s (1997) interpretation no longer hold under the results from the simpler
model. As can be seen from the coefficient estimate β1 , these results also indicate that democracies are
less likely to break down the higher their GDP per capita. In addition, the coefficient estimate α 1
indicates that higher GDP per capita increases the likelihood that countries remain autocracies, and that
this effect is comparatively larger. In this sense, these results do clearly not support the notion that greater
economic wealth increases the likelihood of transitions to political democracy. However, the idea of
stable equilibrium proportion of democracies or a “bell shaped” survival pattern for autocracies no longer
obtains. Rather, the initial distribution could remain relatively stable over time irrespective of economic
performance. Przeworski and Limongi’s (1997) interpretations follow directly from their seemingly
arbitrary choice of functional form for the model rather than any inherent feature of the data as such. Why
should we expect “the true relationship” to be square rather than, say, cubic, or leptokurtotic?
Lack of available data and the relative scarcity of transitions prevents estimating Przeworski and
Limongi’s (1997) original model adding the spatial context of democracy to gauge the potential
contribution of diffusion. However, we can estimate a version of the specification in Equation 7 with
parameters for the spatial context of democracy,
c h
e j
e j
e
j
p$ Ai ,t = Φ β1 ln Pi ,t − 1 + β2 DiR,t− 1 + α 1 ln Pi ,t − 1 × Ai ,t− 1 + α 2 DiR,t − 1 × Ai ,t− 1 + µ i ,t ,
(8)
all the terms as defined above. The results of estimating Equation 8 are displayed in Table 7.
37
Note that conventional goodness of fit measures based on residual deviance to null deviance are meaningless in
this setting, since the model does not include an intercept and there is no clear null alternative for the estimated
model.
32
Table 7: Probit of transitions by spatial context and GDP per capita, 1950-90
CONDITIONING VARIABLES
COEFFICIENT
ESTIMATE
STANDARD
ERROR
GDP per capita t− 1
-.2655
.0112
GDP per capita t− 1 × Regime t− 1
.5493
.0146
Spatial context of democracy t− 1
-.0573
.0184
Spatial context of democracy t− 1 × Regime t− 1
-.0412
.0245
Residual deviance: 574.838, N=4195
The estimated coefficients for the economic variables are virtually undistinguishable from those
in Table 6. Though the estimated coefficients for the spatial context are somewhat reduced, the implied
relationship resembles the previous results. As such, the association between the transition or survival
probabilities for regimes and the regional context of authority structures does not merely stem from
failing to control for geographical clustering in higher values of GDP per capita.
38
Note that conventional goodness of fit measures based on residual deviance to null deviance are meaningless in
this setting, since the model does not include an intercept and there is no clear null alternative for the estimated
model.
33
text
con
ratic
c
o
Dem
5
0
-10
-5
xt
onte
tic c
a
r
c
o
Dem
10
10
10
00
0
GD
50
Pp
00
er
ca
pit
a
5
0
-5
text
con
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c
o
Dem
-10
0 0.050.10.150.20.25
-10
acy
Transitions to democr
15
00
01
00
00
50
GD
00
Pp
er
ca
pit
a
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15
00
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0.9 0.95 1
Democracy prevails
GD
50
Pp
00
er
ca
pit
a
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0.750.80.850.90.95
10
10
00
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Autocracy persists
0.1
0.05
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n
Democracy breaks dow
15
00
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15
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Pp
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Dem
Figure 7: Transition and survival probabilities by context and real GDP per capita
Figure 7 shows how the transition and survival probabilities of democracy and autocracy vary in
two-dimensional space over GDP per capita and the regional composition of authority structures. Looking
at how the transition probabilities vary with respect to GDP along the left axis indicates how Przeworski
and Limongi got part of the story right: Transitions cannot be predicted by wealth alone. In fact, the two
rightmost plot indicate that autocracy actually is much less likely to break down in wealthier countries
than the very poorest, and that transitions to democracy under certain conditions are more likely in poorer
societies. However, given the greater instability of regimes at low levels of GDP per capita, Przeworski
and Limongi are correct in suggesting that democracy is more likely to endure in wealthier states.
Whereas Przeworski and Limongi assumed that regime probabilities depend only GDP per capita,
we can see that effects of GDP per capita on the transitions and survival probabilities vary dramatically
depending on the regional context. The effects of wealth on whether autocracy persists or perishes are
negligible while the regional context is autocratic. Similarly, GDP per capita is unlikely to exert much
effect on the prospects for survival for democracies in highly democratic contexts. Democracies are more
likely to break down in highly autocratic regional contexts, and autocracy is unlikely to be sustained in
highly democratic contexts. The qualitative conclusions with respect to the effects of regional context do
not change, even if the magnitude of the effects are somewhat reduced. Since the change in curvature
34
over the democracy axis is larger than that over GDP per capita and the regional context is considerably
more amenable to change in the short run than economic wealth, these results suggest the spatial context
of democracy provides a dynamic component in transitions and democratization processes. As can be
seen from Table 8, the overall success in prediction of the model is very high, with about 98.6 of all the
observations correctly predicted.
Table 8: Predicted versus observed regime status, 1950-90
OBSERVED
PREDICTED
Democracy
Autocracy
Democracy
1523
27
Autocracy
32
2612
Conclusion
What does this tell us about domestic and international dimensions of democracy and democratization?
Though democratic societies on average are wealthier than non-democratic states, there is less evidence
that changes in socio-economic development yield changes towards democracy. Countries’authority
structures display strong persistence over time, and previous studies that disregard the stickiness of
regime type may have overstated the substantive implications of domestic economic change on the
likelihood of democratic rule. Moreover, democracy and democratization are not exclusively related to
domestic attributes or similar processes unfolding independently within each country, but also affected by
external conditions and events.
The distribution of democracy in the international system is strongly influenced by diffusion
processes among states, especially at the regional level. There is a strong association between a country’s
authority structures and the extent of democracy in the surrounding regional context. Not only do regimes
tend to be similar within regions, but there is also a strong tendency for transitions to occur in ways
yielding regimes more similar to the regional context. Since the regional context is more permeable to
changes in the short term than socio-economic factors, international influences on democracy appear to be
as important as the domestic “social requisites.”
A history of prior conflict and a hostile regional context decreases the likelihood that a country
will be democratic. Though it is more dubious whether this relationship alone dominates the association
between democracy and peace, taking the diffusion of conflict and peace into account enable us to say
more about prospects for democratization and its potential consequences for conflict. I have shown
elsewhere that the composition of countries in regions is more important for conflict proneness than the
35
characteristics of individual countries (Gleditsch 1999). Though democratization can improve the
prospects for peace, the effects are only likely to be substantial when democratization occurs on a broader
regional basis. In addition, democracies are much more likely to break down when they are located in
zones that contain many autocracies, and such reversals to autocracy have the highest likelihood of
conflict.
Though this paper does not fully specify or describe the micro-level processes of democratization
and how international factors influence these, it seems theoretically inappropriate to treat the international
and domestic arena as fully independent with attributes in one “given” or determined by the other. Nor
does it make sense to simply assume that transitions to democracy are random and exclude the regional
context when this is the component that explains the greatest share of the variance. Though we may not
be able to predict transitions and how regions evolve over time, considering the interaction between
domestic processes and regional context allows a better delineation of plausible and observed trajectories.
36
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