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Democracy and Growth: A Lexical Approach
• John Gerring, Boston University
• Svend-Erik Skaaning, University of Aarhus
• Matthew Maguire, Boston University
• Eitan Tzelgov, Gothenburg University
ABSTRACT
Scholars have long debated whether regime-type has any impact on growth. Empirical work
based on established indices of democracy suggests that the relationship is weak or
nonexistent. In this paper, we present a theory for why regime-type might matter for growth
based on the differential incentives facing leaders in democratic and non-democratic settings.
To test this theory, we develop a new index of electoral democracy based on a “lexical”
approach to scaling, in which levels of the index represent successive necessary conditions of
an ordinal scale. In the empirical sections of the paper we show that this index bears a
positive and highly robust relationship to growth, reconcile this result with previous findings
based on other measures of democracy, and explore evidence of an accountability
mechanism.
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For many who study patterns of development around the world it is axiomatic that
“institutions matter.”1 Among institutions, a country’s regime-type is often regarded as
fundamental, especially with respect to securing conditions for long-term growth.2
A variety of reasons may be adduced to support this view. A democratic framework
may assure credible commitment to property rights3; set in place incentives for leaders to
provide growth fostering public goods4; encourage technological change and productivity
growth5; enhance prospects for learning and deliberation, tapping into the wisdom of
crowds6; and encourage norms of trust, equality, fairness, and reciprocity.7
Not everyone agrees with this optimistic perspective. Some argue, with an eye to the
East Asian NICs (newly industrializing countries), that economic growth is more likely to be
achieved through strict authoritarian rule, a necessary condition for instilling discipline in the
labor force, prioritizing long-term savings and investment over current consumption, and
resisting rent-seeking pressures from organized groups.8 Democracy, by contrast, has been
associated with populist policies that favor short-term redistribution over long-term growth9;
policy sclerosis and a clientelist, rent-seeking style of politicking in which side-payments to 1 Acemoglu et al. 2005; North 1990; Rodrik et al. 2004.
2 Acemoglu & Robinson 2012; Barzel 2002; Bueno de Mesquita & Root 2000; Halperin et al. 2004; Knack &
Keefer 1995; North 1990; Oppenheimer & Edwards 2012; Scully 1992; Wittman 1995.
3 Clague et al. 1996; North & Weingast 1989.
4 Baum & Lake 2003; Besley & Kudamatsu 2006; Brown & Mobarak 2009; Deacon 2009.
5 Knutsen 2013; North, 2005.
6 Surowiecki 2004.
7 Oppenheimer & Edwards 2012, ch 5.
8 Amsden 1989; Haggard 1990; Leftwich 2005; Rao 1984.
9 Dornbusch & Edwards 1991.
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special interests trump the provision of collective goods10; and ethnic conflict, social
disorder, and violence.11 There are many reasons to suppose that democracy does not
stimulate positive development outcomes.12
Empirical work on this topic seems to favor the skeptical view. Studies have
uncovered little evidence for the proposition that regime-type positively affects per capita
GDP growth. Although rich countries are generally democratic the direction of causality is
unclear. It could be that democracies are rich because economic development supports
democratization13 and/or enhances democratic consolidation.14 Most studies conclude that
countries with authoritarian polities grow about as rapidly as democracies with similar
background conditions. Of course, democracy may still have positive effects on some
policies or policy outcomes. However, the evidence suggests that these positive effects are
counterbalanced by negative effects such that the net effect of democracy on growth
performance is negative, null, or at best, very small.15 (This point is borne out empirically in
Table 3.)
10 Buchanan, Tollison & Tullock 1980; Olson 1982.
11 Chenoweth 2010; Mansfield & Snyder 2005.
12 Ror wide-ranging reviews see Knutsen 2012; Leftwich 2005; Sirowy & Inkeles 1990.
13 Epstein et al. 2006.
14 Przeworski et al. 2000; Teorell 2010.
15 Alesina & Perotti (1997), Barro (1996), Baum & Lake (2003), Brunetti (1997), Dawson (1999), De Haan &
Siermann (1995a, 1995b), Doucouliagos & Ulubasoglu (2008), Faust (2007), Feng (1997, 2003), Gasiorowski
(2000), Giavazzi, Tabellini (2005), Hausmann, Pritchett & Rodrik. (2005), Helliwell (1994), Krieckhaus (2004),
Kurzman, Werum & Burkhart (2002), Mulligan, Gil & Sala-i-Martin (2004), Norris (2012), Przeworski et al.
(2000), Saint-Paul & Verdier (1993), Tavares & Wacziarg (2001). See also empirical tests summarized in Table 3
(below).
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This paper seeks to better understand the relationship between institutions
associated with democracy and economic performance. We begin by setting forth a simple
theory for why an electoral connection between leaders and electors might result in stronger
growth. In Section II, we present a new index of electoral democracy grounded in a lexical
approach to scaling, where levels of the index represent successive necessary conditions in
an ordinal scale. In Section III, we offer a variety of panel analyses focused on the
relationship between the Lexical index and growth performance. In section IV, we test the
Lexical index side-by-side with other measures of electoral democracy and discuss possible
reasons for their contrasting performance. In the final section, we explore the mechanism of
electoral accountability by comparing the relationship between growth and leadership
turnover in autocracies and democracies.
Our main finding is that when democracy is measured in a lexical fashion it has a
strong, positive, and robust relationship to growth. This suggests that the institutional
framework is fundamentally correct in its assignment of responsibility for long-term
development to features associated with regime-type. However, this result is not replicable
with other indices of democracy, suggesting that the measurement of this key concept
matters critically for causal assessment.
I. Electoral Democracy and Economic Growth
To begin our theoretical discussion we propose a stylized model of the policymaking
process. This model is not intended to reproduce the nuances of the political economy; it is
intended, rather, to lay out the main lines of the argument in a concise fashion. To do so, we
take some expository shortcuts. Readers should take note, for example, that key distinctions
introduced in the following discussion – e.g., between democracy and autocracy, cadre and
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citizen, particularistic and universalistic – are intended to articulate matters of degree rather
than strict dichotomies. This is just one example of a simplification that serves the goal of
parsimony, at some cost to realism. After the model is presented, we offer further
clarifications and respond to possible objections.
Any country-sized polity has a leadership cadre, aka court, or elite.16 This inner circle,
numbering in the dozens or hundreds, has direct access to the leader, i.e., the top
decisionmaker, who may or may not occupy a formal position as head of government.
Typically, the leadership cadre includes family members, friends, advisors, key allies, heads of
ministries, and business associates.17 Those outside the leadership cadre will be referred to as
citizens. This is not to imply that all citizens have equal political power; it merely underlines
the fact that people outside the leadership cadre do not have direct access to the top
decisionmaker. Hence, we divide up members of the polity into two categories – cadre and
citizens.
Leaders pursue policies, a term that in our capacious rendering includes all actions
taken by leaders – formal laws as well informal practices (e.g., embezzlement). Policies may
be understood as particularistic or universalistic.18 Particularistic policies (aka rents) are intended
to selectively benefit a particular individual or group, e.g., through targeted government
16 Dogan 2003.
17 The notion of a cadre is obviously not an easy one to operationalize. One can imagine a series of concentric
circles emanating outward from the leader, with each circle being somewhat more removed from the locus of
power. In any case, the cadre composes a very tiny fraction of the general population, presumably much
smaller than concepts like “selectorate” or “winning coalition” as employed by Bueno de Mesquita et al. (2003).
This, in turn, derives from time-limitations; there is only so much time in a day and hence only so many people
that the leader can meet with on a regular basis.
18 Volden & Wiseman 2007.
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services, special tax breaks, skewed regulatory policies, or outright theft. Universalistic
policies (aka public goods) are intended to benefit all members of society. We assume that
universalistic policies have a more positive impact on growth than particularistic policies.19
Hence, the mix of particularistic/universalistic policies determines the growth rate, all other
things being equal. Note that even though some particularistic policies (e.g., those that
reward capital with higher return or attract foreign direct investments) may lead to higher
growth, universalistic policies are more likely, on average, to induce growth.
Among possible policy mixes, we assume that individuals have the following
preference ordering: (1) particularistic policies targeted on themselves, (2) universalistic
policies, (3) particularistic policies targeted on all constituencies, (4) particularistic policies
targeted on others. This is based on anticipated payoffs, which follow directly from our
assumptions about the impact of particularistic and universalistic policies on growth.
Specifically, if each particularistic policy subtracts from growth, then individuals should favor
particularism over universalism only when they selectively benefit. (Evidently, these are
matters of degree in which the kind of policy, the money expended, the number of
constituents, and so forth matter to its impact on aggregate economic performance.)
Information costs are low for particularistic policies since the benefits are fairly
immediate and tangible. Thus, people can be expected to have strong and stable preferences
on particularistic policies. Information costs are high for universalistic policies since their
impact is not immediate and is often hard to gauge, depending upon complex
macroeconomic models. Consequently, people are expected to have weak and unstable
19 Mauro 1995; Murphy, Shleifer & Vishny 1993; North 1990; Rama 1993.
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preferences for universalistic policies. Instead, they judge policies in a retrospective fashion,
according to subsequent growth performance.20
Democracy is understood as a system in which leaders are chosen through periodic
multi-party elections before a broad electorate, a definition often described as electoral,
minimal, procedural, or Schumpeterian.21 Autocracy is the residual category.
Regime-type matters insofar as it affects the balance of power between the leadership
cadre and the citizenry in setting policy priorities. In an autocracy, power is monopolized by
the leadership cadre. It follows that the country’s policy profile will be weighted toward
particularistic policies whose benefits are targeted on the leadership cadre.22
In a democracy, by contrast, ordinary citizens have greater influence over
policymaking. Specifically, when top offices are brought under the sway of elections, and
when those elections are opened to multi-party competition under minimally free and fair
conditions, accountability between leaders and constituents is enhanced.23 The balance
between universalistic and particularistic policies should therefore be slanted toward the
former, relative to a similarly situated autocracy.
Since universalistic policies are more likely to have a positive impact on growth than
particularistic policies, we anticipate that democracies will outperform autocracies over the
long-term. This is the theory, in its essentials. We turn now to elaborations and clarifications
of various points. 20 Fiorina 1981.
21 Dahl 1956; Przeworski et al. 2000; Schumpeter 1950.
22 Other regime characteristics than the level of electoral democracy might of course matter such as the power
base of autocrats (e.g., Knutsen & Fjelde 2013) or the constitutional arrangements in democracies (e.g.,
Knutsen 2011) but here we exclusively focus on former.
23 Ferejohn 1986; Ranney 1962; Schumpeter 1950.
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One possible objection concerns the envisaged policy outcome for democracies –
universalism. It could be that the diffusion of political power in a democracy leads to a
policy profile characterized by a large number of particularistic policies with different
beneficiaries, a rent-seeking society.24 However, because this outcome is less desirable (for all
members of society) than one in which universalistic policies prevail it is unlikely to form the
basis for a stable equilibrium. That is to say, many democracies are characterized by rampant
clientelism and rent-seeking at some point in their development.25 But so long as this
outcome is viewed unfavorably, and so long as the polity remains democratic, it is subject to
reform. In this vein, it is important to note that crossnational studies of economic voting
generally find that voters evaluate growth performance sociotropically (i.e., according to
news reports of aggregate economic performance, as measured by standard macro-economic
indicators) rather than according to their personal pocketbook.26 This suggests that
economic voting responds more to aggregate macro-economic performance than to
particularistic payoffs.
A second possible objection concerns our claims about electoral accountability.
Accountability is an ambiguous concept and it is not always clear when pre-conditions for
effective principal-agency conditions have been established. Voters (“principals”) may cast
their ballots prospectively rather than retrospectively; they may care about different (even
conflicting) outcomes of concern; they may change their minds; they may be unable to
discern good outcomes from bad outcomes; they may be unable to assign praise and blame
for desirable and undesirable outcomes. Likewise, leaders (“agents”) may be unable to
24 Buchanan, Tollison & Tullock 1980; Olson 1982.
25 Kitschelt & Wilkinson 2006.
26 Lewis-Beck & Ratto 2013; Nadeau, Lewis-Beck & Belanger 2013.
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discern what voters want; they may be unable to achieve what they think voters want; they
may find it more profitable to posture and cast blame rather than to achieve results; they
may have strongly held beliefs or interests that override voters’ demands; and they may not
be concerned with reelection. The electoral cycle may not be well-timed to internalize the
long-term costs and benefits of a chosen policy. Term-limits and fragmented institutions –
within the executive branch, across branches, across parties, within parties, between elected
leaders and non-elected bureaucrats, between national and subnational political units – may
weaken principal-agent relationships. In sum, many factors may cause agents to “shirk.”27
These difficulties notwithstanding, there is much to recommend the theory of
electoral accountability, especially when considering a policy outcome like economic growth.
There is strong evidence that growth benefits citizens – perhaps not as much as it benefits
those in a privileged position but more than the alternative, i.e., no growth.28 While ordinary
citizens may not have strong views, or correct views, on the policies that achieve growth,
they are apt to judge those policies by the outcome in question, as we have theorized. This
grants leaders considerable wiggle-room in devising a set of policies that, in their judgment,
is likely to be most effective in securing growth.29 Growth performance is highly salient to all
citizens, virtually all of whom are in favor of it (growth is not a positional issue). While
public perceptions of a country’s economic performance may be massaged by leaders,
growth is not a matter that can be hidden from view for very long. People notice when times
are good and when times are bad. They are especially attentive when times are especially
good or especially bad. This supports the view that leaders in a multi-party democracy are
27 Ashworth 2012; Lindberg 2013; Moncrieffe 1998; Przeworski, Stokes & Manin 1999; Schedler 1999.
28 Firebaugh & Beck 1994; Dollar & Kraay 2002.
29 Stokes 2001.
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likely to be rewarded by citizens if a country prospers and punished if it does not. Indeed, a
good deal of research shows that favorability toward incumbents, and perhaps election
results, are influenced by the state of the economy.30
In an autocracy, by contrast, the leader’s survival is less dependent upon support
among the general public and more dependent upon support within the leadership cadre.31
In particular, the leader depends upon the willingness of the cadre to exercise violence on
the leader’s behalf. This is a delicate balance. Soldiers must be willing and able to shoot, but
they must aim in the right direction, i.e., toward the crowd not toward the palace. In order to
assure the loyalty of key constituents there is strong pressure to bestow preferential
treatment on those who are in a position to assassinate or depose the leader, i.e., members of
the cadre.
By the same token, there may be few payoffs for an autocratic leader who manages
to achieve economic growth. Note that growth-augmenting policies often require shifting
resources from privileged sectors to less developed sectors. Likely as not, privileged sectors –
e.g., plantation agriculture, old industries, the military, urban elites, and other dominant
social groups – are closely allied to the regime, and likely to be well-represented in the
leadership cadre. Removing their privileges is not likely to be well-received and may prove a
source of political instability. Relatedly, growth-augmenting policies may involve an
expansion of public goods such as education, health, and infrastructure. Over the long run,
these policies are likely to empower those at the bottom of the socioeconomic pyramid,
which may also pose problems for regime maintenance. Moreover, insofar as long-run
growth is achieved in a country – insofar, that is, as its GDP per capita registers a substantial
30 E.g., Brender & Drazen 2008; Duch & Stevenson 2008.
31 Geddes 1999; Greene 2010; Myerson 2008; Smith 2005; Svolik 2008.
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increase – we can anticipate that this will alter the configuration of social power, upsetting
old elites and empowering new elites including a rising middle class and urban working class.
Since both of these social forces are commonly regarded as shock troops for democracy32,
the long-term impact of growth on social and economic modernization – as measured by per
capita GDP – may not bode well for a sitting dictator.
We recognize that the redistribution of wealth occasioned by growth-augmenting
policies and by the creative destruction of capitalism is likely to be resisted by established
elites in any polity, democratic or autocratic. The difference is that where multi-party
elections are in place the power of privileged sectors is counterbalanced by the power of the
ballot, which diffuses political power broadly among members of the electorate. Under such
circumstances, shifting resources from privileged to underprivileged sectors is more possible
and more palatable for a democratically elected leader than it would be for an autocratic
leader. Thus, the political incentives facing a dictator lie more on the side of resource
extraction and clientelistic payoffs to narrow constituencies than broad-based economic
growth.33
Another objection to our theory rests on the idea that “stationary bandits” have an
incentive to promote growth because they own some portion of the total wealth in a
country.34 By this logic, the size of the leader’s personal wealth is directly proportional to the
size of the economy, and from this the autocratic leader has a direct stake in the overall
performance of the economy. The argument applies nicely to situations in which the fisc of
the leader is inseparable from the fisc of the state or the economy at-large (L’etat, c’est le roi),
32 Lipset 1959; Moore 1966; Rueschemeyer, Stephens & Stephens 1992.
33 Keefer & Knack 2007; Padró I. Miquel 2007.
34 Olson 1993.
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as it often was in medieval states.35 However, the argument does not translate well to
contemporary states and economies, where leaders’ wealth is rarely (if ever) tied directly to
GDP. Instead, leaders’ wealth derives from a nominal salary along with whatever they can
purloin. It is the latter, not the former, that distinguishes leaders who gain vast wealth from
those who do not. Granted, the ability to steal is limited by the wealth of a country and in
this limited sense Olson’s logic applies. However, if a leader is able to steal, and inclined to
do so, it is unlikely that he will view the long-term growth of the economy as preferable to
present gains. Theft, by its very nature, is uncertain. Thus, the predatory leader’s choice to
discount future gains heavily is eminently rational. It makes more sense to take what one can
while the coast is clear rather than to serve as a careful steward of the economy in order to
achieve greater gains – by theft – in the future. A leader who wishes to maximize gains for
himself and his associates is therefore more likely to pursue a policy of predation than a
policy oriented toward long-term growth.
Thus, for a variety of reasons democratic leaders may have stronger incentives to
foster growth than autocratic leaders. (This assumption is probed empirically in Section V.)
If so, we may expect to see an association between regime-type and growth performance
among nation-states.
II. Electoral Democracy as a Lexical and Developmental Concept
Having set forth a simple theory of electoral democracy and growth, we turn to the question
of measurement. How should electoral democracy be operationalized?
35 Strayer 1970.
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Approaches to this problem may be classified according to the type of scale that is
adopted: binary, ordinal, and interval. Binary indices rest on the logic of necessary conditions,
all of which are sufficient for the attainment of democracy. For example, one might stipulate
that competitive, multi-party elections for legislative and executive offices, collectively,
qualify a polity as democratic.36 Ordinal indices also rest on an a priori (intuitive or
theoretical) sense of which attributes of democracy are most important.37 In this case,
however, the aggregation schemes are rather ad hoc and difficult to replicate.38 Interval
measures of electoral democracy are derived from the distribution of scores provided by
extant indices (such as those listed above) using techniques of factor analysis or IRT
models.39 Since the aggregation techniques are empirical rather than theoretical one cannot
assign a particular meaning to a particular score, other than more/less of an underlying
distribution. Note also that interval indices are based on other indices that are aggregated in
a variety of ways; they are indices of indices, further complicating the task of interpretation.
Our approach bears closest resemblance to binary scales, from which we borrow the
building block of necessary conditions. However, we regard these conditions as defining
successive thresholds along an ordinal scale, preserving distinctions that are lost in binary
codings of regime-type. Electoral democracy is thus operationalized in a cumulative fashion.
Condition A is necessary and sufficient for level 1; conditions A&B are necessary and jointly
sufficient for level 2; and so forth.
36 E.g., Boix et al. 2013; Cheibub et al. 2010.
37 E.g., Freedom House 2007; Marshall & Jaggers 2007.
38 See Goertz 2005; Munck 2009.
39 E.g., Coppedge et al. 2008; Pemstein et al. 2010.
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To arrive at a theoretically justifiable ordering of these conditions we rely on
judgments of centrality and dependence. One attribute is considered prior to another if it is more
central to the concept of theoretical interest or if it is a logical, functional, or causal pre-
requisite of another, i.e., if B depends on A. In either case, the levels of the scale bear an
asymmetric relationship to each other. Some are more fundamental than others,
differentiating this scaling procedure from Guttman scales40 and Mokken scales, aka ordinal
or nonparametric item response theory.41 Following Rawls42, whose theory of justice invokes
a similar logic, we refer to this scaling procedure as lexical.43
To illustrate the logic of this procedure let us begin with Dahl’s classic distinction
between two essential components of electoral democracy: contestation and inclusion. One
approach to this conceptual distinction is multidimensional, i.e., the creation of separate
indices.44 Another approach is to combine these elements into a single index by multiplying
them together.45 A third approach is to regard them as necessary conditions of a binary
concept of electoral democracy.46 In common with the latter two approaches, we assume
these features have important interaction effects. Specifically, we assume that inclusion has
little impact on the power of constituents to affect policymaking unless and until a minimal
threshold of contestation is achieved. It hardly matters that leaders are elected by universal
suffrage in one-party elections, e.g., contemporary North Korea. Contestation thus lies prior 40 Coppedge & Reinicke 1990; Guttman 1950.
41 Cingranelli & Richards 1999; van Schuur 2003.
42 Rawls 1971.
43 Gerring, Skaaning & Pemstein 2013.
44 E.g., Coppedge et al. 2008.
45 E.g., Vanhanen 2000.
46 E.g., Boix et al. 2013.
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to inclusion in our operationalization of the concept. However, contestation contains
multiple elements that need to be teased apart if we are to recognize and measure potentially
consequential institutions that compose a country’s regime-type. In this way, we attempt to
identify both qualities (distinct categories) and quantities (degrees) of electoral democracy.
The first level in our proposed index is the inauguration of direct elections for a
national-level body. A polity without any sort of national-level elections has no electoral
democracy at all, providing a true (natural) 0 value. The second level is the admission of
multi-party competition for a legislative body. The third level is the extension of multi-party
competition to the selection of the executive, either directly (a presidential system) or
indirectly (a parliamentary system). The fourth level is the inauguration of elections that are
minimally competitive, involving an element of uncertainty over the outcome of elections
and allowing opposition groups a reasonable possibility of gaining control over executive
functions. The fifth level is the extension of suffrage to all adult males or females. The sixth
level is the extension of suffrage to all adult males and females. The resulting ordinal scale is
summarized below:
0. No elections.
1. No-party or one-party elections.
2. Multi-party elections for legislature.
3. Multi-party elections for legislature and executive.
4. Minimally competitive, multi-party elections for legislature and executive.
5. Minimally competitive, multi-party elections with full male or female suffrage for
legislature and executive.
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6. Minimally competitive, multi-party elections with universal suffrage for legislature
and executive.
Detailed information about coding, sources, coverage, and data distributions for the
Lexical index are presented in Appendix A.
It should be clear that the distinctiveness of a lexical approach to scaling lies in the
way the conditions are prioritized into an ordinal scale. While the Freedom House and Polity
indices are also properly classified as ordinal, the categories in these scales are
heterogeneous. There are different ways of obtaining a “3”, whereas in a lexical scale there is
only one combination of attributes that culminates in a “3.” This allows for clear coding
rules, clear aggregation rules, and transparency in the construction of the index, a feature
shared with most binary indices but not with most ordinal or interval indices.
Lexical ordering also means that theoretical judgments – about how to prioritize
different attributes of democracy – are transparent, though of course not beyond dispute. In
considering the choices made here it should be borne in mind that the purpose of this index
is to explain the relationship of regime-type to policy outcomes such as growth. We do not
intend to summarize all aspects of the ambient, multivalent concept of democracy or to
explain all outcomes of interest.
Finally, the stages of the index are not intended to represent the empirical process by
which countries democratize. Rather, they are intended to specify higher and lower levels in
the achievement of electoral accountability. For example, many countries establish universal
suffrage before establishing multi-party elections. However, universal suffrage does not
count in our index until the latter has been reached. The “lexical” reasoning behind this
coding is that universal suffrage does not produce electoral accountability unless and until
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multi-party competition is allowed. Similar judgments underlie the other elements of the
scale.
It follows that the resulting index cannot be empirically validated as most other
scales are (e.g., Guttman, Mokken, or IRT indices). This, in turn, stems from a feature of
democracy that distinguishes it from other concepts (e.g., intelligence or aptitude) that are
amenable to empirical scaling. “Degrees of difficulty” do not always correspond to “degrees
of democracy.”
Although a great deal more could be said about the choice and prioritization of levels
(citation omitted for review purposes), we leave this discussion aside in order to focus on the
concept’s possible explanatory power.
III. Empirical Tests
To test our argument we regress GDP per capita growth against the Lexical index in a series
of initial tests, compiled in Table 1. The chosen estimator is ordinary least squares with
standard errors clustered by country.
The benchmark specification is intentionally sparse so as to avoid the possibility of
post-treatment bias. Consistent with prior work, the right side of this model includes a
measure of per capita GDP (transformed by the natural logarithm) in order to model cross-
country convergence in growth performance. Following standard practice in panel analyses
we also include country and year fixed-effects. Country fixed-effects focus analytic attention
on the longitudinal aspect of the data, i.e., how changes in growth (relative to a country’s
mean level of growth across the observed period) are affected by changes in regime status
(relative to a country’s average regime status across the observed period). Given the
heterogeneous nature of countries it seems more plausible to examine longitudinal variation
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Table 1: Tests of the Lexical Index
1 2 3 4 5 6 7 8 9 10 Growth Madd WDI Madd Madd Madd Madd Madd Madd Madd Madd Period 1822-
2004 1962-2005 1822-2001 1822-2001 1822-
2004 1823-2004
1825-1995
1822-2004
1822-2004
1822-2004
Time-periods Annual Annual Annual Annual Annual Annual 5-year Annual Annual Annual MI No No No No Yes No No No No No Estimator OLS OLS OLS RE OLS OLS OLS OLS GMM TSLS Lexical 0.423*** 0.201** 0.143** 0.168*** 0.372*** 0.432*** 0.108* 0.489*** 1.602*** Index [0.135] [0.088] [0.061] [0.062] [0.128] [0.133] [0.063] [0.108] [0.695] Lexical 0.298** (T-5) [0.145] GDPpc (ln) -6.390*** -3.793*** -2.764*** -0.670*** -4.167*** -
6.613*** -
2.181*** -
6.166*** -
8.736*** -8.306***
(T-1) [1.269] [0.675] [0.544] [0.251] [0.961] [1.258] [0.362] [1.327] [0.478] [0.671] Urban -4.639* -2.185 [2.449] [1.645] Population -1.447*** -0.125 (ln) [0.482] [0.113] Capability 11.626*** 4.513* [3.571] [2.435] European 2.244*** language [0.550] English -0.189 legal origin [0.381] Latitude (ln)
0.128
[0.260] Landlock -0.489 [0.451] Yt-1 0.028** -0.016** [0.012] [0.007] Regional FE
X
Country FE X X X X X X X X X Year FE X X X X X X X X X X Countries (N)
187 175 154 151 212 187 184 185 187 166
Obs (N) 11,809 5,879 9,764 9,645 13,125 11,742 2,141 11,173 11,742 9382 R2 (within) (0.122) (0.089) (0.100) 0.089 0.097 (0.123) (0.190) (0.120) (.1259) Wald Chi2 1711.82
Y = GDP per capita growth. OLS = ordinary least squares analysis. RE = random effects. FE = fixed effects. MI = full dataset imputed with Amelia II algorithm (Honaker et al. 2011). GMM = generalized method of moments (Blundell & Bond 1998). TSLS = two-stage least squares. Standard errors clustered by country except in Model 10. *** p<.01, ** p<.05, * p<.1 (two-tailed test) Model 1 is understood as the benchmark model.
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than cross-sectional variation. Year fixed-effects provide assurance that results are not driven
by period effects. Information about variables in this table and subsequent tables – including
coding, sources, and descriptive statistics – is contained in Appendix B.
Model 1 in Table 1 includes a global sample of countries observed from 1820 to
2008. It is worth noting that although historical data is routinely employed when testing the
modernization thesis47, it is rarely employed when democracy sits on the right side of a
causal model. To measure growth across two centuries we rely on estimates of per capita
GDP drawn from Angus Maddison.48 The resulting analysis suggests that a one-point
increase in the Lexical index translates into a 0.4% increase in growth, a considerable impact
when judged over the long term. Subsequent models introduce variations in this benchmark
model.
Model 2 focuses on the contemporary era (1960-). Here, we adopt widely used
measures of GDP and GDP growth contained in the World Development Indicators.49
While this analysis suggests a somewhat smaller impact of regime-type on growth it is
important to bear in mind that the variation contained within the contemporary sample is
truncated, with 43% of the cases classified as L6 (see Table A1). This may account for the
reduced coefficient, relative to the benchmark model (Model 1).
The next series of tests focus on model specification. In Model 3, we add the
following covariates: Urban (percent living in urban areas), Population, and Capability (an
index of state strength that combines iron/steel production, energy use, military
47 E.g., Epstein et al. 2006.
48 Maddison 2010. An updated and slightly expanded version of the original Maddison dataset (Bolt & Van
Zanden 2013) shows similar patterns when the benchmark model is tested.
49 World Bank 2007.
20
expenditures, military personnel, and total and urban population). The inclusion of
Capability is especially important. This factor, which changes over time, may affect the
propensity of a state to develop democratic institutions as well as to achieve strong growth
performance, thus serving as a potential confounder. It might also be viewed as endogenous
to regime-type, in which case it is correctly regarded as a mechanism rather than a potential
confounder. (For this reason, we do not include it in our benchmark model.)
In Model 4, we add (to those in the previous model) a set of non-varying covariates
including European language (percent speaking a European language), English legal origin
(former British colony), Latitude (distance from equator, natural logarithm), Landlock (lack
of access to an ocean), and regional dummies (Africa, Asia, Latin America, Middle East).
Because these covariates are static we employ a random effects estimator. Results from the
specification tests in Models 3 and 4 are consistent, though there is some instability in the
estimated effect of electoral democracy on growth, as one might expect.
In Model 5, we return to the benchmark model, this time imputing a full sample –
i.e., all sovereign states from 1820 to 2004 – using the Amelia II multiple-imputation
algorithm.50 Sample size increases modestly – from just under 12,000 observations (Model 1)
to just over 13,000 observations (Model 5). Results indicate a slight decrease in the
coefficient for the Lexical index relative to the benchmark model, suggesting that there is
little sample bias.
In Model 6, we introduce a lagged dependent variable (along with annual fixed
effects), which may help to block confounders and to solve problems of autocorrelation.51
The length of our panel obviates concerns about bias often generated when a lagged
50 Honaker et al. 2011.
51 Beck, Katz 1995.
21
operator is combined with unit fixed effects. Results for our key variable are virtually
identical to those reported in our benchmark model. Moreover, results for the key variable
are virtually unaffected when country fixed effects are removed and a random-effects
estimator is adopted (not shown).
In Model 7, the unit of analysis shifts from annual to five-year periods. This is
accomplished by calculating the mean value for growth across a moving five-year period and
running a panel analysis with every fifth year. (Since right-side variables are scarcely affected
by this moving-average they remain in their accustomed format.) Results are diminished but
persistent relative to the benchmark model.
An ever-present threat to inference is the possibility of endogeneity between X and
Y. Evidently, if growth causes democratization then any apparent relationship is spurious.
One approach to this problem is to lag right side variables several periods behind the
outcome. In Model 8, the Lexical index is given a five year lag. While the coefficient is
somewhat diminished, the relationship retains significance. Of course, regime-type changes
slowly so that a country’s Lexical score at T is highly correlated with its score at T-5.
Another approach to modeling endogeneity is to exchange X and Y – from one side
of the model to the other – leaving all other elements intact. Here, we find that any possible
impact of growth on democracy disappears after a single lag (one year), suggesting that any
X/Y endogeneity is at best short-lived and cannot serve as a confounder in Model 8. By and
large, the literature on democratization corroborates this interpretation. While a country’s
level of development probably affects its propensity to create and/or sustain a democratic
form of government52, studies suggest that annual growth performance has little impact on
these outcomes (though it may have repercussions for leadership turnover, as shown in 52 Epstein et al. 2006.
22
Table 4). A more plausible conjecture is that growth performance has a positive, proximal
effect on stability for any regime-type, democratic or autocratic.53
Yet another approach to X/Y endogeneity employs instruments for all right- and
left-side variables, drawn from their lagged and differenced values. The GMM estimator
developed by Blundell & Bond arrives at estimates for the Lexical index that are virtually
identical to our benchmark model, as shown in Model 9.54 (Because GMM estimators are
designed for panels that are short and wide, we replicate this approach with the
contemporary sample and specification shown in Model 2. Here, the estimated effect of
Lexical on Growth is even stronger than that reported in Model 9 [available upon request].)
A final approach to model identification enlists an instrumental variable in a two-
stage analysis. Previous studies suggest that a country’s regime-type is strongly influenced by
neighborhood effects.55 A country in a predominantly democratic neighborhood may be
subjected to strong peer pressure to adopt a democratic form of government, while a
country in a predominantly autocratic neighborhood may face considerable adversity if it
wishes to transition to democracy. A simple approach to modeling diffusion regards all
countries within 500 kilometers as neighbors56; their mean level of democracy (measured by
the Lexical index) becomes the basis for a diffusion variable. This variable is lagged one-
period in order to further exogenize the instrument.57
53 Coppedge 2012; Geddes 1999; Przeworski et al. 2000; Svolik 2008; Teorell 2010.
54 Blundell & Bond 1998.
55 Brinks & Coppedge 2006.
56 Gleditsch & Ward 2006.
57 Our approach loosely follows Acemoglu et al. (2008).
23
This particular instrument is strongly correlated with the variable of theoretical
interest (Pearson’s r=0.58). Our IV analysis, based on the benchmark model, includes per
capita GDP along with year and country fixed-effects. The first stage of a two-stage
regression model produces an R2 (overall) of 0.38, indicating a fairly strong fit between the
chosen instruments and the regressor of interest. The second-stage analysis, shown in Model
10, reveals a point estimate for the impact of Lexical on growth that is somewhat larger than
our benchmark model. This is reassuring, though perhaps exaggerated.
Note, first, that the IV analysis pertains only to countries whose Lexical score is
affected by diffusion. This excludes, by definition, first-adopters, i.e., several countries that
enter the dataset with high Lexical scores. And it excludes countries whose regime-type is
determined primarily by domestic factors. Note also that in order to avoid biased estimates
we must be willing to assume that the process of diffusion has no impact on growth
performance except through regime-type. If the diffusion of democracy mimics the spread
of technology, trade, investment, or other ingredients of growth then the exclusion
restriction is violated. Of course, no instrument for democracy is free of potential bias, and
other options such as English Legal Origin or Muslim may be even more suspect in this
setting since these factors are widely regarded as affecting growth performance. Diffusion
has the advantage of temporal variance, allowing us to retain a time-series format with year
and country fixed-effects. (A second TSLS model based on Model 4, employing a random
effects estimator rather than a fixed-effect estimator, shows similar results.) All things
considered, we regard diffusion as the best-possible instrument in the present context.
However, it is offered in a humble spirit as an additional robustness test, not a benchmark
estimate.
24
A few additional robustness issues are pursued in Appendix C. There, we include a
measure of short-term effects (proxied by ∆Lexical) and long-term effects (proxied by a
“stock” measurement of the Lexical index). Neither covariate disturbs the main finding. We
also examine the individual components of the Lexical index by disaggregating the index into
dummy variables (representing each level in the ordinal scale). In order to compare the
impact of specific levels in the ordinal scale along with the overall impact of the Lexical
index (understood as a continuous predictor) we employ a Bayesian hierarchical model.58
These results confirm that most level deviations are not statistically distinguishable from the
linear specification, justifying our decision to treat the ordinal scale as an interval variable in
benchmark tests contained in Table 1.
A final set of tests, available upon request from the authors, may be quickly
reviewed. Sometimes, regression tests are vulnerable to influential cases. To test this
possibility we remove each country from the sample, seriatim, and re-run the benchmark
model. No changes to the estimate for the Lexical index are discovered.
Sometimes, regression results are subject to arbitrary choices in the method of
estimation. To test this possibility we adopt alternate methods of calculating standard errors
in our benchmark models (historical and contemporary). We find that conventional,
jackknife, and Newey-West methods generally produce lower standard errors, and hence
higher t statistics, when compared with those reported for Lexical in Model 1, Table 1,
where standard errors are clustered by country.
Specification tests are potentially infinite. We present relatively spare models in Table
1 in order to avoid post-treatment bias. We also perform additional tests in order to
demonstrate robustness in the presence of potential confounders such as oil production per 58 Alvarez et al. 2011.
25
capita59, population growth60, judicial independence61, instability62, conflict or civil war63,
investment64, foreign direct investment65, productivity growth66, telephone mainlines67,
government consumption68, trade openness69, foreign aid per capita and per GNI70, literacy71,
life expectancy72, infant mortality73, and fertility.74 The procedure of testing is fairly simple.
Covariates are added, seriatim, to Model 1 (if historical data is available) or Model 2 (if
historical data is not available). Wherever a confounder can be measured in a variety of
different ways (as indicated above) variables are tested separately. This battery of twenty-four
tests demonstrates that although the coefficient for Lexical is sometimes affected (increasing
or decreasing in small amounts) none of these prospective confounders erases the impact of
59 Humphreys 2005.
60 World Bank 2007 & Maddison 2010.
61 Linzer & Staton 2013; Cingranelli & Richards 2010.
62 Banks 1994.
63 Marshall 1999, Gleditsch et al. 2002; Sarkees & Wayman 2010.
64 Summers & Heston 1991.
65 World Bank 2007.
66 Baier et al. 2006.
67 World Bank 2007.
68 World Bank 2007.
69 World Bank 2007; Barbieri & Keshk 2012.
70 World Bank 2007.
71 World Bank 2007.
72 World Bank 2007.
73 World Bank 2007.
74 World Bank 2007.
26
the Lexical index on growth. All specification tests are passed (at conventional statistical
thresholds).
Several features of our data may help explain the stability of results across various
samples, specifications, estimators, and codings. First, our sample is long (T~200 years) and
wide (N~186), offering nearly 12,000 observations. Second, there is considerable year-to-
year variation in the outcome of interest. Indeed, the correlation between growth at T and T-
1 is nearly zero. (It is slightly higher in the nineteenth-century, due to the linear interpolation
of missing data; however, these provide only a small portion – roughly one quarter – of the
total sample.)
Serial correlation in the predictor is somewhat more troublesome, as countries do
not alter regime-types frequently. Even so, among 16,899 country-years, 1,336 regime
changes are registered by the Lexical index, roughly eight percent of the sample. This means
that the average country experiences at least six regime changes during the observed period –
a fair bit of variation. To be sure, the pattern of change trends upward: 880 changes
correspond to an increase in a country’s lexical score while 456 changes correspond to a
decrease, a roughly 2:1 differential. Yet, 456 instances of democratic reversal are still a
considerable number. Note also that since growth does not exhibit a strong secular-historical
pattern we need not fear accidental correlation between left- and right-side variables with
parallel trends. More problematic are year-to-year variations, a feature that annual dummies
should overcome.
In sum, many of the problems traditionally associated with panel data – short panels,
sluggish variables, long-term trends that co-vary75 – are less problematic in this particular
setting. 75 Bertrand et al. 2004.
27
IV. Other Measures of Democracy
We have shown that when features of electoral democracy are arranged in a “lexical” fashion
– as a sequence of necessary conditions within an ordinal scale – a positive relationship to
growth emerges that is robust to a variety of samples, specifications, and estimators (Table
1). This raises a puzzle, for most studies find little or no relationship between regime-type
and growth, as discussed in our preliminary review of the literature.76 In this section, we 76 Somewhat more optimistic views of the democracy/growth relationship can be found in some recent work
(for an extensive review see Knutsen 2012). Studies that operationalize democracy as a historical (stock) variable
often find a positive relationship to growth (Ferree & Singh 2006; Gerring, Bond, Barndt & Moreno 2005;
Persson & Tabellini 2009). Studies sometimes find a conditional relationship between democracy and growth.
For example, Wu (2012) finds an interactive relationship between regime-type and structural factors such as
external threats and natural resource intensity, namely, democracies perform better than autocracies when these
impediments are present but not when they are absent. In this vein, Heo & Tan (2001) conduct a Granger test
of causality, suggesting that in about one-third of the countries in their sample democracy has a causal effect on
economic growth.76 Some studies discern a curvilinear relationship between democracy and growth. For
example, Barro (1997) finds that as one moves from a low to moderate level of democracy growth increases.
But as one moves to a higher level of democracy the relationship becomes negative (see also Barro 1996;
Plumper, Martin 2003).
Several studies that reach positive conclusions about the nexus between regime-type and economic
performance nexus are marred by questionable research designs and/or non-replicable findings. For example,
Benyishay & Betancourt (2010) examine the relationship between one component of the Freedom House
index, “Personal Autonomy and Individual Rights,” and growth, where they find a positive relationship.
However, this particular component – described by the authors as “the extent of personal economic freedoms
such as the choice of ownership form, employment, residence and education, as well as social freedoms such as
choice of marriage partners and family size” (2010: 282) – is peripheral to the usual meaning of democracy.
Moreover, the results are rather shaky given that data for the key independent variable is available only for a
28
compare the Lexical index with extant indices in a systematic fashion in order to shed light
on this discrepancy.
Our review incorporates extant indices of electoral democracy with prominence in
the social science literature and broad country and temporal coverage. Among ordinal
indices, we include the “Polity2” variable from the Polity IV dataset77 and the Political rights
(“PR”) and Civil liberty (“CL”) indices, both produced by Freedom House.78 Among interval
indices, we include the Democracy Index produced by Vanhanen79, the Contestation and
Inclusive indices produced by Michael Coppedge and collaborators80, and the Unified
single year and is tested for a small subsample of sixty countries. Papaioannou and Siourounis (2008) construct
a binary coding of enduring democratization episodes, which they show to be associated with a significant long-
term improvement in growth rates. The problem with this analysis is its construction of the key independent
variable. Countries receive a positive score only if they manage to maintain a democratic regime indefinitely
(until the end of the observed period). This equates democratization with successful democratization, aka
democratic consolidation, making causal inference especially problematic. Rodrik and Wacziarg (2005) find a
positive relationship between democratization episodes and growth in the short-term (the five years following a
transition). While suggestive, this finding is apparently contingent upon the inclusion of several covariates that
may be endogenous to the causal factor of interest. It also begs the question of what the longer-term impact of
regime-transitions might be. Persson and Tabellini (2006) find a positive relationship when growth is regressed
against a binary measure of democracy generated from the Polity2 scale, which is coded 1 when the score is
strictly positive along the -10 to +10 index. Our analyses suggest that these results are not robust (see Table 3).
77 Marshall & Jaggers 2007.
78 Freedom House 2007.
79 Vanhanen 2000.
80 Coppedge et al. 2008.
29
Democracy Scores (“UDS”).81 Among binary indices, we include the Democracy-
Dictatorship (“DD”)82, “BMR”83, “BNR”84, and “PT”85 indices.
Table 2 summarizes key features of these eleven indices, alongside the Lexical index
(for further details see Appendix B). It will be seen that coverage for the Lexical index –
including 17,054 country-year observations from 1800 to 2008 – is greater than or
comparable to all other indices. The final columns of Table 2 display correlations between
the Lexical index and extant indices. Although they co-vary, the correlations are far from
perfect, especially when the highest scoring cases (country-years in which Lexical=6) are
removed from the sample. In this sub-sample, the mean correlation between the Lexical
index and extant continuous indices is 0.52 (Pearson’s r) and the mean correlation with
extant binary indices is 0.35 (Spearman’s r). The lexical approach to electoral democracy is
not just another flavor of vanilla.
The consequences of these varying approaches to measurement are tested in Table 3.
In this set of tests we impose a common sample by focusing on the contemporary era and
imputing a full panel of observations for all sovereign states from 1960 to 2004 using the
Amelia II algorithm.86 Continuous indices are re-scaled from 0-1 (1=most democratic) so
that coefficients can be directly compared. Each regression model follows the specification
of Model 2 in Table 1: growth (from the WDI) is regressed against an index of democracy
81 Pemstein et al. 2010.
82 Cheibub et al. 2010.
83 Boix, Miller & Rosato 2013.
84 Bernhard, Nordstrom & Reenock 2001.
85 Persson & Tabellini 2006.
86 Honaker et al. 2011.
30
along with per capita GDP (from the WDI) and year and country fixed-effects. Results for
the Lexical index are displayed alongside results for extant indices, introduced above.
Table 2: Democracy Indices Compared
SCALE COVERAGE CORRELATION
Type Range Countries Years Obs Full
sample Restricted
sample Lexical (authors) Ordinal 0-6 224 1800-
2008 17,054
Polity2 (Marshall, Jaggers) Ordinal -10-10 182 1800-2012
16,327 .84 .43
PR (Freedom House) Ordinal 1-7 202 1972-2005
5,918 .85 .44
CL (Freedom House) Ordinal 1-7 202 1972-2005
5,918 .79 .30
Democracy Index (Vanhanen)
Interval 0-100* 203 1810-2010
15,209 .81 .60
Contestation (Coppedge et al.)
Interval -1.84-1.96
197 1950-2000
7,167 .91 .65
Inclusive (Coppedge et al.) Interval -3.04-1.91
197 1950-2000
7,167 .61 .56
UDS (Pemstein et al.) Interval -2.10-2.12
198 1946-2008
8,697 .88 .63
DD (Cheibub et al) Binary 0/1 199 1946-2008
9,030 .86 .41
BMR (Boix et al.) Binary 0/1 208 1800-2007
16,308 .84 .52
BNR (Bernhard et al.) Binary 0/1 124 1913-2010
11,932 .80 .25
PT (Persson, Tabellini) Binary 0/1 194 1800-2012
16,327 .76 .50
*=theoretical range. The final columns show the Spearman’s or Pearson’s correlation coefficient
between the Lexical index and other indices. Full samples include all available observations. Restricted samples include country-years in which Lexical<6.
Regression tests in Table 3 confirm that while the Lexical index is correlated with
growth most extant indicators of democracy are not. There is only one exception – Inclusive
(see Model 10). However, this result is probably driven by X/Y endogeneity or by an
unmeasured common-cause confounder. Further tests reveal that when Inclusive is lagged a
31
single period (one year) behind the outcome it loses statistical significance (see Table D4).
Additional robustness tests focused on other indices also reveal inconsistent relationships
between these indices and growth when alterations in sample, specification, or estimator are
introduced (see Tables D1-D3).
32
Table 3: Tests of Extant Indices
1 2 3 4 5 6 7 8 9 10 11 12
Index Polity2 PT Vanhanen BMR BNR DD PR CL Contest -ation Inclusive UDS Lexical
Democracy -0.028 0.314 0.011 0.509 0.120 0.151 0.108 0.045 0.090 0.349** 0.052 0.198** Index [0.022] [0.271] [0.020] [0.334] [0.321] [0.301] [0.093] [0.100] [0.171] [0.138] [0.217] [0.079]
GDPpc(ln), -2.733*** -2.700*** -2.729*** -2.723*** -2.714*** -2.713*** -2.734*** -2.723*** -2.708*** -2.784*** -2.714*** -2.713*** (WDI), T-1 [0.490] [0.489] [0.495] [0.489] [0.488] [0.488] [0.492] [0.490] [0.489] [0.493] [0.489] [0.486]
R2 (within) (0.062) (0.062) (0.062) (0.063) (0.062) (0.062) (0.063) (0.062) (0.062) (0.064) (0.062) (0.064) Y = GDP per capita growth (WDI). Variables defined in Appendix B. All non-binary measures converted to a 0-1 scale so that coefficients can be directly compared. Units of analysis = country-years. Sample: all sovereign countries from 1960-2004 (N=6,836), missing data imputed with Amelia II (Honaker et al. 2011). All models include year and country fixed-effects. Estimator = ordinary least squares, standard errors clustered by country. *** p<.01, ** p<.05, * p<.1 (two-tailed test)
33
In order to make sense of these divergent findings we must enter into a detailed
discussion of measurement. Here, we shall identify features of the Lexical index that set it
apart from other indices. Note that each contrast has a somewhat different target – Feature 1
sets the Lexical index apart from extant indices A and B but not C, while Feature 2 sets the
Lexical index apart from indices B and C but not A. Considered together, however, these
features may explain the divergent outcomes displayed in Table 3 and in adjunct models
presented in Appendix D.
First, the coding of many democracy indices includes elements that are not strictly
electoral in character such as rule of law, civil liberties, conflict, corruption, civil society,
participation, and constraints on the executive. Although it is not the objective of this paper
to test alternative institutional sources of growth, analyses suggest that indices that stray
from the electoral core are not robustly associated with growth.
Second, the Lexical index recognizes important interdependencies among the
properties of electoral democracy. That is, the possession of one attribute is presumed to
affect the way in which other attributes function, and hence its implications for growth. A
classic instance of this is the interaction of multiparty competition and suffrage. Arguably,
the meaning and import of an institution like universal suffrage depends upon whether
multiparty competition is allowed. By contrast, continuous measures tend to combine
features of a regime that have no functional relationship to one another, creating “mashup”
indicators of development with no underlying theoretical rationale.87
Third, while binary indices recognize the interactive nature of political institutions
they reduce all relevant factors into a single dichotomous coding. This tosses out
87 Ravallion 2011.
34
information that may be invaluable for sorting out the relationship between electoral
democracy and growth. Of particular importance are distinctions at the lower end of the
scale, e.g., between a no-election regime such as Saudi Arabia (L0), a regime with single-party
elections such as North Korea (L1), a multi-party election regime that does not control the
choice of executive such as Jordan (L2), and a multi-party election regime that extends to the
legislature and executive but is not minimally competitive such as Rwanda (L3). These sorts
of distinctions, as well as those at the high end of the scale, are conflated by binary indices.
Although the findings of this paper contrast starkly with most extant studies, the
divergent findings are not surprising once one looks closely at the construction of various
indices. Choices in measurement are highly consequential, as previous studies have
demonstrated.88 With respect to growth, our findings suggest that the diverse institutions
associated with electoral democracy are best operationalized as a series of necessary
conditions, establishing the cumulative format of a lexical scale.
V. Mechanisms: An Initial Test
Our theory (Section I) suggests that electoral accountability is the chief characteristic
distinguishing democratic and autocratic regime-types. Accountability cannot be directly
observed, as discussed. Nonetheless, we ought to be able to observe its traces. Specifically, if
we observe an association between growth performance and subsequent leadership turnover
in democracies this may be interpreted as an indication that leaders are being rewarded
(punished) for good (bad) economic performance. By the same token, if we observe no such
association (or a much weaker association) in autocracies this confirms our conjecture that
88 Casper & Tufis 2003.
35
no such mechanism exists where conditions of multiparty competition are not present. And
if this differential pattern is present, we will have corroborated our assumption that
democratic leaders face greater incentives to pursue growth-augmenting (universalistic) policies
than autocratic leaders.
A connection between macroeconomic performance and the fates of incumbents in
democratic polities has been located by some studies89 but not others.90 However, all extant
studies are limited in their purview to democracies (variously defined) and to the postwar
era. Most focus on a single country or a small sample of OECD countries.
In order to expand this empirical terrain – to cover a longer period of historical time
and a larger sample of countries (including both sides of the political regime spectrum) – we
turn to a measure of leadership turnover provided by the Archigos dataset.91 The outcome of
interest is a binary measure of leadership change in the top executive office (1=turnover,
0=no turnover), measured annually for sovereign countries across the past century and a
half.
This measure of leadership turnover is regressed in a logit model against the growth
rate (calculated, as previously, from Maddison) along with covariates measuring per capita
GDP92, decade dummies (to capture time effects), and country fixed-effects. The model thus
estimates the probability of a leadership turnover in a given year conditional on covariates,
which are measured in the previous year to avoid simultaneity.
89 E.g., Brender & Drazen 2008.
90 E.g., Cheibub & Przeworski 1999; Lewis-Beck 1988; Powell & Whitten 1993.
91 Goemans et al. 2009.
92 Maddison 2010.
36
To distinguish between polities with elected leaders versus those without we examine
the disaggregated empirical tests of the Lexical index contained in Table C1 and Figure C1
for potential threshold effects. These analyses suggest that if there is a threshold effect this
effect is registered at the point at which executive and the legislature offices are subjected to
multi-party elections (L3). Accordingly, our sub-sample of non-elective regimes contains
those polities with a Lexical score of 0-2, while our sub-sample of elective (multi-party)
regimes is restricted to polities with a Lexical score of 3-6.
As one might expect, leadership changes are less common in the first group than in
the second group. The first sub-sample contains 741 leadership changes across 5,357
observed country-years, nested within 121 unique country-periods. The mean leadership
tenure in the subset without multi-party elections is approximately 10 years. The sub-sample
of elective regimes contains 1,592 leadership changes across 6,450 observed country-years,
nested in 139 unique country-periods, generating leadership spells that average just over 6
years.
Results of these sub-sample analyses are displayed in Table 4. Among countries
without multi-party elections (Model 1) we find that there is no discernible relationship
between growth and leadership turnover. Indeed, the estimated coefficient for growth is
almost precisely 0. By contrast, among polities with multi-party elections (Model 2), we find
a consistent, robust relationship whereby a decrease in growth rates increase the odd of
leadership tenure. The predicted probabilities estimated in Model 2 are plotted in Figure 1,
along with 95% confidence intervals.
37
Table 4: Growth and Leadership Change
1 2
Sample Autocracies Minimal electoral
democracies
Operationalization Lexical=0-2 Lexical=3-6
Growth (T-1) -0.000 -0.015*** [0.003] [0.005] GDPpc (ln) (T-1) 0.173 -0.156 [0.190] [0.137] Country FE X X Decade FE X X Years 1841-2004 1865-2004 Countries (N) 106 136 Obs (N) 4,086 5,633 Log likelihood -1332.908 -2541.446 Prob >chi2 0.000 0.000
Y: probability of leadership change (Goemans et al. 2010). Estimator: logit, maximum likelihood. *** p<.01, ** p<.05, * p<.1 (two-tailed test).
The estimated causal effect in Model 2 is modest, perhaps due to the fact that
economic growth is imprecisely measured across two centuries. Note also that electoral
defeat is a blunt instrument; incumbents may also be sensitive to the impact of growth
performance on their popularity (measured in the contemporary era by public opinion
surveys). In any case, it is noteworthy that the results shown in Model 2 are robust (a) when
the sample is restricted to the postwar era (1950-2004), (b) when different ways of modeling
time are adopted (e.g., year dummies, a linear trend variable, or no time-periods), (c) when
growth is lagged one or two-periods behind the outcome, (d) when growth is measured with
a three-year moving average, (e) when per capita GDP is omitted from the model, and (f)
when fixed- or random-effects estimators are employed.93
93 Since the analyses in Table 4 hinge on a binary distinction between political regimes it is not surprising to
discover that the point at which the cutoff is inscribed affects the results. Specifically, when the cutoff is moved
38
Figure 1:
-50 0 50 100 150 200
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Marginal effect of growth rates on leadership
growth rates
pred
icte
d pr
obab
ility
of t
urno
ver
up (to L4) or down (to L2) the relationships shown in Table 4 are attenuated. Likewise, when regimes are
classified by other binary indices (e.g., BNR, BMR, DD, PT) the relationship between growth and leadership
turnover among democratic polities is still negative but not always statistically significant. This dovetails with
analyses presented in Table 3, where we saw that democracy matters for growth but only when measured in a
lexical fashion.
39
Although we have by no means provided a full exploration of causal mechanisms, we
have corroborated a key expectation: relationships of accountability between leaders and
citizens exist with respect to growth performance in polities with a minimal degree of
political competition but not in polities without such competition. This suggests that sitting
dictators have nothing to fear from poor economic performance, for they are no more or
less likely to lose office when growth is weak. By contrast, poor growth performance lowers
the probability of an incumbent retaining office in a polity with multi-party elections.
More broadly, the evidence gathered in this study suggests that the structure of
incentives facing political leaders depends upon the institutions they are situated within.
Specifically, leaders subjected to multi-party elections should be more strongly motivated to
achieve growth than autocratic leaders. And this, in turn, may help to account for why we
see a difference in growth performance across different political regimes, as measured by the
Lexical index of electoral democracy.
40
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Appendix A:
The Lexical Index of Electoral Democracy
In this appendix, we clarify coding procedures used for the Lexical index of electoral democracy and
review the properties of this index.
To operationalize the levels of the index we make use of four variables from the PIPE
dataset (LEGSELEC, EXSELEC, OPPOSITION, FRANCHISE [Przeworski et al. 2013]) as well as
a new variable (COMPETITION), which draws on Cheibub et al. (2010) and Boix et al. (2013), as
described below. These five indicators are employed, as follows, in order to construct the Lexical
index:
0. No elections. There are not regular national elections. This includes situations
in which elections are postponed indefinitely or the constitutional timing of
elections is violated in a more than marginal fashion. LEGSELEC = 0 (the
lower house of the legislature is not elected) & EXSELEC = 0 (the chief
executive is not elected – whether directly or indirectly, i.e., by people who
have been elected). Sources: PIPE, country-specific sources.
1. No party or one-party elections. There are regular national elections.
LEGSELEC =1 (the lower house of the legislature is at least partly elected)
or EXSELEC =1 (the chief executive is either directly or indirectly elected,
56
i.e., by people who have been elected). Sources: PIPE, country-specific
sources.
2. Multi-party elections for legislature. Opposition parties are allowed to participate
in legislative elections and to take office. OPPOSITION =1 (there is a
legislature that is at least in part elected by voters facing more than one
choice). Sources: PIPE, country-specific sources.
3. Multi-party elections for legislature and executive. The executive is chosen directly
or indirectly (by an elected legislature) through multi-party elections.
EXSELEC = 1 (the chief executive is either directly or indirectly elected, i.e.,
by people who have been elected). Sources: PIPE, country-specific sources.
4. Minimally competitive elections. The chief executive offices and the seats in the
effective legislative body are filled by elections characterized by uncertainty,
meaning that the elections are, in principle, sufficiently free to enable the
opposition to gain power. COMPETITION =1 (control over the executive
is determined, in practice, by the electorate by means of contested elections).
Sources: Cheibub et al. (2010), Boix et al. (2013), country-specific sources.
5. Male or female suffrage. Virtually all adult male or female citizens are allowed to
vote in national elections. (In no extant cases was universal female suffrage
introduced before universal male suffrage, so in practice this level is reserved
for countries with male (only) suffrage.) MALE SUFFRAGE = 1 (virtually
universal male suffrage) or FEMALE SUFFRAGE = 1 (virtually universal
female suffrage). Sources: PIPE (FRANCHISE), country-specific sources.
57
6. Universal suffrage. Virtually all adult citizens are allowed to vote in national
elections. MALE SUFFRAGE = 1 (virtually universal male suffrage) &
FEMALE SUFFRAGE = 1 (virtually universal female suffrage). Sources:
PIPE (FRANCHISE), country-specific sources.
Clarifications
Several clarifications relative to the foregoing coding criteria may now be added.
Indicators (LEGSELEC, EXSELEC, OPPOSITION, FRANCHISE, COMPETITION)
refer to the status of a country on the last day of the calendar year (31 December) and are not
intended to reflect the mean value of an indicator across the previous 365 days.
Indirect elections do not qualify as “elections” unless the electors endorse specific candidates
or parties, as in US presidential elections. (In the absence of explicit endorsements, indirect elections
usually serve to restrict competition, allowing the in-group to monopolize political power.)
To qualify as an election the electorate may be quite small – though it must be separable
from, and much larger than, the group of officials it is charged with selecting. An example would be
South Africa under Apartheid.
In measuring universal male and female suffrage we take a juridical approach. Suffrage is
achieved when constitutionally prescribed, even though local or informal practices may impede the
achievement of this right (as in the American South prior to the Civil Rights movement). This is
consistent with the usage of the concept by Schumpeter, Dahl (for “polyarchy”), Przeworski, and
also with many extant indices (e.g., Polity2).
Electoral democracy does not presume complete sovereignty. A polity may be constrained in
its actions by other states, by imperial control (as over a colony), by international treaties, or by
world markets. In sum, to say that a polity is an electoral democracy, according to our proposed
58
index, is to say that it functions as such (a) for those who are allowed to vote and (b) for policies
over which it enjoys decision-making power.
Although we employ PIPE as an initial source for coding L0-3 and L5-6, we deviate from
PIPE codings – based on our reading of country-specific sources – in several ways. First, with
respect to executive elections, in the PIPE dataset “Prime ministers are always coded as elected if
the legislature is open.” However, for our purposes we need an indicator that also takes into account
whether the government is responsible to an elected parliament if the executive is not directly
elected, which, for example, has not been the case in a number of monarchies in Europe before
World War I and today in the Middle East. To illustrate, PIPE codes Denmark as having executive
elections from 1849 to 1900 although the parliamentary principle was not established until 1901.
Before then, the government was accountable to the king. Among the current cases with elected
multi-party legislatures not fulfilling this condition, we find Jordan and Morocco. In order to achieve
a higher level of concept-measure consistency, we have thus recoded all country-years (based on
country-specific accounts) for this variable where our sources suggested doing so.
Second, we have filled out all the missing values in the PIPE dataset, meaning that we have a
complete dataset for all conditions for all independent countries of the world in the period 1800-
2008. In general, except for the minor adjustment regarding executive elections mentioned above,
we have followed the more specific coding rules laid out in the PIPE codebook. This work did not
only imply that we used country-specific sources to fill the gaps but also that many years and a
number of countries (such as the German principalities of the 19th century) were added. If we, in
our coding of the missing values, came across information which suggested a recoding of any PIPE
scores, we did so. All values refer to the status of countries as of the end of a particular year.
Whereas the numbers of observations for the employed PIPE indicators range between 14,465 and
15,302, the additional codings mean that our dataset has 17,179 observations for all indicators.
59
Third, in order to measure if elections are competitive, we have constructed a new variable
to capture if there is a positive probability that the opposition can win government power
(Przeworski et al. 2000: 16-17). Like Cheibub et al. (2010), we consider instances of electoral
incumbent turnover as a robust indicator of contested elections. However, like Boix et al. (2013), we
do not consider electoral executive turnover to be either necessary or sufficient for genuinely
contested elections. We also take into account more general impressions of how free elections were
according to country-specific sources. In this respect, we follow Schumpeter (1950; see also
Przeworki et al. 2000; Boix et al. 2013) in establishing a modest threshold, e.g., not insisting on an
entirely level playing field or a high level of respect for civil liberties. Thus, elections are generally
considered competitive if control over the executive after an election reflects the general intentions
of the electorate during the election, as near as can be determined. More specifically, this state of
affairs is reached if voters experience little systematic coercion in exercising their electoral choice
and electoral fraud does not determine who wins.
When contrasted with most continuous measures of democracy the Lexical index is relatively
simple, enhancing the transparency and reproducibility of the index.94 Indeed, much of the coding
draws on available datasets, as indicated above. Coding decisions that move beyond, or over-ride,
extant datasets are factual in nature, resting on institutional features that require historical knowledge
but not subjective judgments on the part of the coder. To be sure, uncertainties are introduced when
source material for a country is weak. But we assume that this sort of bias is random rather than
systematic (as it might be if coder judgments involved questions of meaning and interpretation). In
this respect, the Lexical index echoes a feature of DD, BMR, and BNR. Indeed, it is quite similar to
these indices insofar as it relies on binary codings, which are combined to form the Lexical index.
94 These issues are discussed at length in Cheibub et al. (2010).
60
Equally important, the coding procedure is clearly separable from the outcome of concern.
Note that with many democracy and governance indices – particularly those that assume a
continuous distribution – there is a strong possibility that coders may view the state of democracy in
Country X as inseparable from the general state of affairs in that country including its economic
performance. When things are going well X may receive a higher score; when things are going
poorly it may receive a lower score – even if its political institutions are substantially unchanged
(Kurtz, Schrank 2007). The coding of the Lexical index offers little opportunity for this sort of
measurement error because coding decisions rest on clear-cut thresholds and because the features
that are being coded are not amenable to “state of affairs” confounders.
The dataset
To identify independent countries from 1800 to the present we rely on Gleditsch (2013) and
Correlates of War (2011), supplemented from 1800 to 1815 by various country-specific sources.
Each country is coded for the length of its sovereign or semisovereign existence within the 1800-
2008 timespan, generating a dataset with 228 countries and 17,176 country-years.95
Frequency distributions of the Lexical index across the entire historical period (1800-2008)
and the contemporary period (1960-2008) are provided in Table A1. It will be seen that several
categories have relatively small membership in the historical sample – L2 (9% of country-years), L4
(5%), and L5 (3%). While merging these categories with one of their neighboring levels would
produce a more parsimonious index – and would not compromise the empirical results reported in
Table 1 – it was judged essential on theoretical grounds to maintain distinctions contained in these
levels. (This provides another reminder of the strong role of theoretical priors in the construction of
a lexical scale.)
95 In comparison, the coverage of the Polity2 variable for the same period is 189 countries and 15,823 country-years.
61
Since our empirical analysis of the relationship between democracy and growth leans heavily
on this historical evidence it is important that the reader have a feel for the distribution of case-types
over time, as illustrated in Figure A1.
62
Table A1:
Frequency Distribution of the Lexical Index of Electoral Democracy
1800-2008 1960-2008
N % N % 0 4,501 26.20 1,211 15.18 1 2,920 17.00 1,748 21.90 2 1,507 8.77 312 3.91 3 2,594 15.10 1,168 14.64 4 863 5.02 60 0.75 5 503 2.93 15 0.19 6 4,291 24.98 3,466 43.43
Total 16,899 100.00 7,980 100.00
63
Figure A1:
Distribution of Countries Across the Lexical Index of Electoral Democracy, 1800-2008
64
Appendix B: Information about Variables
65
Table B1:
Variables, Definitions, Sources
DEMOCRACY INDICES
BMR. To qualify as democratic a country must satisfy the following conditions: “(1) The executive is directly or indirectly elected in popular elections and is responsible either directly to voters or to a legislature, (2) The legislature (or the executive if elected directly) is chosen in free and fair elections, (3) A majority of adult men has the right to vote” (Boix, Miller, Rosato 2013: 1531). Democracy_Boix
BNR. A regime is coded as democratic if it satisfies the polyarchy criterion (Dahl 1971), i.e., regimes “that permit a high level of contestation and include a large part of the adult population” (Bernhard et al. 2001: 783), with some recoding to convert the original “breakdown” variable into a democracy/non-democracy variable (authors). BNR_democracy
CL. Civil liberties, an index measuring the legal and practical protections of human rights (Freedom House 2007), reversed scale. Civil_liberty_FH_reverse
Contestation. An index derived from the first component of a principal components analysis including a large number of democracy indicators (Coppedge et al. 2008). cam_contest
DD. Democracy-dictatorship index. To qualify as democratic a country must satisfy the following conditions: “(1) The chief executive must be chosen by popular election or by a body that was itself popularly elected, (2) The legislature must be popularly elected, (3) There must be more than one party competing in the elections, (4) An alternation in power under electoral rules identical to the ones that brought the incumbent to office must have taken place” (Cheibub et al. 2010: 69). DD
Inclusive. An index derived from the second component of a principal components analysis including a large number of democracy indices (Coppedge et al. 2008). cam_inclusive
Lexical. Lexical index of electoral democracy, as described in text and Appendix A. Lexical
Lexical change. Coded 1 for any year in which a country’s score on the Lexical index changes from the previous year (including the first year of a series), 0 otherwise (authors). Lexical_change
Lexical stock. A country’s cumulative democratic history. Constructed by adding up a country’s score on the Lexical index from 1800 (or independence) to the present year, with a 1% annual depreciation rate (authors). Lexical_stock
Lexical diffusion. The mean level of Lexical for all countries within 500 kilometers of the country being coded (Gleditsch, Ward 2006). Lexical_a
PR. Political Rights, an index measuring the extent of political rights (Freedom House 2007), reversed scale. Pol_Rts_FH_reverse
Polity2. Polity2 index, combining Autocracy and Democracy variables, from the Polity IV dataset (Marshall, Jaggers 2007). Polity2
PT. Coded 1 for all years in which Polity2 > 0, and 0 otherwise (coded by authors but derived from Persson, Tabellini 2006). Polity2_dich_0
UDS. Unified Democracy Score, derived from an IRT model including a large number of democracy indicators (Pemstein et al. 2010). uds_mean
Vanhanen. Democracy index, the product of (1) the vote-share or seat-share of all but the largest party and (2) the share of adult population that voted (Vanhanen 2000). Democracy_Index_Vanhanen
OTHER VARIABLES
Capability. Capability index, combining iron/steel production, energy use, military expenditures, military personnel, and total and urban population (COW). capability_cow
English legal origin. English legal origin (La Porta et al 1999). English_legal_origin
66
European language. Percent speaking a European language (CIA WorldFactbook). European_language
GDP per cap (ln) (contemporary). GDP per capita (World Bank 2007), transformed by natural logarithm. GDPpc_ln_07
GDP per cap (ln) (historical). GDP per capita (Maddison 2010), missing data within a time-series interpolated and missing data for contemporary countries imputed from World Bank (2007), transformed by natural logarithm. GDPpc_imputed_Madd_ln
Growth (contemporary). GDP per capita growth (World Bank 2007). GDPpc_Growth_WDI_07
Growth (historical). GDP per capita growth calculated by authors from GDP per capita (historical), as described above. GDPpc_Madd_imp_Growth
Landlock. 1 if country is landlocked, 0 otherwise (Acemoglu, Johnson, Robinson 2002). Landlock
Latitude (ln). Distance from the equator, transformed by natural logarithm (authors). Latitude_ln
Population (contemporary). Total population (World Bank 2007), transformed by natural logarithm. Pop_Total_WDI_07_imp_ln
Population (historical). Total population (Maddison 2010), missing data within a time-series interpolated, transformed by natural logarithm. Pop_Madd_ipo_ln
Protestant. Percent Protestant (CIA WorldFactbook). Protestant
Regional dummies. Africa, Asia, Latin America, Middle East (authors). Africa Asia LatinAm MiddleEast
Urban (contemporary). Urban population, percent of total (World Bank 2007). Urban_Pop_WDI_07
Urban (historical). Urban population, percent of total (COW). pop_cow_urban
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Table B2: Descriptive Statistics
Democracy Indices Obs Mean SD Min Max BMR 13,344 0.349 0.477 0 1 BNR 7,593 0.504 0.500 0 1 CL 5,656 3.050 1.959 0 6 Contestation 6,712 0.145 1.060 -1.843 1.961 DD 8,830 0.435 0.496 0 1 Lexical 14,164 2.966 2.310 0 6 Lexical change 14,164 0.078 0.268 0 1 Lexical stock 14,164 101.435 102.170 0 485.427 Lexical diffusion 11,046 2.846 1.597 0 6 Inclusive 6,712 0.126 0.998 -3.035 1.911 PR 5,656 3.047 2.252 0 6 Polity2 12,759 -0.356 7.176 -10 10 PT 12,759 0.424 0.494 0 1 UDS 8,393 0.027 0.978 -2.103 2.117 Vanhanen 12,914 8.744 11.765 0 49
Other Variables Capability 12,823 0.015 0.040 0 0.384 English legal origin 13,038 0.240 0.427 0 1 European language 13,145 0.336 0.423 0 1.064 GDP per cap (ln) (contemporary) 5,946 7.434 1.560 4.035 10.988 GDP per cap (ln) (historical) 11,177 7.784 0.960 5.307 10.688 Growth (contemporary) 5,947 1.821 6.090 -50.486 89.828 Growth (historical) 11,130 1.994 15.125 -64.829 865.855 Landlock 13,051 0.151 0.358 0 1 Latitude (ln) 12,757 -1.440 0.897 -4.500 -0.341 Population (ln) (contemporary) 13,122 15.487 1.790 7.761 20.989 Population (ln) (historical) 11,181 8.907 1.520 4.127 14.059 Protestant 13,118 0.123 0.316 0 1 Africa 13,863 0.180 0.384 0 1 Asia 13,051 0.114 0.318 0 1 Latin America 13,051 0.197 0.398 0 1 Middle East 13,036 0.104 0.306 0 1 Urban (contemporary) 6,915 46.807 24.716 2.080 100 Urban (historical) 12,755 0.144 0.155 0 1.018
68
Appendix C:
Additional Tests of the Lexical Index
In this appendix we pursue a variety of additional tests as robustness checks to Table 1. These are
summarized in Table C1.
The Lexical/growth relationship may be confounded by short-term effects associated with
periods of transition. A tradition of research emphasizes the dislocating nature of democratic
transitions, especially where state institutions are weak (Mansfield & Snyder 2002). One may surmise
that transitions to autocracy also involve short-run dislocations with negative effects on growth.
Since our interest is in long-term equilibrium relationships it is important to parse out these short-
term effects. This can be accomplished by constructing a change variable, coded 1 if a country’s
score on the Lexical index changes in that year, and 0 otherwise. When this variable is included in
the benchmark model we find that regime-change indicator is indeed negatively associated with
growth performance, as shown in Model 1. However, the inclusion of a change variable scarcely
disturbs the estimate for the Lexical index, suggesting that the latter is picking up longer-term causal
effects, as intended.
69
We must also consider the possibility that our index of electoral democracy is serving as a
proxy for lagged effects of regime-type that have accrued over a long period of time. Given the
stickiness of regime measures it is always difficult to differentiate short- and long-run effects. One
approach is to explicitly measure the historical aspect of democracy with a “stock” variable (Gerring
et al. 2005). To create such a measure we add up each country’s score on the Lexical index from
1800 to 2008 with a one percent annual depreciation rate. This means that a country’s regime stock
stretches back for up to two centuries (depending upon its date of independence). Results, shown in
Model 2, confirm the notion of a legacy effect. In Model 3, we include both level and stock
measures of the Lexical index. Reassuringly, both effects persist and estimates are virtually
unchanged, suggesting that problems of collinearity are minimal. Electoral democracy as measured
by the Lexical index registers an effect on growth that is both contemporaneous and historical.
We turn next to an analysis focused on the components of the Lexical index. In order to
compare the impact of specific levels in the ordinal scale along with the overall impact of the Lexical
index (understood as a continuous predictor) we employ a Bayesian hierarchical model (Alvarez et
al. 2011). Accordingly, the estimated impact of each level of the index (L0-6) is a random intercept
term. In this way, we estimate the linear, monotonic effect of the Lexical index as well as the
individual deviations from that effect at each level. The size of these deviations is determined by the
data96 and the individual estimates for each level are pooled toward the overall linear impact.
Estimates generated by this model are presented in Model 4 of Table C1. An accompanying
figure (Figure C1) demonstrates that the Bayesian shrinkage estimates are essentially a weighted
average of our linear, pooled, benchmark model (Model 1, Table 1) and an unpooled model with
dummies for each level. These results confirm that most level deviations are not statistically
distinguishable from the linear specification. The exceptions are L3, which exerts a stronger effect
96 The deviations are drawn from a normal distribution with mean zero and a common variance.
70
on the dependent variable than the average linear effect, and L6, which exerts a weaker effect, as
illustrated in Figure C1.
71
Table C1:
Robustness Tests
1 2 3 4
Estimator OLS OLS OLS Bayesian hierarchical
Lexical index 0.416*** 0.394*** 0.422*** [0.137] [0.136] [0.53] Lexical stock 0.011*** 0.009** [0..004] [0.004] L0 (dummy) 0.017 [0.106] L1 (dummy) -0.225* [0.121] L2 (dummy) -0.371 [0.239] L3 (dummy) 0.856*** [0.147] L4 (dummy) -0.006 [0.269] L5 (dummy) -0.049 [0.321] L6 (dummy) -0.221** [0.079] Lexical change -1.696*** [0.436] GDPpc (ln), T-1 -6.494*** -6.689*** -6.921*** -6.334*** [1.276] [1.382] [1.393] [0.455] Obs (N) 11,737 11,822 11,737 11,809 R2 (within) (0.124) (0.121) (0.123)
Y = GDP per capita growth. Estimator = OLS (ordinary least squares) with standard errors clustered by country or Bayesian hierarchical (Alvarez et al. 2011). All models include year and country fixed effects. Samples include 186-187 countries observed from 1822-2004. *** p<.01, ** p<.05, * p<.1 (two-tailed test)
72
Figure C1:
Model Comparisons ─ Bayesian Shrinkage, Pooled and Unpooled models
The solid line depicts the linear trend from the pooled model (Model 1, Table 1). Estimates from the Bayesian shrinkage model (accompanied by 95% confidence intervals) are drawn relative to this trend. The triangles (jittered to the right for visibility) represent the estimates from the unpooled model (Model 4, Table C1).
73
Appendix D:
Additional Tests of Alternate Indices
74
Table D1:
Tests of the Polity2 Index 1 2 3 4 5 6 7 8 9 Growth Madd WDI Madd Madd Madd Madd Madd Madd Madd Period 1822-2004 1962-2005 1822-2001 1822-2001 1822-2004 1823-2004 1825-1995 1822-2004 1822-2004 Time-periods Annual Annual Annual Annual Annual Annual 5-year Annual Annual MI No No No No Yes No No No No Estimator OLS OLS OLS RE OLS OLS OLS OLS GMM Polity2 0.116*** -0.041 -0.004 0.017 0.100** 0.117*** 0.029 0.124*** [0.042] [0.026] [0.021] [0.021] [0.042] [0.041] [0.021] [0.037] Polity2 0.576 (T-5) [0.481] GDPpc (ln) -6.767*** -3.345*** -2.572*** -0.626** -5.070*** -7.023*** -2.275*** -6.621*** -8.107*** (T-1) [1.410] [0.637 [0.558] [0.267] [1.148] [1.389] [0.394] [1.493] [0.490] Urban -4.696* -2.581 [2.652] [1.726] Population -1.452*** -0.120 (ln) [0.526] [0.126] Capability 10.962*** 3.459** [3.693] [1.649] European 2.719*** language [0.606] English -0.106 legal origin [0.394] Latitude (ln) 0.113 [0.264] Landlock -0.486 [0.466] Yt-1 0.030** -0.007 [0.012] [0.007] Regional FE X Country FE X X X X X X X X Year FE X X X X X X X X X Countries (N) 165 153 152 149 212 165 162 163 165 Obs (N) 11,094 5,302 9,426 9,311 13,125 11,041 2,034 10,539 11,041 R2 (within) 0.131 0.094 0.099 0.090 0.095 0.131 0.198 0.129 Wald Chi2 1713.60 Y = GDP per capita growth. OLS = ordinary least squares analysis. RE = random effects. GMM = generalized method of moments (Blundell & Bond 1998). FE = fixed effects. MI = full dataset imputed with Amelia II (Honaker et al. 2011). Standard errors clustered by country. *** p<.01, ** p<.05, * p<.1 (two-tailed test)
75
Table D2:
Tests of the PT Index 1 2 3 4 5 6 7 8 9 Growth Madd WDI Madd Madd Madd Madd Madd Madd Madd Period 1822-2004 1962-2005 1822-2001 1822-2001 1822-2004 1823-2004 1825-1995 1822-2004 1823-2004 Time-periods Annual Annual Annual Annual Annual Annual 5-year Annual Annual MI No No No No Yes No No No No Estimator OLS OLS OLS RE OLS OLS OLS OLS GMM PT Index 1.293** 0.088 0.269 0.404 1.250*** 1.284** 0.438* 1.495*** [0.519] [0.324] [0.245] [0.252] [0.470] [0.514] [0.257] [0.471] PT Index 0.576 (T-5) [0.481] GDPpc (ln) -6.709*** -3.282*** -2.609*** -0.650** -5.064*** -6.956 -2.277*** -6.621*** -8.039*** (T-1) [1.399] [0.637] [0.555] [0.265] [1.144] [1.379] [0.401] [1.493] [0.489] Urban -4.758* -2.560 [2.654] [1.740] Population -1.439*** -0.121 (ln) [0.524] [0.127] Capability 11.171*** 3.480** [3.803] [1.626] European 2.699*** language [0.595] English -0.143 legal origin [0.391] Latitude (ln) 0.097 [0.265] Landlock -0.477 [0.467] Conflict
Yt-1 0.029** -0.007 [0.013] [0.007] Regional FE X Country FE X X X X X X X X Year FE X X X X X X X X X Countries (N) 165 153 152 149 212 165 162 163 165 Obs (N) 11,094 5,302 9,426 9,311 12,125 11,041 2,034 10,539 11,041 R2 (within) 0.131 0.093 0.099 0.090 0.095 0.131 0.198 0.129 Wald Chi2 1712.38 Y = GDP per capita growth. OLS = ordinary least squares analysis. RE = random effects. GMM = generalized method of moments (Blundell & Bond 1998). FE = fixed effects. MI = full dataset imputed with Amelia II (Honaker et al. 2011). Standard errors clustered by country. *** p<.01, ** p<.05, * p<.1 (two-tailed test)
76
Table D3:
Tests of the DD Index 1 2 3 4 5 6 7 8 9 Growth Madd WDI Madd Madd Madd Madd Madd Madd Madd Period 1947-2004 1962-2005 1947-2001 1947-2001 1947-2004 1947-2004 1955-1995 1952-2004 1947-2004 Time-periods Annual Annual Annual Annual Annual Annual 5-year Annual Annual MI No No No No Yes No No No No Estimator OLS OLS OLS RE OLS OLS OLS OLS GMM DD 0.736 0.097 -0.152 0.216 0.720 0.798 -0.222 0.698 [0.851] [0.330] [0.327] [0.292] [0.857] [0.844] [0.298] [0.777] DD (T-5) -0.561 [0.803] GDPpc (ln) -9.869*** -3.665*** -3.337*** -0.110 -8.897*** -10.273*** -2.946*** -10.506*** -11.937*** (T-1) [1.956] [0.659] [0.802] [0.252] [1.889] [1.937] [0.563] [2.326] [0.699] Urban -6.855* -1.829 [3.468] [1.716] Population -2.311** 0.034 (ln) [0.948] [0.135] Capability 11.843 -1.864 [26.406] [9.012] European 1.907*** language [0.552] English -0.247 legal origin [0.369] Latitude (ln) 0.081 [0.228] Landlock -0.332 [0.420] Yt-1 0.034*** -0.019** [0.011] [0.009] Regional FE X Country FE X X X X X X X X Year FE X X X X X X X X X Countries (N) 187 175 154 151 198 187 184 185 187 Obs (N) 7,772 5,865 6,491 6,392 7,930 7,742 1,289 6,936 7.742 R2 (within) 0.128 0.087 0.083 0.060 0.113 0.129 0.170 0.127 Wald Chi2 1206.81 Y = GDP per capita growth. OLS = ordinary least squares analysis. RE = random effects. GMM = generalized method of moments (Blundell & Bond 1998). FE = fixed effects. MI = full dataset imputed with Amelia II (Honaker et al. 2011). Standard errors clustered by country. *** p<.01, ** p<.05, * p<.1 (two-tailed test)
77
Table D4:
Tests of the Inclusive Index 1 2 3 4 5 6 7 8 9 Growth WDI WDI WDI WDI WDI WDI WDI WDI WDI Period 1962-2000 1962-2000 1962-2000 1962-2005 1962-2000 1965-2000 1962-2001 1962-2005 1963-2000 Time-periods Annual Annual Annual Annual Annual 5-year Annual Annual Annual MI No No No Yes No No No No No Estimator OLS OLS RE OLS OLS OLS OLS OLS GMM Inclusive 0.425** 0.406** 0.276* 0.337** 0.361** 0.099 0.490** [0.178] [0.179] [0.148] [0.147] [0.154] [0.147] [0.191] Inclusive 0.153 (T-1) [0.167] Inclusive -0.013 (T-5) [0.104] GDPpc (ln) -4.430*** -4.795*** 0.176 -2.634*** -4.771*** -4.451*** -3.993*** -3.168*** -7.362*** (T-1) [0.771] [0.903] [0.211] [0.458] [0.721] [0.750] [0.726] [0.686] [0.308] Urban -11.870** -2.071 [4.852] [1.915] Population -4.575*** -0.083 (ln) [1.376] [0.154] Capability 55.428 5.474 [55.411] [10.289] European 1.916*** language [0.625] English -0.333 legal origin [0.421] Latitude (ln) -0.089 [0.220] Landlock -0.181 [0.479] Yt-1 0.272*** 0.170*** [0.043] [0.011] Regional FE X Country FE X X X X X X X X Year FE X X X X X X X X X Countries (N) 171 142 140 198 171 170 171 173 171 Obs (N) 4,873 4,216 4,170 6,921 4,854 1,066 4,970 5,310 4,754 R2 (within) 0.088 0.110 0.069 0.065 0.148 0.211 0.079 0.070 Wald Chi2 1298.99 Y = GDP per capita growth. OLS = ordinary least squares analysis. RE = random effects. GMM = generalized method of moments (Blundell & Bond 1998). FE = fixed effects. MI = full dataset imputed with Amelia II (Honaker et al. 2011). Standard errors clustered by country. *** p<.01, ** p<.05, * p<.1 (two-tailed test)