Chapter Two:
METHODOLOGICAL ISSUES
In this chapter we discuss our operationalization of the two key concepts that form the
backbone of the analysis – democracy and development. Following, we discuss methodological
issues raised by the two empirical approaches employed by the subsequent chapters, time-series
cross-section analysis and case study methods.
DEMOCRACY: CONCEPTUALIZATION
Democracy, as is often observed, is a protean term; its contrary, authoritarianism or
autocracy, even moreso.1 To be sure, since the Greeks there has been general consensus with
respect to what might be regarded as the core meaning of democracy, namely, “rule by the people.”
Left unclear is exactly how this phrase should be interpreted. What does it mean for the people to
rule in the context of a polity the size of a nation-state?
We restrict our focus to explicitly political settings, to representative democracy, and to
issues that are largely procedural in nature.2 From there, our approach to definition follows a
minimal/maximal strategy, identifying (a) features that are common to all usages of the concept
(essential attributes) and, subsequently, (b) those that are implied by the ideal-type.3 By bounding
the concept in this fashion we hope to finesse some of the interminable debates that surround this
contested concept – though we do not suppose that this method of concept definition will ever
resolve these longstanding debates.
Minimally, representative democracy is usually understood as a situation in which elective
bodies are sovereign (they are not controlled by military or religious bodies or a monarchy), multiple
parties (or candidates) compete for political power through regularly scheduled elections held before
a broad electorate, with rules that are fair to each of the contestants. The key underlying dynamic is
competition: when effective competition among parties or candidates exists, democracy is said to
exist. This vision of democracy is associated with the pioneering work of Joseph Schumpeter and
with contemporary writers such as Robert Dahl and Adam Przeworski, and is sometimes referred to
as electoral democracy or polyarchy.4
On problems of definition see Collier, Adcock (1999), Collier, Levitsky (1997), Lively (1975). On
problems of operationalization see Bollen (1993), Bollen, Paxton (2000), Gleditsch, Ward (1997), Munck,
Verkuilen (2002), Treier, Jackman (2003).
2 By restricting ourselves to procedural issues we wish to exclude questions concerning the economic
system pertaining in a society and the level of socioeconomic equality. It may be, as some have argued, that
democracy is difficulty to maintain in a society where property rights are not respected, or where extreme
inequalities of wealth exist. However, in order to maintain a useful concept – useful, that is, for socialscientific purposes – it is important to avoid definitions that encompass too broad a semantic territory. If
democracy becomes a synonym for a certain type of society then it is impossible to talk about a regime-type
having any sort of causal effect. It may be useful as a descriptive concept but it will not be useful in causal
arguments.
3 Gerring (2001: ch 4), Gerring, Barresi (2003).
4 Dahl (1971), Przeworski et al. (2000), Schumpeter (1942/1950).
1
Maximally, democracy has been associated with many additional features, e.g., a free and
independent press, a well-informed electorate, strong protection for civil liberties, universal adult
suffrage, low entry barriers to new parties and independent candidates, equal access to the media and
to sources of campaign finance for all parties and candidates, strict adherence to the one person/one
vote principle (e.g., elimination of malapportionment), equal opportunity to participate in politics at
all levels, a high level of actual participation, citizenship rights available to all permanent residents,
vertical and horizontal accountability, and so forth. This more demanding understanding of
democracy – going well beyond the ideal of competitiveness -- is sometimes described as “real,”
“full,” “deep,’ “complete,” or “thick” democracy.
A consensus seems to have been reached, at least among scholars, that democracy in the
context of a nation-state entails, at the very least, multi-party elections with reasonably fair rules of
procedure. Beyond that, there is great debate about how many (if any) additional attributes might
profitably be added to this minimal concept. No resolution of this debate is in sight. The problem,
from our perspective, is that while we have various measures of democracy (tout court) we do not
have disaggregated measures of democracy’s manifold components – free elections, free press, party
competition, sovereignty, and so forth.5 This prevents us from administering a systematic,
crossnational test of which elements of democracy might be most important for achieving
development. Moreover, even if we had such disaggregated measures such tests might not be very
revealing, for sub-components of democracy tend to be highly correlated.6 Countries with effective
multi-party competition also tend to be countries with a free press, so differentiating the causal
effects of these two factors would be difficult. Democracy, as it appears in the world today, is a
“syndrome.”
This fact provides some methodological defense for our loose conceptualization of the key
concept. If all facets of democracy cohere, then perhaps it matters less how one defines and
measures the key concept. In any case, our theoretical assumption is that democracy is a complex
concept, with both “minimal” and “maximal” attributes. We expect that a country that possesses
more of these attributes has greater developmental potential than a country with fewer of such
attributes, even though we cannot say precisely what role each of these attributes might play, or how
much causal weight to assign to each one.
This inclines us to consider democracy as a continuous, rather than dichotomous, concept. There
are many attributes that define democracy, and most of these attributes are properly considered
matters of degree. Thus, although ordinary language forces us to speak of regimes as “democratic”
or “authoritarian,” we see no justification for imposing clear-cut distinctions between this sub-types.
Such definitional boundaries are inevitably highly arbitrary. They lump together a large set of
regimes that may be quite heterogeneous within each category; and between categories there are
inevitably borderline cases that are hard to classify neatly into one or the other group. Moreover,
the classificatory principles -- multi-party elections only?, multi-party elections plus civil liberties?,
government turnover from one party to another? -- are difficult to justify, ex ante.
DEMOCRATIC STOCK: MEASUREMENT ISSUES
We argued in chapter one that regimes have long histories, and these histories must be
accounted for if we are to understand the causal effect of regime-type on development. We argued
Cite final report by the Committee on the Evaluation of USAID Democracy Assistance Programs
(CEUDAP), convened by the National Academy of Sciences.
6 Coppedge (forthcoming).
5
2
in the previous section that the concept of regime-type, as it might be expected to influence
development, is multi-faceted and continuous. This means that we must arrive at an empirical
measure of the concept that is sensitive both to differences of duration and of degree.
As it happens, only one crossnational indicator of democracy satisfies these two desiderata.
This is the well-known Polity2 variable drawn from the Polity IV data set.7 Polity2 measures the
extent to which democratic or autocratic “authority patterns” are institutionalized in a given country
in a given year, taking into account how the executive is selected, the degree of checks on executive
power, and the form of political competition. It is highly sensitive (employing a twenty-one-point
scale), global in reach (including all sovereign polities except microstates), and extends back to the
nineteenth century.
Unfortunately, the Polity dataset also imposes two serious costs. First, the rules used to
create the key variable are dizzyingly complex. The Polity User’s Manual makes a valiant effort to
explicate coding procedures, but the methods remain rather difficult to unpack. Second, there are
serious questions regarding measurement error in the index.8 Granted, questions might be raised
with respect to all extant, and all conceivable, democracy indices. Polity2 is no worse than the rest
and probably better than the average. It is, indeed, the industry standard, owing to the strengths
noted above. Reassuringly, the Polity2 variable correlates highly with other existing measures of
democracy.9 Thus, there is no reason to suspect systematic errors in this index that might affect the
substantive findings of this study, even though we are not entirely happy with the coding of the
variable. (Some supplementary coding of the Polity2 variable was conducted by the authors in order
to rectify missing observations. These coding decisions are explained in the chapter appendix.)
To create a stock measurement of democracy from this variable we add up each country’s
Polity2 score from 1900 to the present year, with a 1 percent annual depreciation rate. This means
that a country’s regime stock stretches back over the course of a century. The year 1900 is chosen as
a threshold year ushering in a period (1) in which mass democracy becomes a world-historical
phenomenon (no longer restricted to the U.S. and a few European states), (2) in which it is not
unreasonable to assume a causal relationship between democracy and development, and (3) in which
the data exist to test such a relationship.10 [We may choose to extend the index back to 1850 or
1800. We may also experiment with different depreciation schedules and perhaps with nonmonotonic formulas.]
In order to clarify how this coding procedure translates into democratic stock for the
countries in our sample, we include a graph with scores for four countries that illustrate diverse
regime trajectories. Figure 2.1 depicts democratic stock for the United States (democratic
throughout the century), China (authoritarian throughout the period), Chile (intermittently
democratic), and Botswana (democratic since independence in 1966). We include scores for the
entire century even though our empirical tests, described in subsequent chapters, cover only the
postwar era (1950–2000). Note that the slope of the curve moderates as a country accumulates
more democratic experience, as in the case of the United States toward the end of the twentieth
century. A continuous period of democratic rule translates into a monotonic, but not linear,
variable.
Marshall, Jaggers (2000).
Bollen, Paxton (2000), Bowman, Lehoucq, Mahoney (2005), Munck; Verkuilen (2002), Treier,
Jackman (2003).
9 Intercorrelations of Polity2 with other democracy indicators are as follows: “Political Rights”
(Freedom House) = -.85; “Liberal Democracy” (Bollen) =.92; “Democracy index” (Vanhanen) = .85.
10 We see no reason to suppose that a longer period of measurement—which might, in principle,
stretch back to 1850—would alter any of the findings presented here.
7
8
3
Figure 2.1:
Democratic Stock: An Illustration
800
600
400
200
0
-200
-400
Chile
China
United States of America
2000
1995
1990
1985
1980
1975
1970
1965
1960
1955
1950
1945
1940
1935
1930
1925
1920
1915
1910
1905
1900
-600
Botswana
Democratic stock (based on 1 percent annual depreciation rate) for four countries, 1900–2000. (Note that regression
tests conducted in subsequent chapters are limited to the years 1950–2000.)
4
The democratic stock of a country, as illustrated in Figure 2.1, provides our best guess about
the role of regime history in conditioning developmental outcomes. Countries with more stock
should provide better governance and should realize better policy outcomes, all other things being
equal.
DEVELOPMENT
The concept of development has come to play the same role that “modernization” and
“progress” played for an earlier generation of scholars and policymakers. This, by itself, is
noteworthy, and may engender skepticism. One is naturally suspicious of a single term that is asked
to bear such enormous semantic burdens. Indeed, “development” is used nowadays as an offhand
synonym for all things praiseworthy.
On the other hand, if one is to take a stand on matters that affect the poorer regions of the
world one is compelled to find a label that distinguishes those aspects of politics, economics, and
society that are encouraging from those that are not. Development, therefore, will be understood to
include any policy or policy outcome that furthers the material wellbeing of a population, with
particular attention to less advantaged sectors. (The emphasis on welfare, with special attention to
the less advantaged, follows a “prioritarian” conception of justice.)11
An empirical defense might also be mounted for the utility of the concept. It is an oft-noted
fact that good things go together, as do bad things. Countries with high per capita GDP are also
likely to enjoy low corruption, high social trust, high life expectancy, good schools, and other
desiderata. There would appear to be high and low equilibria. This can be seen in the rather high
inter-correlations of the diverse variables employed as outcomes in this study (see Table 2.2, chapter
appendix). A factor analysis of these variables reveals only ?? separate dimensions, the first of which
accounts for??% of the variance (see Table 2.3, chapter appendix). In laymen’s terms, societies are
holistic; it is no surprise, therefore, that the process of development is also holistic. This quality of
societies provides a strong justification for holistic terms like progress, modernization, and
development. We need to be able to talk about diverse features that nonetheless co-vary.
Even so, while we employ the abstract concept of development in our theoretical discussion,
the empirical project of this book is highly disaggregative, taking very little for granted. That is, we
do not suppose that because a society is wealthy it must, therefore, also be highly educated. Each
chapter examines a separate policy and/or policy outcome, along with one or more measures of that
concept. These include economic growth (chapter 3), economic policy (chapter 4), public
infrastructure (chapter 5), policy continuity (chapter 6), policies to protect and conserve the
environment (chapter 7), education (chapter 8), public health (chapter 9), and opportunities for
women (chapter 10). A complete set of concepts and indicators is included in Table 2.1.
11
Gerring (forthcoming), Parfit (2000).
5
Table 2.1:
Measures of Development
Concept
Indicator(s)
Chapter
Economic growth
GDPpc growth rate
3
Economic policy
Trade openness
Foreign direct investment
Investment risk
4
Infrastructure
Electricity
5
6
Policy continuity
Environmental policy CO2
7
Sulfur emissions
Education
School attainment
8
Public health
Infant mortality
9
Gender equality
Female population (% of total)
Life expectancy ratio (F/M)
Female labor force participation
Fertility
Schooling gap (F/M)
10
6
Of course, many additional matters might be considered under the rubric of development.
Any such list is bound to be partial. This book is limited to topics related to domestic policy; thus,
we do not consider the “democratic peace.” It is also limited to topics for which reliable empirical
indicators are available for a broad sample of countries -- preferably in a time-series format, so that
historical changes can be interrogated. We exclude factors such as income inequality, where data
quality is questionable and good time-series data is lacking for all but a handful of countries. We
exclude other variables, even if they exist in a time-series format, if they are difficult to distinguish
empirically from measures of democracy, risking endogeneity between left- and right-side variables.
Indeed, many indicators of good governance include, as a component of the coding scheme, a
measure of political or civil rights or leadership accountability.12 Consequently, these issues are
difficult to disentangle.
Some of the developmental outcomes on our list are classifiable as policies and others as
policy outcomes; still others occupy a position somewhere in between. For our purposes, this is not
a consequential distinction. What is important is the underlying normative assumption that all
chosen outcomes have a net positive (or negative) effect on human welfare: they are, on the whole,
good (or bad) for people, with special attention to the least advantaged. Of course, this does not
mean that they are equally good (bad). Saving infants from premature death may be more important
than a high level of economic growth, for example.13 We refrain from issuing judgments on these
matters.
Similarly, we do not wish to imply that all the indicators chosen for analysis are invariably
good (bad) for people. One can imagine circumstances in which growth might negatively affect a
society, at least for a period of time, for example. This does not challenge the general conclusion
that economic growth is, on the whole, better than economic stagnation or recession. It should also
be pointed out that just because a policy or policy outcome has a net positive effect on society does
not mean it is the best of all possible policies or policy outcomes. Returning to the example of
economic growth, it may be observed that there are different kinds of growth, some of which are
more sustainable and more equitable than others. We make no judgments on this issue and on other
similar issues that arise with other measures of economic, political, and human development. In an
analysis of this scope, one is constrained to accept the data more or less as it presents itself.
One final question deserves mention. The eight policy areas are treated as if they were
(more or less) causally independent of one another. Of course, we know that this is not entirely
true. A country’s growth performance, for example, is likely to have repercussions for each of the
other outcomes under investigation. This is the most obvious interaction effect, though others may
be inferred. In order to neutralize the cumulative effect of economic growth we include a per capita
GDP control in all analyses. Sometimes, we also include a control for recent growth performance.
Other interaction effects are deemed to be small enough to ignore. Note that because of the high
inter-correlations among various developmental outcomes, a “full” model (with all developmental
outcomes included) would not be informative.
An alternative approach to the interaction problem would involve the construction of a
comprehensive multiple-equation model, where each component is modeled separately.
Unfortunately, the construction of a comprehensive model of development requires so many -essentially untestable -- assumptions that any results from the model would be viewed as highly
suspect. No such model has, to our knowledge, ever been undertaken, much less satisfactorily
12
13
E.g., governance variables collected by the PRS/ICRG group (PRS 2004).
Gerring (2007a).
7
achieved. Under the circumstances, it seems judicious to approach each developmental outcome as
if it were independent of the others.14
TIME-SERIES CROSS-SECTION (TSCS) ANALYSIS
In subsequent chapters, each of the foregoing measures of development is treated as a
separate dependent variable. Sample periods begin in 1950, or the earliest year for which data are
available, and extend to 2003. Analyses include all countries for which sufficient data are available,
providing samples that generally extend to most of the sovereign states of the world (over 150
country-cases).
Additional tests are conducted on an artificially generated “complete” sample, including all
sovereign countries, to ensure that results are not contingent upon a biased sub-sample of the
world’s countries. These imputed samples are viewed as robustness checks, and are reported only
where they differ substantially from the original (“natural”) sample.15
Where time-series data exists, we prefer to test hypotheses about the relationship of
democracy and development in a fixed-effect format. This means that dummy variables for each
country are inserted into the model, providing a unique intercept for each unit. This helps to
neutralize the problem of spatial heterogeneity and dramatically reduces the specification dilemma
inherent in all nonexperimental analyses. It also goes some way toward solving selection problems.
Specifically, any underlying factor that affects either a country’s regime trajectory and/or its
developmental trajectory will be captured in the unique intercept provided for that country, so long
as that underlying factor holds relatively constant across the period of analysis (1960-2003). This
means that geographic factors, longstanding disease vectors, cultural/sociological factors, colonial
histories, as well as other deeply-rooted historical factors should not bias the results of our analyses.
Of course, this does nothing to solve the problem of finding adequate controls that vary over
time. One approach is the introduction of annual dummy variables for each year. However, this
presumes that temporal effects are uniform across all cases, which may or may not be the true.
Evidently, different outcomes presuppose different contributing factors. Thus, we postpone our
discussion of time-varying controls for subsequent chapters, each of which is focused on a different
set of outcomes. In each analysis, once a benchmark model is identified, a series of sensitivity tests
is employed so as to ensure that results are not the product of arbitrary choices in model
specification.
Another sort of econometric issue concerns endogeneity between the variable of principal
theoretical interest—democracy stock—and various outcomes under investigation. Is democracy
causing polities to become more stable, or is increasing stability causing polities to democratize (or
remain democratic)? Fortuitously, a stock measure of democracy that extends back in time (up to a
century) is less subject to problems of causal endogeneity than a contemporaneous measure of
Readers may speculate about whether this imposes a liberal or conservative bias on our findings. If
all dependent variables are viewed as highly correlated indicators of the same thing (“development”) then the
effect of democracy on each individual outcome may be somewhat less than is suggested by the regression
results reported in subsequent chapters. If, however, there are positive interactions among the various
developmental outcomes – if, that is, higher growth causes improved public health, and improved public
health causes higher growth – then the models in subsequent chapters under-estimate the true causal effect of
democracy on development. Because of the difficulty of modeling all of these interactions, we take no
position on this question.
15 The “Amelia” program is employed for imputing missing data (Honaker et al. 2001).
14
8
democracy. Even so, one must be concerned. Thus, we introduce a lag of one time-period for all
predictors. As a robustness check, this time lag is increased to one decade, and the magnitude of the
coefficient examined. A final method involves the use of lagged instruments for each variable in the
model, pioneered by Arellano and Bond (1991).16
The most troubling aspect of the analysis concerns the non-random quality of the
“treatment.” By employing a global sample we are implying that all countries are capable of
democracy. However, the countries that manage to democratize, to do so at an early date, and to
maintain that regime-type over time probably differ from other countries in systematic ways.
Plausibly, those countries with greater developmental potential are more likely to accumulate
democratic stock. If so, then democracy stock may serve as a proxy for “developmental potential,”
biasing the coefficients on this key variable.
One approach to overcoming this identification problem could be to model the selection
process by which countries become more or less democratic over time, or to use quasi-random
sources of data variation (e.g. through the use of selection models, matching techniques, or
instrumental variables).17 Unfortunately, each of these techniques brings in its train an additional set
of statistical issues. It is very difficult to identify a defensible time-varying instrument for democracy
that we can employ in our fixed effects models, an instrument that relies only on within-country
variation in democracy stock to identify the effects of democratization. It is also difficult to find
appropriate matches for countries with varying degrees of democratic stock in order to perform
case-control analysis. (Recall that our independent variable of interest is continuous, not
dichotomous.)
Thus, we are led to adopt a more traditional approach to the problem of statistical modeling
– the inclusion of relevant controls. It is important to keep in mind that the fixed-effect format
captures all country-specific time-invariant unobservables that might be related to outcomes of
interest. This includes geographic and sociocultural factors (insofar as the latter are static). In order
to capture time-varying factors we include specification tests with a variety of controls (depending
upon the outcome of interest) that change over time such as GDP per capita, growth, population,
and a measure of contemporaneous democracy.
Of course, we do not suppose that we will be able to model all the exogenous influences on
a country’s developmental trajectory. There is always the possibility of missing variables. However,
a key point to notice is that this set of control variables includes a number of indicators that are
highly sensitive to short-term factors that might influence development. Because one of these
controls is a variable measuring a country’s current level of democracy (which we shall denote as X2),
this should purge the stock measure of democracy of any un-measured “proxy” effects. Suppose,
for example, that a country solves some important problem that is blocking its developmental
potential, e.g., it resolves a lingering ethnic conflict. Let us represent this development by the
variable X3. And suppose that we do not have an independent measure of X3 (indeed, data on this
subject are sketchy). It is quite possible that X3 would enable that country to democratize, as well as
contribute to its performance on various outcome measures of development (growth, infant
16 Another approach for dealing with this problem involves the use of instrumental variables in twostage least-squares estimation. Regrettably, it is difficult to discover a set of instruments for democracy stock
in a fixed-effect, time-series framework. Our sense is that 2SLS analysis with bad instruments (instruments
that are correlated with the outcome or that are poorly correlated with the variable of theoretical interest)
creates new problems that are often worse than the existing econometric problems. Thus, we do not hold
much hope for this form of analysis in this particular instance.
17 Extensive use is made of selection models in work by Przeworski and colleagues (e.g., Przeworski
et al. 2000).
9
mortality, education, et al.). Thus, in a simple bivariate model (with no control variables), the
absence of X3 would presumably boost the strength of our key theoretical variable, democracy stock
(which we shall denote by X1), resulting in biased estimates. However, if a contemporaneous
measure of democracy (X2) is included in the model, this should register any short-term effects of X3
on X1. Thus, the coefficient for X1, the principal variable of interest, should be unbiased.
CASE STUDY ANALYSIS
While most of the empirical evidence gathered together in this book derives from crosscountry data analyses, it is important to recognize that there is a limit to what the time-honored
research design of time-series cross-section regressions can accomplish. In particular, this genre of
research is often mute with respect to causal mechanisms. Why do good policies and policy
outcomes seem to follow as democratic experience accumulates? The problem is that most of the
putative causal mechanisms are difficult to measure, and thus cannot be tested in a cross-country
regression format.
Our recourse is to think closely about the process of policymaking within individual country
cases, paying close attention to changes that may occur as a country’s democratic regime
consolidates (accumulates democratic experience). This is done in a schem
Countries chosen for exploration in this study are regarded as pathway cases, embodying the
“treatment” and the outcome of theoretical interest while isolating possible confounders that might
inhibit causal assessment.18 In this context, a useful country has the following characteristics. It a)
transitions from a long period of authoritarian rule to democracy, b) retains that regime-type over a
longish period, c) experiences a fairly strong record of development in the post-transition or postconsolidation phase, and d) is not associated with other exogenous factors (“confounders”) that
might account for its subsequent development success. We were also interested in drawing
countries from diverse regions of the world and with diverse historical backgrounds so as to make
plausible the idea that the causal mechanisms explored here might be generalizable.19 Three cases fit
the bill especially well: Brazil, India, and Mauritius.
Of course, it might be objected that we are choosing cases on the dependent variable –
successes only, no failures – and thus biasing the results of this study. However, it must be kept in
mind that the purpose of these case study investigations is not to demonstrate a covariational
relationship between X (democratic stock) and Y (various developmental outcomes); this has already
been amply proven in multiple cross-case studies (cited above). Rather, the purpose is to shed light
on the causal mechanisms that may be at work in this persistent, and presumably causal,
relationship.20 Indeed, the utility of pathway approaches to case analysis always depends upon prior
work of a “covariational” nature; only in the context of these findings would it make sense to
choose cases that bear out the predictions of a general model.
Gerring (2007).
In this respect, our case-selection strategy follows a “diverse-case” approach (Gerring 2007: ch 5).
20 Other case-selection strategies where the outcomes on various cases are different, such as a mostsimilar system design, may also be employed for the purpose of elucidating causal mechanisms. However, in
this instance, one does not find appropriate control cases to match with countries (like India, Brazil, and
Mauritius) that undergo a change on the independent variable of interest. Consequently, the more useful
variation is temporal (before and after a democratic transition and during the course of democratic
consolidation), rather than spatial (across country cases). For further discussion see Gerring (2007).
18
19
10
The logic of this strategy of case-selection becomes clear if one considers countries that do
not satisfy one or more of the above-listed criteria (a-d). Consider, first, countries that have
remained authoritarian throughout the modern period such as China. This sub-sample provides no
variation on the theoretical variable of interest. Thus, any study of these countries would be
constrained to adopt a counterfactual style of analysis. (What would China have been like had it
adopted a democratic form of government?) This is very far from the experimental ideal.
Consider, second, countries that see-saw back and forth between authoritarianism and
democracy such as Thailand, Turkey, or Argentina. Here, one finds lots of mini-treatments.
However, they are not the sort of treatments envisioned by the theory, which concern the causal
effects of long-term democratic rule. Consequently, the relationship between theory and evidence will
be difficult to interpret. (Of course, Brazil does some see-sawing as well; however, its history prior
to 1985 was predominantly authoritarian, and its history after the apertura has been strongly
democratic.)
Consider, finally, countries that fit our stipulated pattern – they are consolidated democracies
with reasonably good developmental performance – but have highly unusual characteristics,
characteristics that are likely to affect either the character of the regime or the developmental
trajectory of that country. In this category one might place Botswana (whose government has never
experienced a change of party control), the United States (which has spawned a whole literature on
“American exceptionalism”), and Switzerland (a top candidate for the world’s strangest polity
[Steinberg 1996]). Granted, every country is exceptional; yet, some countries are more exceptional
than others. In these cases, the number of possibly confounding variables is too great to allow for a
case-based causal analysis. We cannot “control,” even in a loose and informal sense, for the
unusual-ness of these cases.
As stated, our selection of countries is also driven by the need to encompass at least some
portion of the immense variety of democratization experiences in the modern world. To that end,
we choose countries from disparate regions of the developing world (Asia, Africa, and Latin
America), different historical eras (corresponding roughly to the different “waves” of
democratization) and diverse socioeconomic and cultural backgrounds. One of the benefits of a
diverse sample is that it is more likely to represent the theoretically relevant features of a larger
population (Gerring 2007: ch 5).
Our claim, then, is that Brazil, India, and Mauritius are pathway cases, and their utility for
causal analysis can be defended according to the logic of this research design. At the same time, we
must acknowledge that our careful selection of cases is not equivalent to a truly experimental
research design. Perhaps the most recalcitrant problem is that democratization never occurs by
itself. There are always other institutional changes that affect not only the subsequent workings of
democratic institutions but also, more directly, the developmental trajectory of that country. Among
these, perhaps the most important is the process of gaining independence. Many long-term
democracies, including India and Mauritius, became democratic at the point of gaining
independence or shortly thereafter. This means that the process of democratization is difficult to
differentiate from the process of becoming a sovereign state. Complicating matters further, some of
these states, again including India and Mauritius, experienced a degree of democracy prior to
independence, as discussed below. This means that the “treatment” of interest has been
administered gradually, over a number of years, rather than all at once.
Notwithstanding these important caveats, the case study method has much to recommend it.
Note that our approach to the case study falls somewhere in between the purely exploratory and the
purely confirmatory. Exogenous and endogenous variables – democratic stock and growth -- are
stipulated a priori, on the basis of a body of existing work. The central point of interest –
mechanisms lying in between these factors – is approached in a more inductive fashion.
11
WHAT IS THE TREATMENT?
The most vexing question of all has to do with the intrinsic opacity of the “treatment.”
What does it mean for a country to achieve a larger democracy score? Recall that there are several
ambiguities of conceptualization and measurement. Democracy is difficult to define including many
different dimensions of uncertain import, and posing a very challenging aggregation problem. The
composition of our chosen indicator of democracy, drawn from the Polity IV dataset, reflects this
ambiguity. But there is an additional ambiguity that goes beyond conceptualization and
measurement. This becomes clear if one compares the treatment under consideration here – degree
and duration of democracy – with a garden-variety research question, e.g., the effect of electoral
system laws on party system size (the number of parties gaining representation in parliament) within
a democratic polity. It is clear in the latter case what the treatment consists of because it is possible
(at least theoretically) to alter the treatment – electoral system law – in any existing democracy
without making any other changes to the system. Electoral system law is a discrete element of the
polity. Now consider the concept of interest here, regime-type. It is unlikely that one can alter a
polity’s regime-type without also altering other things (things unrelated definitionally to democracy)
about the polity.
As an example of this problem, consider the case of Iraq over the past several years. In Iraq,
the occupying powers, led by the United States, have attempted to install a democracy in a country
that was previously authoritarian. From one perspective, this may be viewed as an experimental
manipulation of the treatment that a researcher could exploit. However, it is self-evident that
changing a country’s regime-type involves much more than simply a change in the formal rules
contained in its constitution. (Note that many countries – indeed, almost all – are formally
democratic, even though some of these would not be classified as democratic by any standard
employment of the term.) Because democracy is a state of being, rather than a state of law, and
because attaining that state of being is dependent upon many things that are external to the
definition of democracy, these other things are impossible to separate empirically from the treatment
itself. Thus, in the case of Iraq, many things will have to happen in order for this country to attain,
and retain, a democratic form of government (even if one employs the most minimalist definition of
the term).
Because the treatment – degree and duration of democracy – cannot be isolated from other
factors that support (or undermine) democracy, it is impossible to test the hypothesis of interest
without also considering these other factors. Unfortunately, it is also impossible to know precisely
what these other factors are (because we do not have a very precise or certain notion of what causes
democracy). Our expedient of last resort is to subsume these concomitant factors under the ceteris
paribus assumption. We surmise that being (and remaining) a democracy leads to better governance,
all other things being equal – in full knowledge that we cannot say for sure what these other things
might be.
For all these reasons, we do not wish to overplay the strength of the evidence presented in
subsequent chapters. Crossnational regression analysis of observational data, with all the attendant
ambiguities of conceptualization, measurement, and ceteris paribus conditions, is a very crude
approximation of the true experiment with a manipulated treatment and randomized control. Even
so, we believe that this research design, with all its flaws, is the best of all possible research designs.
Insofar as one might wish to know how (and if) regime-type affects development, one must take
seriously the evidence provided by nation-states and their historical experiences. Insofar as one
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must incorporate into a single analysis these diverse cases, the TSCS framework with fixed
“country” effects offers a plausible model of causal relations.
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Chapter appendix:
Additional Methodological Issues
SUPPLEMENTARY CODING OF THE DEMOCRACY VARIABLE
To correct for Polity2’s exclusion of microstates, an exclusion that might bias our sample,
we impute democracy scores for these excluded cases using other democracy indices that are
conceptually and empirically close to the Polity2 measure: (1) the Freedom House Political Rights
indicator, (2) Ken Bollen’s Liberal Democracy variable, (3) Tatu Vanhanen’s Competition variable,
(4) Arthur Banks’s Legislative Effectiveness variables (I and II), and (5) Banks’s Party Legitimacy
variable.21 These various measures of democracy take into account the degree to which citizens can
participate freely in the political process, the extent of suffrage, the competitiveness of national-level
elections, the degree of party competitiveness, and the degree to which the legislature affects public
policy. This imputation process adds about five hundred observations (less than 10 percent of the
original sample) to the original Polity2 variable.
Because the historical component of this index weighs heavily on our understanding of the
concept and because the Polity data set ignores nonsovereign states in its coding procedures, we
supplement the Polity2 coding with our own coding of several nation-states that were previously
part of contiguous empires. The procedure is as follows. For each year that a nation-state belonged
to an imperial power, it receives the same Polity2 score as its imperial ruler; for example, Estonia
receives the same score as the Soviet Union from 1941 through 1990. We use this procedure only
for nation-states that were contiguous with the empire to which they belonged, under the
assumption that contiguous colonies are likely to be governed in the same manner as the imperial
power itself (a dynamic less likely to be true for overseas colonies).
This recoding affects the following countries: Albania (1900–1912, Ottoman Empire),
Andorra (1900–present, France), Armenia (1900–1990, Russia/USSR), Azerbaijan (1900–1990,
Russia/USSR), Belarus (1900–1990, Russia/USSR), Bosnia-Herzegovina (1908–17, AustriaHungary; Yugoslavia 1929–91), Croatia (1900–1917, Austria-Hungary; Yugoslavia, 1929–91), Czech
Republic (1900–1917, Austria-Hungary), Slovakia (1900–1917, Austria-Hungary), Estonia (1900–
1916 and 1941–90, Russia/USSR), Finland (1900–1916, Russia), Georgia (1900–1990,
Russia/USSR), Iraq (1900–1917, Ottoman Empire), Palestine/Israel (1900–1917, Ottoman Empire),
Kazakhstan (1900–1990, Russia/USSR), Kyrgyzstan (1900–1990, Russia/USSR), Latvia (1900–1917
and 1941–90, Russia/USSR), Lithuania (1900–1917 and 1941–90, Russia/USSR), Macedonia (1922–
90, Yugoslavia), Moldova (1900–1945, Romania; 1946–90, USSR), Mongolia (1900–1920, China),
Bangladesh (1947–71, Pakistan), Slovenia (1900–1917, Austria-Hungary; 1929–91, Yugoslavia), Syria
(1900–1917, Ottoman Empire), Tajikistan (1900–1990, Russia/USSR,), Turkmenistan (1900–1990,
Russia/USSR), Ukraine (1900–1917 and 1920–90, Russia/USSR), Uzbekistan (1900–1990,
Russia/USSR), and East Timor (1976–99, Indonesia).
For noncontiguous colonies we assign a Polity2 score of 0 for all preindependence years.
While this procedure is admittedly somewhat arbitrary, it has relatively little effect on an analysis that
focuses only on postindependence years in a fixed-effect format (for obvious reasons, there is no
data on developmental outcomes prior to a country’s achievement of formal sovereignty). Note that
the preindependence years constitute a largely static component of a country’s score for any given
Freedom in the World, survey methodology, on the Freedom House Web site:
www.freedomhouse.org/research/freeworld/2000/methodology.htm. Bollen (1993), Vanhanen (1990),
Banks (1994).
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(postindependence) observation; thus, it is captured in that country’s unique intercept. This means
that any inaccuracies introduced by our arbitrary scoring of preindependence years will have
relatively little effect on empirical results reported in subsequent data tables—most of which employ
a fixed-effect format. Indeed, an alternative coding of this variable assigns a score of –10 (the
lowest score on the Polity2 index) to missing data from pre-independence years. Virtually identical
results are obtained in most estimations. Thus, although we are conscious of the arbitrary quality of
this coding procedure, we are confident that it does not jeopardize the main results reported in
subsequent chapters. (For the nonfixed effect regression tests displayed in subsequent chapters, the
arbitrary scoring of these preindependence years matters much more. This constitutes yet another
reason for preferring a regression specification with country fixed-effects.)
[Note: We also plan to impute missing data for all countries prior to independence using the
following instruments: latitude (distance from the equator), settler mortality (Acemoglu, Johnson,
Robinson 2001), continent (dummy variables for Africa, Latin America, et al.), and colonial history
(dummy variables for English, French, Spanish, et al.). Alternatively, we may be able to build on
data collected by Steven Wilkinson in elections in colonies prior to independence. Either method
should provide a more reasonable estimate of pre-independence regime-type than is provided
currently by the default coding of 0. Another project for the next iteration of the data analysis is to
extend the “stock” measure back to 1850.]
DESCRIPTIVE STATISTICS FOR KEY VARIABLES
Table 2.2:
N, Mean, Standard Deviation
[Includes democracy stock, democracy level, and all dependent variables, but none of the controls.]
Table 2.3:
Correlation table
[Includes democracy stock, democracy level, and all dependent variables, but none of the controls.]
Table 2.4:
Factor analysis of Dependent Variables
[Time-series variables only. We should use the imputed “full” dataset, since case-wise deletion will
reduce the sample dramatically.]
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