Democratic Transitions
Author(s): David L. Epstein, Robert Bates, Jack Goldstone, Ida Kristensen and Sharyn
O'Halloran
Source: American Journal of Political Science, Vol. 50, No. 3 (Jul., 2006), pp. 551-569
Published by: Midwest Political Science Association
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Democratic Transitions
David L. Epstein Columbia University
Robert Bates Harvard University
Jack Goldstone George Mason University
Ida Kristensen Columbia University
Sharyn O'Halloran Columbia University
Przeworski et al. (2000) challenge the key hypothesis in modernization theory: political regimes do not transition to
democracy as per capita incomes rise, they argue. Rather, democratic transitions occur randomly, but once there, countries
with higher levels of GDP per capita remain democratic. We retest the modernization hypothesis using new data, new
techniques, and a three-way rather than dichotomous classification of regimes. Contrary to Przeworski et al. (2000) we
find that the modernization hypothesis stands up well. We also find that partial democracies emerge as among the most
important and least understood regime types.
he study of democratization is one of the most
venerable literatures in comparative politics. It is
also one of the most vigorous, as controversies
over theory and method interact with empirical research
in debates over the origins and determinants of democratic
forms of government. In recent years, however, an
uncharacteristic lull seems to have descended on this vibrant
field-a lull we attribute to the need to absorb the
pivotal contribution of Przeworski et al. (2000) (hereafter
referenced PACL). Despite the challenges posed by Boix
(2002) and Boix and Stokes (2003), rather than igniting
debate, as would be right and proper, PACL appear instead
to have quenched it.
Among the most notable of PACL's findings is that
modernization-specifically, an increase in per capita
GDP-is not a causal factor in the process of democratization.
Rather, they argue, the positive association between
income and democracy results from the reduced
likelihood of more modern countries sliding back, as it
were, into undemocratic forms of government once having
(randomly) become democratic. This finding is now
treated as received wisdom.
We challenge that finding. The grounds for our dissent
are both methodological and substantive. PACL employ
a dichotomous classification of political systems, in
which governments are either democratic or authoritarian,
with rather stringent requirements for being included
in the former category. All countries failing to meet the
necessary conditions for being a full democracy are then
deemed autocratic.
This approach, however, ignores the possibility of
an intermediate category, "partial democracies," which
possess some, but not all, of the properties that characterize
full democracies. Not only are such regimes becoming
more numerous, there is also growing evidence
that they behave differently from either full democracies
or full autocracies. Mansfield and Snyder (1995), for
instance, show that partial democracies are more likely
to become involved in armed conflicts with other countries.
Bacher (1998) argues that countries in the similar
Freedom House category of "partially free" regimes
are most likely to enact policies that harm the environment.
Goldstone et al. (2000) demonstrate that partial
democracies are more prone to political instability, revolutions,
and ethnic wars. And Zakaria (2003), terming
such regimes "illiberal democracies," warns that they can
be just as oppressive and contemptuous of human rights
as any dictatorship.
In this article, we first review and critique the work
of PACL. We indicate that they mistakenly interpret their
own estimates in a manner that predisposes them to reject
the modernization hypothesis. Shifting from their dichotomous
to our trichotomous measure of democracy,
we recreate their result; employing Markov estimation,
as do they, we then demonstrate that our trichotomous
measure is to be preferred.
Epstein, Kristensen, and O'Halloran: Department of Political Science, Columbia University; Bates: Department of Government, Harvard
University; Goldstone: School of Public Policy, George Mason University.
American Journal of Political Science, Vol. 50, No. 3, July 2006, Pp. 551-569
02006, Midwest Political Science Association ISSN 0092-5853
551
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552 DAVID L. EPSTEIN, ROBERT BATES, JACK GOLDSTONE, IDA KRISTENSEN, AND SHARYN O'HALLORAN
What we learn from these efforts is that higher per
capita incomes increase the likelihood of a movement
away from autocracy as well as decrease the likelihood of a
movement away from democracy. That is, we find reason
(contra PACL) to support the modernization hypothesis.
In our view, democracy is a process, not an end state. And
as is often the case, the journey is more important than
the destination.
We also learn that the frontier of this line of inquiry
has shifted away from the study of autocracies and democracies
and toward the study of partial democracies. As we
show here, the behavior of these systems largely determines
the level, rate, and properties of democratization.
While thus influential, partial democracies, being highly
heterogeneous, are poorly understood. The study of democratization,
we therefore conclude, should place them
at its focus.
The following section reviews the relevant literature
on modernization theory. After reviewing the results of
previous research, we summarize the data used in our
analysis and our statistical techniques: tobit, Markov, and
duration models. We then present our own findings. The
last section concludes by emphasizing the significance of
partial democracies.
Modernization Theory
Modernization theory was first developed by Lerner
(1958), a behavioral scientist studying the role of the media
in development (see also Deutsch 1961). Lerner designated
as modern those societies whose people are literate,
urban-dwelling, and better off, in the sense of commanding
higher incomes. The later works of economists,
such as Rostow (1960), Kuznets (1966), and Chenery and
Taylor (1968), focused on economic modernization. In
so doing, they emphasized the importance of structural
change and associated the rise of per capita incomes with
the decline of the agrarian economy and the rise of urban
industry.
The classic statement of the relationship between
modernization and politics originates from Lipset (1959),
who first established the link between the level of per
capita income and democracy in a global cross-section
of nations. Lipset hypothesized that as societies develop
economically, their citizens no longer tolerate repressive
political regimes. The rise in per capita GDP, he argued,
triggers a transition to democracy.
Pioneering the small-N research tradition in comparative
historical sociology, Moore (1966) related democratization
to the rise of the middle class and to the terms of its
political incorporation, a result upheld by Rueschemeyer,
Stephens, and Stephens (1992). More common is the use
of large-N data sets, with important contributions from
Cutright (1963), Dahl (1971), and Burkart and LewisBeck
(1994), among others. Londregan and Poole (1996)
perform an especially careful test of the relation between
income and democracy and find a significant, albeit modest,
effect.
Against this background, Przeworski and his coauthors
advanced an important new argument.' Reminding
us that correlation does not necessarily imply causation,
PACL note that countries may become democratic
due to reasons unrelated to their level of economic development.
Once prosperous, however, if democracies
with higher levels of GDP per capita were to avoid slipping
back into autocracy, then over time the relationship
between GDP and democracy would emerge. It would
do so even though economic growth does not cause
democratization.
We agree with PACL that a true test of modernization
theory should examine both the impact of GDP on
democratization and its ability to promote the consolidation
of established democracies. However, we take issue
with their conclusion that economic development does
not play a significant role in transitions away from autocracy.
We dissent because we find their own work flawed
and because a more refined measure of regime type generates
evidence of the impact of GDP that their measure
obscures.
As mentioned, Boix and Stokes (2003) also challenge
the PACL findings. Their criticism is somewhat muted,
however, as they essentially agree with PACL that the impact
of GDP on democratization in the postwar period
is negligible, even though it may be statistically significant.
They argue that it is the prewar period-from the
late nineteenth century through the 1940s-in which the
impact of GDP on democracy is most powerful. Although
we agree with Boix and Stokes that the patterns of GDP
and democratization are clear in the prewar period, we
argue that the same patterns are important in the postwar
era as well.
The Work of PACL
As we have observed, PACL (2000) employ a dichotomous
regime classification. If (1) the chief executive is
'See Przeworski et al. (1996), Przeworski and Limongi (1997), and
especially Przeworski et al. (2000).
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DEMOCRATIC TRANSITIONS 553
TABLE 1 Results from PACL Table 2.12
Autoc. -+ Autoc. --
Democ. -+ Democ. Democ.
Indep. Var. Autoc. (Original) (Corrected)
Constant -1.144** -2.524** -2.524**
(0.000) (0.000) (0.000)
GDP -0.201 0.329 0.329**
(0.162) (0.484) (0.004)
GDP2 -0.003 -0.029 -0.029
(0.874) (0.191) (0.069)
GDP Growth -0.042** -0.021** -0.021*
(0.003) (0.000) (0.015)
N 1584 2407 2407
Pseudo R2 0.19 0.05 0.05
Note: P-values in parentheses. *denotes significance at the 0.05
level; **denotes significance at the 0.01 level.
elected; (2) the legislature is elected; (3) there is more
than one political party; and (4) an incumbent regime has
lost power, then the country is deemed democratic; otherwise,
it is classified authoritarian. Using this definition,
PACL claim that increases in per capita GDP do not influence
transitions from autocracy to democracy; rather,
they help countries that are already democratic remain so.
They base their conclusions on Tables 2.12 and 2.17 from
Chapter 2 of their book. The former, reproduced as the
first two columns of Table 1, performs a Markov probit
regression of regime type on lagged values of per capita
GDP, its square, and year-to-year GDP growth:
P (Dit) = (D {Po + P1GDP + 32GDP2 + 33Growth + P41D
+ P3lIDGDP + 36IDGDP2 P37D Growth},(1)
where P(Dit) signifies the probability that country i is
a dictatorship in year t, (D(.) is the cumulative normal
distribution, and ID is an indicator variable for dictatorship
in the previous period.2 As indicated in the first two
columns of Table 1, PACL report the coefficients on GDP
and GDP2 in this regression as insignificant when predicting
transitions both to and from democracy. PACL take
this as evidence that the level of GDP per capita does not
influence democratic transitions.
Note that when ID = 1 in equation (1), the coefficient
on GDP will be P1 + P5, the coefficient on GDP2
will be 32 + 36, and likewise for the constant (3o + 34)
and Growth (13 + 37). PACL's Table 2.12 correctly
reports these summed coefficients in the columns labeled
"Transitions to democracy" (the second column
of our Table 1), but the repo
for 34 through P7 alone, rath
coefficients.
To calculate the P-values for transitions to democracy,
one must perform a Wald test on the hypothesis that
the sum of the appropriate coefficients is 0.3 For example,
the coefficient on p1 in equation (1) is -0.201 with a
P-value of 0.162, and the coefficient on P5 is -0.128
with a P-value of 0.484. The sum of the coefficients is
-0.329, and PACL then correctly reverse the sign to indicate
the impact of GDP on transitions from dictatorship
to democracy.4
What these results tell us is that the impact of GDP
on transitions to dictatorships is not significantly different
from 0, and that the impact of GDP on transitions to
democracy is not significantly different from its impact
on transitions to dictatorship; that is, -0.329 is not significantly
different from -0.201. But in this context we
are interested in whether the sum of these coefficients is
different from 0: that is, whether GDP is a significant predictor
of transitions to democracy. And a Wald test of the
hypothesis that 31 + ps = 0 shows that it can be rejected
with a P-value of 0.004.
Substituting the corrected P-values into the analysis
yields the results reported in the last column of Table 1. As
shown, these results actually run counter to PACL's central
hypothesis: GDP influences transitions to democracy but
not transitions to autocracy.
Both the GDP and GDP2 terms, however, contribute
to the total impact of GDP on transitions. To evaluate
this impact, we employ the delta method, which involves
evaluating the derivative P/IaGDP. For equation (1), the
derivative is
(o' (o + 31 GDP + ?2 GDP2 + 13 Growth)
. (13 + 22GDP) (2)
when ID = 0, and
' [(O + 4 (+ 1 ? + (35)GDP +
+ (13 + 137)Growth] [(P1 + P
(3)
when ID = 1. Performing these calculations, we find that
the overall coefficient on GDP for transitions to autocracy
is -0.0034 with a standard error of 0.0015, and for
2Relative to PACL's Table 2.12, the coefficients on GDP and GDP2
in Table 1 are multiplied by 1000.
3A Wald test is used to determine whether a linear combination of
coefficient values is equal to some constant. Here we wish to test
the restriction that, for instance, pi3 + P5 = 0. See Greene (2003,
484-88). All Wald tests were performed using the postestimation
test command in Stata 9.0. Note that these same P-values can also
be calculated by running two probits, one when the regime at time
t - 1 is democratic and another when it is a dictatorship.
4The -0.329 coefficient indicates the impact of GDP on transitions
from dictatorship to dictatorship, which is equal and opposite to its
impact on transitions to democracy.
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554 DAVID L. EPSTEIN, ROBERT BATES, JACK GOLDSTONE, IDA KRISTENSEN, AND SHARYN O'HALLORAN
TABLE 2 Results from PACL Table 2.17
Autoc. -- Democ. Autoc. -* Democ.
Indep. Var. Democ. -* Autoc. (Original) (Corrected)
Constant 0.114 3.414** 3.414**
(0.899) (0.002) (0.000)
GDP -0.547** -0.033** -0.033
(0.000) (0.000) (0.445)
GDP Growth -0.022 0.018* 0.018
(0.181) (0.027) (0.079)
Leadership Turnover 0.975** -0.527** -0.527**
(0.001) (0.000) (0.007)
Religious 0.026** -0.001* -0.001
Fractionalization (0.010) (0.014) (0.816)
% Catholic 3.937* -0.369 -0.369
(0.048) (0.105) (0.707)
% Protestant 2.626* 0.038 0.038
(0.039) (0.118) (0.965)
% Moslem 5.087* -0.147 -0.147
(0.016) (0.932) (0.890)
New Country -0.012 0.432 0.432*
(0.978) (0.365) (0.039)
British Colony -0.842* -0.164 -0.164
(0.048) (0.153) (0.423)
Previous Transitions 0.897** -0.362** -0.362**
(0.000) (0.000) (0.000)
% World -3.735* -3.040 -3.040*
Democracies (0.047) (0.750) (0.011)
N 1584 2407 2407
Pseudo R2 0.19 0.05 0.05
Note: P-values in parentheses. *denotes
transitions to democracy the coeffi
a standard error of 0.0034. The tota
regime change is thus significant in bo
than insignificant both ways as repor
PACL's Table 2.17, reproduced a
columns of Table 2, reports the res
Markov regression, this time witho
host of other covariates. The author
the coefficient on GDP is now signifi
tions, but discount this result, saying
of magnitude larger for democracies"
indicate the basis for this statement.s
As with Table 2.12, however, PACL f
significance level of the sum of the r
The corrected version of these results
column of Table 2. This time the revise
favorable to their central hypothesis:
predictor of transit
These results, howev
highly sensitive to
thus leave open the
in transitions to de
A Trichotomous Measure
of Democratization
Among the most hotly debated issues in the study of democratization
is that of the choice of measures (see, for
5Indeed, this is one of the criticisms leveled at PACL by Boix and
Stokes (2003). We discuss their results at further length below.
6For example, in most specifications the inclusion of the Previous
Transitions variable (labeled "STRA" in PACL) makes the coefficient
on GDP insignificant. But an examination of the data patterns
indicates that the greater the number of previous transitions, the
less of an effect GDP has on the outcome. This in turn suggests
including an interactive term, and indeed when this term is added
all three variables (GDP, STRA, and GDP * STRA) are significant.
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DEMOCRATIC TRANSITIONS 555
example, Bollen and Jackman
1999; Munck and Verkuilen 200
by defining and summarizing
tion. We then describe our me
ous history of democratization
variables of our analysis.
Identifying Partial De
PACL employ a dichotomous
Consider, however, the 85 au
Geddes (1999, 115-16) records a
the "third wave." Of these, 34
regimes, and 30 as stable democ
remained contested and unstab
four descended into "warlordism." Geddes's discussion
thus reminds us of the significance of partial democracies,
a category that dichotomous measures fail to-indeed,
cannot-capture.7
Using the Polity IV scaling of regimes from -10
to +10, we categorize regimes as Autocracies (Polity value
-10 to 0), Partial Democracies (+1 to +7), or (Full)
Democracies (+8 to +?10).8 The Polity score is based on
three components: measures of executive constraints, political
competition, and the quality of political participation.
In autocracies, the executive retains a high level of
political discretion, often due to the absence of a strong judiciary
or powerful legislature. There is no organized competition
for political office. And political participation is
orchestrated by those who hold power. In full democracies,
the executive faces binding constraints on the use of
power; there are institutionalized forms of political competition;
and citizens openly propound and associations
openly champion civic causes.
Between these end points, there remain gradations:
in partial democracies, the chief executive may be elected,
but then face weak constraints; and his selection may not
result from open and organized competition, but rather
from lobbying by a politicized military or from selection
by a committee of a ruling party. Alternatively, the election
itself could be uncompetitive, either because of political
manipulation by the authorities or because political parties
were highly factionalized. As of 2002 (Polity scores in
parentheses), Ethiopia (1), Nigeria (4), Venezuela (6), and
Russia (7) illustrate what is meant by "partial democracy."
While the selection of the cut points must ultimately
be arbitrary, we provide three justifications. When the
Polity score registers 7 or below, then the country fails to
attain a maximum score on any of its three component
measures. Countries with 8 or higher reach a maximum
value on at least one of them; 9 or higher, on at least
two; and 10 on all three. Secondly, the Polity scores yield
classifications that correspond well with those employed
by others. Most importantly, as noted in the discussion
below, we can readily recover the classification employed
by PACL using the Polity scores. Lastly, our argument
is robust to changes in the cut points: we estimated the
regressions reported later in the article with the cut points
between adjacent categories moved one or two units in
either direction, and the results did not change. In this
sense, our findings are not the result of an arbitrary choice
of measure.
Thus defined, partial democracies comprise 14.3% of
country-years in our sample, which includes 169 countries
from 1960 to 2000. As Figure 1 shows, the percentage
of partial democracies among the world's societies has
grown markedly in recent years: it had a minimum value
of 3.6% in 1976 and rose to its maximum of 26.1% in
2000, with a notable increase after the fall of the Soviet
Union. The "third wave" peopled the globe with partial
democracies.
Whereas Figure 1 shows the overall patterns of democratization,
Table 3 examines the dynamics of change
from one regime category to another. It shows the distribution
of autocracies, partial democracies, and democracies,
conditioning on the previous year's category. The
table reveals that both autocracies and full democracies
are stable in the short run: an average of 97.3% of all
autocracies remain autocratic the next year, while an average
of 98.2% of all democracies remain democratic;
thus around 2% of countries in these categories change
in a given year. Partial democracies are over four times
less stable, however, with 9.6% of them changing into an
autocracy or full democracy the following year.
These differences become even more pronounced
when we expand the time horizon to five years. About
11% of all autocracies change into partial or full democracies
after five years, and 7% of democracies change category
five years later. The most volatile group, again by a
large margin, is partial democracy: almost 40% of these
change category after five years. Movements in or out of
the category of partial democracy account for 80% of the
transitions in our sample.9
These data highlight the importance of partial
democracies: more volatile than either pure democracies
7This division is also emphasized in Collier and Levitsky (1997).
8Note that by our definition, partial democracies are truly an intermediate
category, even relative to PACL's regime classification
formula. In the country-years for which our data sets overlap, 97%
of regimes that we code as autocratic PACL also code as autocracies,
and 92% of our full democracies are democracies in their data too.
But of our partial democracies, 52% are democracies and 48% are
autocracies by PACL's measure.
'Appendix A provides a complete categorization of countries by
their number and type of transitions.
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556 DAVID L. EPSTEIN, ROBERT BATES, JACK GOLDSTONE, IDA KRISTENSEN, AND SHARYN O'HALLORAN
FIGURE 1 World Democratization Trends, 1960-2000
100",
90"
L)
O, h
0 0050 .
, 40
m Democracy
YPar
0-) Lo r- 0',. .) "1. .O.C".. . .. ..LO LC o LAI a _J
Yea
ZIC
31)"1o
?O
20" ! [] BIIsI ssIIss Democracyyo~vz I [] aria
10 lo, I Autocracy
r r~r~~r r r r rir r-r ll 11111 l~l~i m
Year
TABLE 3 Regime Category
Transitions-One-Year Lag
Current Year
Partial
Previous Year Autocracy Democracy Democracy
Autocracy 97.3% 2.1% 0.7%
(3,121) (66) (22)
Partial Democracy 6.4% 90.4% 3.3%
(49) (695) (25)
Democracy 1.1% 0.8% 98.2%
(16) (12) (1,496)
Total 3,186 773 1,543
Note: Numbers in parentheses are cell counts.
or autocracies, they account for an increasing
of current regimes and the lion's share of regime
tions. Rather than dichotomizing countries into d
racies and autocracies, then, our dependent variab
be trichotomous, including a middle category for
democracy. This variable is called Regime Category
data set, with Autocracies coded as 0, Partial De
cies as 1, and Full Democracies as 2. In creating a t
category democracy measure, we heed the advice
Elkins (2000), who warns that dichotomous m
may obscure correlations that intermediate-grade
reveal, and Collier and Adcock (1999), who sugg
for studies of democratic transitions, more coarsemeasures
are appropriate.
Previous Democratization
Many observers argue that a country's previous transition
history may affect current efforts at democratization,
as prior failures may spur or weaken future attempts.
Goldstone and Kocornik-Mina (2005) have shown that
many countries experiencing democratic transitions are
"bouncers" or "cyclers" that move back and forth between
autocracy and democracy on multiple occasions.
We somehow need to capture prior volatility and failed
efforts at achieving democracy.
Despite its importance, however, a country's history
of negative experience with democratization is hard to
measure. Simply counting movements between categories
will miss unstable behavior that consists of substantial
movements toward (or away from) democracy within a
single category: say from a Polity score of -10 to 0 and
then back to -10. Counting the value of all changes in
Polity scores, on the other hand, will treat successful and
large transitions to democracy as indicating just as much
volatility as a country that experiences several smaller
movements toward democracy that failed and fell back.
We therefore settled on a variable "Previous Transitions,"
which for country i in year t is the cumulative sum
of the absolute values of negative changes in the Polity
score for country i from 1960 up to and including year t.
To illustrate the construction of this variable, Figure 2 provides
the values of both the Polity score and the Previous
Transitions variable for Turkey for each sample year. As
the figure shows, the Polity score for Turkey varied widely
over this period, from 4 up to 9, down to -2, back up to 9,
back down to -5, up to 9, and then finally down to 7. Most
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DEMOCRATIC TRANSITIONS 557
FIGURE 2 Illustration of Previous Transitions
variable for Turkey, 1960-2000
Polity Score - PrevTrans
30 -
25 26 2
20 -
0 11
10 - 9 .
C l
0
0
.5
-10
1955 1965 1975 1985 1995 2000
Year
measures would show that Turkey fell out of democracy
twice during the sample period, and, indeed, our measure
rises at just those points where the Polity score falls. At
any given time then, the variable provides an indicator of
the country's prior and cumulative negative experiences
with democratization.0
Other Independent Variables
As independent variables, we employ the standard set of
modernization indicators: log of GDP per capita, year-toyear
GDP growth, the percent of the population living in
cities, and log of population density." Our focus will, of
course, be on per capita GDP.
As controls, we use our Previous Transitions measure
of prior experiences with democratization (as in
Acemoglu and Robinson 2001); log of trade openness,
defined as exports plus imports over GDP (as in Rodrik
1997); and a variable indicating whether over 75% of
national income is derived from sales of minerals or
petroleum. This latter variable captures the "resource
curse" hypothesis (as in Ross 1999; Boix and Stokes 2003),
which argues that countries deriving a large share of national
income from easily extractable natural resources
TABLE 4 Summary Statistics
Std.
Variable Mean Dev. Min. Max. N
Polity Score -0.45 7.58 -10 10 5671
Regime Category 0.70 0.88 0 2 5671
Log of Per Capita GDP 8.14 1.04 5.64 10.21 4417
Percent Change in GDP 0.02 0.06 -0.52 1.01 4475
Percent Urban Pop. 44.94 24.29 2.3 100 5245
Log of Population 3.61 1.46 -0.49 8.77 5600
Density
Log of Trade Openness 3.98 0.62 0.43 6.16 4902
Previous Transitions 3.96 6.41 0 31 5671
Resource Curse 0.23 0.42 0 1 5671
tend to be undemocratic and un
descriptive statistics for all variab
Statistical Method
We address two distinct questi
tries democratic, and what fac
democracies against backsliding
refers to democratization; the s
We use two techniques to addr
and Markov analyses-and one
duration analysis.
First, we shall examine demo
method that takes into account t
that is, our scale is limited to
perhaps artificially. We employ
model for these estimations, whi
Polity values rather than categor
Like PACL, we also use Marko
However, as described above, in
model (democracy and dictatorshi
state model. This allows us to estimate six distinct transitions:
Autocracy to Partial Democracy, Partial Democracy
to Democracy, and Autocracy to Democracy, as well as the
10We note also that our Previous Transitions measure correlates
with PACL's similar "STRA" variable (for sum of
transitions to authoritarianism) at 90.5% for all overlapping
country-years.
"One might also add percent of GDP originating from agriculture
to this list, but its correlation with urbanization is over 90%. Thus
we use only urbanization in our analysis.
12Data Sources: Polity Score-Polity V, IRIS, University of
Maryland; GDP-Penn World Tables; Urban PopulationPopulation
Division of the Department of Economic and Social
Affairs of the United Nations; Population Density-Hybrid of UN
Population Division, World Development Indicators, and Banks
population density series (WDI is used if UND is not available;
BNK is used if WDI is not available); Trade Openness-Hybrid
data series of World Development Indicators and Penn World Tables
trade openness (WDI is the primary source; PWT is used if
WDI is missing); Resource Curse-United Nations: Trade and Development
Statistics.
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558 DAVID L. EPSTEIN, ROBERT BATES, JACK GOLDSTONE, IDA KRISTENSEN, AND SHARYN O'HALLORAN
reverse of each. The Markov model treats each of these six
transitions as distinct and identifies causal factors associated
with each kind of change.
Developed in biometrics, duration models estimate
the impact of factors affecting survival. In our setting we
wish to determine the factors that affect the survival of
newly fledged democracies. Our analysis differs from the
classic medical setting, though, in that each "patient" (or
country, for us) can experience more than one episode
of failure; it can fall out of democracy more than once
(witness Turkey). Hence we employ a repeated-failures
variant of the standard duration model.13 We wish to capture
unit-specific effects, i.e., whether some countries are
more "frail" (to return to a medical setting), in the sense
of being (to return once again to our application) more
prone to autocracy.
Continuous Models with Data Censoring
Tobit models account for the possibility that the data are
censored at either or both ends of their range of values.
That is, we assume, for country i at time t:
Y~ Xit4 + Eit, Eit ~ N(0, 92), (4)
Yit = Yi ifa < Yi~ b; Y = a if Yi < a;
Yit = b if Yi* > b, (5)
where Yi* is the implicit, or underlying value of the dependent
variable, Yit is its observed value, and a and b
are the upper and lower bounds of the observation interval,
respectively. (For the Polity scale used in this study,
a = -10 and b = +10.) This gives rise to the likelihood
function:
- - (Yit-Xi.))] u [((a - XitP))
a<_Yt<b Y" <,a
Xhe [(- (b - Xit))
Y* >b
The first term corresponds to nonlimit observations, the
second to observations at the lower limit a, and the third
to observations at the upper limit b.14
The tobit methodology gives accurate estimates for
processes in which data are limited to some predetermined
range. It also allows for the estimation of the percent of
censored observations, in order to determine the degree to
which the upper and lower limits constrain the estimation.
This part of the estimation, then, takes advantage of
the full 21-point Polity scale. However, the technique assumes
that moves up the Polity scale are caused by factors
equal and opposite to those driving moves down the scale.
As PACL have shown, it is often the case that a given factor
may have a different impact on transitions toward, or
away from, greater democracy. We therefore supplement
the tobit analysis with a Markov switching model.
Markov Transition Models
The Markov model employs a smaller number of possible
democratization categories and then estimates the probability
of moving from any given state to another state in a
single period. In these models, history matters: the conditions
present in one period can affect the probabilities of
different types of transitions in the subsequent period.'5
Markov models thus estimate equations of the form:
F [Pr(Yit = bI Yt,_- = a)] = Oab + XitPa, (6)
where a and b are possible regime types and F(.) is a
function from the [0, 1] interval to the real line, such
as the logit (F(z) = log TzZ) or probit (F(z) = )-'(z))functions.
To expedite the analysis, we follow Clayton (1992)
and work with cumulative transition probabilities. Assume
that there are C ordered categories of the dependent
variable (C = 3 for our study), labeled 0, 1,..., C - 1.
It then becomes convenient to express the equations in
terms of Y* variables, where Y* = 1 if Y < a. In our data,
for example, if we let Yit = 0 indicate that country i is an
autocracy at time t, Yit = 1 indicate partial democracy,
and Yit = 2 indicate full democracy, then the translation
from Y to Y* is given in Table 5.
As by definition, Pr(Y < a) = Pr(Y < a - 1) +
P r( Y = a), we can recover the individual transition probabilities
from the set of cumulative probabilities. We therefore
estimate equations of the form:
F[Pr(Yit = b I Yi,_1 < a)] = Oab + Xit a, (7)
for b = 0, 1, 2 and a = 0, 1, which is equivalent to equation
(6), substituting values of Y* for values of Y in the
previous period. One could estimate equation (7) separately
for each regime type, or, as with dichotomous
Markov regressions, combine the data for each value of
b into a single equation, including interactions of the
independent variables and lagged values of Y*:
13These models are becoming increasingly popular in political science,
and our treatment of them owes much to recent work by
Box-Steffensmeier and Zorn (2002).
14See Greene (2003, 764-66) for a good textbook discussion of these
models.
"'Variables associated with other historical aspects of a country's
development, such as previous transitions to democracy, can be
added to the model as independent variables. See the discussion of
our Previous Transitions variable above.
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DEMOCRATIC TRANSITIONS 559
TABLE 5 Definition of Y* Variables
Y: 0 1 2
YT: 1 0 0
YT: 1 1 0
F[Pr(Yit = b I Yi,_ = yit
= b+L Yit-1 Y X
f=o ==o
for b = 0, 1, 2. Under this form
tOab = Oab - 0(a+l)b, so a significan
indicates that adjacent categories
Similarly, P = 3Pc-1 and _ya = P3
y values indicate that an independ
effects on transition probabilities
of the lagged dependent variable.
A distinctive advantage of this ap
we can test whether adjacent cate
variable should be collapsed. As we
enables us to test the validity of o
fication; to compare it to PACL's
and to explore the significance of
our three-way division of regim
Markov model of regime type on
of the adjacent categories should
add a fourth type-"partial autocra
between -6 and 0-the same test indicates that the full
and partial autocracies should indeed be combined into
a single regime category.
Equation (8) can be run separately for each value of b
or with an ordered probit, where the dependent variable is
the ordered regime category. We begin with a "fully saturated"
model, with right-hand side variables consisting of
the lagged regressors (GDP, growth, urbanization, etc.),
the lagged values of the indicator variables Y*, lagged Y*,
and all interactions between the regressors and indicators.
From this initial model, with its profusion of interactive
terms, one tests down, eliminating insignificant interactions
to arrive at a more parsimonious specification.16 It
is the result of this procedure that we report.
Survival Analysis
To investigate the determinants of consolidation, we employ
duration models. As mentioned above, our application
differs from that in biometrics in two important ways:
we think that countries might have unit-specific heterogeneity,
and we know that they may experience repeated
failure.
In duration models, unit-specific effects are captured
by "frailty" terms, written as
hi(t) = Xi( t)vi,
where hi(t) is the hazard rate for observation i at time
t and vi is an individual-specific factor which operates
multiplicatively on the hazard. In biometrics, this term
captures the patient-specific susceptibility to a disease; in
our setting, it refers to a country's susceptibility to autocracy.
If countries differ in their frailties, but these terms
are left out of the estimating equation, then there will be
more variability in the actual hazard than is captured by
the model (Omori and Johnson 1993). Over time, differences
in frailty will cause observations to "select out" of
the data; that is, low-frailty cases will stay in, while highfrailty
ones will drop out. The model will then underestimate
the hazard, with a corresponding overestimate of
survival times. Not only is the shape of the hazard function
incorrectly estimated; if the vi terms are correlated with
the independent variables, then the estimated coefficients
will also be biased.
Analogously with panel data, these unit-specific effects
can be estimated via fixed or random effects. Following
Lancaster (1990), we adopt the random-effects
approach, which involves choosing a specific distribution
for the vi's; the most commonly used is the gamma (1, 0)
distribution.'"
For the estimation, we first fit a standard proportional
hazards model and then choose a set of possible
values for 0. For each of these values, we generate an estimated
"predicted frailty" for each observation. We then
fit a second duration model, this time including the estimated
vi terms as an additional covariate, with a fixed
coefficient of 1.0 (that is, as an offset):
h(t) = ho(t)vi exp(Xi3).
We then repeat these steps for each value of 0 until
convergence.
A second distinctive characteristic of our data is that
we can have repeated failures-countries can fall out of
democracy more than once-and we would not wish to
impose a priori the requirement that these failures be independent
of one another. In particular, methods that
ignore correlations among repeated failures will tend to
underestimate the standard errors.
To address this property, we require that our frailty
terms not be independent, but rather correlated across all
16This procedure is elaborated in Chapter 10 of Diggle, Liang, and
Zeger (2002).
'7See also Vaupel et al. (1979) and Manton, Stallard, and Vaupel
(1981).
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560 DAVID L. EPSTEIN, ROBERT BATES, JACK GOLDSTONE, IDA KRISTENSEN, AND SHARYN O'HALLORAN
observations from a single country. The unit of observation
thus becomes a "country-spell," that is, a sequential
run of years in which a given country remains in a single
regime. We then restrict the estimated frailty terms to
be constant (in parlance, "shared") for all observations
from a given country. The approach, once again, is to
test down, i.e., to start with a proportional hazards model
with shared frailties, and if these are not significant, then
to remove this requirement and estimate a less restricted
model instead.
Regression Results
We proceed to analyze the results from the tobit, Markov,
and survival analyses described above. In each case, we
estimate three models: GDP alone; GDP plus the other
modernization variables; and GDP, modernization, and
political control variables. The Tobit regressions also include
a lagged dependent variable and control for regional
effects: Africa, East Asia, Europe, Former Soviet Union,
Latin America, and Near East.'8
Tobit Regressions
The tobit results are given in Table 6. As indicated, GDP is
significant in all specifications: countries are more likely
to be democratic the higher their level of economic development.
Moreover, the overall model fit is good, with a
pseudo-R2 of about 40%, high enough to capture significant
amounts of variation, but not so high that one would
suspect that the lagged dependent variable was doing all
the work.
The regression results also highlight some interesting
regularities. First, note that higher GDP growth
rates are associated with autocracies. On its own this
finding might be puzzling, but see the Markov analysis
below, which indicates that this relation holds
only for countries starting in autocracy. Looking at the
other two modernization variables, urbanization is negatively
related to democracy and population density
is positively related, but neither relation is statistically
significant.
As for the political variables, the results on our measure
of previous attempts at democratization indicate that
countries that experience previous falls from democracy
tend to be more democratic (but, again, see the discussion
of this variable in the Markov regressions below). Trade
openness is also associated with more democracy. And
TABLE 6 Tobit Regression Analysis of Factors
Affecting Democratic Transitions
(Regional Fixed Effects Omitted)
Model
(1) (2) (3)
Lagged Polity Score .964 .962 .962
(.006)*** (.007)*** (.007)***
GDP Per Capita .362 .385 .32
(.063)*** (.099)*** (.102)***
GDP Growth -2.458 -2.72
(.782)*** (.783)***
Pct. Urban Pop. -0.001 -.003
(.004) (.004)
Population Density .059 .032
(.035)* (.036)
Trade Openness .292
(.077)***
Previous .013
Transitions (.007)**
Resource Curse -.067
(.113)
N 4259 3789 3789
Pseudo-R2 .396 .397 .398
Note: All independent variables lagg
the "resource curse" is negat
but the coefficient is insigni
These findings corroborate
economic development to d
course, subject to PACL's o
variables may describe wel
democracy but do not pred
racy. This possibility is addre
analysis below.
Markov Regressi
As a first look at the data, co
a local regression (lowess) p
and the probability of transi
racy. The most obvious patte
have a significant impact on
both into and out of democra
magnitudes. This initial view
cism regarding PACL's claim
from but not into democrac
PACL also make much of
racy has ever fallen with a
$6,055, the prevailing level o
it transitioned to autocracy i
the probability of transition"sThe countries in each region are listed in Appendix B.
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DEMOCRATIC TRANSITIONS 561
FIGURE 3 Impact of GD
Transition Probabiliti
LC)
0
Cu
I- I
.OLLog
of GDP Per Capita
Type of Transition
Into Democracy ................... Out of Democracy
once a country passes this key income level. As shown in
Figure 3, though, no sharp drop-off is evident; the probability
of leaving democracy declines smoothly as GDP
increases, without any indication that one level of wealth
is more critical than another.
PACL also claim that the income levels at which countries
transition out of autocracy show significantly more
variation than the levels at which countries transition out
of democracy. Figure 4 shows that the data do not support
PACL's claim: the distribution of GDP values for
FIGURE 4 Distribution of GDP for Transitions To and From Democracy
-9 Variance = 0.712
-2 -1 0 1 2 3
(a) Log of G
(09 Variance = 0.742
C,)
-2 -1 0 1 2 3
(b) Log of G
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562 DAVID L. EPSTEIN, ROBERT BATES, JACK GOLDSTONE, IDA KRISTENSEN, AND SHARYN O'HALLORAN
TABLE 7 Markov Regression Analysis
Model
(1) (2) (3)
Lagged Yo -2.686 -2.979 -2.866
(.073)*** (.758)*** (.105)***
Lagged Yi 2.226 4.134 3.836
(.85)*** (1.491)*** (1.538)**
GDP Per Capita .800 1.118 .997
(.096)*** (.202)*** (.218)***
GDP Per capita * Y1 -.622 -.971 -.803
(.105)*** (.219)*** (.231)***
GDP Growth -.127 -.385
(.973) (.986)
GDP Growth * Yo -2.232 -1.844
(1.302)* (1.305)
Pct. Urban Pop. -.016 -.012
(.007)** (.008)
Pct. Urban Pop. * Y1 .019 .013
(.008)** (.008)
Population Density .017
(.034)
Population Density * Yo .075
(.049)
Trade Openness .228
(.129)*
Trade Openness * Y1 -.236
(.142)*
Previous Transitions -.023
(.013)*
Previous Transitions * Yo .033
(.009)***
Previous Transitions * Y1 .026
(.015)*
Resource Curse -.185
(.098)*
N 4299 3789 3789
Pseudo-R2 .773 .776 .780
Note: All independent variables lagg
transitions to democracy actually
variance than the distribution of income for transitions
to autocracy (0.712 vs. 0.742).
We begin our Markov analysis with all possible interactions
between the regressors and lagged values of Y3 and
YT, and then test down to a more parsimonious model.
Recall that if, for example, the interaction between GDP
and Y* (GDP * Y*) is significant, this means that GDP
has a different effect on the level of democracy if the
regime is autocratic in the previous period, as opposed
to partially or fully democratic. Similarly, if GDP * Y* is
significant, GDP has a different impact when the regime is
fully democratic in the previous period, as opposed to the
other two alternatives. Consequently, if both GDP* Y*I
and GDP * Y* are significant, GDP has a different effect
for all three lagged regime types.
The results of this analysis are illustrated in Table 7
in raw form, showing the significance of the direct and
interactive effects, and in Table 8 in a more easily interpretable
format. Beginning with the former, we see that
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DEMOCRATIC TRANSITIONS 563
TABLE 8 Summary of Markov
Model
(1) (2) (3)
A
0.18*** 0.15** 0.19**
GDP Per Capita P
D 0.80*** 1.04*** 1.00"**
A -2.34*** -2.23**
GDP Growth
P
-0.189 -0.386
D
A
0.002 -0.0001
Percent Urban Pop. P
D -0.015** -0.013*
A 0.095***
Population Density
0.021
D
A
-0.010
Trade Openness P
D 0.227*
A 0.035***
Previous Transitions P 0.002
D -0.024*
A
Resource Curse P -0.186*
D
Note: Coefficients refer to the relevant sums of direct and
interactive effects. * = 0.10; ** = 0.05; *** = 0.01. A = Autocracy;
P = Partial Democracy; D = Full Democracy.
the significance levels of the coefficients on the modernization
variables are similar in all three models. In
particular, GDP per capita is highly significant in all
specifications.
Table 8 distills the results from the analysis, showing
only the relevant (sums of) coefficients from the direct
and interactive effects. Coefficients that straddle table
rows have similar effects for the adjacent categories. In all
three models, for example, GDP has a similar impact on
democratization when the country in question was autocratic
or partially democratic in the previous period,
as opposed to fully democratic. If the country was autocratic
or partially democratic, the coefficient is 0.18
(the sum of GDP and GDP * Y* in Table 7); if the country
was fully democratic, then the coefficient on GDP in
Model 1 is 0.80 (the direct effect from Table 7). Both are
significant, as they are in Models 2 and 3 as well, indicating
that higher GDP does produce more democratic regimes,
no matter what the starting point, and no matter which
sets of covariates are added to the estimation equation.
The other findings in the table are also interesting and
help shed light on the results from the Tobit analysis above.
The coefficient on growth, for example, is significant only
for countries starting as autocracies, in which case it inhibits
democratic transitions; otherwise, growth is not a
significant factor. This result explains the negative coefficient
on growth in Table 6. Urbanization, on the other
hand, appears to undermine democracies but has no effect
on other regime categories. And population density,
significant in Model 2 only, promotes transitions out of
autocracy but has no impact on partially or fully democratic
regimes.
Turning to the political variables, trade openness
helps stabilize full democracies, but it does not help autocratic
or partially democratic regimes move up the ladder.
The results for our Previous Transition variable illustrate
the power of the Markov approach. Previous transitions
destabilize autocracies, have no impact on partial democracies,
and make full democracies more likely to backslide;
in other words, they are a destabilizing force. Thus a single
variable can have different impacts (in fact, opposite
signs) depending on the starting point in the previous period.
Finally, the resource curse tends to make all regime
categories more autocratic.
Why do our results from the Markov analysis vary so
markedly from PACL's? They, after all, test a similar model
to ours. Perhaps the difference comes from our coding of
the dependent variable: we use Polity scores, while PACL
employ their own measure of autocracy and democracy.
If we substitute our Polity measure into their regressions,
though, combining partial and full democracies into a
single democratic category, the estimation results from
PACL's model specification still hold. In particular, even
with a Polity version of the dependent variable, lagged
GDP is shown to be a significant predictor of transitions
out of democracy, but not to democracy.
Conversely, we dropped the "partial" category in our
data set. With this specification, the coefficient on GDP
is, as PACL concluded, significant for transitions to autocracy,
but not to democracy. In both data sets, then,
one can reproduce PACL's results using a dichotomous
regime classification with Polity data and thus rule out
the possibility that differences in the measure of democracy
account for differences in our findings.
Continuing in this fashion, we subdivide the two
PACL regime categories into four: (1) PACL autocracies
that we did not list as partial democracies; (2) PACL
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564 DAVID L. EPSTEIN, ROBERT BATES, JACK GOLDSTONE, IDA KRISTENSEN, AND SHARYN O'HALLORAN
autocracies that we list as partial democracies; (3) PACL
democracies that we list as partial democracies; and (4)
PACL democracies that we did not list as partial democracies.
PACL combine categories one and two versus three
and four, while we combine two and three together, but
leave one and four as distinct categories. If our categorization
is correct, then we should see relatively more transitions
out of category one into categories two, three, or
four than we would see from categories one and two to
categories three or four. And, in fact, 2.63% of regimes
transition out of category one, which is a 49% increase
over the 1.76% that transition out of categories one or
two.
Moreover, when we run a Markov regression with
GDP as an independent variable, we find that the coefficients
separating categories two and three are uniformly
insignificant, while those separating category one from
category two and category three from category four are
uniformly significant. This finding supports the use of
our tripartite regime classification rather than PACL's dichotomous
specification.
Finally, note the elusive nature of partial democracies.
Although we can gain some understanding of the
factors that make autocracies (or full democracies) become
partially democratic, we have little information as
to the factors that would lead partial democracies to either
slide down to autocracy or to move up to full democracy.
In fact, examining the saturated regression with all direct
and interactive effects, we find that none of the coefficients
on partial democracy are significant on their own.
Numerous, volatile, and shaping the dynamics of regime
transitions, the determinants of the behavior of the partial
democracies elude our understanding.
Duration Analysis
We now turn to the duration analysis, which highlights
the factors that help countries stay democratic. Given our
trichotomous measure of democracy, there are two ways
in which we could approach this issue: what prevents full
democracies from sliding back to partial democracies or
autocracies, and what prevents partial democracies from
sliding back to autocracy?
PACL also perform duration analysis, and they argue
that new democracies are in fact more likely to fail than
more established ones, but that once GDP per capita is
taken into account, this difference disappears. We examine
this conclusion using the same three models as in the
previous section.
Table 9 shows the results of estimating the probabilities
that states fall out of full democracy. The frailty
TABLE 9 Survival Analysis of Transitions Out
of Full Democracy
Model
(1) (2) (3)
GDP Per -1.853 -2.784 -2.895
Capita (.328)*** (.65)*** (.777)***
GDP Growth -6.8 -6.526
(7.136) (7.785)
Pct. Urban .041 .031
Pop. (.022)* (.026)
Population .023 .02
Density (.236) (.309)
Trade -1.265
Openness (.505)**
Previous .067
Transitions (.04)*
Resource Curse .448
(.855)
N 1483 1356 1356
Log Likelihood -78.326 -63.167 -58.778
0 .383 .67 .895
Note: All independent var
***=.01.
terms were significant in all specifications, and so they are
retained in the estimation equations.
Note first that in all specifications, higher GDP per
capita reduces the probability that countries fall out of
democracy. Other than this finding, however, the results
offer few clues as to the factors that help prevent backsliding.
Higher urban populations are a risk factor for democracies
in Model 2, and trade openness is a stabilizing factor
in Model 3, but no other coefficients are significant.
Figure 5 graphs the impact of GDP on transitions,
plotting the smoothed hazard rates both with and without
adjustment for GDP per capita.19 The top figure shows
that the risk of falling out of democracy rises at first, then
declines after the first seven years as a full democracy. The
bottom figure tells a very different story. Here, after adjusting
for GDP per capita, the risk of failure rises steadily
for about 20 years, and then plateaus. To put it another
way, it is the average increase in per capita GDP that causes
the reduction in the hazard rate in the top figure. The key
to consolidation of new democracies, it would appear, is
a strong economy.
'19The adjustment sets GDP per capita at its mean value and calculates
hazard rates for all other variables. The fact that the curve
flattens out is evidence that variation in GDP accounts for the Ushaped
function in the top figure.
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DEMOCRATIC TRANSITIONS 565
FIGURE 5 Adjusted and
of Democracy
Smoothed Hazard Estimate for Leaving Democracy
Unadjusted
a:
U,
0
I
0 10'o 20 30Years
Adjusted for GDP per Capita
cc
0--
o
10 2'0 30
Years
Note: Gaussian Kernel Smoothing
We now repeat the above analysis for partial democracies,
to see if similar factors keep semidemocratic
societies from falling back into autocracy.20 Here, the
frailty terms were never significant and were dropped
from the estimation.
Table 10 presents the results. Compared with transitions
from full democracy, the predictive ability of these
models is much smaller. The coefficient on GDP per capita
is again negative in all specifications, but marginally significant
in Model 2 and insignificant in Model 3. The
only other significant variables are trade openness and
the resource curse in Model 3, the former associated with
less risk of falling into autocracy, the latter with increased
risk.21 And the log likelihoods of all models are smaller
than those in Table 9.
Neither does Figure 6 offer much evidence as to the
factors that affect the consolidation of partial democracies.
The unadjusted hazard rate in the top half of the
figure rises gently, turning negative only after 13 years (at
which point there are relatively few data points remaining
in the sample). This general pattern does not change,
even after adjusting not just for GDP, but for all independent
variables in Model 3. As in the Markov analysis, then,
the factors affecting transitions out of partial democracy
remain poorly understood.
TABLE 10 Survival Analysis of Transitions
from Partial Democracy to Autocracy
Model
(1) (2) (3)
20Since we are interested in the question of stabilizi
racies, transitions to full democracy are treated a
vations in this part of the analysis.
21One might also reasonably inquire whether cou
partial democracies after falling out of full dem
ferent patterns in their hazard rates. To evaluate
a variable FullToPart was added to the analysis,
significant.
GDP Per -.695 -.692 -.603
Capita (.221)*** (.356)* (.395)
GDP Growth 3.939 4.295
(3.651) (3.799)
Pct. Urban -.002 -.006
Pop. (.014) (.017)
Population .008 .034
Density (.131) (.166)
Trade -.882
Openness (.351)**
Previous -.006
Transitions (.026)
Resource Curse .903
(.513)*
Obs. 703 627 627
e(ll) -153.779 -131.85 -128.113
Note: All independent variables lagged o
***=.01.
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566 DAVID L. EPSTEIN, ROBERT BATES, JACK GOLDSTONE, IDA KRISTENSEN, AND SHARYN O'HALLORAN
FIGURE 6 Adjusted and Unadjusted Hazard Rates for Transitions from Partial
Democracy to Autocracy
Smoothed Hazard Estimate for Leaving Partial Democracy
Unadjusted
o
LO
0 5 10 15 20
Years
Adjusted for All Independent Variables
M ?L
0 5 10 15 20
Years
Note: Gaussian Kernel Smoothing
Conclusion
This article has returned to the analysis of the relationship
between modernization and democracy. In doing so, it has
reappraised the central argument of the works of PACL,
the standard against which all other work in this field
should be measured.
We find that PACL themselves erred in their own analysis,
failing to correctly estimate the standard errors of
the coefficients reported in their Markov model, and that
when doing so, they erred in a way that led them to report
the impact of GDP on democratization as insignificant.
As did PACL, we too employed Markov methods; when
we did so, we found reason to prefer a three-category
classification of democratic regimes to the dichotomous
categorization that they employed. Classifying countries
into autocracies, democracies, and partial democracies,
we demonstrated that higher incomes per capita significantly
increased the likelihood of democratic regimes,
both by enhancing the consolidation of existing democracies
and by promoting transitions from authoritarian
to democratic systems.
Our trichotomous measure proved valuable for an
additional reason: it highlighted the significance of the
middle category--the partial democracies-a category
whose properties and significance were necessarily obscured
in the PACL analysis. These are "fragile" democracies,
or perhaps "unconsolidated democracies." Whatever
one wishes to call them, they emerge from our analysis as
critical to the understanding of democratic transitions.
More volatile than either straight autocracies or democracies,
their movements seem at the moment to be largely
unpredictable. One of our major conclusions, then, is that
it is this category--the partial democracies-upon which
future research should focus.
We also note that this is one area of scholarly inquiry
with important implications for policy. In the present era,
countries are reforming both politically and economically.
Politically, they seek democracy. Economically, they pursue
policies to promote economic growth. Should these
reforms be sequential or simultaneous, and if sequential,
which-the political or the economic-should come first?
PACL's (2000) conclusion that economic growth does not
aid democratization, while democratic political institutions
foster growth, puts them squarely in the "politics
first" camp.
In arguing with PACL's conclusions, though, we do
not mean to imply that economic reforms should take
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DEMOCRATIC TRANSITIONS 567
primacy over the political, as the p
the issues of reciprocal causality in
Indeed, the current literature on po
exactly these causality issues by try
instrument for institutions, broadl
in turn affected by the level of eco
Rodrik, Subramanian, and Trebbi
use the widely known Acemoglu, J
(2001) measure of settler mortality
tutional quality. They find that ins
important as determinants of econ
either geography or trade. Glaese
Silanes, and Shleifer (2004) disagree
claiming that in fact better property
ships help spur economic growth, w
the emergence of democratic gover
To this fruitful, ongoing debate w
that leaving autocracy is not the sa
racy. Between these two lie parti
often act in a manner distinct from
more or less democratic than the
ics, while shaping contemporary po
understood.
Appendix A: Descriptive Statistics
on Transitions
We have 169 countries in the data set.22 Their patterns of
stability and transitions are as follows:
* 41 very stable countries: same Polity value throughout
the data set
- 23 very stable full democracies with Polity value of
1023
- One very stable democracy with Polity value of 824
- Four very stable partial democracies existing between
eight and 11 years25
- 13 very stable autocracies26
* 42 stable countries: same category (autocracy, partial,
or democracy) throughout the data set but Polity value
changes
- Four stable democracies27
- Four stable partial democracies28 (Note: none of
these countries is more than 10 years old.)
- 34 stable autocracies29
After identifying the very stable and the stable countries,
we want to characterize the countries making transitions
between categories. Two features seem to be of
interest: how many categories the country visited during
our time period and the direction of the changes. Concerning
the latter feature, we distinguish between somewhat
stable countries making a single transition during
the time period studies and unstable countries making
several transitions.
* 56 shifting between two categories
- Nine shifting between partial and democracy
* Four somewhat stable: three countries up to
democracy30 and one down to partial31
* Four unstable countries going up once and down
once32
- 37 shifting between partial and autocracy
* 22 somewhat stable: 15 up to partial33 and seven
down to autocracy34
* 17 unstable35
22Twenty-five countries that existed during our sample period had
no Polity scores: Andorra, Antigua & Barbuda, Bahamas, Barbados,
Belize, Bosnia and Herzegovina, Brunei, Cape Verde, Dominica,
Grenada, Liechtenstein, Maldive Islands, Malta, Monaco, Palau,
San Marino, Sao Tome-Principe, Seychelles, Solomon Islands, St.
Kitts-Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname,
Vanuatu, and Western Samoa.
23Countries existing in all 41 years are Australia, Austria, Belgium,
Canada, Costa Rica, Denmark, Finland, Iceland, Ireland, Italy,
Japan, Netherlands, New Zealand, Norway, Sweden, Switzerland,
and United Kingdom. Countries existing less than all 41 years are
Czech Republic (8), Germany (11), West Germany (30), Lithuania
(10), Papua New Guinea (26), and Slovenia (10).
24Latvia (10).
25Estonia (10), Ethiopia after 1993 (8), Macedonia (10),
Namibia (11).
26Countries existing in all 41 years are Bhutan, Cuba, Libya, and
Saudi Arabia. Countries existing less than all 46 years are Eritrea
(8), East Germany (29), Kyrgyzstan (10), Qatar (30), United Arab
Emirates (30), Uzbekistan (10), Vietnam (25), South Vietnam (16),
and Yemen (11).
27 Israel, Jamaica, Mauritius, and Trinidad.
28 Georgia, Moldova, Russia, and Ukraine.
29Afghanistan, Algeria, Angola, Bahrain, Burundi, Cameroon,
Chad, China, Congo-Kinshasa, Egypt, Gabon, East Germany,
Guinea, Iraq, Jordan, Kazakhstan, North Korea, Kuwait, Laos,
Liberia, Mauritania, Morocco, Oman, Rwanda, Swaziland, Syria,
Tajikistan, Togo, Tunisia, Turkmenistan, USSR, North Vietnam,
North Yemen, South Yemen, and Former Yugoslavia.
30Botswana, France, Slovakia, and South Africa.
31Malaysia.
32Columbia, France, India, and Sri Lanka.
33Central African Republic, C6te d'Ivoire, Croatia, Djibouti,
El Salvador, Guinea-Bissau, Honduras, Indonesia, Iran, Malawi,
Mali, Mozambique, Paraguay, Tanzania, and Yugoslavia.
34Belarus, Equatorial Guinea, Kenya, Singapore, Somalia, Syria, and
Zimbabwe.
35Up once and down once: Armenia, Azerbaijan, Benin, Burkina
Faso, Guyana, Pakistan (pre-1972), and Zambia. More shifts:
Albania, Cambodia, Comoros, Congo-Brazzaville, Ghana, Haiti,
Nepal, Sierra Leone, and Uganda.
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568 DAVID L. EPSTEIN, ROBERT BATES, JACK GOLDSTONE, IDA KRISTENSEN, AND SHARYN O'HALLORAN
- 11 shifting between autocracy and democracy
* Nine somewhat stable: seven countries up to
democracy36 and two down to autocracy37
* Two unstable countries going two categories up
once and two down once38
30 shifting between all three categories
- Only in one direction
* Six countries up39 and none the other way
- 24 back and forth
* 12 countries making three transitions between
categories40
* Four making four transitions41
* Four making five transitions42
* Two making six transitions43
* One making seven transitions44
Appendix B: Countries
in Each Region
Africa: Angola, Benin, Botswana, Burkina Faso, Burundi,
Cameroon, Central African Republic, Chad,
Comoros, Congo-Brazzaville, Congo-Kinshasa, C6te
d'Ivoire, Djibouti, Equatorial Guinea, Eritrea, Ethiopia,
Gabon, The Gambia, Ghana, Guinea, Guinea-Bissau,
Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania,
Mauritius, Mozambique, Namibia, Niger, Nigeria,
Rwanda, Senegal, Sierra Leone, Somalia, South Africa,
Sudan, Swaziland, Tanzania, Togo, Uganda, Zambia,
Zimbabwe
East Asia: Australia, Burma, China, Fiji, Indonesia, Japan,
North Korea, South Korea, Laos, Malaysia, Mongolia,
New Zealand, Papua New Guinea, Philippines, Singapore,
Solomon Islands, Taiwan, Thailand, Vietnam
Europe: Albania, Austria, Belgium, Bulgaria, Croatia,
Cyprus, Czech Republic, Czechoslovakia, Denmark,
Finland, France, Germany, Greece, Hungary, Iceland, Ireland,
Italy, Macedonia, Netherlands, Norway, Poland,
Portugal, Romania, Slovakia, Slovenia, Spain, Sweden,
Switzerland, Turkey, United Kingdom, Yugoslavia
Former Soviet Union: Armenia, Azerbaijan, Belarus,
Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania,
Moldova, Russia, Tajikistan, Turkmenistan, Ukraine,
USSR(Soviet Union), Uzbekistan
Latin America: Argentina, Bolivia, Brazil, Chile,
Colombia, Costa Rica, Cuba, Dominican Republic,
Ecuador, El Salvador, Guatemala, Guyana, Haiti,
Honduras, Jamaica, Mexico, Nicaragua, Panama,
Paraguay, Peru, Trinidad, Uruguay, Venezuela
Near East: Afghanistan, Algeria, Bahrain, Bangladesh,
Bhutan, Egypt, India, Iran, Iraq, Israel, Jordan, Kuwait,
Lebanon, Libya, Morocco, Nepal, Oman, Pakistan, Qatar,
Saudi Arabia, Sri Lanka, Syria, Tunisia, United Arab
Emirates, Yemen
36Bolivia, Bulgaria, Czechoslovakia, Hungary, Portugal, Senegal,
and Spain.
37Burma and Laos.
38Lesotho and Uruguay.
39Mexico, Mongolia, Nicaragua, Poland, Romania, and Taiwan.
40Bangladesh, Brazil, Chile, Colombia, Fiji, Gambia, Greece,
Madagascar, Niger, Panama, Philippines, and Venezuela.
41Dominican Republic, Guatemala, Pakistan (post-1972), and
Sudan.
42Argentina, Ecuador, Nigeria, and South Korea.
43Peru and Turkey.
44Thailand.
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