World-system position and democracy,1972–2008

Internal and external dynamics

Arguably, the global spread of democracy could be explained in purely functional terms, as democratic principles seem appealing to a broad collection of interests. ‘An increasingly democratic world appears to offer better prospects for peace, human rights, geopolitical stability, and increased trade and economic development’ (O’Loughlin et al., 1998: 546). Nevertheless, the spatial and temporal clustering of democratic transitions (O’Loughlin et al., 1998; Schwartzman, 1998; Wejnert, 2005) suggests that other factors are at play. Scholars generally converge around two sets of explanations for understanding democratic growth, emphasizing either a) internal dynamics involving socio-economic development and income distribution, or b) external dynamics involving cross-national diffusion via socialization, exchange, and proximity.

I posit that a country’s world-system position may influence democratic growth, at least in part, through these internal and external dynamics. Given that core nations feature higher levels of economic development and industrial sophistication (Mahutga and Smith, 2011), lower levels of income inequality (Mahutga et al., 2011), and greater integration in a variety of cross-national networks (e.g. economic, political, and cultural) (see Kick, 1987; Snyder and Kick, 1979; Van Rossem, 1996), core nations should experience greater democratic growth than semiperipheral and peripheral nations via these mediating factors. In fact, core nations not only feature higher levels of economic development, but world-system position significantly affects subsequent economic growth (Clark, 2010; Clark and Beckfield, 2009), thereby causing core and peripheral economies to diverge even further apart from one another. Thus, core nations not only enjoy the internal characteristics that make them better equipped to initiate and/or sustain democratic transitions, but they are also embedded in cross-national networks that expose them more heavily to democratic models. In this way, a country’s status or position in the world-system functions as a composite measure that holistically captures these internal and external dynamics. But how do these dynamics influence democratization?

Research that examines internal mechanisms typically focus on the role that both socio-economic development and differentiation may play in promoting or hindering democratic growth. Dahl’s (1971) model of democracy (or ‘polyarchy’) is largely captured by these dual factors, proposing that the emergence of democracy is made more likely by several conditions, including 1) high economic development and the prevalence of certain cultural values (e.g. belief in democratic principles, spirit of trust and compromise), as well as 2) low levels of economic inequality and ethnic/religious pluralism.

However, explanations related to socio-economic development typically garner more attention. Most notably, Lipset (1959) proposed that economic growth, industrialization, urbanization, and advances in literacy or schooling can serve as important precursors for the emergence of a stable democracy. From this perspective, economic modernization is thought to generate a diverse set of powerful groups that eventually make political demands. In particular, socio-economic development produces a large and educated middle class, oftentimes densely populated in urban centers, that is capable of organizing and challenging elites. In addition, economic prosperity ushers in a set of ‘post-materialist’ values that are conducive to the formation of democratic principles, such as self-expression, individual autonomy, political participation, tolerance, and trust (Inglehart, 1997; Inglehart and Welzel, 2005).

Development-based explanations attained wide appeal over the years, as studies consistently found support for Lipset’s (1959) idea that socio-economic development promotes (or is associated with) democracy (Barro, 1999; Bollen, 1979, 1983, 1995; Bollen and Jackman, 1985; Burkhart, 1997; Burkhart and Lewis-Beck, 1994; Crenshaw, 1995; Cutright, 1963; Cutright and Wiley, 1969; Diamond, 1992; Gonick and Rosh, 1988; Li and Reuveny, 2003; Pevehouse, 2002b; Russett, 1965). By contrast, others question the causal efficacy of development, arguing that it is a necessary, but not sufficient, condition for democratization (Rueschemeyer et al., 1992), or that democracies are simply more likely to survive in wealthier nations, but that they do not emerge as a result of economic growth (Przeworski et al., 2000). Moreover, by some accounts, economic modernization does not guarantee democracy, but instead depends on the strength of various class groups (Moore, 1966; Stephens, 1989) during the shift to industrial production. In short, while socio-economic development is widely viewed as an important factor in understanding democratic growth, others are quick to nuance this relationship or stress other internal factors.

Indeed, a number of scholars suggest that a country’s level of stratification also shapes its political development. Theoretically, an unequal distribution of income hinders democratic growth because elites must resort to repressive measures to maintain social order. Moreover, in highly unequal societies, non-elites lack the economic resources to effectively engage in political action (e.g. organize the citizenry, communicate effectively through mass media). Surprisingly, though, previous studies examining the relationship between democracy and income inequality produced mixed results. A number of studies showed that income inequality negatively affects democratic growth (Barro, 1999; Boix, 2003; Li and Reuveny, 2003; Muller, 1995a, 1995b), or that democracy and income inequality negatively affect one another (Muller, 1988; Rubinson and Quinlan, 1977). However, most of this work does not consider much of the post-1990 era when levels of inequality and democracy rose together throughout much of the world, especially in Eastern Europe. Moreover, other work finds that income inequality exerts no significant impact on democracy (Bollen and Jackman, 1995; Crenshaw, 1995), or that there is no significant relationship between the two measures in either direction (Bollen and Jackman, 1985). Ultimately, though, there is reason to suspect that domestic characteristics, such as socio-economic development and income inequality, may mediate much of the relationship between a country’s world-system position and its democratic growth.

Another way in which world-system position may indirectly impact political development is through external dynamics. Given that core nations are more deeply integrated in international networks than non-core states, measures that capture various forms of cross-national diffusion may mediate some of the effect of world-system position. Studies that incorporate external dynamics are becoming quite popular. In fact, when taking into account the cross-national diffusion of democratic models, the effects of social-economic development can attenuate substantially (Wejnert, 2005). Diffusion-based arguments emphasize the importance of a range of factors, including geographic proximity, cross-national communication, trade relations, international organization ties, colonial history, and hegemonic influence.

In one line of research, scholars examine whether cross-national economic exchange impacts democratization. Theoretically, trade and foreign investment promote democracy by a) strengthening domestic institutions that foster democratic growth, such as impartial courts and bureaucracies that support the rule of law, transparency, property rights, as well as civil and human rights, and b) exposing countries to democratic partners and other actors in the international community, thereby enhancing the flow of information and ideas that emanate from global civil society (Li and Reuveny, 2003). However, previous work examining the impact of trade and capital flows on democracy produced inconsistent results, reporting effects that are positive, mixed, or conditional (Balaev, 2009; Li and Reuveny, 2003; Rudra, 2005).

A more popular idea is that democratic principles spread worldwide through international governmental organizations (IGOs). Research covering the previous two centuries shows that countries tied to democratic partners through IGO memberships are more likely themselves to democratize (Torfason and Ingram, 2010; Wejnert, 2005). In some cases, international organizations positively affect the transition to, and longevity of, new democracies by pressuring regime leaders to institute political reforms, which, in turn, enhances their legitimacy (Pevehouse, 2002a, 2002b; Simensen, 1999). Indeed, IGOs are capable of enforcing democratic norms among its member states via sanctions, diplomacy, mediation, as well as shaming (Donno, 2010). However, others argue that the IGO network primarily diffuses models through socialization. ‘Only a handful of IGOs are capable of coercing their member states, but hundreds can interpret and transfer normative information about models of the state. We agree that some IGOs can be channels for coercion, but we believe that most of these organizations’ impact on global convergence occurs through normative mechanisms. This conclusion is certainly true in the case of democracy diffusion’ (Torfason and Ingram, 2010: 372). Accordingly, there is evidence that even international non-governmental organizations (INGOs) are associated with democratic growth (Li and Reuveny, 2003).

Other forms of diffusion may not mediate much of the effect of world-system position on democracy, but may be operative nonetheless. For example, one dynamic known for spreading democracy is diffusion via geographic proximity. By the 1990s, the world had dichotomized into two regions consisting of a democratic zone (the Americas, Europe, and Australasia) and an autocratic zone (Africa, the Middle East, and Asia). Moreover, these zones conform to spatial clustering patterns that persist even when controlling for economic development (O’Loughlin et al., 1998). Regional patterns suggest that democratic (and autocratic) transitions depend, at least in part, on what happens in neighboring states, as countries tend to emulate their neighbors (Gleditsch and Ward, 2006). Theoretically, spatial proximity may be an important mechanism of diffusion given that neighboring countries feature a greater number of linkages than more distant countries (Wejnert, 2005). Spatial clustering of democratic transitions could also be explained by ‘tipping effects’, whereby the presence of one transition increases the likelihood of subsequent transitions. Indeed, the costs of not transitioning to democracy (e.g. receiving aid, establishing trade relations) may become steeper as more countries within the region become democratic (Gleditsch and Ward, 2006). Not surprisingly, a number of studies find empirical evidence of spatial patterns in the spread of democratic models (Brinks and Coppedge, 2006; Gleditsch and Ward, 2006; Li and Reuveny, 2003; Pevehouse, 2002a; Torfason and Ingram, 2010; Wejnert, 2005).

Collectively, the internal and external dynamics reviewed here suggest a wealth of factors that potentially mediate the relationship between world-system dynamics and democratic growth. However, net of a country’s level of economic development or exposure to democratic models, does a country’s position in the world-system directly impact its political development? In the next section, I review world-system theory in relation to democratic growth.

Structural location in the world-system

Although a number of theoretical perspectives play a role in explaining the global spread of democracy, critical macro-comparative approaches tend to contribute less to this narrative. Dependency and world-system theories remain popular for examining economic development (Mahutga and Smith, 2011) and income inequality (Lee et al., 2007), but scholars less actively adopt these frameworks for investigating the contours of democracy’s global spread. In this section, I elaborate a more direct link between a country’s position in the world-system and its level of democracy, developing the following arguments: 1) an unequal distribution of political power in the periphery helps sustain exploitive core/periphery relations, 2) when democratic transitions do occur in the periphery, they are often limited or cosmetic in nature, and 3) the classic democratizing effects of socio-economic development tend to be distorted in the periphery.

According to world-system theory, countries occupy one of three zones within the world economy: core, semiperiphery, and periphery. Core states refer to the set of wealthy and powerful countries that feature dense economic ties with other nations. Peripheral states, by contrast, refer to a larger collection of poor and isolated nations, whose exchange relations are concentrated with the core. Semiperipheral states, meanwhile, occupy an intermediate position (Clark and Beckfield, 2009). A world-system approach suggests that core/periphery relations in the world economy may help explain cross-national variation in democratic growth and why non-democratic regimes persist in some parts of the world.

First, core/periphery relations are thought to promote autocratic regimes in the non-core (Bollen and Jackman, 1985). Economic production is more profitable for foreign investors entering the non-core when labor conditions there are poor and political power is concentrated among local elites. Thus, peripheral nations rely on attracting foreign investment by supplying cheap and docile labor to core investors who are seeking to reduce their production costs (London and Ross, 1995; Shandra et al., 2003). Consequently, maintaining an attractive labor pool encourages repressive labor policies and/or the lax enforcement of existing labor standards, which may require significant repression of political rights and civil liberties. Accordingly, recent work shows that trade dependence with a hegemonic partner negatively affects democratic growth (Balaev, 2009).

Second, although democratic models diffuse widely, the form they take and/or the extent to which they are actually implemented may vary considerably across countries. Democratic systems that are installed within non-core nations may be ‘loosely coupled’ from the Western models they intend to mimic. Among developing countries, political reforms sometimes produce ‘low-intensity democracies’ that feature minor or cosmetic change to the existing political structure (O’Loughlin et al., 1998). Some scholars emphasize the importance of American hegemonic influence in promoting democratic models via foreign aid packages (e.g. Scott and Steele, 2011). However, Robinson (1996: 49) suggests that the United States actually promotes ‘a system in which a small group actually rules and mass participation in decision-making is confined to leadership choice in elections carefully managed by competing elites’. Such electoral systems, Robinson explains, represent little more than a stable form of social control intended to suppress mass political movements in the developing world that seek to install popular democracy. In this way, elites in less developed countries attempt to ‘lock-in’ a limited form of democracy.

Third, non-core status in the world-system may also disrupt or distort the effects of development on democratization. As Bollen (1983: 470) explains, ‘the core’s support of elites in the periphery and semiperiphery is thought to hinder the processes associated with socioeconomic development that contribute to democratic forms of government’. While economic development traditionally produces groups that politically challenge elites (e.g. the bourgeoisie, skilled workers, organized labor), these groups do not perform the same function in the non-core because they are relatively weak vis-à-vis core-supported elites. Indeed, past research shows that occupying a non-core position reduces the positive effect of economic development on democracy (Burkhart, 1997; Burkhart and Lewis-Beck, 1994). Thus, not only do core nations benefit from higher levels of socio-economic development, but the impact of development may be greater there.

Finally, a country’s structural location in the world-system captures a range of ‘unobservable’ processes (Mahutga et al., 2011) that may involve complex interactions among variables or that incorporate core/periphery mechanisms operating at a systemic level. For example, core nations are thought to democratize more readily than the non-core so that the class interests of non-elites in the core will diverge from those of non-elites located in the periphery and semiperiphery, thereby preventing the formation of a unified international class consciousness (Bollen, 1983). However, such dynamics are difficult to detect and may be invisible to observers.

While the preceding arguments paint an image of uneven political development, it is also true that democratic transitions have occurred throughout most regions of the world by now (Schwartzman, 1998; Wejnert, 2005). Scholars talk of a ‘global rise’ in democracy (Torfason and Ingram, 2010), which spread over the last two centuries in a series of ‘waves’ (Huntington, 1991). To be sure, the frequent disregard of women’s suffrage by scholars distorts the timing of democratic transitions and calls into question the standard narrative (Paxton, 2000). More generally, though, the identification of democratic waves is compatible with a world-system approach that examines how an international political structure formed and evolved over time (Modelski, 2000, 2005).

Moreover, the most recent democratic ‘wave’ beginning in the 1970s can be interpreted from a world-system perspective as occurring in the stagnant B-phase of the Kondratiev wave, whereby political reforms arose primarily among emerging nations (Schwartzman, 1998). This B-phase is linked to the ascendance of the ‘middle strata’ in international trade (Kim and Shin, 2002), where production activities were relocated from traditional core nations. ‘These semiperiphery nations, by virtue of global economic convergence, have lost their quality of ‘‘semiperipheralness’’ and taken on some qualities of the core, such as an enlarged working class and an industrial bourgeoisie that can contest the power of the landed aristocracy. Growth of these new sectors reduced the level of economic ‘‘disarticulation,’’ thereby removing the obstructions to democracy’ (Schwartzman, 1998: 176). Thus, democratization since the 1970s may be linked with upward mobility in the world-system, whereby democratic transitions occurred predominantly in a number of emerging nations that began to exhibit more core-like characteristics. In sum, there is reason to suspect that world-system dynamics continue to shape cross-national variation in democratic growth even in an era of rapid political development.

Nevertheless, past research provides limited support for world-system expectations. Bollen’s (1983) seminal piece demonstrates a positive association between a country’s world-system position and its level of democracy, a finding replicated in subsequent studies (Bollen and Jackman, 1985; Gonick and Rosh, 1988). However, others critique these results on the grounds that world-system position is simply a crude proxy for economic development (Weede and Kummer, 1983). Other research shows that measures of world-system position and economic dependency are unrelated to levels of democracy (Gasiorowski, 1988). Moreover, others find that world-system position is not a significant or robust predictor of democratic growth (Crenshaw, 1995; Muller, 1995a; Paxton, 2002). More recently, Wejnert (2005) finds that core countries experienced higher levels of democracy between 1800 and 1999, but that peripheral countries experienced faster democratic growth during this time, a finding contrary to world-system expectations. ‘Being a peripheral country can be a ‘‘benefit’’ – the rate of those countries’ democratization accelerates faster than in other countries’ (Wejnert, 2005: 67). Overall, the evidence linking world-system position and democracy is mixed (Wejnert, 2005), and its impact on democratic change is notably absent.

However, previous studies testing for the effects of world-system position typically rely on some version of Snyder and Kick’s (1979) orthodox measure, oftentimes with revisions from Bollen (1983) and/or additions from Bollen and Appold (1993). As Clark and Beckfield (2009) explain, the continued use of Snyder and Kick’s measure is problematic due, in part, to the measure’s age and informal construction. In particular, the international trade network is now considerably more dense than was the case in prior decades (Kim and Shin, 2002), featuring substantial world-system mobility (Clark, 2010; Mahutga and Smith, 2011; Smith and White, 1992), suggesting that the use of a more recent and/or dynamic measure of world-system position may uncover the importance of the core/periphery hierarchy for understanding democratic growth since the 1970s.

In response to these concerns, Clark and Beckfield (2009) construct a new trichotomous measure of world-system position using data from the international trade network covering the 1980–1990 period. In their study, Clark and Beckfield (2009) find that their new measure outperforms the orthodox measure in predicting cross-national variation in economic growth. In this study, I replicate Clark and Beckfield’s original methodology for the 1990–2000 period and update their measure to incorporate new countries that emerged during the post-Cold War era. I then ‘blend’ these two measures together to form a dynamic trade-based indicator of world-system position that I use to predict democratic growth during the sample period. In the next section, I outline details from my replication, describe the other variables used in the analyses, and review my estimation strategy.

Independent variables

Dependent variable

Control variables

Several controls are logged to reduce skew, as noted below.

Sample

The primary models featuring the trade-based version of world-system position in the Freedom House sample consist of 5445 observations across 161 countries during the 1972–2008 period. The pooled data are unbalanced with some states contributing more observations than others. However, countries contribute 33.8 observations on average during the sample period, and every country is present for at least three years. The secondary models featuring the trade-based version of world-system position in the Polity IV sample consist of 5124 observations across 154 countries during the 1972–2008 period. These data are also unbalanced, but countries contribute 33.3 observations on average during the sample period, and every country is present for at least three years.

Estimation strategy

Democracy is a limited dependent variable, featuring a censored distribution in which more than 20 percent of the observations are clustered at either the lower or upper limit of the distribution. In my Freedom House sample of 5445 cases, 500 are left-censored at one (the minimum rating), and 768 are right-censored at seven (the maximum rating). In my Polity IV sample of 5124 cases, 143 are left-censored at -10 (the minimum rating), and 950 are right-censored at 10 (the maximum rating). Theoretically, we can imagine a variable, ‘propensity to democratize’, that would vary across all observations. For most cases, we can use the observed democracy rating as a measure of propensity. However, among those observations that are right-censored with a maximum rating (or left-censored with a minimum rating), we are unable to assess variation in propensity, even though some of these cases represent societies that democratized (or autocratized) more comprehensively and/or show greater (or lesser) support of democratic principles than other societies that likewise fully democratized (or autocratized).

For example, Freedom House’s original scoring gives all states that earn a combined political rights score of 36 to 40 the highest rating, while giving those states that earn a combined political rights score of zero to five the lowest rating. This already censors variation to some degree. More importantly, though, even among those states that receive a score of 40 (which indicates that a state received a perfect score of four on all 10 political rights criteria), this simply means that the state features good practices and laws for most or all elements pertaining to each criterion. Once a state achieves good practices, enacts good legislation, and does so for most political rights components, there is nothing to distinguish this country from another that reforms its practices further, passes a new law, or simply expands its good practices and laws across every domain. In short, the propensity to democratize (or not democratize) among these limit cases is effectively censored.

Unfortunately, conventional regression methods fail to account for the qualitative difference between limit and non-limit observations (Greene, 2008). Specifically, least squares estimators are biased and inconsistent under such circumstances (Greene, 2008; Hill et al., 2008). Of course, if one were solely interested in modeling a state’s likelihood of having fully democratized, limit observations would not be problematic. However, such an estimation strategy (e.g. probit) would come at the expense of discarding all variation among the non-limit observations (Tobin, 1958).

Tobit models represent an appropriate solution to modeling limited dependent variables, such as democracy, because of their two components: 1) a discrete function that determines whether or not the dependent variable is observed (e.g. whether a state becomes fully democratic or autocratic), and 2) a continuous function that determines the value of the dependent variable among cases where the outcome is not observed (e.g. the democracy rating among states that are not fully democratized or autocratized) (Tobin, 1958). For example, the observable outcome y_i equals one (or seven) in Freedom House if the latent value is unobserved (i.e. censored at the minimum or maximum rating), whereas y_i takes on the latent value in all other cases (i.e. when the latent value is in between the minimum and maximum rating).

y i = 1 if y i * ≤ 1 y i = y i * if y i * > 1 and y i * < 7 y i = 7 if y i * ≥ 7

(1)

Because my dataset contains multiple observations for different countries across time, I employ random-effects tobit models (RETMs) to accommodate the panel structure of the censored data. The random-effects model represents one estimation strategy designed to correct for the problem of heterogeneity bias (the confounding effect of unmeasured time-invariant variables). When estimating RETMs, it is possible to compare the performance of the panel estimator (random-effects tobit) to the pooled estimator (tobit) by calculating ρ, which is the fraction of the overall variance indicated by the panel estimator. When ρ equals zero, the panel-level variance (i.e. country-level variance) is unimportant, and the panel estimator is not different from the pooled estimator. In the trade-based models presented below, however, the proportion of the overall variance explained by the panel-level variance ranged from .602 to .855. Moreover, a series of likelihood-ratio tests comparing the two estimators confirm that the panel estimator is a significant improvement over the pooled estimator in all models (p < .001).

The random effects estimator in these models uses adaptive quadrature to compute the log likelihood and its derivatives. For all models presented below, I set the number of quadrature points to 48. If estimates noticeably change when altering the number of quadrature points, the quadrature is not reliably approximating the likelihood. Thus, in separate diagnostics, I checked the sensitivity of my results by replicating the Freedom House version of the fully specified trade-based model (Table 3, model 3) when setting the number of quadrature points to 32 and 64. The coefficients for all measures fluctuate by less than .01 percent, suggesting that the quadrature approximation is accurate (the largest fluctuation occurs with the coefficient for trade openness, whose estimate is .00005281% smaller when setting the number of integration points to 32).

Trends and patterns

I begin by reviewing recent trends and patterns in democratic growth during the 1972–2008 period. Among the 125 countries with both a Freedom House rating and a world-system classification throughout the sample period, the average level of democracy in this group rose from 3.61 to 4.50, an increase of 24.7 percent, or almost one full point, and covering almost 15 percent of the entire scale (1–7). This high level of democratic growth is consistent with other accounts describing this period (Torfason and Ingram, 2010; Wejnert, 2005). However, the overall trend masks important cross-national variation. Figure 1 shows the average Freedom House rating during the sample period by world-system position, using the trade-based version introduced in this study. Of the 125 countries noted above, 108 are consistently categorized as belonging to either the core (N = 39), semiperiphery (N = 17), or periphery (N = 52). Figure 1 depicts democracy trends for these 108 states.

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Figure 1.

Democracy rating (by world-system position), 1972–2008.

First, we can compare differences in democracy levels across world-system zones. Consistent with theoretical expectations, core states feature the highest average levels of democracy, followed by the semiperiphery, and then the periphery. At no point does the semiperiphery pass the core, and at no point does the periphery pass the semiperiphery. Second, we can compare differences in change over time across these zones. Again, consistent with theory, the core’s average level of democracy increased by well over one full point (which is greater than the world average), while the semiperiphery and periphery increased their averages by less than one point (which is less than the world average). Overall, Figure 1 suggests that the semiperiphery and periphery are not narrowing the democracy gap, but that, if anything, the discrepancy may be widening slightly.

Table 1 compares democratic growth across these 108 countries in greater detail. The first column reports each zone’s change score, referring to the average increase in each group’s democracy rating between 1972 and 2008. As noted above, the core’s increase of 1.26 is greater than the increases exhibited by the semiperiphery (0.50) or the periphery (0.80). In the second column, I report the growth rate in each zone’s average democracy rating between 1972 and 2008. From this perspective, the periphery’s growth rate (26.7%) is almost equal to that of the core’s (27.7%), and both are substantially greater than the semiperiphery’s growth rate (14.2%). Again, though, neither non-core zone demonstrates convergence with the core. Moreover, the fact that the core democratized the most during this period is all the more impressive when considering that over 30 percent of the core (12 countries) already achieved the maximum Freedom House rating by 1972 (see the third column), making growth impossible for almost one-third of this group. Conversely, almost no countries in the non-core reached this ceiling by the beginning of the sample period. By 2008, seven more core countries achieved the maximum democracy rating, while the non-core exhibited very little change in this regard.

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Table 1.

Democratic growth (by world-system position), 1972–2008

	Change score (1972–2008)	Growth rate (1972–2008)	Max. rating (1972)	Max. rating (2008)	Not Free (1972)	Not Free (2008)	Free (1972)	Free (2008)
Core (N = 39)	1.26	27.7 %	12 (30.8 %)	19 (48.7 %)	13 (33.3 %)	4 (10.3 %)	19 (48.7 %)	30 (76.9 %)
Semiperiphery (N = 17)	0.50	14.2 %	0 (0.0 %)	2 (11.8 %)	7 (41.2 %)	5 (29.4 %)	4 (23.5 %)	4 (23.5 %)
Periphery (N = 52)	0.80	26.7 %	2 (3.8 %)	2 (3.8 %)	30 (57.7 %)	17 (32.7 %)	11 (21.2 %)	11 (21.2 %)

Notes: Change score refers to each group’s average increase in their democracy rating between 1972 and 2008; Growth rate refers to each group’s average growth rate in their democracy rating between 1972 and 2008; Max. rating refers to the number of states in each group that had received the maximum rating (7) in the given year; Not Free refers to the number of states in each group that were considered ‘not free’ (i.e. receiving a Freedom House rating between 1 and 2.5) in the given year; Free refers to the number of states in each group that were considered ‘free’ (i.e. receiving a Freedom House rating between 5.5 and 7) in the given year; Core = Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, China, Denmark, Egypt, Finland, France, Greece, Hungary, India, Indonesia, Iran, Ireland, Italy, Japan, Malaysia, Mexico, Netherlands, New Zealand, Norway, Pakistan, Poland, Portugal, Romania, Saudi Arabia, Singapore, South Korea, Spain, Sweden, Switzerland, Thailand, Turkey, United Kingdom, and United States; Semiperiphery = Algeria, Bangladesh, Colombia, Côte d’Ivoire, Cyprus, Kenya, Kuwait, Libya, Nigeria, Panama, Peru, Philippines, Sri Lanka, Tunisia, Uruguay, Venezuela, and Zimbabwe; Periphery = Afghanistan, Albania, Barbados, Benin, Bolivia, Brunei, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Chad, Congo (DR), Congo (R), Dominican Republic, El Salvador, Equatorial Guinea, Ethiopia, Fiji, Gabon, Gambia, Guinea, Guyana, Haiti, Honduras, Iceland, Jamaica, Laos, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mauritius, Mongolia, Myanmar, Nepal, Nicaragua, Niger, Oman, Paraguay, Qatar, Rwanda, Senegal, Sierra Leone, Somalia, Sudan, Togo, Trinidad-Tobago, Uganda, and Zambia.

Finally, if we use Freedom House’s three-tier categorization of Not Free (rating of 1.0–2.5), Partly Free (rating of 3.0–5.0), and Free (rating of 5.5–7.0), we can examine change in each zone’s likelihood of their countries exiting the Not Free category and/or entering the Free category. As expected, peripheral states (57.7%) were more likely to be classified as Not Free in 1972 than either the semiperiphery (41.2%) or the core (33.3%). By 2008, all three groups reduced these percentages, but the core reduced their percentage of Not Free states by more than two-thirds (13 to 4), while the periphery cut their percentage by less than half (30 to 17), and the semiperiphery by about one quarter (7 to 5). In 1972, core countries had a higher percentage of its countries classified as Free (48.7%) than either the semiperiphery (23.5%) or the periphery (21.2%). And, by 2008, this disparity grew, as 11 more countries from the core became Free, while the semiperiphery and periphery experienced no aggregate change at all. In sum, if the rise in democratic growth since the 1970s produced cross-national convergence in political development, it would seem to be due to either a) convergence occurring within world-system zones, or b) convergence occurring between the semiperiphery and periphery, because c) neither non-core zone appears to be converging with the core.

Analyses

In Tables 2 and 3, I report results from a series of RETMs examining the relationship between world-system position and democratic growth. Each cell reports the unstandardized coefficient with the standard error in parentheses. In Table 2, I replicate Wejnert’s (2005) finding of political convergence across world-system zones with the orthodox measure of world-system position. In Table 3, I repeat these models with the trade-based version of world-system position.

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Table 2.

Random-effects tobit models of democracy, 1972–2008

	Freedom House			Polity IV
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Year	.451*** (.018)	.103 (.079)	.069 (.074)	2.856*** (.069)	1.567** (.459)	1.931*** (.440)
Orthodox semiperiphery (0 = core)	−4.673*** (.669)	−4.595*** (.666)	−1.113* (.444)	−20.386*** (2.779)	−19.530*** (2.786)	−10.817*** (2.713)
Orthodox periphery (0 = core)	−5.693*** (.596)	−5.621*** (.594)	−.998* (.460)	−26.194*** (2.575)	−25.377*** (2.584)	−14.275*** (2.842)
Orthodox semiperiphery x year		.463*** (.087)	.562*** (.079)		1.565** (.480)	1.111* (.453)
Orthodox periphery x year		.331*** (.082)	.411*** (.075)		1.236** (.466)	.843† (.442)
Latin America			−2.054*** (.439)			−12.577*** (2.644)
Africa			−3.698*** (.452)			−21.165*** (2.699)
Middle East			−3.086*** (.461)			−17.389*** (2.709)
East Asia			−3.094*** (.436)			−17.995*** (2.600)
Eastern Europe			−2.421*** (.529)			−14.730*** (2.956)
GDP PC			.385*** (.052)			−.867*** (.226)
Gini			.607** (.193)			3.448*** (.765)
IGO memberships			.606*** (.100)			−.320 (.396)
Trade openness			−.060 (.047)			−.153 (.175)
FDI inflows			.471* (.185)			.230 (.701)
Regional average			.448*** (.024)			2.697*** (.159)
Commonwealth member			1.079*** (.227)			4.519*** (1.241)
Period (1989–1991)			−.140* (.061)			.002 (.236)
Interpolated/extrapolated cases			−.414*** (.051)			−1.200*** (.205)
Imputed cases			−2.529*** (.358)			−6.846*** (1.804)
Left-censored	434	434	434	87	87	87
Uncensored	3,442	3,442	3,442	3,424	3,424	3,424
Right-censored	720	720	720	890	890	890
N (states)	4596(125)	4596(125)	4596(125)	4401(121)	4401(121)	4401(121)
BIC	12,031	12,017	11,487	20,239	20,244	19,889

†

p < .1; * p < .05; ** p < .01; *** p < .001 (two-tailed tests).

Note: Each cell reports the unstandardized coefficient with the standard error in parentheses.

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Table 3.

Random-effects tobit models of democracy, 1972–2008

	Freedom House			Polity IV
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Year	.429*** (.017)	.531*** (.033)	.491*** (.034)	2.839*** (.066)	3.258*** (.137)	3.424*** (.141)
Trade semiperiphery (0 = core)	−1.210*** (.151)	−1.194*** (.151)	−.843*** (.138)	−2.219*** (.584)	−2.152*** (.584)	−.725 (.537)
Trade periphery (0 = core)	−1.013*** (.170)	−1.107*** (.170)	−.800*** (.152)	−1.950** (.658)	−2.234** (.658)	.183 (.606)
Trade semiperiphery x year		−.300*** (.051)	−.150** (.049)		−1.102*** (.201)	−1.110*** (.191)
Trade periphery x year		−.080* (.040)	−.067† (.040)		−.333* (.163)	−.493** (.162)
Latin America			−2.581*** (.425)			−23.693*** (2.564)
Africa			−4.068*** (.434)			−32.491*** (2.569)
Middle East			−4.011*** (.454)			−29.163*** (2.667)
East Asia			−3.881*** (.443)			−27.685*** (2.646)
Eastern Europe			−3.118*** (.423)			−25.136*** (2.512)
GDP PC			.356*** (.046)			−.749*** (.208)
Gini			.676*** (.172)			3.081*** (.667)
IGO memberships			.133* (.063)			−.170 (.252)
Trade openness			.008 (.042)			−.194 (.158)
FDI inflows			−.105 (.123)			−.519 (.462)
Regional average			.438*** (.023)			2.874*** (.145)
Commonwealth member			1.104*** (.259)			4.846** (1.455)
Period (1989–1991)			−.096† (.056)			.041 (.212)
Interpolated/extrapolated cases			−.331*** (.045)			−.993*** (.177)
Imputed cases			−1.786*** (.359)			−6.455** (1.874)
Left-censored	500	500	500	143	143	143
Uncensored	4177	4177	4177	4031	4031	4031
Right-censored	768	768	768	950	950	950
N (states)	5445(161)	5445(161)	5445(161)	5124(154)	5124(154)	5124(154)
BIC	14,397	14,378	13,822	23,691	23,676	23,139

†

p < .1; * p < .05; ** p < .01; *** p < .001 (two-tailed tests).

Note: Each cell reports the unstandardized coefficient with the standard error in parentheses.

Table 2 consists of six models. In model 1, I regress democracy (Freedom House rating) on the orthodox version of world-system position. In model 2, I introduce the interaction terms. In model 3, I introduce the controls. In models 4–6, I replicate these models with the Polity IV ratings. Model 1 shows that states in the orthodox semiperiphery and periphery are significantly less democratic than the orthodox core. Model 2 then replicates Wejnert’s (2005) finding of a democratic core and a less-democratic non-core converging over time. The results show that the orthodox semiperiphery is significantly less democratic than the orthodox core (b = −4.595; p < .001), but that this negative effect significantly attenuates over time (b = .463; p < .001). Likewise, the orthodox periphery is significantly less democratic than the orthodox core (b = −5.621; p < .001), but this negative effect also weakens (b = .331; p < .001). Thus, these initial results show a large democratic gap between the core and non-core becoming smaller across the sample period. Finally, model 3 shows that these effects persist even when the control measures are included. Although the controls weaken the main effects of the semiperiphery and periphery, the interaction effects remain substantial. In models 4–6, I replace the Freedom House ratings with those from Polity IV. The results are similar, except that the periphery-by-year interaction becomes marginally significant in the fully specified model. Overall, these results suggest a large democracy gap across the world-system that becomes significantly smaller during the sample period.

In models 3 and 6, most of the control measures perform as expected. The regional dummies show the expected democracy gap between the West and all five non-Western regions. As expected, GDP PC is a positive, significant predictor of democratic growth in model 3 (p < .001). However, in model 6, when the Polity IV ratings are used, the effect of GDP PC is significant and negative (p < .001). I revisit this unexpected finding below. In both models 3 and 6, the Gini is positively associated with democracy at the highest level of significance (p < .001). This contrasts with previous findings that report a negative association between these two measures. However, most of these studies do not consider much of the post-1990 era when inequality and democracy levels started to rise together. I revisit this finding below, as well.

Models 3 and 6 show more consistent support for the diffusion variables. IGOs (p < .001), FDI inflows (p < .05), regional average (p < .001), and Commonwealth membership (p < .001) all exert positive effects on democracy in model 3, while the latter two remain significant in model 6 (p < .001). Only trade openness is non-significant in both models. These results are consistent with the following notions: 1) democratic models flow through international organizations and economic exchange; 2) democratization in a country’s regional neighbors appears to be influential, thereby capturing an important spatial dynamic in the spread of democracy; and 3) British hegemonic influence is operative in diffusing democratic principles. Overall, the above effects appear when controlling for the collapse of communism during the 1989–1991 period, and when including technical controls for the two procedures designed to address missing data: interpolation/extrapolation and imputation.

How do these results compare when I use the updated trade-based version of world-system position? Table 3 addresses this question. Model 1 presents the main effects of world-system position. These results show that the trade-based semiperiphery (b = −1.210; p < .001) and periphery (b = −1.013; p < .001) exhibit significantly lower levels of democracy than the core. In model 2, I introduce the interaction terms. The main effects of the semiperiphery and periphery remain negative (p < .001), but the interaction terms indicate that these initial gaps did not decline, and that the core-semiperiphery gap (b = −.300; p < .001) and core-periphery gap (b = −.080; p < .05) actually increased slightly.

In model 3, we can see how much of the relationship between world-system position and democracy is mediated by the control measures. While the main effects of the semiperiphery and periphery are not greatly affected, the coefficients of the interaction terms become notably smaller. Specifically, the semiperiphery-by-year coefficient is now half as large, but still significant (b = −.150; p < .01), while the periphery-by-year coefficient is reduced by a smaller amount, but becomes marginally significant in the process (b = −.067; p < .10). Nevertheless, the results indicate that the democracy gap between the core and non-core did not dissipate over time. When using the Polity IV ratings in models 4–6, the results are similar except that the control measures mediate more of the main effect of world-system position than the effects of the interaction terms. In sum, both sets of models indicate that countries in the semiperiphery and periphery are not converging with the core, but are perhaps diverging slightly.

The control measures perform similarly in these models, except that FDI inflows are now a non-significant predictor of democracy in both the Freedom House and Polity IV models. More importantly, the results for GDP PC remain mixed, positively affecting a country’s Freedom House ratings, but negatively affecting a country’s Polity IV ratings. Meanwhile, income inequality remains positively associated with democracy in both models.

In separate analyses, I checked for influential observations in these models via robust regression, whereby outliers are dropped or down-weighted. The procedure begins by fitting a regression, calculating Cook’s D, and excluding any observation for which D > 1. Next, the procedure calculates weights for each remaining case based on the absolute value of the residuals. Weights range from 0 to 1, with larger residual values getting down-weighted more, and dropped cases receiving a weight of 0.

I then re-estimated the RETMs from Table 3 when excluding those observations given a weight of a) .10 or less, b) .25 or less, and c) .50 or less. For the Freedom House replications, no outliers were detected for models 1 and 2 at any of these three specified levels, indicating that the widening gap between the core and non-core is not driven by outliers. In model 3, 10 observations were flagged as outliers at the .10 level, along with 22 observations at the .25 level, and 86 observations at the .50 level. In all three instances, when re-running model 3 without these observations, the periphery-by-year coefficient drops out of significance, suggesting that the control measures mediate more of the world-system effect when outliers are excluded.

For the Polity IV replications, no outliers were detected at the .10 level for any of the three models. Two observations were flagged in model 3 at the .25 level, along with two observations in model 1 at the .50 level, and 34 observations in model 3 at the .50 level. In all three instances, though, when re-running these models without these observations, the results were substantively identical. In short, regardless of whether outliers are excluded from the above models, semiperipheral and peripheral states are diverging (not converging) with the core in their levels of political development. At most, outliers may be masking some of the mediation by the control measures in explaining the divergence between the core and periphery.

Replications

To assess the robustness of my findings, I perform three sets of replications, employing an alternative measure of world-system position (Mahutga and Smith, 2011), an alternative measure of democracy (Bollen’s Liberal Democracy Series), and an alternative estimation strategy (fixed-effects logit).

In the first replication, I consider an alternative measure of world-system position: Mahutga and Smith’s (2011) categorical measure. Although this measure covers a smaller number of countries in the models presented below (86–90), it is valuable for disaggregating world-system position into finer groupings, especially the top-tier. The measure refers to a state’s structural location in the international trade network based on five commodity categories (high tech/heavy manufacturing, sophisticated extractive, simple extractive, low-wage/light manufactures, and animal products and byproducts) across three years: 1965, 1980, and 2000. ³ The result is a six-group partition, with each group featuring an average Freedom House rating across the sample period that conforms to world-system expectations: core (6.84), core contenders (5.79), semiperiphery (4.49), strong periphery (4.27), weak periphery (3.95), and weakest periphery (2.99). Importantly, the Mahutga and Smith measure is highly correlated with my trade-based measure (r = .839), with the correspondence growing stronger over time (r = .806 in the 1970s; r = .878 in the 2000s).

Of particular interest is the partition made in the top-tier between the core and core contenders. Core nations refer to the ‘strongest countries in the world led by the United States’ (Mahutga and Smith, 2011: 264), while core contenders refer to ‘some developed European countries… and many of the more dynamic economies of the developing world, including China (by 1980), Hong Kong, India, along with Brazil, South Korea, and Singapore (by 1980)’ (Mahutga and Smith, 2011: 264). The core’s average Freedom House rating across the sample period is 6.84, with 69.2 percent of all its country-year observations given the maximum rating, another 29.2 percent given a rating of 6.5, and the remaining observations all given a 6.0. By contrast, the average Freedom House rating across the sample period for the core contenders is 5.79, with less than half (47.2%) of its country-year observations given the maximum rating, and 20.6 percent of observations given a rating of 4.5 or less. Thus, most of the democratic growth from this top-tier of states must come from the core contenders, just as the upwardly mobile ‘new core’ countries are responsible for the bulk of democratic growth in my trade-based measure. Consequently, in the first replication, I use the Mahutga and Smith measure of world-system position, with core contenders serving as the excluded reference category. We should expect to see core contenders converging with the core in their level of democracy, while at the same time widening the gap between themselves and those countries residing in the semiperiphery and periphery. ⁴

Table 4 reports the results. Model 1 shows that core states feature significantly higher levels of democracy than core contenders (b = 1.552; p < .001), while core contenders feature significantly higher levels of democracy than all groups in the semiperiphery and periphery (p < .01 or greater). How do these gaps change over time? In model 2, we see that while core contenders are converging with the core (b = −.703; p < .001), core contenders are also widening the democracy gap between themselves and all four groups in the semiperiphery and periphery, with three of the four comparisons reaching significance (p < .001). When introducing the controls in model 3, the semiperiphery-by-year coefficient is reduced by 52.9 percent (b = −.144; p < .10), the strong periphery-by-year coefficient is reduced by 42.8 percent (b = −.249; p < .01), and the weak periphery-by-year coefficient is reduced by 58.0 percent (b = −.198; p < .05). Nevertheless, all three comparisons remain significant (p < .10 or greater), while the other two comparisons remain mostly unaffected. The Polity IV models, presented in the subsequent columns, report similar results. Core contenders are converging with the core, but diverging from the other groups, with the exception that the democracy gap between the core contenders and the semiperiphery does not significantly widen over time. In sum, these models suggest that most of the cross-national convergence in political development that occurred during the sample period is within the core (between the advanced core and the dynamic core contenders). ⁵

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Table 4.

Replication with alternative world-system measure (Mahutga and Smith, 2011)

	Freedom House			Polity IV
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Year	.396*** (.020)	.727*** (.064)	.620*** (.068)	3.077*** (.092)	3.696*** (.290)	4.532*** (.314)
Core (0 = core contender)	1.552*** (.390)	1.746*** (.383)	.792* (.316)	22.089*** (4.678)	22.138*** (4.527)	9.257** (3.100)
Semiperiphery (0 = core contender)	−.343** (.132)	−.120 (.140)	.245† (.135)	−.425 (.567)	.634 (.630)	.651 (.583)
Strong periphery (0 = core contender)	−.980*** (.177)	−.770*** (.180)	−.074 (.171)	−3.466*** (.757)	−2.705** (.786)	−1.078 (.737)
Weak periphery (0 = core contender)	−.759*** (.194)	−.444* (.200)	.209 (.191)	−2.087* (.851)	−1.184 (.893)	.155 (.844)
Weakest periphery (0 = core contender)	−.882*** (.232)	−.553* (.236)	.091 (.226)	−3.367** (1.031)	−2.442* (1.068)	−1.385 (1.024)
Core x year		−.703*** (.103)	−.615*** (.099)		−4.000*** (.648)	−3.362*** (.594)
Semiperiphery x year		−.306*** (.083)	−.144† (.080)		.078 (.372)	−.096 (.349)
Strong periphery x year		−.435*** (.075)	−.249** (.075)		−.832* (.340)	−1.484*** (.334)
Weak periphery x year		−.471*** (.076)	−.198* (.078)		−.635† (.346)	−1.240*** (.347)
Weakest periphery x year		−.101 (.078)	.056 (.081)		−.686* (.347)	−1.610*** (.360)
Latin America			−2.548*** (.454)			−24.040*** (3.443)
Africa			−4.230*** (.529)			−37.906*** (3.722)
Middle East			−3.802*** (.506)			−29.585*** (3.819)
East Asia			−3.702*** (.496)			−29.520*** (3.788)
Eastern Europe			−2.239** (.860)			−16.749** (6.082)
GDP PC			.346*** (.081)			−3.012*** (.404)
Gini			.643** (.213)			4.747*** (.921)
IGO memberships			.247* (.121)			.355 (.536)
Trade openness			−.061 (.070)			.586† (.307)
FDI inflows			−1.010*** (.271)			−4.953*** (1.359)
Regional average			.366*** (.026)			2.800*** (.192)
Commonwealth member			.983** (.293)			6.484** (2.244)
Period (1989–1991)			.065 (.068)			.366 (.292)
Interpolated/extrapolated cases			−.225*** (.061)			−.820** (.270)
Imputed cases			−2.401*** (.581)			−9.845* (4.469)
Left-censored	107	107	107	119	119	119
Uncensored	2487	2487	2487	2171	2171	2171
Right-censored	715	715	715	870	870	870
N (states)	3309(90)	3309(90)	3309(90)	3160(86)	3160(86)	3160(86)
BIC	8,131	8,082	7,856	13,033	13,024	12,735

†

p < .1; * p < .05; ** p < .01; *** p < .001 (two-tailed tests).

Note: Each cell reports the unstandardized coefficient with the standard error in parentheses.

The effects of the control measures are similar to those reported above, with two exceptions. First, FDI inflows negatively affect democracy ratings in both models 3 and 6 at the highest level of significance. And second, trade openness exerts a positive effect at a marginal level of significance in model 6. These results, combined with those from above, suggest that the impact of economic exchange on democracy is ambiguous.

In the second replication, I consider an alternative measure of democracy: Bollen’s (1998) Liberal Democracy Series (LDS). While the Freedom House and Polity IV ratings remain popular, a number of scholars are critical of these measures. For example, Freedom House ratings are criticized as flawed due to political bias (Bollen, 1993; Bollen and Paxton, 2000), while the Polity IV is known for not considering female suffrage when assigning countries a democracy score (Paxton, 2000; Paxton et al., 2003). By contrast, although the LDS covers a shorter period of time (1972–1988), it minimizes the systematic error present in other democracy measures (Bollen, 2009). Nevertheless, the LDS is highly correlated with the ratings from both Freedom House (r = .908) and Polity IV (r = .909) in my dataset. Scores for this measure range widely, from 0 (less democratic) to 100 (more democratic), allowing me to estimate ordinary random-effects models (using a generalized least squares estimator) that include a first-order autocorrelation correction.

Table 5 presents results that are similar to those already discussed. In model 1, both the trade-based semiperiphery (b = −31.941; p < .001) and periphery (b = −36.759; p < .001) feature significantly lower levels of democracy than the core. In model 2, we see that these gaps do not narrow, but instead grow wider. In fact, the gap between the core and the semiperiphery widens significantly (b = −7.579; p < .05), as it does between the core and periphery (b = −8.762; p < .01), even during this shorter time period. In model 3, the control measures mediate much of this effect, reducing the semiperiphery-by-year coefficient by 35.5 percent (and causing it to drop out of significance), and reducing the periphery-by-year coefficient by 34.8 percent. Nevertheless, these results are consistent with those from the Freedom House and Polity IV models in that the semiperiphery and periphery are not closing the democracy gap with the core.

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Table 5.

Replication with alternative democracy measure (LDS), 1972–1988

	Model 1	Model 2	Model 3
Year	5.312*** (1.187)	11.206*** (2.128)	8.866*** (2.157)
Trade semiperiphery (0 = core)	−31.941*** (8.575)	−39.043*** (9.251)	−12.049† (6.912)
Trade periphery (0 = core)	−36.759*** (6.213)	−44.892*** (6.689)	−10.585† (5.810)
Trade semiperiphery x year		−7.579* (3.705)	−4.888 (3.659)
Trade periphery x year		−8.762** (2.665)	−5.709* (2.692)
Latin America			−19.647** (7.458)
Africa			−48.470*** (8.311)
Middle East			−45.236*** (7.317)
East Asia			−32.648*** (7.454)
Eastern Europe			−57.208*** (9.041)
GDP PC			5.328*** (1.341)
Gini			−12.592* (5.585)
IGO memberships			1.520 (1.913)
Trade openness			−.965 (1.286)
FDI inflows			−1.079 (6.281)
Regional average			2.770*** (.390)
Commonwealth member			23.670*** (3.797)
Interpolated/extrapolated cases			−1.997† (1.199)
Imputed cases			−17.857*** (5.043)
N (states)	2260(137)	2260(137)	2260(137)
R 2	.193	.194	.631

†

p < .1; * p < .05; ** p < .01; *** p < .001 (two-tailed tests).

Notes: All replications reported in Table 5 are random-effects models that include a first-order autocorrelation correction. Each cell reports the unstandardized coefficient with the standard error in parentheses.

Interestingly, while most of the control measures perform similarly in this replication, the Gini is a notable exception, now exerting a significant negative effect on democracy in model 3 (p < .05), contrary to the results reported above, but consistent with theory. Does restricting attention to the 1972–1988 period account for this result? In separate analyses, I estimated the fully specified models from Table 3 (models 3 and 6) during the pre-1990 era. In both models, the effect of Gini is negative (p < .001). These findings suggest that perhaps the negative association between income inequality and democracy during the pre-1990 era is historical and not generalizable to other eras. Conversely, it may be that the post-1990 era is unique in seeing both inequality and democracy rise together across a number of countries. Still another interpretation is that any association that exists between these two measures is spurious, with different associations manifesting accordingly across different eras. Unfortunately, the results here do little to advance the long-standing debate on this topic.

The other curious finding we can now revisit is the negative effect of GDP PC in the Polity IV model, which is both contrary to theory, and contrary to the positive effect that GDP PC exerts in both the Freedom House and LDS models. One explanation for the negative effect of GDP PC on the Polity IV rating is misspecification. In a previous study, Muller (1995a) finds that the relationship between economic development and democracy is actually curvilinear (negative, before turning positive) because income inequality (which, in theory, negatively affects democracy) is highest among middle-income countries. Appendix B shows a scatterplot with a fitted quadratic line between GDP PC (log) and Polity IV ratings for the year 2000. There is clearly a curvilinear association, as high levels of GDP PC are associated with very low and very high Polity IV ratings. In addition to Muller’s (1995a) purported explanation for this association, another contributing factor may be the presence of a number of oil-rich countries in the Middle East that feature high levels of GDP PC, but low Polity IV ratings. Indeed, the six countries in the top-left corner of the scatterplot in Appendix B are Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and United Arab Emirates. Thus, I replicated the Polity IV models when including a second-order polynomial term for GDP PC and report these results in Appendix C. Model 1 replicates the fully specified model from Table 2 (featuring the orthodox measure of world-system position), model 2 replicates the fully specified model from Table 3 (featuring the trade-based measure of world-system position), and model 3 replicates the fully specified model from Table 4 (featuring the Mahutga and Smith measure of world-system position). I omit results for the control measures to preserve space, but they are available upon request. As these results show, GDP PC is now properly specified, but the effects of world-system position in all three models are similar to those reported above.

In the third replication, I consider an alternative estimation strategy: fixed-effects logit models. While this approach dichotomizes the dependent variable into democratic versus non-democratic states, this method allows me to examine longitudinal change using censored data, as outcomes that do not vary (states that are either always democratic or never democratic) are dropped from the model. By contrast, ordinary fixed-effects models retain observations that never vary (e.g. states that receive the maximum Freedom House rating throughout the entire sample period). Another advantage of logit is the ability to use multiple imputation to fill missing values for several of the control variables, as I do for these models. Following the convention set forth by Freedom House, I code all country-years with scores ranging from 5.5 to 7.0 as democratic, while all other scores are coded as non-democratic. Similarly, following Polity IV standards, I code all country-years with scores ranging from 7 to 10 as democratic, while all other scores are coded as non-democratic. And in the LDS, I code all country-years with scores ranging from 70 to 100 as democratic, while all other scores are coded as non-democratic. To discern democratic growth for each world-system zone (as defined by the trade-based measure of world-system position), I run fixed-effects logit models on three sub-samples (core, semiperiphery, and periphery) and interpret the coefficient for year as the indicator of democratization for that zone.

Table 6 reports the results. The top panel reports results when democracy is measured with the Freedom House ratings. The first two models are restricted to a sample of core states, the next two models only include the semiperiphery, and the last two models feature the periphery. Without controls, core states feature greater democratization across time (b = 1.701; p < .001) than either the semiperiphery (b = .185; NS) or the periphery (b = .326; p < .001), as indicated by the coefficient for year. Moreover, when the time-varying controls are included, democratization in the core remains significant (b = 2.755; p < .001), but not the semiperiphery (b = .014; NS) or periphery (b = −.083; NS). The middle panel replicates these models with the Polity IV ratings, but the results are similar, as democratization in the core is greater than either of the other two zones, with or without controls. Finally, the bottom panel shows similar results for the LDS. In sum, when analyzing longitudinal change through this alternative estimation strategy, core nations continue to show greater democratization during the sample period than either of the two non-core zones.

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Table 6.

Fixed-effects logit models of democracy

Freedom House (1972–2008)	Core		Semiperiphery		Periphery
Year	1.701*** (.160)	2.755*** (.468)	.185 (.114)	.014 (.198)	.326*** (.085)	−.083 (.113)
GDP PC		.707 (.930)		2.168** (.712)		.962* (.463)
Gini		.654 (2.362)		−2.180 (1.556)		−.337 (.947)
IGO memberships		6.153*** (1.596)		1.413† (.749)		−.192 (.433)
Trade openness		−3.794*** (.902)		.113 (.563)		.089 (.420)
FDI inflows		35.231** (11.773)		5.676 (4.227)		6.326** (2.371)
Regional average		1.968*** (.395)		.018 (.269)		1.159*** (.237)
Period (1989–1991)		.002 (.509)		−.098 (.416)		.179 (.316)
N (states)	629 (18)	629 (18)	478 (16)	478 (16)	944 (29)	944 (29)
Polity IV (1972–2008)	Core		Semiperiphery		Periphery
Year	2.716*** (.247)	3.496*** (.624)	.640*** (.135)	.202 (.196)	1.428*** (.122)	1.253*** (.132)
GDP PC		.504 (1.517)		2.179* (1.043)		.274 (.523)
Gini		10.650*** (2.920)		−6.334** (1.884)		2.094* (1.050)
IGO memberships		6.754** (2.156)		−.657 (.620)		.334 (.463)
Trade openness		2.532* (.996)		.880 (.654)		.561 (.382)
FDI inflows		−5.778 (9.723)		4.636 (4.489)		4.080* (2.016)
Regional average		2.948*** (.559)		1.739*** (.470)		.574† (.298)
Period (1989–1991)		−.134 (.654)		.477 (.508)		−.881* (.377)
N (states)	592 (17)	592 (17)	444 (15)	444 (15)	908 (28)	908 (28)
LDS (1972–1988)	Core		Semiperiphery		Periphery
Year	2.187*** (.506)	10.490*** (2.824)	1.481** (.486)	2.300* (.946)	1.710*** (.416)	1.421* (.601)
GDP PC		−5.877 (3.641)		−12.240*** (3.430)		14.976*** (3.793)
Gini		−31.375** (11.165)		.347 (3.138)		3.958 (3.773)
IGO memberships		11.817† (6.616)		.700 (3.597)		−7.630** (2.906)
Trade openness		−17.709** (5.341)		−.932 (1.817)		−5.172** (1.531)
FDI inflows		178.484** (57.345)		9.379 (29.828)		5.577 (9.292)
Regional average		3.349** (.985)		2.377*** (.612)		1.013* (.441)
N (states)	170 (10)	170 (10)	153 (9)	153 (9)	184 (11)	184 (11)

†

p < .1; * p < .05; ** p < .01; *** p < .001 (two-tailed tests).

Note: Each cell reports the unstandardized coefficient with the standard error in parentheses. In each model, the dependent variable is coded dichotomously (0 = undemocratic; 1 = democratic), indicating whether or not a country has crossed the following rating thresholds: Freedom House (5.5 or higher), Polity IV (7 or higher), and LDS (70 or higher).