I N S T I T U T E
Economic Development and 
Democracy: An Electoral Connection
Carl Henrik Knutsen, John Gerring,
Svend-Erik Skaaning, Jan Teorell,
Matthew Maguire, Michael Coppedge 
and Staffan I. Lindberg
Working Paper 
SERIES 2015:16
THE VARIETIES OF DEMOCRACY INSTITUTE 
November 2015
Varieties of Democracy (V-Dem) is a new approach to conceptualization and measurement of democracy. It is co-hosted by the University of Gothenburg and University of Notre Dame. With a V-Dem Institute at University of Gothenburg with almost ten staff, and a project team across the world with four Principal Investigators, fifteen Project Managers (PMs), 30+ Regional Managers, 170 Country Coordinators, Research Assistants, and 2,500 Country Experts, the V-Dem project is one of the largest ever social science research-oriented data collection programs.      Please address comments and/or queries for information to: V-Dem Institute Department of Political Science  University of Gothenburg Sprängkullsgatan 19, PO Box 711 SE 40530 Gothenburg Sweden E-mail: contact@v-dem.net       
V-Dem Working Papers are available in electronic format at www.v-dem.net.   Copyright © 2015 University of Gothenburg, V-Dem Institute. All rights reserved.  
  1 
Economic Development and Democracy:  An Electoral Connection∗    Carl Henrik Knutsen Professor of Political Science University of Oslo  John Gerring Professor of Political Science Boston University  Svend-Erik Skaaning Professor of Political Science Aarhus University  Jan Teorell Professor of Political Science Lund University  Matthew Maguire PhD Student Boston University   Michael Coppedge Professor of Political Science University of Notre Dame  Staffan Lindberg Professor of Political Science Director, V-Dem Institute University of Gothenburg          
                                                * This research project was supported by Riksbankens Jubileumsfond, Grant M13-0559:1, PI: Staffan I. Lindberg, V-Dem Institute, University of Gothenburg, Sweden; by Swedish Research Council, 2013.0166, PI: Staffan I. Lindberg, V-Dem Institute, University of Gothenburg, Sweden and Jan Teorell, Department of Political Science, Lund University, Sweden; by Knut and Alice Wallenberg Foundation to Wallenberg Academy Fellow Staffan I. Lindberg, V-Dem Institute, University of Gothenburg, Sweden; by University of Gothenburg, Grant E 2013/43.  
  2 
Abstract 
This study takes a new tack on the question of modernization and democracy, focused on the outcome of theoretical interest. We argue that economic development affects the electoral component of democracy but has minimal impact on other components of this diffuse concept. This is so because development (a) alters the power and incentives of top leaders and (b) elections provide a focal point for collective action. The theory is tested with two new datasets – Varieties of Democracy and Lexical Index of Electoral Democracy – that allow us to disaggregate the concept of democracy into meso- and micro-level indicators. Results of these tests corroborate the theory: only election-centered indices are correlated with economic development. This may help to account for apparent inconsistencies across extant studies and may also shed light on the mechanisms at work in a much-studied relationship.    
  3 
Introduction 
In the heyday of modernization theory it was widely accepted that economic development would favor a democratic form of government (Lipset 1959). In subsequent decades, this thesis was severely challenged. Early on, Barrington Moore (1966) and Guillermo O’Donnell (1973) questioned the logic of the argument. More recent challenges focus on empirical relationships discernible from the crossnational data. Adam Przeworski and collaborators argue that richer countries are more likely to maintain democratic rule but that the initial transition to democracy is unrelated to a country’s level of economic development (Przeworski & Limongi 1997; Przeworski et al. 2000). Acemoglu, Johnson, Robinson & Yared (hereafter AJRY) claim that even this relationship is spurious, disappearing once country fixed-effects are incorporated into statistical models (AJRY 2008, 2009; see also Alexander, Harding & Lamarche 2011; Moral-Benito & Bartolucci 2012).1 Countering these challenges to the orthodoxy, others argue that the relationship between economic development and democracy is restored if historical data stretching back to the nineteenth century is incorporated or if different estimators are employed (Benhabib et al. 2011; Boix 2011; Boix & Stokes 2003; Che et al. 2013; Epstein et al. 2006; Faria et al. 2014; Treisman 2015).  As things now stand, the modernization debate rests upon a complex set of modeling choices, e.g., which time-periods to include, how to overcome the censored nature of democracy indices, what temporal units of analysis to employ, what corresponding lag structure to adopt, whether to apply linear or non-linear models, and which dynamic models to employ. Left out of this long-running debate is any serious consideration of the outcome.  A priori, there is no reason to expect the impact of economic development to be uniform across all dimensions of democracy (Aidt & Jensen 2012). Since democracy is a broad concept, open to many interpretations and operationalizations, the issue is non-trivial. We propose that the differential response of various aspects of democracy to changes in economic development, typically operationalized by per capita GDP, may help to account for the fragility of this relationship, as well as for the ongoing and seemingly irresolvable debate about possible mechanisms at work in the development-democracy nexus. Specifically, we argue that economic development primarily affects contested elections. Its impact on other aspects of democracy is much weaker, and perhaps nonexistent.  Our explanation hinges on the incentives of leaders and the collective action dilemmas of citizens. We argue, first, that economic development enhances the power resources of citizens                                                 1 In this view, the correlation between income and democracy is the product of some unmeasured confounder that affects both income and democracy. 
  4 
vis-à-vis leaders. We argue, second, that economic development affects the opportunity costs of a leader contemplating the prospect of relinquishing power. In rich countries these opportunity costs are lowered because leaders can obtain remunerative employment elsewhere. However, these shifts in opportunity costs and in the relative power resources of leaders versus citizens do not lead to more democratic institutions unless citizens are able to overcome their collective action dilemma. Elections, unlike other aspects of democracy, provide a focal point for collective action, allowing citizens to hold leaders accountable. This helps to explain why economic development is associated with the achievement of competitive elections but not with other institutions associated with democracy, which do not provide a convenient focal point for collective action.  If our argument is correct, indices that lump several features of democracy together (e.g., Polity and Freedom House), as well as indices that focus on non-electoral elements of democracy (e.g., constitutionalism, civil liberties, participation, deliberation, political equality), will reveal a weak or attenuated empirical relationship to economic development. Only indices that are tightly focused on the electoral component of democracy should be strongly correlated with previous levels of economic development. Testing this set of hypotheses requires disaggregating the concept of democracy so that its component features can be separately examined. To do so we enlist two new datasets, Varieties of Democracy (“V-Dem”) (Coppedge et al. 2015) and the Lexical Index of Electoral Democracy (Skaaning, Gerring & Bartusevi ius 2015). With these new data sources, we conduct extensive empirical tests across a global sample of countries extending back over two centuries. These analyses support our contention that only indicators tightly focused on competitive multi-party elections are robustly and positively associated with economic development. This finding not only helps to reconcile divergent results in the literature but also sheds new light on causal mechanisms that may be at work in this much-debated relationship.   In Section I, we present our theory. In Section II, we present the data and a benchmark model. In Section III, we probe the robustness of this result. In Section IV, we conduct head-to-head contests between electoral and composite measures of democracy. In Section V, we disaggregate the key index of electoral democracy in order to analyze its component parts, allowing us another peak into the mechanisms that may be at work. In Section VI we distinguish between democratic upturns and downturns. Section VII concludes with a brief discussion of future directions for research on the modernization thesis. 
  5 
I. Economic Development and Democracy 
What aspects of regime change are promoted by economic development? The question, so far as we can tell, is under-theorized and under-explored. Yet, democracy is a many-splendored concept. Although usually approached as a single entity, recent work distinguishes a variety of elements that may enable rule by the people. This includes electoral contestation, constitutionalism (horizontal accountability, rule of law, civil liberties), state capacity, participation, deliberation, and political equality (Coppedge & Gerring et al. 2011; Cunningham 2002; Diamond & Morlino 2004; Held 2006; Munck 2015). Although these features are undoubtedly correlated, they are not perfectly correlated. Countries scoring high on one dimension may score low, or middling, on another (examples include early-19th century Britain and Apartheid South Africa, which both scored relatively high on contestation but low on participation). Consequently, it is plausible to suppose that economic development might impact some dimensions more strongly than others. We argue that economic development favors electoral aspects of democracy. To be clear, we are not proposing that economic development has no impact at all on the other factors listed above. What we are proposing is that this effect, if present, pales in comparison with the impact of economic development on free and fair elections. Our theoretical discussion thus focuses on explaining these differential effects rather than on factors that might apply broadly to all aspects of democracy. To facilitate this argument we distinguish two players: citizens (understood here as permanent residents of a sovereign territory, whether formally recognized by the state as citizens or not) and leaders (those who control the executive at a particular point in time along with their entourage of family, friends, and advisors). We provide a verbal account of the argument here. (Elsewhere, we construct a formalized version modelled as a sequential game with incomplete information between citizens and a leader that can manipulate different democratic rights [authors]). We assume, first, that citizens of a polity are more likely to prefer a democratic regime-type than its leaders, other things being equal. Thus, while the preferences of both citizens and leaders may have evolved dramatically over the past two centuries (presumably, in a democratic direction), we assume that their relative preferences remain constant. Note that leaders may derive rents from controlling office (Rowley et al. 1988) as well as the intrinsic rewards inhering in power and status, all of which may incline them to prefer holding onto their positions even in the face of popular opposition. By contrast, surveys of mass publics generally show strong 
  6 
support for democracy, especially when contrasted with other possible options (Chu et al. 2008; Inglehart 2003; Norris 2011).  We assume, second, that economic development increases the relative power resources of citizens vis-à-vis leaders. A richer, better educated, more urbanized, more connected citizenry is, by virtue of these traits, more powerful (Inglehart & Welzel 2005; Rueschemeyer et al. 1992). There are many reasons for this, but all point to the idea that wealthier and better educated urbanites are in a better position to engage in oppositional activities (Glaeser et al. 2007). Although economic development may also enhance the power resources of leaders, leaders in poor countries are already in control of considerable resources, especially in autocratic states, where they are generally free to build up police power and to engage in predation (Bueno de Mesquita et al. 2003). Thus, we expect economic development to have a differential effect on the power resources of citizens and leaders, with citizens improving their relative position as a society develops. In addition to altering the relative power of citizens and leaders, economic development affects the direct costs and opportunity costs for a leader as s/he ponders whether to subvert electoral democracy (e.g., by not holding elections or committing electoral fraud). Note that the ideal of electoral democracy hinges upon the willingness of the current leader to relinquish office. If the incumbent is willing to hold an election and abide by its results electoral democracy stands a strong chance of succeeding. If not, electoral democracy cannot succeed, almost by definition. It follows that any factor affecting the direct costs and opportunity costs of a leader is highly relevant (Boix & Stokes 2003; Przeworski & Limongi 1997). Regarding direct costs, economic development increases the costs of electoral fraud. This is most obvious in the case of vote-buying, a common strategy of electoral fraud. Mired in poverty, even public-spirited citizens may sell their votes for a modest sum. Well-off citizens, by contrast, are less likely to do so, or will require larger payments. In rich countries, therefore, the direct costs associated with election manipulation are higher – even taking into account the enhanced resources available to a leader (or ruling party) in a rich society (Jensen & Justesen 2014). Electoral fraud may also be less tolerated among wealthier, well-educated middle class citizens on ideological grounds (Aidt & Jensen 2012; Inglehart & Welzel 2005; Stokes et al. 2013; Weitz-Shapiro 2013). A good deal of research suggests that the opportunity costs of a leader contemplating leaving office are also affected by economic development. In a poor country, jobs with the state are often one of the few sources of substantial income. Evidence for this proposition may be found by comparing the salaries of parliamentarians. In rich (OECD) countries, members of 
  7 
parliament earn about three times the annual per capita income in their country, while in poor countries MPs earn about fourteen times the per capita income (Gerring, Oncel & Morrison 2015). We can expect that the salary differential between rich and poor countries is at least equally big with respect to the salaries of executives, party leaders, and senior staff. A leader exiting office in a poor country may therefore have few options available by which to maintain the lifestyle to which he or she – and his/her coterie – has become accustomed. By contrast, in a rich society leaders who (voluntarily) leave office are likely to find ample sources of remuneration. They may serve on boards of directors, sell their services to consulting and lobbying firms, collect fees for writing and speaking, and so forth (Diermeier et al. 2005; Eggers & Hainmuller 2009). Many leaders find their financial opportunities enhanced after vacating their seat of power (Palmer & Schneer 2015).  The anticipated payoffs from leaving office may influence a leader’s decision about whether or not to manipulate an election in order to ensure his/her hold on power, e.g., by vote-buying, intimidation, or ballot-stuffing. Elections identified as manipulated clearly increase the risk of riots, demonstrations, revolutions, and coups (see, e.g., Beaulieu 2014; Tucker 2007; Wig & Rød 2015) – which, if successful, have dire personal consequences for leaders (see Goemans et al. 2011). These may be risks that leaders of poor countries are willing to take, given the high opportunity costs of exiting office. In rich countries, however, where former leaders can expect to assume well-paid private-sector jobs, a high-risk approach to maintaining office is probably less enticing. Hence, all else equal, a more developed economy (a) increases the power of citizens vis-à-vis leaders and (b) changes the incentive structure for leaders, making less likely that they will cling to office by any means necessary. Even so, in order for citizens to affect the character of national institutions they must overcome collective action dilemmas (e.g., Medina 2007). Citizens cannot impose their will when operating alone but may do so when acting in concert. If citizens are able to coordinate, enhanced power resources at the individual level, attendant upon economic development, are likely to translate into sustained impact at a societal level. A critical feature distinguishing electoral institutions from other institutions is the role that elections play as a focal point for citizen action, mitigating collective action problems that would otherwise constrain popular mobilization.2 This feature acts as a protection against democratic backsliding, helping to ensure that electoral institutions, once established, are respected. 
                                                2 On problems of collective action pertaining to democracy, see Chong (1991), Fearon (2011), and Weingast (1997). On the role of elections, and electoral fraud, as focal points, see Thompson & Kuntz (2005) and Tucker (2007). On focal points more generally see Schelling (1960). 
  8 
The focal role of elections stems from five key features of the electoral process. First, elections are high-stake endeavors, authorizing governments to enact policies influencing the distribution of resources and the sanctioning of values. Second, they are highly visible. One can hardly hold an election in secret. Indeed, elections are likely to be intensively canvassed by the media and by informal networks (which may provide alternative sources of information if the official sources are biased). Third, actions that impair the quality of an election – e.g., widespread vote-buying, voter intimidation, denial of access to the ballot to a major party or candidate – are fairly easy to discern. Although clever leaders have developed subtle ways of manipulating elections (see Birch 2011; Gandhi & Lust-Okar 2009; Lehoucq 2003; Schedler 2013; Simpser 2013), gross infringements are hard to obscure. The most severe infringement upon the principle of free and fair elections – outright cancellation – is also the most visible. Fourth, elections occur across a short period of time and culminate in a single event, the announcement of a winner. At this point, when emotions are running high, it is natural for large numbers of people to mobilize if their preferences are not respected (see, e.g., Beaulieu 2014; Thompson & Kuntz 2005; Tucker 2007). Mobilization is more likely if the will of the majority is denied, for then this majority enjoys the comfort and safety of numbers. Once a tipping point of engagement is reached – making it difficult for the police or army or para-military squads to control a crowd – peripheral actors may enter the fray with minimal risk (Bunce & Wolchik 2011; Beaulieu 2014; Kuran 1989; Lohmann 1994; Tucker 2007).  These characteristics set elections apart from other aspects of democracy, and the prospect of collective action ought to make leaders think twice before manipulating them. By way of contrast, let us consider a non-electoral feature of democracy such as civil liberties. Leaders may infringe upon the right of free speech selectively, arresting only a few individuals at a time and allowing others to bask in (false) security. They may choose an opportune moment, when public attention is focused on another event of great salience (e.g., a natural disaster, international conflict, sporting event). They may even create the conditions for that moment by instigating a distracting event. They may also abridge civil liberties in a clandestine manner, e.g., through disappearances managed by para-military groups or private contracts, thus avoiding direct responsibility. Using various tools of repression, great damage may be done to the democratic ideal of civil liberty without a high level of public awareness and without a single galvanizing event that might prompt the general public to take action. Infringements of civil liberty – in contrast to elections – may be achieved stealthily, for there are no natural focal points. To reprise, we argue that economic development spurs democratization in the electoral realm, but not necessarily in other realms. This is so because the relative power resources of 
  9 
citizens as well as the leaders’ direct costs and opportunity costs of repression are higher in more developed societies and because of the focal quality of elections, which helps citizens overcome collective action problems. All of these features should incentivize elected leaders to respect the election process and its results and should also provide citizens with an opportunity to shoo incumbents out of office if they fail to do so.  Importantly, focal points operate only where elections are already in place. Otherwise, there is no event around which constituencies can mobilize. This suggests that economic development might have greater impact on the consolidation of electoral democracy (once elections are established) than on the initial transition to electoral rule, following a line of argument initiated by Przeworski and associates (Przeworski et al. 2000; Przeworski 2005).   II. A Benchmark Model 
Our main hypothesis centers on a particular dimension of democracy which we have characterized as electoral and which we define narrowly as “clean multiparty elections.” Electoral democracy refers here to the quality of the electoral process itself, not the extent of participation in that election (i.e., suffrage or turnout). We expect that measures focused mainly on the electoral features of democracy will be strongly related to economic development, while measures focused on other aspects of democracy, as well as more comprehensive indices that include both electoral and non-electoral elements, will be only weakly related, or not at all related, to economic development.  Following Lipset (1959), we shall assume that economic development involves a set of factors including income, industrialization (and attendant changes to class structure), changing sectoral composition, education, communications infrastructure, and urbanization. Since these factors are causally inter-related (in ways that would be difficult to model) and highly correlated (and hence difficult to disentangle), we adopt the usual expedient by which per capita GDP serves as a proxy for the composite concept of economic development. Our chosen indicator is drawn from the Maddison Project (Bolt & van Zanden 2014), transformed by the natural logarithm. Following standard practice (Boix 2011; Treisman 2015), missing data within a time-series is linearly interpolated. Robustness tests focused on urbanization are included in the appendix (Tables B20-B21). Other good proxies for economic development with long time series and extensive cross-country coverage are hard to identify. It should be noted that we are not concerned with short-term changes in per capita GDP, i.e., economic growth, or with 
  10 
various factors sometimes associated with, but conceptually distinct from, economic development such as wealth distribution or violent conflict. There is no well-established benchmark model for testing the association between income and democracy, or other determinants of democracy for that matter (Gassebner et al. 2012). Following Boix (2011) and AJRY (2009), we employ a high threshold test in our benchmark model because we want to minimize the possibility of spurious findings. The chosen model features an ordinary least squares estimator along with country and year fixed effects, a lagged dependent variable, and robust standard errors clustered at the country level. Right-side variables are lagged one period behind the outcome and data is analyzed annually. The benchmark specification is intentionally sparse, disregarding additional factors that might serve as potential confounders but might also introduce post-treatment confounding or greatly truncate the sample. Note that our models include a lengthy time-series, extending for more than 100 years and in some cases up to two centuries, which should provide sufficient within-country information in a fixed-effects framework to mitigate the so-called Nickell bias (Nickell 1982).  We begin by assembling indicators that focus on non-electoral components of democracy. This includes four meso-level indices from the V-Dem dataset that attempt to measure Liberal, Participatory, Deliberative, and Egalitarian components of democracy (Coppedge et al. 2011; 2015a,b). Additional indices capitalize on the richness of V-Dem data to measure more specific aspects of democracy including Individual Liberty and Rule of Law, Judicial Constraints, Legislative Constraints, Free Expression, Alternative Sources of Information, Free Association, Executive Selection, and (de jure) Adult Suffrage. Detailed definitions of all variables used in this paper are located in Table A1 and descriptive statistics in Table A2. Note that all democracy measures are re-scaled to a 0-1 scale so that coefficients can be directly compared.  Results of these initial tests are shown across the first row of Table 1. Among these twelve non-electoral indicators of democracy only Judicial Constraints is predicted (with the expected sign) by a country’s per capita GDP. Somewhat surprisingly, higher income predicts lower suffrage – a result that we suspect is spurious. Next, we examine a set of composite indices commonly used to measure democracy in its entirety (following different understandings of the concept). This includes Polity2 from the Polity IV dataset (Marshall, Gurr & Jaggers 2014), the Unified Democracy Scores (“UDS”) from Pemstein et al. (2012), and the Political Rights and Civil Liberties indices from Freedom House (2014). While each of these indices has a somewhat different focus they are all highly aggregated, including a wide variety of underlying concepts and measures. Results of these tests, shown in 
  11 
columns 13-16 in Table 1, suggest that democracy, considered in its entirety, is not clearly identified as a by-product of economic development.  Of course, there are many additional issues to consider pertaining to samples (e.g., Boix 2011), estimators (e.g., Heid et al. 2012), specifications (e.g., Boix & Stokes 2003), and other matters. These are taken up in the next section of the paper. However, the results shown here indicate that whatever relationship may exist between economic development and macro-indices of democracy is not especially strong. Thus far, the skeptical view of modernization theory, introduced at the outset, is upheld. 
 12 
 Table 1:  Varieties of Democracy 
 NON-ELECTORAL  1 2 3 4 5 6 7 8 9 10 11 12 
Outcome Liberal Component (V-Dem) Participatory Component (V-Dem) Deliberative Component (V-Dem) Egalitarian Component (V-Dem) Ind. Liberty Rule of Law (V-Dem) Judicial Constraints (V-Dem) Legislative Constraints (V-Dem) Free Expression (V-Dem) Alternative Information (V-Dem) Free Association (V-Dem) Executive Selection (V-Dem) Adult Suffrage (V-Dem) GDPpc(ln) 0.003 -0.000 0.001 -0.001 -0.001 0.004* 0.004 0.001 -0.001 0.001 0.006 -0.007**  (0.002) (0.001) (0.003) (0.001) (0.002) (0.002) (0.003) (0.003) (0.002) (0.003) (0.007) (0.003) Years 111 111 111 111 111 111 111 111 111 111 111 111               COMPOSITE MOSTLY ELECTORAL PURELY ELECTORAL   13 14 15 16 17 18 19 20 21    
Outcome Polity2 (Polity IV) UDS (Pemstein) Political Rights (FH) Civil Liberties (FH) BMR (Boix) Lexical (Skaaning) Electoral Contestation (V-Dem) Competitive Elections (Skaaning) Clean Elections (V-Dem)    GDPpc(ln) 0.002 0.001 -0.004 0.002 0.007 0.010** 0.007** 0.013** 0.010***     (0.003) (0.002) (0.006) (0.005) (0.005) (0.005) (0.003) (0.005) (0.004)    Years 211 62 37 37 207 211 111 211 111     Ordinary least squares regression with lagged dependent variable, country and year fixed effects, and standard errors clustered by country. *.1, **.05, ***.01 (two-sided tests).  Units of analysis: country-years.  Right-side variables measured at T-1.  Scales normalized to 0-1 (1=most democratic)  
  13 
In the third section of Table 1 (“mostly electoral”) we examine indices that are focused primarily – but not exclusively – on the electoral component of democracy. We begin with the binary democracy indicator from Boix, Miller & Rosato (2013).1 Their measure (“BMR”) captures whether the legislature and executive are chosen (directly or indirectly) in free and fair elections in which at least a majority of adult men are enfranchised. Note that the inclusion of suffrage is the only departure from a purely electoral indicator (following our definition). Next, we examine the Lexical index (Skaaning et al. 2015), which is based on a cumulative aggregation of indicators capturing whether national elections are held, opposition parties are allowed to run, elections are competitive, and suffrage is inclusive. Again, the inclusion of a suffrage criterion is the only departure from a purely electoral measure. Finally, we employ an index of Electoral Contestation based on different V-Dem indicators including measures of Freedom of Association (including repression of political parties), Clean Elections, and Executive Selection. These are combined through multiplication based on the idea that they are necessary and mutually dependent conditions for contestation. Results from these tests are shown in columns 17-19 of Table 1. All electoral indices bear a positive relationship to economic development, though one (BMR) does not surpass the usual threshold of statistical significance. In the final section of Table 1 (“purely electoral”) we examine indicators that are tightly focused on electoral democracy. Competitive Elections focuses on the existence of competitive multi-party elections without any consideration of the extent of suffrage. Specifically, the index is coded 1 in any situation where the chief executive offices and seats in the effective legislative body are filled by multi-party elections characterized by uncertain outcomes – meaning that the elections are, in principle, sufficiently free to enable the opposition to gain government power. Next, we measure Clean Elections, understood as the absence of registration fraud, systematic irregularities, government intimidation of the opposition, vote buying, and election violence. The index is formed from a Bayesian factor analysis of these component indicators, drawn from the V-Dem dataset. Note that Competitive Elections is a component of the ordinal Lexical index and Clean Elections is a component of Electoral Contestation. These narrower indices are thus nested within the broader indices that we classified as “mostly electoral.” Results of these final tests, 
                                                1 It rather closely follows (except for including the participation criterion and some adjustments on how to capture the contestedness of elections) an earlier formulation provided by Przeworski and colleagues (Przeworski et al. 2000), subsequently known as the Democracy-Dictatorship (DD) measure (Cheibub et al. 2010). We do not include DD here, due to its shorter time series (post-WWII). However, when running tests on similar samples, we do find that BMR is somewhat more strongly related to income than DD. One plausible explanation of this is the stronger weight put on observed government alternation. Knutsen and Wig (2015) show that young democracies with strong economic performances are more likely to be misclassified as dictatorships by DD, which could lead to attenuation bias also when using DD to test the modernization thesis. 
  14 
shown in columns 20-21 of Table 1, support our argument, as they are all strongly correlated with prior levels of per capita GDP.  To get a sense of the estimated size of the (long-term) causal effect, Figure 1 plots the marginal effect of logged GDP per capita on the long-run predicted equilibrium level of the Clean Elections index based on our benchmark model – Model 1, Table 3. Since our benchmark includes a lagged dependent variable, the coefficient for income only reveal the short-term (yearly) effect – 0.010 for each unit increase in logged income. The long-run effect, however, is 0.010/(1-0.881), where 0.881 is the coefficient on the lagged dependent variable, which amounts to roughly 0.080 on the 0-1 Clean Elections index (with a standard error of 0.032). This effect is plotted in Figure 1, surrounded by 95% confidence intervals.2   Figure 1:  Long-run Effects 
  Long-run effects of economic development (proxied by per capita GDP) on electoral democracy (proxied by Clean Elections).  
                                                2 The standard errors of the long-run coefficient are calculated using the nlcom command in Stata 13. They are very similar but slightly larger than those from a Bewley-transformation (De Boef and Keele 2008), where the lag of the dependent variable is used to instrument for its change. The same goes for the long-run equilibrium levels, where the root mean squared error (RMSE) based on the standard errors from Table 1, Model 21, scaled by (1-0.881), yields a  slightly larger estimate than the RMSE from the Bewley tansformation. Figure 1 is based on this slightly more conservative estimate of the RMSE, arrived at through the margins and marginsplot commands in Stata 13. 
0
.
2
.
4
.
6
.
8
L
o
n
g
-
r
u
n
 
l
e
v
e
l
 
o
f
 
c
l
e
a
n
 
e
l
e
c
t
i
o
n
s
 
(
V
-
D
e
m
)
250 500 1000 2000 4000 8000 16000 32000
GDP per capita in USD (Maddison)
Predictive Margins with 95% CIs
  15 
To put this in perspective, an extremely poor country, at $250 USD per capita GDP, is expected to hover around 0.23 on the Clean Elections index – approximately the level observed in Mexico under the PRI in the 1980s. Quadrupling that income level, to $1000 USD, the expected long-run level of Clean Elections rises to 0.34 – equivalent to the status of Kenya after Arap Moi (but prior to 2007). A median income country by 2010’s standards, at roughly $7300 USD per capita, is expected to score around the 0.5 midpoint of the Clean Elections scale – corresponding (roughly) to Ghana in the late 1990’s. Given the secular-historical rise of the world economy, these results suggest that economic development brings with it a substantial shift in the quality of elections.   III. Additional Tests 
We have demonstrated that measures narrowly focused on the electoral component of democracy are more closely associated with changes in per capita GDP than non-electoral measures or composite indices that include electoral and non-electoral elements. But, we have tested only one format: ordinary least squares with a lagged dependent variable, country and year fixed effects, and clustered standard errors. In this section, we explore alternate estimators, samples, and specifications. Our attention is focused on Competitive Elections and Clean Elections since they are narrowly targeted on the concept of theoretical interest. (A similar battery of robustness tests is also conducted on other indices, with results shown in Appendix B.)   Table 2 focuses on Competitive Elections. Model 1 replicates our initial test – Model 20 from Table 1. Subsequent models introduce variations in this benchmark. Model 2 excludes the lagged dependent variable. Model 3 substitutes a trend variable for annual dummies. Model 4 includes a number of control variables that, following the literature, may affect a country’s regime-type: Corruption (Birch 2011), Land Inequality (Ansell & Samuels 2014), neighbor Diffusion (Brinks & Coppedge 2006), Internal Conflict and External Conflict (Reuveny & Li 2003), Natural Resources (Ross 2001). Descriptions of these variables and their sources can be found in Table A1.   
  16 
Table 2:  Competitive Elections 
Estimator OLS OLS OLS OLS OLS OLS OLS OLS IV Sample Full Full Full Full Full Full 5-year MI Full  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.013** 0.148*** 0.104*** 0.022* 0.167***  0.064*** 0.040*** 0.187**  (0.005) (0.036) (0.035) (0.011) (0.048)  (0.020) (0.008) (0.090) GDPpc (ln)       0.165***       L20      (.047)    Lagged Y 0.890***   0.840***   0.578*** 0.544***   (0.009)   (0.012)   (0.031) (0.031)  Trend   0.002***          (0.001)       Corruption    -0.090*** -0.775***            (0.031) (0.172)     Land     -0.000 -0.000**        Inequality    (0.000) (0.000)     Diffusion    2.108** 10.488**         (0.926) (4.644)     Internal     0.008 -0.020        Conflict    (0.010) (0.034)     External     -0.007 -0.039        Conflict    (0.008) (0.034)     Natural     0.000 0.000        Resources    (0.000) (0.002)     Country FE ü ü ü ü ü ü ü ü ü Year FE ü ü  ü ü ü ü ü ü Countries 157 157 157 132 132 158 156 216 136 Years 211 211 211 99 99 193 42 213 191 Obs 12947 13081 13081 6683 6695 12053 2509 23445 9610 R2 (within) 0.849 0.287 0.239 0.765 0.237 0.289 0.521 0.628 0.252 Cragg-Donald             156.1  Outcome: Competitive Elections.  Estimators: OLS (ordinary least squares, with standard errors clustered by country), IV (instrumental variable, results from second stage).  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted.  Right-side variables measured at T-1.  
Model 5 repeats this specification without the lagged dependent variable. Model 6 returns to the benchmark model but lags per capita GDP two decades behind the outcome. Model 7 reconstructs the annual panel as a five-year panel (after converting variables to 5-year moving averages). Given the sluggish nature of right- and left-side variables, this might be regarded as a more plausible formulation. Model 8 imputes missing data with the Amelia II algorithm (Honaker & King 2010), extending our benchmark sample with an additional 10,000+ observations. Model 9 presents the second stage of an instrumental variables analysis, where (following Acemoglu et al. 2008), instruments are constructed by using the weighted income of trading partners to capture exogenous international shocks to domestic income. All tests shown in Table 2 reveal a positive relationship between per capita GDP and Competitive Elections. Remarkably, all robustness tests suggest a stronger relationship between 
  17 
these two variables – judging solely by coefficient estimates – than in our benchmark model (reproduced as Model 1 in Table 2), although coefficients are not directly comparable across dynamic and non-dynamic models.  The tests in Table 2 apply an ordinary least squares estimator, a choice that might seem odd given the binary outcome of interest. OLS provides ease of interpretation, computational simplicity (allowing for unit and time fixed effects along with annual data), and consistency with estimators used for other outcomes (e.g., in Table 1 and Appendix B). Moreover, a linear-probability model provides a sensible estimate of the conditional expectation function without relying heavily on assumptions about the distribution of the error term to produce estimates, as do logit, probit, and other maximum-likelihood models. Granted, the assumptions required for its use are more plausible in settings where the treatment is randomly assigned (Angrist & Pischke 2009: 94-107). To relieve concerns, tests in Table 2 (except the multiple-imputation and instrumental-variable models) are replicated with a logit estimator. Results, shown in Table B22, corroborate OLS estimates. Table 3 focuses on Clean Elections. Model 1 again replicates our initial test from Table 1. Subsequent models introduce variations in this benchmark, following the template of Table 2 but with a few variations, as discussed below. Clean Elections is a continuous variable, so there is no need to introduce non-linear estimators. However, the variable presents an uneven distribution, with multiple values at the left bound of 0, representing a non-electoral regime. To assure that reported results are not solely the product of an electoral transition (from no elections to elections), Model 7 in Table 3 replicates the benchmark model with a sub-sample of observations in which an electoral regime was in place (elections were on course).           
  18 
Table 3:  Clean Elections 
Estimator OLS OLS OLS OLS OLS OLS OLS OLS GMM OLS IV Sample Full Full Full Full Full Full Y>0 5-year 5-year MI Full  1 2 3 4 5 6 7 8 9 10 11 GDPpc (ln) 0.010*** 0.100*** 0.074*** 0.015** 0.119***  0.011*** 0.034** 0.083*** 0.009*** 0.116**  (0.004) (0.026) (0.026) (0.006) (0.030)  (0.003) (0.014) (0.015) (0.003) (0.058) GDPpc (ln)       0.083**         L20      (0.037)      Lagged Y 0.879***   0.837***   0.953*** 0.579*** 0.643*** 0.741***   (0.010)   (0.015)   (0.006) (0.034) (0.060) (0.022)  Trend   0.002***            (0.000)         Corruption     -0.103*** -0.688***          Index    (0.021) (0.108)       Land     -0.000** -0.000**         Inequality    (0.000) (0.000)       Diffusion    0.676 4.189           (0.500) (2.787)       Internal     -0.001 -0.008         Conflict    (0.005) (0.015)       External     -0.001 -0.027         Conflict    (0.005) (0.018)       Natural     -0.000 -0.000         Resources    (0.000) (0.001)       Country FE ü ü ü ü ü ü ü ü  ü ü Year FE ü ü  ü ü ü ü ü ü ü ü Countries 152 152 152 132 132 153 149 152 152 205 130 Years 111 112 112 99 99 115 111 22 22 114 92 Obs 11271 11375 11375 6630 6649 10439 8560 2211 2211 21143 7789 R2 (within) 0.847 0.320 0.262 0.818 0.417 0.351 0.863 0.549  0.853 0.189 Cragg-Donald           127.6  Outcome: Clean Elections index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), IV (instrumental variables, second stage), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), Y>0 (scores for Clean Elections that surpass 0), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted.  Right-side variables measured at T-1.    
The continuous nature of Clean Elections allows for the use of a system generalized method of moments estimator (Blundell & Bond 1998), reported in Model 9 of Table 3. This version of GMM is regarded as appropriate for studying sluggish variables. We follow a standard approach for GMM models with long time series in re-coding annual data at five-year intervals (as in Model 8). This reduces the number of time series units and thus the number of instruments, and allows for valid identification (following the assumptions of the model). We enter income and the lagged dependent variable as endogenous and allow two lags for instrumentation. This yields 145 instruments, below the number of cross-sectional units (153), which is the rule-of-thumb threshold (Roodman 2009). The Ar(2) test p-value is .56 and the Hansen J-test p-value is .39, suggesting that Model 9 provides consistent estimates (this holds also for other GMM specifications that we tested).  
  19 
Overall, the results for Clean Elections are highly robust. Across eleven models shown in Table 3, per capita GDP is related to higher-quality elections in every test, surpassing standard thresholds of significance. As with Competitive Elections, we find that robustness tests generally show an enhanced relationship between these two factors relative to the benchmark model (Model 1). Since economic development is a protean concept, amenable to many operationalizations, it is possible that these results may reflect some peculiarity of this particular indicator, drawn from the Maddison project. To alleviate this concern, we replicate the battery of tests in Tables 2 and 3 using Urbanization rather than national income as the key predictor. (Urbanization, the share of population living in cities, is the main alternative to per capita GDP if one requires a measure of economic development with good historical coverage.) Results, shown in Tables B20-21, are generally robust. At this point, we have subjected two indicators of central theoretical concern – Competitive Elections and Clean Elections – to a litany of empirical tests. But alternatives to these two measures have been tested in only one format, our benchmark model. This incongruity is remedied in a series of tables in Appendix B, where tests contained in Tables 2-3 are replicated for alternate measures of democracy. The general picture that emerges from this interrogation confirms the initial findings presented in Table 1. Non-electoral indicators of democracy, with the notable exception of Judicial Constraints, are not well-predicted (in the expected direction) by per capita GDP (Tables B1-B12). Nor are composite indices (Tables B13-B16). By contrast, indices that focus mostly on the electoral component of democracy are consistently predicted by a lagged measure of per capita GDP (Tables B17-B19). Indeed, Lexical and Electoral Contestation prove to be almost as robust as our “purely electoral” indicators (Competitive Elections and Clean Elections).  The general picture emerging from all these tests is that the relationship between economic development and democracy is dependent on an electoral connection. The more closely an indicator homes in on the purely electoral component of democracy the more sensitive it is to changes in economic development.     
  20 
IV. Head-to-Head Contests 
Measures of democracy are highly correlated, as many studies have pointed out. As such, one must be wary of over-interpreting fine differences in performance across indicators of very similar latent concepts – each of which, we must presume, is affected by potential measurement error. One approach to this problem is to include both measures in the same model so that partial effects (the impact of X controlling for Z) can be calculated. In our setting, this common strategy is more complicated since we are comparing rival measures of the outcome (Y) rather rival measures of a causal factor. Even so, the strategy of testing rival hypotheses head-to-head in the same model is viable.  Table 4:  Head-to-Head Contests 
Outcome Competitive Elections Clean Elections Polity2  1 2 3 4 GDPpc (ln) 0.065*** 0.085*** 0.006 -0.046**  (0.024) (0.019) (0.021) (0.022) Polity2 0.940*** 0.485***    (0.042) (0.028)   Competitive Elections   0.461***        (0.024)  Clean Elections    0.802***     (0.045) Country FE ü ü ü ü Year FE ü ü ü ü Countries 155 149 155 149 Years 211 112 211 112 Obs 12543 9739 12543 9739 R2 (within) 0.599 0.581 0.632 0.537  Ordinary least squares regression with country and year fixed effects, standard errors clustered by country. *.1, **.05, ***.01 (two-sided tests). Right-side variables measured at T-1. Units of analysis: country-years.   
In Table 4, we build on the benchmark model to test electoral measures of democracy – Competitive Elections and Clean Elections – against the most common composite measure of democracy, Polity2. In Model 1, Competitive Elections is regressed on per capita GDP along with Polity2 plus country and year fixed effects. In Model 2, the analysis is replicated with Clean Elections as the outcome indicator. In both analyses, the relationship between per capita GDP and electoral democracy is robust, even when “controlling” for a composite measure of democracy on the right side of the model. Models 3 and 4 repeat this exercise in reverse. Here, Polity2 forms the outcome while Competitive elections and Clean elections serve as the controls. Here, the result does not survive. Indeed, the relationship turns negative in Model 4. 
  21 
 The set of results presented in Table 4 offers further evidence of our claim that the relationship between economic development and democracy is not evenly distributed across all aspects of democracy. Composite indices such as Polity2 are not robust to the inclusion of electoral democracy, while measures of electoral democracy are robust to the inclusion of a composite measure.   V. Inside the Box 
The Clean Elections index offers a unique opportunity to peak inside the box of an intriguing relationship. Note that this index is composed of eight variables, each of which is measured separately in the V-Dem dataset. By testing our benchmark model with each of these outcome variables (separately) we may gain additional insight into the causal mechanisms at work in this relationship.  Four indicators tap into problems of electoral integrity that may be characterized as violence or fraud. Government intimidation inquires whether opposition candidates, parties, or campaign workers were subjected to repression, intimidation, violence, or harassment by the government, the ruling party, or their agents. Other violence asks whether the campaign period, election day, and post-election process were free from other types of violence related to the conduct of the election and the campaign. Vote buying inquires into evidence of vote and/or turnout buying in an election. This refers to the distribution of money or gifts to individuals, families, or small groups in order to influence their decision to vote/not vote, or whom to vote for. Other irregularities refers to other irregularities on the part of incumbent and/or opposition parties. Specific examples include use of double IDs, intentional lack of voting materials, ballot-stuffing, misreporting of votes, and false collation of votes. We have strong theoretical reasons to believe that these factors are affected by the incentives of leaders and the relative power of leaders and citizens, which in turn are responsive to economic development, as articulated in Section I.  Three of the indicators that compose the Clean Elections index measure the capability of a state to manage the election process. Voter registry asks whether there was a reasonably accurate voter registry in place at the time of an election and whether it was in fact utilized. EMB capacity measures whether the Electoral Management Body in charge of administering national elections has sufficient staff and resources to administer a well-run national election. EMB autonomy measures the ability of the Election Management Body to apply election laws and administrative rules impartially in national elections, separate from pressures exerted by the government or 
  22 
governing party. While it is plausible to suppose that economic development might enhance state capacity, this lies outside the ambit of our theory. Thus, we have no strong priors on the relationship of these variables to per capita GDP. The final indicator comprising the Clean Elections index is Free and fair elections. This provides a summary judgment of whether – taking all aspects of the pre-election period, election day, and post-election process into account – the national election was free and fair. It does not consider the extent of suffrage but only the fairness of an election for those who are entitled to vote. We regard this as an overall measure of electoral democracy, and hence falling within the ambit of our theoretical framework.  In Table 5, we regress each of these outcomes on per capita GDP in our benchmark model (lagged dependent variable, country and year fixed effects, and clustered standard errors). Not all of these variables pass standard tests of statistical significance, suggesting that the meso-level concept – Clean Elections – is more responsive to economic development than several of its components. This could be a product of measurement error, which is generally minimized when a variety of measures are combined in a single index. Note also that these components may perform a substitutive function. When leaders clamp down on (or open up to) electoral democracy they may prioritize one or the other of these factors, leading to variability across time and across countries that serves as noise in the crossnational estimator. For incumbents wanting to manipulate election results, picking one option from the “menu of manipulation” may be sufficient for ensuring election victory (Schedler 2002). For instance, leaders could opt either to stuff ballot boxes or to use party thugs to deter opposition members from voting in the first place; these strategies act as substitutes. Table 5:  Clean Elections, Disaggregated 
 Fraud & Violence Capacity General 
Outcome Government Intimidation Other Violence Vote Buying Other Irregularities Voter Registry EMB Capacity EMB Autonomy Free & Fair  1 2 3 4 5 6 7 8 GDPpc (ln) 0.027*** 0.029*** 0.034*** 0.032*** 0.012 -0.003 0.007 0.029***  (0.009) (0.009) (0.007) (0.009) (0.008) (0.007) (0.008) (0.010) Lagged Y 0.924*** 0.901*** 0.917*** 0.918*** 0.910*** 0.960*** 0.950*** 0.914***  (0.007) (0.008) (0.007) (0.007) (0.009) (0.005) (0.005) (0.008) Countries 152 152 152 152 152 151 151 152 Years 111 111 111 111 111 111 111 111 Obs 11271 11271 11271 11271 11271 11227 11230 11271 R2 (within) 0.869 0.839 0.858 0.856 0.879 0.952 0.937 0.855  Outcomes: components of the Clean Elections index.  Ordinary least squares regression with country and year fixed effects, standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Right-side variables measured at T-1. Units of analysis: country-years.     
  23 
Even so, it is worth comparing those indicators that pass our threshold test to those that do not. In line with our expectations, Table 5 shows that all indicators associated with electoral violence and fraud bear a strong relationship to economic development (Models 1-4) while indicators of state capacity do not (Models 5-7). The overall measure of election quality – Free and Fair – is also strongly correlated with per capita GDP, though this result does not help in disentangling causal mechanisms as it rests at roughly the same level of aggregation as our summary index (Clean Elections).  This set of tests provides additional fodder for our argument that a richer economy empowers citizens to deter leaders from engaging in blatant manipulation of elections and weakens the incentives of leaders to do so. By contrast, other aspects of election quality that derive more from state capacity bear little relationship to per capita income. Even when we disaggregate the index of theoretical interest we find that the “electoral connections” theory makes accurate predictions.   VI. Upturns and Downturns 
Finally, we investigate whether the relationship between income and electoral democracy is symmetric or asymmetric. Does economic development affect the probability of upturns (transitions to greater democracy, aka “democratization”) as well as of downturns (to greater autocracy, aka “democratic survival”), as argued by Boix (2011), Boix & Stokes (2003), and Epstein et al. (2006)? Or does it only affect the probability of downturns, as argued by Przeworski and colleagues (Przeworski et al. 2000; Przeworski 2005)? According to our theory, elections cannot serve as focal points in a non-elective regime. Where the established method for selecting leaders is by appointment or inheritance, there is no recognized event that might galvanize opposition at a single point in time. Thus, we expect that the impact of economic development is asymmetric – assisting in the consolidation of an electoral regime but not (or only minimally) in the initial transition to an electoral regime. To analyze this question we return to our preferred measures of electoral democracy – Competitive Elections and Clean Elections – along with a third measure that registers the existence of an Electoral Regime (a regime in which regular elections are on course). Units of analysis are comprised of election-years, as previously. But we also conduct tests with elections as the units of analysis. (Recall that annual data is generated from election data by filling in non-election years with scores from the previous election – unless there is an interruption in the electoral regime, in which case the period of interruption is coded as 0). 
  24 
Following Boix (2011: 822), we run two regressions for each dependent variable to differentiate movements in either direction (toward, or away from, democracy). The “Up” model re-codes the outcome to register instances of positive change since the previous year, setting all cases of no change or negative change to zero. The “Down” model re-codes the outcome to register instances of negative change since the previous year, setting all cases of no change or positive change to zero. By comparing the coefficients on GDP across these two regressions we can differentiate the influence of economic development on democratization and on backsliding (away from the democratic ideal).  Table 6:  Upturns and Downturns 
Outcome Competitive Elections Electoral Regime Clean Elections Clean Elections Sample 1801-2011 1901-2011 1901-2011 1901-2011 Units Country-year Country-year Country-year Election-year Direction Up Down Up Down Up Down Up Down  1 2 3 4 5 6 7 8 GDPpc (ln) 0.004 0.009*** -0.008 0.012*** 0.002 0.008*** -0.002 0.011***  (0.004) (0.003) (0.006) (0.004) (0.004) (0.002) (0.009) (0.003) Lagged Y -0.057*** -0.052*** -0.139*** -0.054*** -0.084*** -0.037*** -0.110*** -0.052***  (0.004) (0.006) (0.009) (0.005) (0.007) (0.005) (0.016) (0.010) Countries 157 157 156 156 152 152 149 149 Years/elections 211 211 111 111 111 111 56 56 Obs 12947 12970 11792 11797 11271 11283 2720 2723 R2 (within) 0.047 0.051 0.110 0.031 0.076 0.029 0.090 0.089  Ordinary least squares regression with country and year fixed effects, standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Right-side variables measured at T-1.   • “Up” (toward greater democracy): !!,!! = !! ∗ !!!! + !! ∗ !"#!!! + !! + !! + !!,!, where D is the democracy measure (dependent variable), and !!,!! = max (!! ,!!!!). • “Down” (avoiding backsliding): !!,!! = !! ∗ !!!! + !! ∗ !"#!!! + !! + !! + !!,!, where !!,!! = min (!! ,!!!!). • !! and !! are country-and year-fixed effects.  Results from these analyses, shown in Table 6, clearly support the asymmetric hypothesis.3 Higher income discourages downturns but does not encourage upturns. This is so regardless of whether we focus on dichotomous measures – Competitive Elections (Models 1-2) and Electoral Regime (Models 3-4) – or the more fine-grained Clean Elections index (Models 5-8). It is so regardless of whether the sample includes the twentieth century only (Models 3-8) or the entire modern period (Models 1-2). And, it is so regardless of whether years (Models 5-6) or elections (Models 7-8) provide the units of analysis. (The latter tests suggest that the asymmetric relationship is not solely the product of electoral interruptions, which are not included in the 
                                                3 Coefficients on the lagged dependent variable in Table 6 are negative because these models look at change in the dependent variable as the outcome, as opposed to the other tests in this paper where current level is the dependent variable. 
  25 
election-year panel analysis.) In other words, as per capita GDP rises it becomes less likely that election quality will deteriorate.4  VII. Conclusion 
Since democracy is a diffuse, multi-dimensional concept it stands to reason that if economic development affects democracy, the causal connections are likely to be stronger for some aspects of democracy than for others. Only by disaggregating the concept can this crucial issue be addressed.   In this study, we find that the relationship between economic development and democracy is robust only with respect to the electoral component of democracy, narrowly construed as the existence of competitive national elections and the procedural integrity of the electoral process. Other aspects of democracy such as those associated with the participatory, deliberative, liberal, and egalitarian ideals or with state capacity are not related, or are only weakly related, to national income and its correlates (e.g., urbanization). This may help to explain why empirical tests employing composite indices such as Polity2 or Freedom House show inconsistent results, leading to a long and seemingly irresolvable debate over modernization theory, referenced at the outset. We also find that while economic development prevents democratic backsliding it does not show a significant relationship to democratization, corroborating the thesis of asymmetric effects (Przeworski et al. 2000).  As part of the contribution of this study, we propose a theoretical framework to explain the differential effects of economic development on democracy. This framework, presented in Section I, suggests that economic development reduces the relative power and alters the utility calculus of leaders, who are in a position to respect or subvert multi-party elections. In a developed society, the direct costs of subversion (e.g., through vote-buying) are raised while the opportunity costs of leaving office are lowered (by virtue of offering remunerative nongovernmental career options). Likewise, the focal role of elections provides a coordination mechanism for citizens who wish to see the will of the people respected. All of these mechanisms are election-centered, having little applicability to other elements of democracy or to state capacity (often viewed as a facilitating condition of democracy).   This explanation is put forth in a stipulative fashion, based on extant studies, and is consistent with the evidence presented here. However, the mechanisms are not directly measured 
                                                4 However, Models 3-4 indicates that there is also an ‘interruptions/coup effect’; income does not incur the introduction of elections, but it decreases the chances of electoral interruptions (coups, autogolpes, etc.). 
  26 
and tested. Future research should aim to get further inside the box so as to show the micro-level connections between economic development and improved prospects for democracy.   
  27 
VIII. References  
Acemoglu, Daron, Simon Johnson, James A. Robinson, Pierre Yared. 2008. “Income and Democracy.” American Economic Review 98(3): 808–842. Acemoglu, Daron, Simon Johnson, James A. Robinson, Pierre Yared. 2009. “Reevaluating the Modernization Hypothesis.” Journal of Monetary Economics 56(8): 1043–1058. Aidt, Toke S., Peter S. Jensen. 2012. “From Open to Secret Ballot: Vote Buying and Modernization.” Working paper, Department of Economics, University of Cambridge. Url: http://www.econ.cam.ac.uk/research/repec/cam/pdf/cwpe1221.pdf  Alexander, Marcus, Matthew Harding, Carlos Lamarche. 2011. “Quantile Regression for Time-Series-Cross-Section Data.” International Journal of Statistics and Management System 6(1–2): 47–72. Angrist, Joshua D., Jorn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton: Princeton University Press. Baskaran, Thushyanthan. 2014. “Taxation and Democratization.” World Development 56: 287–301. Beaulieu, Emily. 2014. Electoral Protest and Democracy in the Developing World. New York: Cambridge University Press. Benhabib, Jess, Alejandro Corvalan, Mark M. Spiegel. 2011. Reestablishing the Income-Democracy Nexus. Federal Reserve Bank of San Francisco Working Paper 2011-09. Url: http://www.frbsf.org/economic-research/files/wp11-09bk.pdf  Birch, Sarah. 2011. Electoral Malpractice. Oxford: Oxford University Press. Blundell, Richard, Stephen Bond. 1998. “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models.” Journal of Econometrics 87(1): 115-143. Boix, Carles. 2003. Democracy and Redistribution. Cambridge: Cambridge University Press.  Boix, Carles, Susan C. Stokes. 2003. “Endogenous Democratization.” World Politics 55(4): 517-49. Boix, Carles, Michael K. Miller, Sebastian Rosato. 2013. “A Complete Data Set of Political Regimes, 1800-2007.” Comparative Political Studies 46(12): 1523-1554. Bolt, J., J. L. van Zanden. 2014. “The Maddison Project: collaborative research on historical national accounts.” The Economic History Review 67(3): 627–651. Brinks, Daniel, Michael Coppedge. 2006. “Diffusion Is No Illusion: Neighbor Emulation in the Third Wave of Democracy.” Comparative Political Studies 39(4): 463-89. Bunce, Valerie, Sharon Wolchik. 2011. Defeating Authoritarian Leaders in Postcommunist Countries. New York: Cambridge University Press. Campbell, Donald T. 1966. “Pattern Matching as an Essential in Distal Knowing.” In K.R. Hammond (ed), The Psychology of Egon Brunswick. New York: Holt, Rinehart and Winston. 
  28 
Che, Yi, Yi Lub, Zhigang Tao, Peng Wang. 2013. “The Impact of Income on Democracy Revisited.” Journal of Comparative Economics 41(1): 159–169. Cheibub, Jose Antonio, Jennifer Gandhi, James Raymond Vreeland. 2010. “Democracy and Dictatorship Revisited.” Public Choice 143(1-2): 67-101. Chong, Dennis. 1991. Collective Action and the Civil Rights Movement. Chicago: University of Chicago Press. Chu, Y.-H., Michael Bratton, Marta Lagos, S. Shastri, Marc Tessler. 2008. “Public Opinion and Democratic Legitimacy.” Journal of Democracy 19(2): 74-87. Coppedge, Michael, John Gerring, with David Altman, Michael Bernhard, Steven Fish, Allen Hicken, Matthew Kroenig, Staffan I. Lindberg, Kelly McMann, Pamela Paxton, Holli A. Semetko, Svend-Erik Skaaning, Jeffrey Staton, and Jan Teorell. 2011. “Conceptualizing and Measuring Democracy: A New Approach.” Perspectives on Politics 9(2): 247-67. Coppedge, Michael, John Gerring, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Carl Henrik Knutsen, Kelly McMann, Daniel Pemstein, Megan Reif, Svend-Erik Skaaning, Jeffrey Staton, Eitan Tzelgov, Yi-ting Wang, Brigitte Zimmerman. 2015a. Varieties of Democracy: Methodology v4. Varieties of Democracy (V-Dem) Project. Url: https://v-dem.net/media/filer_public/76/53/7653197c-f274-4a6e-a65b-47144809f896/v-dem_methodology_v4_12.pdf  Coppedge, Michael, John Gerring, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Carl Henrik Knutsen, Kelly McMann, Daniel Pemstein, Megan Reif, Svend-Erik Skaaning, Jeffrey Staton, Eitan Tzelgov, Yi-ting Wang, Brigitte Zimmerman. 2015b. Varieties of Democracy: Codebook v4. Varieties of Democracy (V-Dem) Project.Url: https://v-dem.net/media/filer_public/76/53/7653197c-f274-4a6e-a65b-47144809f896/v-dem_methodology_v4_12.pdf  Cunningham, Frank. 2002. Theories of Democracy. London: Routledge. Dahl, Robert A. 1971. Polyarchy: Participation and Opposition. New Haven: Yale University Press. Davenport, Christian. 2007. “State Repression and Political Order.” Annual Review of Political Science 10: 1-23. Diamond, Larry, Leonardo Morlino. 2004. “Quality of democracy: an overview.” Journal of Democracy 15(4): 20-31. Diermeier, Daniel, Michael Keane, Antonio Merlo. 2005. “A Political Economy Model of Congressional Careers.” American Economic Review 95(1): 347–73. Eggers, Andrew, Jens Hainmuller. 2009. “MPs for Sale: Estimating Returns to Office in Post-War British Politics.” American Political Science Review 103(4): 513-533.  
  29 
Epstein, David L., Robert Bates, Jack Goldstone, Ida Kristensen, Sharyn O’Halloran. 2006. “Democratic Transitions.” American Journal of Political Science 50(3): 551-69. Hugo J. Faria, Hugo M. Montesinos-Yufa, Daniel R. Morales. 2014. “Should the Modernization Hypothesis Survive Acemoglu, Johnson, Robinson, and Yared? Some More Evidence.” Econ Journal Watch 11(1): 17-36. Fearon, James. 2011. “Self-Enforcing Democracy.” Quarterly Journal of Economics 126: 1661-1708. Freedom House. 2014. Freedom in the World Survey, http://www.freedomhouse.org  (last visited November 23rd, 2015). Gandhi, Jennifer. 2008. Political Institutions under Dictatorship. New York: Cambridge University Press. Gandhi, Jennifer, Ellen Lust-Okar. 2009. “Elections under Authoritarianism.” Annual Review of Political Science 12: 403-422. Gassebner, Martin, Michael J. Lamla, James R. Vreeland. 2011. “Extreme Bounds of Democracy.” Journal of Conflict Resolution 57(2): 171-195. Gerring, John, Erzen Oncel, Kevin Morrison. 2014. “The Global Leadership Project: A Comprehensive Database of Political Elites.” Unpublished paper, Department of Political Science, Boston University. Url: http://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2491672_code2293009.pdf?abstractid=2491672&mirid=1  Glaeser, Edward L., Giacomo Ponzetto, Andrei Shleifer. 2007. “Why Does Democracy Need Education?” Journal of Economic Growth 12(2): 77-99. Heid, Benedikt, Julian Langer, Mario Larch. 2012. “Income and democracy: Evidence from system GMM estimates.” Economics Letters 116(2): 166–169. Held, David. 2006. Models of Democracy. Cambridge: Polity Press Honaker, James, Gary King. 2010. “What to Do about Missing Values in Time-Series Cross-Section Data.” American Journal of Political Science 54(2): 561–581. Inglehart, Ronald. 2003. “How Solid is Mass Support for Democracy—And How Do We Measure It?” PS: Political Science and Politics 36(1): 51-57. Inglehart, Ronald, Christian Welzel. 2005. Modernization, Cultural Change, and Democracy. Cambridge: Cambridge University Press.  Jensen, Peter, Mogens Justesen. 2014. “Poverty and Vote Buying: Survey-based Evidence from Africa.” Electoral Studies 33(1): 220-232.  
  30 
Knutsen, Carl Henrik, Tore Wig. 2015. “Government Turnover and the Effects of Regime Type: How Requiring Alternation in Power Biases against the Estimated Economic Benefits of Democracy.” Comparative Political Studies 48(7): 882-914. Kuran, Timur. 1989. “Sparks and Prairie Fires.” Public Choice 61(1): 41-74. Lehoucq, Fabrice. 2003. “Electoral Fraud: Causes, Types, and Consequences.” Annual Review of Political Science 6: 233-56 Lipset, Seymour Martin. 1959. “Some Social Requisites of Democracy: Economic Development and Political Legitimacy.” American Political Science Review 53(1): 69-105. Little, Andrew. 2012.  “Elections, Fraud, and Election Monitoring in the Shadow of Revolution.” Quarterly Journal of Political Science 7(3):249-283. Lohman, Susanne. 1994. “The Dynamics of Informal Cascades.” World Politics 47(1): 42-101. Marshall, Monty, Ted Gurr & Keith Jaggers. 2014. Polity IV project: Dataset users’ manual. Url: http://www.systemicpeace.org/inscr/p4manualv2012.pdf  Medina, Luis Fernando. 2007. A Unified Theory of Collective Action and Social Change. Ann Arbor: University of Michigan Press. Miller, Michael. 2015. “Democratic Pieces: Autocratic Elections and Democratic Development since 1815.” British Journal of Political Science 45(3): 501-30. Moore, Barrington. 1966. Social Origins of Dictatorship and Democracy. Boston: Beacon Press. Moral-Benito, Enrique, Cristian Bartolucci. 2012. Income and Democracy: Revisiting the Evidence. Banco de Espana Working Paper No. 1115. Url: http://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/11/Fich/dt1115e.pdf   Munck, Gerardo. 2016. “What is Democracy? A Reconceptualization of the Quality of Democracy.” Democratization 23(1): 1-26. Nickell, Stephen. 1982. “The Determinants of Occupational Success in Britain.” Review of Economic Studies 49(1): 43-53. Norris, Pippa. 2011. Democratic Deficit. New York: University of Cambridge Press. O’Donnell, Guillermo. 1973. Modernization and Bureaucratic-Authoritarianism: Studies in South American Politics. Berkeley: Institute of International Studies. Palmer, Maxwell, Benjamin Schneer. 2015. “Capitol Gains: The Returns to Elected Office from Corporate Board Directorships.” Journal of Politics (forthcoming).  Pemstein, Daniel, Stephen A. Meserve, James Melton. 2010. “Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type.” Political Analysis 18(4): 426-449. Przeworski, Adam. 2005. “Democracy as an Equilibrium.” Public Choice 123(3/4): 253-273. 
  31 
Przeworski, Adam, Fernando Limongi. 1997. “Modernization: Theories and Facts.” World Politics 49(1): 155-83. Przeworski, Adam, Michael Alvarez, Jose Antonio Cheibub, Fernando Limongi. 2000. Democracy and Development: Political Institutions and Material Well-Being in the World, 1950-1990. New York: Cambridge University Press. Reuveny Raphael, Li Quan. 2003. “The Joint Democracy-Dyadic Conflict Nexus.” International Studies Quarterly 47(3): 325–346. Roodman, David. 2009. “A Note on the Theme of too many Instruments.” Oxford Bulletin of Economics and Statistics 71(1): 135-158. Ross, Michael L. 2001. “Does Oil Hinder Democracy?” World Politics 53(3): 325-361. Rowley, Charles, Robert D. Tollison, Gordon Tullock. 1988. The Political Economy of Rent-Seeking. Springer. Rueschemeyer, Dietrich, Evelyne Huber Stephens, John D. Stephens. 1992. Capitalist Development and Democracy. Chicago: University of Chicago Press.  Schaffer, Frederic C. (ed). 2007. Elections for Sale: The Causes and Consequences of Vote Buying. Lynne Rienner. Schedler, Andreas. 2002. “The Menu of Manipulation.” Journal of Democracy 13(2): 36-50. Schedler, Andreas. 2013. The Politics of Uncertainty: Sustaining and Subverting Electoral Authoritarianism. Oxford: Oxford University Press. Schelling, Thomas. 1960. The Strategy of Conflict. Cambridge: Harvard University Press. Schumpeter, Joseph A. 1942/1950. Capitalism, Socialism and Democracy. New York: Harper & Bros.  Simpser, Alberto. 2013. Why Governments and Parties Manipulate Elections: Theory, Practice, and Implications. Cambridge: Cambridge University Press. Skaaning, Svend-Erik, John Gerring, Henrikas Bartusevi ius. 2015. “A Lexical Index of Electoral Democracy.” Comparative Political Studies 48(12): 1491-1525. Stokes, Susan C., Thad Dunning, Marcelo Nazareno, Valeria Brusco. 2013. Brokers, Voters, and Clientelism: The Puzzle of Distributive Politics. New York: Cambridge University Press. Teorell, Jan. 2010. Determinants of Democratization: Explaining Regime Change in the World, 1972-2006. Cambridge: Cambridge University Press. Thompson, Mark R., Philipp Kuntz. 2009. “More than Just the Final Straw: Stolen Elections as Revolutionary Triggers.” Comparative Politics 41(3): 253-72. Treisman, Daniel. 2015. “Income, Democracy, and Leader Turnover.” American Journal of Political Science 59(4): 927-42- 
  32 
Tucker, Joshua A. 2007. “Enough! Electoral Fraud, Collective Action Problems, and Post-Communist Colored Revolutions.” Perspectives on Politics 5(3): 535-51.  van Ham, Carolien, Staffan Lindberg. 2015. “From Sticks to Carrots: Electoral Manipulation in Africa, 1986–2012.” Government and Opposition 50(2): 521-48. Weingast, Barry M. 1997. «The Political Foundations of Democracy and the Rule of Law.» American Political Science Review 91(2): 245-63.  Weitz-Shapiro, Rebecca. 2013. Curbing Clientelism in Argentina: Politics, Poverty, and Social Policy. Cambridge: Cambridge University Press. Wig, Tore, Espen Rød. 2015. “Cues to Coup Plotters.” Journal of Conflict Resolution (forthcoming).   
  33 
 APPENDIX A:  Data Table A1:  Variable Definitions 
Democracy Indices Polity2 (Polity IV). Measures the extent to which democratic or autocratic “authority patterns” are institutionalized in a given country. It takes into account how the executive is selected, the degree of checks on executive power, and the form of political competition (Marshall et al. 2014). polity2 UDS (Pemstein). A democracy index comprised of multiple indicators and aggregated through a Bayesian IRT measurement model (Pemstein et al. 2010). uds_mean Political Rights (FH). An annual comparative assessment of political rights based on a 1 to 7 scale (Freedom House 2014). fh_pr Civil Liberties (FH). An annual comparative assessment of civil liberties based on a 1 to 7 scale (Freedom House 2014). fh_cl Liberal Component (V-Dem). The liberal principle of democracy emphasizes the importance of protecting individual and minority rights against the tyranny of the state and the tyranny of the majority. The liberal model takes a “negative” view of political power insofar as it judges the quality of democracy by the limits placed on government. This is achieved by constitutionally protected civil liberties, strong rule of law, an independent judiciary, and effective checks and balances that, together, limit the exercise of executive power. This index is formed by averaging the following indices: equality before the law and individual liberties (v2xcl_rol), judicial constraints on the executive (v2x_jucon), and legislative constraints on the executive (v2xlg_legcon). v2x_liberal Participatory Component (V-Dem). The participatory principle of democracy emphasizes active participation by citizens in all political processes, electoral and non-electoral. It is motivated by uneasiness about a bedrock practice of electoral democracy: delegating authority to representatives. Thus, direct rule by citizens is preferred, wherever practicable. This model of democracy thus takes suffrage for granted, emphasizing engagement in civil society organizations, direct democracy, and subnational elected bodies. This index is formed by averaging the following indices: civil society participation (v2x_cspart), direct popular vote (v2xdd_dd), elected local government power (v2xel_locelec), and elected regional government power(v2xel_regelec). v2x_partip Deliberative Component (V-Dem). The deliberative principle of democracy focuses on the process by which decisions are reached in a polity. A deliberative process is one in which public reasoning focused on the common good motivates political decisions—as contrasted with emotional appeals, solidary attachments, parochial interests, or coercion. According to this principle, democracy requires more than an aggregation of existing preferences. There should also be respectful dialogue at all levels—from preference formation to final decision—among informed and competent participants who are open to persuasion. To measure these features of a polity we try to determine the extent to which political elites give public justifications for their positions on matters of public policy, justify their positions in terms of the public good, acknowledge and respect counter-arguments; and how wide the range of consultation is at elite levels. The index is formed by point estimates drawn from a Bayesian factor analysis model including the following indicators: reasoned justification (v2dlreason), common good justification (v2dlcommon), respect for counterarguments (v2dlcountr), range of consultation (v2dlconslt), and engaged society (v2dlengage). v2xdl_delib Egalitarian Component (V-Dem). The egalitarian principle of democracy addresses the distribution of political power across social groups, i.e., groups defined by class, sex, religion, and ethnicity. This perspective on democracy emphasizes that a formal guarantee of political rights and civil liberties are not always sufficient for political equality. Ideally, all social groups should have approximately equal participation, representation, agenda-setting power, protection under the law, and influence over policymaking and policy implementation. If such equality does not exist, the state ought to seek to redistribute socio-economic resources, education, and health so as to enhance political equality. The index is formed by point estimates drawn from a Bayesian factor analysis model including indicators of power distribution according to socioeconomic position (v2pepwrses), power distribution according to social group (v2pepwrsoc), social group equality in respect for civil liberties (v2clsocgrp), equal access to education (v2peedueq), equal access to health (v2pehealth), power distribution according to gender (v2pepwrgen), share of budget allocated to public/common goods (v2dlencmps), and the share of welfare programs that provide universal rather than means-tested benefits (v2dlunivl). v2x_egal Individual Liberty/Rule of Law (V-Dem). To what extent are laws transparent and rigorously enforced and public administration impartial, and to what extent do citizens enjoy access to justice, secure property rights, 
  34 
freedom from forced labor, freedom of movement, physical integrity rights, and freedom of religion? The index is formed by taking the point estimates from a Bayesian factor analysis model of the indicators for rigorous and impartial public administration (v2clrspct), transparent laws with predictable enforcement (v2cltrnslw), access to justice for men/women (v2clacjstm, v2clacjstw), property rights for men/women (v2clprptym, v2clprptyw), freedom from torture (v2cltort), freedom from political killings (v2clkill), from forced labor for men/women (v2clslavem v2clslavef), freedom of religion (v2clrelig), freedom of foreign movement (v2clfmove), and freedom of domestic movement for men/women (v2cldmovem, v2cldmovew). v2xcl_rol Judicial Constraints (V-Dem). To what extent does the executive respect the constitution and comply with court rulings, and to what extent is the judiciary able to act in an independent fashion? The index is formed by taking the point estimates from a Bayesian factor analysis model of the indicators for executive respects constitution (v2exrescon), compliance with judiciary (v2jucomp), compliance with high court (v2juhccomp), high court independence (v2juhcind), and lower court independence (v2juncind). v2x_jucon Legislative Constraints (V-Dem). To what extent is the legislature and government agencies (e.g., comptroller general, general prosecutor, or ombudsman) capable of questioning, investigating, and exercising oversight over the executive? The index is formed by taking the point estimates from a Bayesian factor analysis model of the indicators for legislature questions officials in practice (v2lgqstexp), executive oversight (v2lgotovst), legislature investigates in practice (v2lginvstp), and legislature opposition parties (v2lgoppart). v2xlg_legcon Free Expression (V-Dem). To what extent does government respect press & media freedom, the freedom of ordinary people to discuss political matters at home and in the public sphere, as well as the freedom of academic and cultural expression? The index is formed by taking the point estimates from a Bayesian factor analysis model of the indicators for print/broadcast censorship effort (v2mecenefm), internet censorship effort (v2mecenefi), harassment of journalists (v2meharjrn), media self-censorship (v2meslfcen), freedom of discussion for men/women (v2cldiscm, v2cldiscw) and freedom of academic and cultural expression (v2clacfree). v2x_freexp Alternative Sources of Information (V-Dem). To what extent is the media (a) un-biased in their coverage (or lack of coverage) of the opposition, (b) allowed to be critical of the regime, and (c) representative of a wide array of political perspectives? The index is formed by taking the point estimates from a Bayesian factor analysis model of the indicators for media bias (v2mebias), print/broadcast media critical (v2mecrit), and print/broadcast media perspectives (v2merange). v2xme_altinf Free Association (V-Dem). To what extent are parties, including opposition parties, allowed to form and to participate in elections, and to what extent are civil society organizations able to form and to operate freely? The index is formed by taking the point estimates from a Bayesian factor analysis model of the indicators for party ban (v2psparban), barriers to parties (v2psbars), opposition parties autonomy (v2psoppaut), elections multiparty (v2elmulpar), CSO entry and exit (v2cseeorgs) and CSO repression (v2csreprss). Since the multiparty elections indicator is only observed in election years, its values have first been repeated within election regime periods (as defined by v2x_elecreg). v2x_frassoc_thick Executive Selection (V-Dem). Is the chief executive appointed through popular elections (either directly or indirectly)? There are six different chains of appointment/selection to take into account in constructing this index, all of which are scaled to vary from 0 to 1. First, whether the head of state is directly elected (a=1) or not (a=0). Second, the extent to which the legislature is popularly elected (b), measured as the proportion of legislators elected (if legislature is unicameral), or the weighted average of the proportion elected for each house, with the weight defined by which house is dominant (if legislature is bicameral). Third, whether the head of state is appointed by the legislature, or the approval of the legislature is necessary for the appointment of the head of state (c1=1, otherwise 0).  Fourth, whether the head of government is appointed by the legislature, or the approval of the legislature is necessary for the appointment of the head of government (c2=1, otherwise 0). Fifth, whether the head of government is appointed by the head of state (d=1) or not (d=0). Sixth, whether the head of government is directly elected (e=1) or not (e=0). Define hosw as the weight for the head of state. If the head of state is also head of government (v2exhoshog==1), hosw=1. If the head of state has more power than the head of government over the appointment and dismissal of cabinet ministers, then hosw=1; if the reverse is true, hosw=0. If they share equal power, hosw=.5. Define the weight for the head of government as hogw=1-hosw. v2x_accex Adult Suffrage (V-Dem). What share of adult citizens (as defined by statute) has the legal right to vote in national elections? This question does not take into consideration restrictions based on age, residence, having been convicted for crime, or being legally incompetent. It covers legal (de jure) restrictions, not restrictions that may be operative in practice (de facto). The scores reflect de jure provisions of suffrage extension in percentage of the adult population as of January 1 in a particular year. The adult population (as defined by statute) is defined by citizens in the case of independent countries or the people living in the territorial entity in the case of colonies. Universal suffrage is coded as 100%. Universal male suffrage only is coded as 50%. Years before electoral provisions are introduced are scored 0%. The scores do not reflect whether an electoral regime was interrupted or not. Only if new constitutions, 
  35 
electoral laws, or the like explicitly introduce new regulations of suffrage, the scores were adjusted accordingly if the changes suggested doing so. If qualifying criteria other than gender apply (such as property, tax payments, income, literacy, region, race, ethnicity, religion, and/or ‘economic independence’), estimates have been calculated by combining information on the restrictions with different kinds of statistical information (on population size, age distribution, wealth distribution, literacy rates, size of ethnic groups, etc.), secondary country-specific sources, and – in the case of very poor information – the conditions in similar countries or colonies. v2x_suffr BMR (Boix et al.). Dichotomous democracy measure based on contestation and participation. Countries coded democratic have (1) political leaders that are chosen through free and fair elections and (2) a minimal level of suffrage (Boix, Miller & Rosato, 2013). e_mibmr Lexical (Skaaning et al.). A lexical index of electoral democracy based on six conditions and seven levels: (L0) no elections; (L1) no-party or one-party elections; (L2) multiparty elections for legislature; (L3) multiparty elections for legislature and executive; (L4) minimally competitive, multiparty elections for legislature and executive; (L5) minimally competitive, multiparty elections with full male or female suffrage for legislature and executive; and (L6) minimally competitive, multiparty elections with universal suffrage for legislature and executive (Skaaning et al. 2015). lexical_scale Competitive Elections (Skaaning et al.). An index of electoral competition coded 1 in any situation where the chief executive offices and seats in the effective legislative body are filled by multi-party elections characterized by uncertain outcomes – meaning that the elections are, in principle, sufficiently free to enable the opposition to gain government power (Skaaning et al. 2015). competitive_elections Electoral Contestation (V-Dem). An index of electoral contestation, which combines, through multiplication, measures of Freedom of Assocation (v2x_frassoc_thick), Clean Elections (v2xel_frefair), and Executive Selection (v2x_accex). v2x_contest Clean Elections (V-Dem). To what extent are elections free and fair? Free and fair connotes an absence of registration fraud, systematic irregularities, government intimidation of the opposition, vote buying, and election violence. The index is formed by taking the point estimates from a Bayesian factor analysis model of the indicators for EMB Autonomy (v2elembaut), EMB Capacity (v2elembcap), Election Voter Registry (v2elrgstry), Election Vote Buying (v2elvotbuy), Election Other Voting Irregularities (v2elirreg), Election Government Intimidation (v2elintim), Election Other Electoral Violence (v2elpeace), and Election Free and Fair (v2elfrfair). Since the bulk of these indicators are only observed in election years, the index scores have then been repeated within election regime periods (as defined by v2x_elecreg). v2xel_frefair Components of Clean Elections index Government Intimidation (V-Dem). In this national election, were opposition candidates/parties/campaign workers subjected to repression, intimidation, violence, or harassment by the government, the ruling party, or their agents? Responses: (0) Yes: the  repression and intimidation by the government or its agents was so strong that the entire period was quiet; (1) Yes, frequent:  there was systematic, frequent and violent harassment and intimidation of the opposition by the government or its agents during the election period; (2) Yes, some: there was periodic, not systematic, but possibly centrally coordinated – harassment and intimidation of the opposition by the government or its agents; (3) Restrained: there were sporadic instances of violent harassment and intimidation by the government or its agents, in at least one part of the country, and directed at only one or two local branches of opposition groups; (4) None: there was no harassment or intimidation of opposition by the government or its agents, during the election campaign period and polling day. v2x_elintim Other Violence (V-Dem). In this national election, was the campaign period, election day, and post-election process free from other types (not by the government, the ruling party, or their agents) of violence related to the conduct of the election and the campaigns (but not conducted by the government and its agents)? Responses: (0) No: there was widespread violence between civilians occurring throughout the election period, or in an intense period of more than a week and in large swaths of the country; it resulted in a large number of deaths or displaced refugees; (1) Not really: there were significant levels of violence but not throughout the election period or beyond limited parts of the country; a few people may have died as a result, and some people may have been forced to move temporarily; (2) Somewhat: there were some outbursts of limited violence for a day or two, and only in a small part of the country; the number of injured and otherwise affected was relatively small; (3) Almost: there were only a few instances of isolated violent acts, involving only a few people; no one died and very few were injured; (4) Peaceful: no election-related violence between civilians occurred. v2x_elpeace Vote Buying (V-Dem). In this national election, was there evidence of vote and/or turnout buying? Responses: (0) Yes: there was systematic, widespread, and almost nationwide vote/turnout buying by almost all parties and candidates; (1) Yes, some: there were non-systematic but rather common vote-buying efforts, even if only in some 
  36 
parts of the country or by one or a few parties; (2) Restricted: money and/or personal gifts were distributed by parties or candidates but these offerings were more about meeting an ‘entry-ticket’ expectation and less about actual vote choice or turnout, even if a smaller number of individuals may also be persuaded; (3) Almost none: there was limited use of money and personal gifts, or these attempts were limited to a few small areas of the country; in all, they probably affected less than a few percent of voters; (4) None: there was no evidence of vote/turnout buying. v2x_elvotbuy Other Irregularities (V-Dem). In this national election, was there evidence of other intentional irregularities by incumbent and/or opposition parties, and/or vote fraud? Responses: (0) Yes: there were systematic and almost nationwide other irregularities; (1) Yes, some: there were non-systematic, but rather common other irregularities, even if only in some parts of the country; (2) Sporadic: there were a limited number of sporadic other irregularities, and it is not clear whether they were intentional or disfavored particular groups; (3) Almost none: there were only a limited number of irregularities, and many were probably unintentional or did not disfavor particular groups' access to participation; (4) None: there was no evidence of intentional other irregularities; unintentional irregularities resulting from human error and/or natural conditions may still have occurred. v2x_elirreg Voter Registry (V-Dem). In this national election, was there a reasonably accurate voter registry in place and was it used? Responses: (0) No: there was no registry, or the registry was not used; (1) No: there was a registry but it was fundamentally flawed (meaning 20% or more of eligible voters could have been disenfranchised or the outcome could have been affected significantly by double-voting and impersonation); (2) Uncertain: there was a registry but it is unclear whether potential flaws in the registry had much impact on electoral outcomes; (3) Yes, somewhat: the registry was imperfect but less than 10% of eligible voters may have been disenfranchised, and double-voting and impersonation could not have affected the results significantly; (4) Yes: the voter registry was reasonably accurate (less than 1% of voters were affected by any flaws) and it was applied in a reasonable fashion. v2x_elrgstry EMB Capacity (V-Dem). Does the Election Management Body (EMB) have sufficient staff and resources to administer a well-run national election? Responses: (0) No: there are glaring deficits in staff, financial, or other resources affecting the organization across the territory; (1) Not really: deficits are not glaring but they nonetheless seriously compromised the organization of administratively well-run elections in many parts of the country; (2) Ambiguous: there might be serious deficiencies compromising the organization of the election but it could also be a product of human errors and co-incidence or other factors outside the control of the EMB; (3) Mostly: there are partial deficits in resources but these are neither serious nor widespread; (4) Yes: the EMB has adequate staff and other resources to administer a well-run election. v2elembcap EMB Autonomy (V-Dem). Does the Election Management Body (EMB) have autonomy from government to apply election laws and administrative rules impartially in national elections? Responses: (0) No: the EMB is controlled by the incumbent government, the military, or other de facto ruling body; (1) Somewhat: the EMB has some autonomy on some issues but on critical issues that influence the outcome of elections, the EMB is partial to the de facto ruling body; (2) Ambiguous: the EMB has some autonomy but is also partial, and it is unclear to what extent this influences the outcome of the election; (3) Almost: the EMB has autonomy and acts impartially almost all the time. It may be influenced by the de facto ruling body in some minor ways that do not influence the outcome of elections; (4) Yes: the EMB is autonomous and impartially applies elections laws and administrative rules. v2elembaut Free & Fair (V-Dem). Taking all aspects of the pre-election period, election day, and the post-election process into account, would you consider this national election to be free and fair? Responses: (0) No, not at all: the elections were fundamentally flawed and the official results had little if anything to do with the 'will of the people' (i.e., who became president; or who won the legislative majority); (1) Not really: while the elections allowed for some competition, the irregularities in the end affected the outcome of the election (i.e., who became president; or who won the legislative majority); (2) Ambiguous: there was substantial competition and freedom of participation but there were also significant irregularities; it is hard to determine whether the irregularities affected the outcome or not; (3) Yes, somewhat: there were deficiencies and some degree of fraud and irregularities but these did not in the end affect the outcome; (4) Yes: there was some amount or human error and logistical restrictions but these were largely unintentional and without significant consequences. v2x_elfrfair Causal Factors GDPpc(ln).  Gross domestic product per capita, transformed by the natural logarithm, missing data interpolated within a time-series.  Source: Maddison Project (Bolt & van Zanden 2014). e_migdppcln_ipo Corruption (V-Dem).  Includes indicators of corruption in the executive, the legislature, the judiciary, and the public sector at-large, aggregated with Bayesian factor analysis and then constructed as a historical stock with a 10% annual depreciation rate. v2x_icorr Land Inequality. A measure of land inequality, which combines the urbanization rate (Vanhanen 2003) with the 
  37 
percentage of cultivated land area comprised by family farms (also Vanhanen 2003), according to the formula: (100-[urbanization rate])*(100-[family farms]). land_inequality Diffusion variables. Diffusion of a variable for country X measured as a sum of that variable for all countries except country X, weighted by the distance (in kilometers) between the capital of each country and that of country X. [variable name]_geo Internal Conflict. Coded 1 if the country suffered in an internal armed conflict in a given year, 0 otherwise. The original source codebook (Brecke 2001) states that no war is coded as 0 and war is coded as 1. However, the data contains only 1’s along with missing data (no 0’s). Following the authors’ instructions (personal communication), we re-code missing observations as non-conflict (0) for countries where at least one year in the original times series (which runs from 1500 until present) was coded as 1. Sources: Clio Infra (clio-infra.eu), drawing on Brecke (2001), compiled by V-Dem. conflict_int External Conflict.  Coded 1 if the country participated in an international armed conflict in a given year, 0 otherwise. The original source codebook (Brecke 2001) states that no war is coded as 0 and war is coded as 1. However, the data contains only 1’s along with missing data (no 0’s). Following the authors’ instructions (personal communication), we re-code missing observations as non-conflict (0) for countries where at least one year in the original times series (which runs from 1500 until present) was coded as 1. Sources: Clio Infra (clio-infra.eu), drawing on Brecke (2001), compiled by V-Dem. conflict_ext Natural Resources. Dependence on natural resources, measured by revenues from oil, gas, coal, and metals as a percentage of GDP (Miller 2015). e_resdep2 Urbanization.  Urban population divided by total population. Data on urban population and total population from Clio Infra (clio-infra.eu); missing data within a time-series interpolated using a linear model. urban_clio_ipo   
  38 
Table A2:  Descriptive Statistics 
 Obs. Mean SD Min Max DEMOCRACY INDICATORS      Polity2 (Polity IV) 15,903 0.477 0.352 0 1 UDS (Pemstein) 8,802 0.502 0.232 0 1 Political Rights (FH) 6,986 0.537 0.374 0 1 Civil Liberties (FH) 6,986 0.543 0.326 0 1 Liberal Component (V-Dem) 16,992 0.438 0.280 0.000 0.984 Participatory Component (V-Dem) 20,009 0.240 0.197 0.000 0.828 Deliberative Component (V-Dem) 16,437 0.491 0.298 0.019 0.994 Egalitarian Component (V-Dem) 16,509 0.490 0.295 0.021 0.993 Individual Liberty/Rule of Law (V-Dem) 16,515 0.491 0.290 0.003 0.993 Judicial Constraints (V-Dem) 16,333 0.493 0.290 0.010 0.986 Legislative Constraints (V-Dem) 12,114 0.499 0.300 0.023 0.990 Free Expression (V-Dem)  15,969 0.492 0.296 0.018 0.993 Alternative Sources of Information (V-Dem) 15,986 0.493 0.305 0.033 0.989 Free Association (V-Dem) 16,172 0.495 0.310 0.043 0.976 Executive Selection (V-Dem) 16,358 0.518 0.483 0 1 Adult Suffrage (V-Dem) 16,474 0.639 0.436 0 1 BMR (Boix et al.) 15,739 0.317 0.465 0 1 Lexical (Skaaning et al.) 18,142 0.457 0.391 0 1 Competitive Elections (Skaaning) 18,142 0.347 0.476 0 1 Electoral Contestation (V-Dem) 16,018 0.209 0.299 0 0.957 Clean Elections (V-Dem) 16,317 0.309 0.333 0 0.989 Government Intimidation (V-Dem) 16,325 0.202 0.900 -2.293 3.276 Other Violence (V-Dem) 16,325 0.392 0.756 -2.163 2.615 Vote Buying (V-Dem) 16,325 0.298 0.854 -1.900 2.776 Other Irregularities (V-Dem) 16,325 0.189 0.864 -2.079 2.518 Voter Registry (V-Dem) 16,325 0.257 0.831 -2.233 2.724 EMB Capacity (V-Dem) 16,204 0.136 1.078 -1.742 3.210 EMB Autonomy (V-Dem) 16,210 -0.090 1.138 -1.997 2.864 Free & Fair (V-Dem) 16,317 0.167 0.978 -2.058 2.589 CAUSAL FACTORS      GDPpc (ln) 17,932 7.510 1.011 5.315 10.667 Corruption index 16,403 0.518 0.284 0.014 0.986 Land Inequality 9,764 5,040.182 2,474.755 0 9,603 Internal Conflict 30,753 0.064 0.245 0 1 External Conflict 30,753 0.098 0.297 0 1 Natural Resources 13,541 3.560 9.714 0 100 Urbanization rate 39,879 0.234 0.233 0.002 1 Diffusion variables:          Polity2 (Polity IV) 40,660 0.009 0.011 0.000 0.100     UDS (Pemstein) 11,970 0.020 0.024 0.002 0.260     Political Rights (FH) 7,600 0.029 0.037 0.005 0.312     Civil Liberties (FH) 7,600 0.030 0.036 0.006 0.313     Liberal Component (V-Dem) 21,850 0.017 0.011 0.003 0.078     Participatory Component (V-Dem) 21,850 0.011 0.008 0.001 0.067     Deliberative Component (V-Dem) 21,850 0.018 0.013 0.003 0.107     Egalitarian Component (V-Dem) 21,850 0.018 0.013 0.002 0.081     Individual Liberty/Rule of Law (V-Dem) 21,850 0.018 0.012 0.003 0.084     Judicial Constraints (V-Dem) 21,850 0.018 0.011 0.004 0.075     Legislative Constraints (V-Dem) 21,850 0.014 0.011 0.002 0.096     Free Expression (V-Dem)  21,850 0.018 0.012 0.003 0.089     Alternative Sources of Information (V-Dem) 21,850 0.017 0.012 0.003 0.109     Free Association (V-Dem) 21,850 0.018 0.013 0.002 0.105     Executive Selection (V-Dem) 21,850 0.019 0.016 0.002 0.132     Adult Suffrage (V-Dem) 21,850 0.023 0.020 0.001 0.137     BMR (Boix et al.) 39,472 0.007 0.017 0.000 0.311     Lexical (Skaaning et al.) 40,660 0.011 0.022 0.000 0.315     Competitive Elections (Skaaning) 40,630 0.009 0.020 0.000 0.313 
  39 
    Electoral Contestation (V-Dem) 21,850 0.008 0.008 0.000 0.053     Clean Elections (V-Dem) 21,850 0.012 0.010 0.000 0.065  Democracy indices are normalized to 0-1, where 1=most democratic.                       
  40 
APPENDIX B:  Robustness Tests Table B1:  Liberal Component (V-Dem) Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.003 0.074*** 0.048** 0.004 0.098***  0.018* 0.037*** 0.005**  (0.002) (0.023) (0.024) (0.004) (0.027)  (0.011) (0.010) (0.002) GDPpc (ln)      0.082**      L20      (0.036)    Lagged Y 0.942***   0.935***   0.676*** 0.780*** 0.804***  (0.005)   (0.009)   (0.025) (0.046) (0.021) Trend   0.002***          (0.000)       Corruption    -0.014* -0.576***            (0.008) (0.111)     Land     -0.000 -0.000***        Inequality    (0.000) (0.000)     Diffusion    0.217 4.742         (0.387) (2.989)     Internal     -0.000 -0.019        Conflict    (0.003) (0.015)     External     -0.001 -0.033**        Conflict    (0.003) (0.016)     Natural     0.000 -0.001        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 154 154 154 132 132 154 154 154 205 Years 111 112 112 99 99 115 22 22 114 Obs 11571 11664 11664 6752 6752 10617 2288 2288 21143 R2 (within) 0.920 0.288 0.187 0.900 0.384 0.301 0.616  0.905  Outcome: Liberal Component index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.     
  41 
Table B2:  Participatory Component (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full Y>0 5-year 5-year MI  1 2 3 4 5 6 7 8 9 10 GDPpc  -0.000 0.011 -0.001 0.001 0.058***  0.000 0.001 0.018** 0.000   (ln) (0.001) (0.014) (0.014) (0.002) (0.019)  (0.001) (0.006) (0.008) (0.001) GDPpc       0.024       (ln) L20      (0.021)     Lagged Y 0.957***   0.947***   0.956*** 0.739*** 0.812*** 0.890***  (0.004)   (0.007)   (0.004) (0.024) (0.048) (0.013) Trend   0.003***           (0.000)        Corruption    -0.008 -0.246***             (0.005) (0.069)      Land     -0.000* -0.000***         Inequality    (0.000) (0.000)      Diffusion    0.047 3.371          (0.234) (2.760)      Internal     0.002 0.000         Conflict    (0.002) (0.008)      External     -0.000 -0.023*         Conflict    (0.002) (0.013)      Natural     -0.000 -0.001         Resources    (0.000) (0.000)      Country FE ü ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü ü Countries 159 159 159 132 132 160 155 159 159 205 Years 111 112 112 99 99 115 111 22 22 114 Obs 11998 12095 12095 6751 6751 10997 11545 2370 2370 21143 R2 (within) 0.952 0.483 0.402 0.931 0.476 0.479 0.953 0.758  0.959  Outcome: Participatory Component index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), Y>0 (scores for Participatory Component that surpass 0), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.     
  42 
Table B3:  Deliberative Component (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.001 0.056** 0.020 0.006 0.115***  0.011 0.034*** 0.003  (0.003) (0.027) (0.029) (0.005) (0.038)  (0.012) (0.010) (0.003) GDPpc (ln)      0.058      L20      (0.045)    Lagged Y 0.943***   0.928***   0.668*** 0.767*** 0.798***  (0.004)   (0.007)   (0.023) (0.039) (0.020) Trend   0.004***          (0.001)       Corruption    -0.018* -0.688***            (0.010) (0.142)     Land     -0.000* -0.000***        Inequality    (0.000) (0.000)     Diffusion    0.170 4.893**         (0.280) (2.263)     Internal     0.004 -0.009        Conflict    (0.004) (0.021)     External     -0.000 -0.033        Conflict    (0.004) (0.022)     Natural     -0.000 -0.001        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 11449 11543 11543 6751 6751 10524 2262 2262 21143 R2 (within) 0.930 0.361 0.274 0.901 0.396 0.363 0.654  0.864  Outcome: Deliberative Component index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.     
  43 
Table B4:  Egalitarian Component (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) -0.001 0.012 0.022 0.000 0.006  -0.001 0.007 0.001  (0.001) (0.022) (0.020) (0.002) (0.028)  (0.007) (0.005) (0.003) GDPpc (ln)      0.006      L20      (0.031)    Lagged Y 0.962***   0.963***   0.776*** 0.947*** 0.722***  (0.003)   (0.005)   (0.016) (0.022) (0.027) Trend   0.005***          (0.000)       Corruption    -0.008* -0.388***            (0.004) (0.070)     Land     -0.000 -0.000        Inequality    (0.000) (0.000)     Diffusion    -0.246* 0.261         (0.143) (2.234)     Internal     0.005** 0.002        Conflict    (0.002) (0.011)     External     0.002 -0.019        Conflict    (0.002) (0.014)     Natural     0.000 0.001        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 11447 11541 11541 6749 6750 10522 2261 2261 21143 R2 (within) 0.972 0.611 0.595 0.970 0.686 0.631 0.849  0.878  Outcome: Egalitarian Component index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.    
  44 
Table B5:  Individual Liberty/Rule of Law (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) -0.001 0.059** 0.036 0.000 0.099***  0.005 0.021** 0.003  (0.002) (0.025) (0.026) (0.003) (0.029)  (0.010) (0.009) (0.002) GDPpc (ln)      0.068*      L20      (0.040)    Lagged Y 0.961***   0.952***   0.738*** 0.873*** 0.799***  (0.003)   (0.007)   (0.021) (0.044) (0.022) Trend   0.003***          (0.001)       Corruption    -0.004 -0.540***            (0.008) (0.129)     Land     0.000 -0.000***        Inequality    (0.000) (0.000)     Diffusion    0.145 5.522*         (0.278) (3.196)     Internal     0.002 -0.050***        Conflict    (0.003) (0.015)     External     -0.001 -0.035**        Conflict    (0.003) (0.016)     Natural     0.000 -0.001        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 11449 11543 11543 6751 6751 10524 2262 2262 21143 R2 (within) 0.944 0.324 0.230 0.915 0.380 0.327 0.690  0.893  Outcome: Individual Liberty/Rule of Law index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.    
  45 
Table B6:  Judicial Constraints (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.004* 0.091*** 0.071*** 0.005 0.089***  0.020** 0.031*** 0.007***  (0.002) (0.022) (0.021) (0.003) (0.025)  (0.009) (0.011) (0.002) GDPpc (ln)      0.099***      L20      (0.033)    Lagged Y 0.956***   0.934***   0.753*** 0.918*** 0.737***  (0.006)   (0.011)   (0.023) (0.041) (0.031) Trend   -0.000          (0.000)       Corruption    -0.016** -0.521***            (0.008) (0.095)     Land     -0.000** -0.000***        Inequality    (0.000) (0.000)     Diffusion    0.184 3.192         (0.238) (2.179)     Internal     0.001 -0.006        Conflict    (0.003) (0.012)     External     -0.002 -0.028*        Conflict    (0.003) (0.016)     Natural     -0.000 -0.001        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 11429 11524 11524 6751 6751 10524 2258 2258 21143 R2 (within) 0.916 0.139 0.0801 0.887 0.304 0.154 0.606  0.894  Outcome: Judicial Constraints index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.   
  46 
Table B7:  Legislative Constraints (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.004 0.065* 0.027 0.006 0.082**  0.013 0.021* 0.012***  (0.003) (0.033) (0.032) (0.005) (0.040)  (0.013) (0.013) (0.003) GDPpc (ln)      0.112**      L20      (0.045)    Lagged Y 0.960***   0.956***   0.772*** 0.915*** 0.701***  (0.004)   (0.006)   (0.022) (0.031) (0.025) Trend   0.002***          (0.001)       Corruption    -0.015 -0.626***            (0.011) (0.148)     Land     0.000 -0.000***        Inequality    (0.000) (0.000)     Diffusion    0.118 5.846*         (0.381) (3.448)     Internal     -0.001 0.014        Conflict    (0.004) (0.023)     External     -0.001 -0.039*        Conflict    (0.003) (0.021)     Natural     -0.000 -0.001        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 9551 9839 9839 5834 5969 9133 1801 1801 21143 R2 (within) 0.940 0.253 0.154 0.927 0.359 0.284 0.694  0.814  Outcome: Legislative Constraints index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.   
  47 
Table B8:  Free Expression (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.001 0.077*** 0.031 0.003 0.145***  0.013 0.036*** 0.007***  (0.002) (0.029) (0.033) (0.005) (0.042)  (0.012) (0.010) (0.002) GDPpc (ln)      0.090*      L20      (0.049)    Lagged Y 0.958***   0.948***   0.717*** 0.821*** 0.802***  (0.004)   (0.006)   (0.024) (0.043) (0.021) Trend   0.002***          (0.001)       Corruption    0.000 -0.577***            (0.007) (0.159)     Land     -0.000 -0.000***        Inequality    (0.000) (0.000)     Diffusion    0.076 5.998*         (0.290) (3.275)     Internal     0.002 -0.035*        Conflict    (0.004) (0.020)     External     -0.001 -0.054**        Conflict    (0.004) (0.021)     Natural     -0.000 -0.003**        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 11244 11339 11339 6601 6605 10340 2221 2221 21143 R2 (within) 0.939 0.292 0.127 0.915 0.348 0.305 0.657  0.864  Outcome: Free Expression index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.  
  48 
Table B9:  Alternative Sources of Information (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) -0.001 0.020 -0.023 0.003 0.120***  -0.000 0.028*** 0.005*  (0.002) (0.029) (0.032) (0.004) (0.040)  (0.012) (0.010) (0.003) GDPpc (ln)      0.039      L20      (0.049)    Lagged Y 0.955***   0.945***   0.724*** 0.812*** 0.789***  (0.004)   (0.006)   (0.026) (0.050) (0.022) Trend   0.003***          (0.001)       Corruption    0.004 -0.449***            (0.007) (0.154)     Land     -0.000 -0.000***        Inequality    (0.000) (0.000)     Diffusion    0.309 6.712**         (0.321) (2.781)     Internal     0.005 0.012        Conflict    (0.004) (0.018)     External     -0.001 -0.043**        Conflict    (0.004) (0.020)     Natural     -0.000 -0.003**        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 11244 11339 11339 6601 6605 10340 2221 2221 21143 R2 (within) 0.938 0.325 0.154 0.915 0.341 0.331 0.678  0.869  Outcome: Alternative Sources of Information index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.  
  49 
Table B10:  Free Association (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.001 0.044 -0.008 0.003 0.102**  0.010 0.031*** 0.007**  (0.003) (0.030) (0.033) (0.005) (0.043)  (0.014) (0.009) (0.003) GDPpc (ln)      0.063      L20      (0.050)    Lagged Y 0.951***   0.938***   0.673*** 0.730*** 0.800***  (0.005)   (0.007)   (0.028) (0.059) (0.020) Trend   0.003***          (0.001)       Corruption    0.004 -0.533***            (0.009) (0.138)     Land     -0.000** -0.000***        Inequality    (0.000) (0.000)     Diffusion    -0.093 4.624*         (0.296) (2.699)     Internal     0.005 0.009        Conflict    (0.004) (0.020)     External     -0.003 -0.022        Conflict    (0.005) (0.022)     Natural     -0.000 -0.001        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 11226 11330 11330 6585 6605 10338 2202 2202 21143 R2 (within) 0.932 0.315 0.131 0.907 0.346 0.346 0.627  0.870  Outcome: Free Association index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.  
  50 
Table B11:  Executive Selection (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.006 0.033 0.019 0.043*** 0.223***  -0.001 0.051** 0.007  (0.007) (0.042) (0.039) (0.015) (0.061)  (0.026) (0.022) (0.005) GDPpc (ln)      0.041      L20      (0.065)    Lagged Y 0.849***   0.800***   0.476*** 0.466*** 0.757***  (0.009)   (0.015)   (0.029) (0.039) (0.017) Trend   0.005***          (0.001)       Corruption    -0.059 -0.455**            (0.041) (0.175)     Land     -0.000 -0.000        Inequality    (0.000) (0.000)     Diffusion    0.256 3.436*         (0.688) (1.898)     Internal     -0.027** -0.042        Conflict    (0.013) (0.031)     External     -0.012 -0.040        Conflict    (0.010) (0.034)     Natural     0.000 0.001        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 11295 11402 11402 6716 6717 10394 2226 2226 21143 R2 (within) 0.778 0.189 0.169 0.690 0.134 0.195 0.376  0.785  Outcome: Executive Selection index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.  
  51 
Table B12:  Adult Suffrage (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) -0.007** -0.111*** -0.067** -0.009** -0.074*  -0.025** 0.001 -0.010**  (0.003) (0.030) (0.030) (0.004) (0.039)  (0.012) (0.009) (0.004) GDPpc (ln)      -0.087**      L20      (0.039)    Lagged Y 0.922***   0.918***   0.664*** 0.736*** 0.776***  (0.005)   (0.009)   (0.017) (0.028) (0.018) Trend   0.009***          (0.001)       Corruption    -0.015** -0.220**            (0.007) (0.093)     Land     -0.000 -0.000*        Inequality    (0.000) (0.000)     Diffusion    0.042 1.181         (0.203) (2.252)     Internal     0.006 0.024        Conflict    (0.004) (0.020)     External     -0.000 -0.005        Conflict    (0.004) (0.020)     Natural     0.000** 0.001*        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 152 152 152 132 132 153 152 152 205 Years 111 112 112 99 99 115 22 22 114 Obs 11438 11532 11532 6750 6750 10513 2260 2260 21143 R2 (within) 0.944 0.579 0.520 0.948 0.623 0.623 0.780  0.842  Outcome: Mass Suffrage index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.  
  52 
Table B13:  Polity2 (Polity IV) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.002 0.071** 0.021 0.008 0.094**  0.016 0.064*** 0.009***  (0.003) (0.032) (0.028) (0.006) (0.037)  (0.014) (0.013) (0.003) GDPpc (ln)      0.098**      L20      (0.039)    Lagged Y 0.928***   0.893***   0.666*** 0.663*** 0.732***  (0.006)   (0.010)   (0.029) (0.050) (0.022) Trend   0.003***          (0.001)       Corruption                       Land     -0.019 -0.536***        Inequality    (0.016) (0.136)     Diffusion    -0.000*** -0.000***         (0.000) (0.000)     Internal     0.083 5.139**        Conflict    (0.442) (2.347)     External     0.011** 0.037*        Conflict    (0.005) (0.021)     Natural     -0.009 -0.028        Resources    (0.006) (0.026)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 155 155 155 132 132 156 154 154 216 Years 211 211 211 99 99 193 42 42 213 Obs 12676 12823 12823 6647 6666 11854 2465 2465 23445 R2 (within) 0.912 0.354 0.275 0.845 0.282 0.355 0.655  0.798  Outcome: Polity2 index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.  
  53 
Table B14:  UDS (Pemstein) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.001 0.033* 0.003 0.003 0.026  0.012 0.054*** 0.013***  (0.002) (0.017) (0.016) (0.004) (0.023)  (0.011) (0.010) (0.003) GDPpc (ln)      0.025      L20      (0.026)    Lagged Y 0.892***   0.869***   0.523*** 0.638*** 0.513***  (0.009)   (0.012)   (0.044) (0.075) (0.022) Trend   0.003***          (0.000)       Corruption    -0.022* -0.334***            (0.012) (0.081)     Land     0.000 -0.000        Inequality    (0.000) (0.000)     Diffusion    0.378 4.417**         (0.317) (2.221)     Internal     0.000 -0.009        Conflict    (0.003) (0.010)     External     -0.004 0.001        Conflict    (0.003) (0.016)     Natural     -0.000 -0.000        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 156 156 156 131 131 155 155 155 205 Years 62 63 63 53 53 63 11 11 114 Obs 7390 7538 7538 4840 4846 6883 1296 1296 21143 R2 (within) 0.862 0.282 0.216 0.829 0.322 0.309 0.502  0.755  Outcome: UDS index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.   
  54 
Table B15:  Political Rights (FH) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) -0.004 0.008 -0.009 -0.022 -0.064  -0.002 0.092*** 0.026***  (0.006) (0.033) (0.031) (0.014) (0.050)  (0.021) (0.020) (0.005) GDPpc (ln)      -0.021      L20      (0.036)    Lagged Y 0.849***   0.797***   0.436*** 0.652*** 0.481***  (0.013)   (0.021)   (0.042) (0.064) (0.018) Trend   0.006***          (0.001)       Corruption    -0.067* -0.383**            (0.035) (0.173)     Land     -0.000 -0.000        Inequality    (0.000) (0.000)     Diffusion    0.186 0.661         (0.583) (2.660)     Internal     -0.009 -0.057**        Conflict    (0.008) (0.028)     External     -0.005 0.024        Conflict    (0.012) (0.033)     Natural     0.001*** 0.003***        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 157 157 157 132 132 157 155 155 205 Years 37 39 39 25 25 40 7 7 114 Obs 5247 5540 5540 2746 2749 5733 994 994 21143 R2 (within) 0.774 0.137 0.125 0.695 0.170 0.139 0.297  0.666  Outcome: Political Rights index, inverted scale.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.   
  55 
Table B16:  Civil Liberties (FH) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.002 0.028 0.030 -0.018 -0.025  0.012 0.053*** 0.022***  (0.005) (0.027) (0.025) (0.012) (0.043)  (0.017) (0.015) (0.004) GDPpc (ln)      0.012      L20      (0.031)    Lagged Y 0.845***   0.791***   0.468*** 0.673*** 0.415***  (0.012)   (0.018)   (0.035) (0.047) (0.019) Trend   0.005***          (0.001)       Corruption    -0.014 -0.215            (0.023) (0.138)     Land     -0.000 -0.000        Inequality    (0.000) (0.000)     Diffusion    0.124 2.677         (0.631) (2.410)     Internal     -0.006 -0.060***        Conflict    (0.007) (0.021)     External     -0.001 -0.020        Conflict    (0.011) (0.031)     Natural     0.001*** 0.002        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 157 157 157 132 132 157 155 155 205 Years 37 39 39 25 25 40 7 7 114 Obs 5247 5540 5540 2746 2749 5733 994 994 21143 R2 (within) 0.788 0.179 0.154 0.685 0.126 0.182 0.416  0.663  Outcome: Civil Liberties index, inverted scale.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.  
  56 
Table B17:  BMR (Boix et al.) 
Estimator OLS OLS OLS OLS OLS OLS Logit OLS Sample Full Full Full Full Full Full 5-year MI  1 2 3 4 5 6 7 8 GDPpc (ln) 0.007 0.109*** 0.084** 0.012 0.139**  1.400*** 0.046***  (0.005) (0.041) (0.041) (0.010) (0.054)  (0.439) (0.008) GDPpc (ln)      0.175***     L20      (0.051)   Lagged Y 0.904***   0.869***   2.268*** 0.507***  (0.007)   (0.010)   (0.219) (0.029) Trend   0.003***         (0.001)      Corruption    -0.068*** -0.821***           (0.025) (0.178)    Land     -0.000* -0.000***       Inequality    (0.000) (0.000)    Diffusion    1.237 9.517**        (0.749) (4.598)    Internal     0.008 0.015       Conflict    (0.008) (0.029)    External     -0.006 -0.034       Conflict    (0.006) (0.032)    Natural     0.000 0.000       Resources    (0.000) (0.001)    Country FE ü ü ü ü ü ü ü ü Year FE ü ü  ü ü ü ü ü Countries 156 156 156 132 132 155 76 216 Years 207 207 207 99 99 187 41 213 Obs 12232 12351 12351 6735 6737 11010 1550 23445 R2 (within) 0.873 0.312 0.279 0.805 0.255 0.322  0.578  Outcome: BMR index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.   
  57 
Table B18:  Lexical (Skaaning) 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 GDPpc (ln) 0.010** 0.104*** 0.064** 0.016* 0.124***  0.040** 0.097*** 0.009***  (0.005) (0.027) (0.025) (0.009) (0.037)  (0.017) (0.019) (0.003) GDPpc (ln)      0.079**      L20      (0.037)    Lagged Y 0.849***   0.814***   0.479*** 0.442*** 0.715***  (0.010)   (0.014)   (0.037) (0.064) (0.017) Trend   0.003***          (0.000)       Corruption    -0.069** -0.625***            (0.028) (0.142)     Land     -0.000*** -0.000***        Inequality    (0.000) (0.000)     Diffusion    1.144* 6.447**         (0.665) (2.915)     Internal     0.006 -0.015        Conflict    (0.009) (0.028)     External     -0.003 -0.015        Conflict    (0.007) (0.024)     Natural     -0.000 0.000        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 157 157 157 132 132 158 156 156 216 Years 211 211 211 99 99 193 42 42 213 Obs 12947 13081 13081 6683 6695 12053 2509 2509 23445 R2 (within) 0.825 0.378 0.305 0.740 0.266 0.374 0.523  0.799  Outcome: Lexical index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.   
  58 
Table B19:  Electoral Contestation (V-Dem) 
Estimator OLS OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full Y>0 5-year 5-year MI  1 2 3 4 5 6 7 8 9 10 GDPpc (ln) 0.007** 0.095*** 0.069*** 0.014*** 0.147***  0.007** 0.025* 0.061*** 0.007***  (0.003) (0.022) (0.022) (0.005) (0.026)  (0.003) (0.013) (0.014) (0.003) GDPpc (ln)      0.110***       L20      (0.033)     Lagged Y 0.912***   0.893***   0.956*** 0.640*** 0.689*** 0.777***  (0.008)   (0.010)   (0.006) (0.030) (0.056) (0.021) Trend   0.003***           (0.000)        Corruption    -0.053*** -0.589***             (0.015) (0.112)      Land     -0.000** -0.000***         Inequality    (0.000) (0.000)      Diffusion    0.401 6.131*          (0.492) (3.600)      Internal     -0.001 -0.010         Conflict    (0.004) (0.013)      External     -0.003 -0.037**         Conflict    (0.004) (0.018)      Natural     -0.000* -0.002**         Resources    (0.000) (0.001)      Country FE ü ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü ü Countries 152 152 152 132 132 153 144 152 152 205 Years 111 112 112 99 99 115 111 22 22 114 Obs 11076 11193 11193 6551 6572 10212 7146 2168 2168 21143 R2 (within) 0.900 0.395 0.338 0.884 0.465 0.411 0.915 0.643  0.875  Outcome: Electoral Contestation index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), Y>0 (scores for Electoral Contestation that surpass 0), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.   
  59 
Table B20:  Urbanization and Competitive Elections 
Estimator OLS OLS OLS OLS OLS OLS Logit OLS Sample Full Full Full Full Full Full 5-year MI  1 2 3 4 5 6 7 8 Urbaniz 0.077*** 0.711*** 0.712*** 0.040 0.284  1.424 0.195***  (0.026) (0.202) (0.164) (0.067) (0.326)  (1.767) (0.050) Urbaniz      0.648***      L20      (0.218)   Lagged Y 0.892***   0.843***   2.243*** 0.577***  (0.008)   (0.012)   (0.198) (0.031) Trend   0.001***         (0.000)      Corruption    -0.090*** -0.797***           (0.031) (0.184)    Land     -0.000 -0.000       Inequality    (0.000) (0.000)    Diffusion    1.959** 9.613**        (0.899) (4.453)    Internal     0.006 -0.023       Conflict    (0.010) (0.034)    External     -0.007 -0.050       Conflict    (0.007) (0.035)    Natural     0.000 0.001       Resources    (0.000) (0.002)    Country FE ü ü ü ü ü ü ü ü Year FE ü ü  ü ü ü ü ü Countries 188 188 188 135 135 188 95 213 Years 211 211 211 99 99 193 42 216 Obs 16165 16357 16357 7087 7101 16161 2063 23445 R2 (within) 0.850 0.288 0.253 0.765 0.222 0.282  0.669  Outcome: Competitive Elections index.  Estimators: OLS (ordinary least squares), logit (conditional logit), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.  
  60 
Table B21:  Urbanization and Clean Elections 
Estimator OLS OLS OLS OLS OLS OLS OLS GMM OLS Sample Full Full Full Full Full Full 5-year 5-year MI  1 2 3 4 5 6 7 8 9 Urbaniz 0.034** 0.285** 0.305** 0.082*** 0.387**  0.155*** 0.265*** 0.062***  (0.016) (0.128) (0.124) (0.030) (0.165)  (0.058) (0.058) (0.020) Urbaniz      0.275**       L20      (0.127)    Lagged Y 0.897***   0.841***   0.636*** 0.622*** 0.742***  (0.009)   (0.015)   (0.031) (0.061) (0.022) Trend   0.003***          (0.000)       Corruption    -0.106*** -0.730***            (0.022) (0.122)     Land     -0.000 -0.000        Inequality    (0.000) (0.000)     Diffusion    0.632 4.357         (0.486) (2.829)     Internal     -0.001 -0.013        Conflict    (0.005) (0.015)     External     -0.002 -0.035*        Conflict    (0.004) (0.018)     Natural     -0.000 -0.000        Resources    (0.000) (0.001)     Country FE ü ü ü ü ü ü ü  ü Year FE ü ü  ü ü ü ü ü ü Countries 160 160 160 135 135 160 160 160 205 Years 111 112 112 99 99 115 22 22 114 Obs 15011 15193 15193 7061 7081 15530 2926 2926 21143 R2 (within) 0.873 0.354 0.320 0.820 0.401 0.365 0.611  0.856  Outcome: Clean Elections index.  Estimators: OLS (ordinary least squares), GMM (generalized method of moments), standard errors clustered by country.  *.1, **.05, ***.01 (two-sided tests).  Sample: Full (all available data), 5-year (data aggregated at 5-year intervals, after constructing 5-year moving averages), MI (missing data imputed with the Amelia multiple imputation algorithm).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.   
  61 
Table B22:  Competitive Elections (logit models) 
Sample Full Full Full Full Full 5-year Full  1 2 3 4 5 6 7 GDPpc (ln) 0.945*** 1.749*** 0.194 1.691*** 2.649*** 1.682***   (0.334) (0.463) (0.383) (0.428) (0.611) (0.508)  GDPpc (ln),        2.263***    L20       (0.705) Lagged Y 6.338***   5.900***  2.345***   (0.252)   (0.358)  (0.269)  Trend   0.054***        (0.010)     Corruption    -5.131*** -11.125***          (1.272) (2.264)   Land     -0.000 -0.000      Inequality    (0.000) (0.000)   Diffusion    106.088*** 141.724**       (40.382) (67.440)   Internal     0.397 -0.034      Conflict    (0.414) (0.446)   External     -0.571 -0.854*      Conflict    (0.503) (0.502)   Natural     0.028 0.004      Resources    (0.019) (0.044)   Country FE ü ü ü ü ü ü ü Year FE ü ü  ü ü ü ü Countries 86 87 89 60 60 78 82 Years 152 152 211 99 99 31 154 Obs 7351 7434 8831 3842 3848 1370 6910 R2 (pseudo) 0.827 0.519 0.502 0.802 0.559 0.562 0.529 Log likelihood -839.2 -2363 -2857 -517.4 -1154 -396.6 -2198  Outcome: Competitive Elections index.  Logistic regression, standard errors clustered by country. *.1, **.05, ***.01 (two-sided tests).  Units of analysis: country-years, unless otherwise noted. Right-side variables measured at T-1.