In order to focus our discussion, we first present and evaluate results pertaining to the debates on income and Islam. Next, we identify indicators that are robust across all democracy measures in our main setup on the full time series (1960–2015), before we present various extensions. The online appendix presents summary tables and figures for readers interested in evaluating the robustness of additional specific independent variables. The supplementary syntax can be used to reproduce graphs similar to those presented below for any variable of interest. We report robustness in terms of (unweighted median) \({\bar{\beta }}\)- and \({\bar{\sigma }}\)-values, or the equivalent \({\bar{t}}\)-values, where \({\bar{t}}=\frac{\bar{{\beta }}}{\bar{{\sigma }}}\) across all relevant estimations for which a variable is a focus variable. In order to avoid dichotomizing evidence according to conventional significance thresholds, we also discuss the magnitudes of \({\bar{\beta }}\) and how they change when particular controls are entered (see Wasserstein and Lazar 2016).

We visualise findings with coefficient plots of \({\bar{\beta }}\) for each of the 16 possible democracy measure and time period combinations.Footnote 4 The 95% confidence intervals are calculated using \({\bar{\sigma }}\). Coefficient colors (black, dark grey, and grey) indicate level of significance (.05, .10, and insignificant). Similarly, we display the results for facilitating and fatal variables as coefficient plots, wherein colors indicate how findings change when controlling for a specific variable. A major change (black) occurs when an aggregate insignificant variable becomes significant at .05 (facilitating) or when an aggregate significant variable at .05 becomes insignificant (fatal). A minor change (dark grey) occurs when a insignificant variable becomes significant at .10 (facilitating), or when a significant variable at .05 changes to significant at .10 (fatal).

Do higher incomes lead to democratization and democratic stability?

Figure 1 summarizes results for the relationships between GDP per capita and democratization (left panel) and democratic survival (right panel). The left panel illustrates that our conclusions regarding the income–democratization relationship depend on the choice of democracy measure and sample time period. In the full time series, income does not relate to democratization systematically when using the Polity, DD/ACLP and BMR democracy measures. However, we note that \({\bar{\beta }}\) is positive across democracy measures and, when the V-Dem measure is used, also statistically significant. Compared to V-Dem, the odds of obtaining a positive and significant (at .05) coefficient of income on democratization (across 1960–2015) is reduced by 74% to 85% when instead using Polity, DD/ACLP, or BMR.Footnote 5 Sample differences cannot account for this pattern as they are similar across the different measures.

What time period we draw data from also affects results: \({\bar{\beta }}\) is consistently positive and statistically significant across democracy measures in the Cold War period. The coefficient also is quite large; one standard deviation increase in GDP per capita, on average, increases the odds of democratization (in the Cold War period) by 1.39. That result could, for instance, be related to several developed Eastern European countries democratizing at the end of the Cold War (Boix and Stokes 2003). In the post-Cold War period, by contrast, the effect of GDP per capita on democratization is negative for three of the four democracy measures. These findings highlight that the effect may have changed over time, potentially reflecting changes in the international system (Boix 2011) and new technology (Rød and Weidmann 2015). One plausible hypothesis is the following: Until the last few decades, increasing income levels have corresponded strongly with changes in social structures (e.g., the rise of organized industrial workers and the middle class), which led to stronger pressures for democracy. With the growing automatization and digitization of production processes and market exchanges, however, a relatively small elite may now generate and accumulate considerable wealth; higher incomes thus may no longer correspond as strongly with social structural changes.

Fig. 1 Coefficient plots of \({\bar{\beta }}\) with 95% CI across all specifications for GDP pc and democratization (left) and GDP pc on democratic stability (right). Black = significant at .05 level. Dark grey = significant at .10 level. Grey = insignificant Full size image

Fig. 2 Facilitating variables for GDP pc on democratization. Black = significant at .05 level. Dark grey = significant at .10 level. Grey = insignificant Full size image

One potential problem with the aggregate results presented above is that they can “hide” subsets of regressions in which income relates to democratization. Do any controls facilitate a positive and significant relationship? Fig. 2 plots \({\bar{\beta }}\) and \({\bar{\sigma }}\) for each of the four democracy measures, for 67 subsets of specifications defined by having a specific control included. Several interesting patterns emerge, with numerous controls facilitating noticeable changes in \({\bar{\beta }}\) and their significance levels. The most important pattern in Fig. 2 is that the largest positive and significant \({\bar{\beta }}\) values appear for regressions that include controls identifying whether income is a function of natural resource extraction or not (natural resource, industrialization and urbanization variables). We are about 2.5 times more likely to find that income is significant at .05 when comparing models controlling for any resource curse indicator to other models. Furthermore, across all four democracy measures, the coefficient enlarges more than fourfold, from a median of 0.05 in specifications excluding resource-related controls to 0.22 in specifications including them. Since natural resource income can impede broader economic development and make autocracies less likely to democratize (Ross 2001; Boix and Stokes 2003), one may argue plausibly that a proper test should include such controls. The results indicate a clear relationship between GDP per capita and democratization, and the odds of democratization increases by 1.25 when GDP per capita increases by one standard deviation. A second notable pattern is that negative \({\bar{\beta }}\) values appear when controlling for concepts identifying other features of economic development (communication technology proliferation, education). The latter result indicates that the relationship between income and democratization is likely to be related to broader development processes, and that any direct effect observed when accounting for other aspects of development is zero or negative.

Regarding income and democratic survival, the right panel in Fig. 1 shows that the aggregate \({\bar{\beta }}\) is positive and statistically significant for all four democracy measures for the 1960-2015 period. \({\bar{\beta }}\) is quite large; a standard deviation change in GDP per capita increases the odds that a democracy survives the next year by a median of 1.7. Although the 95% confidence intervals overlap zero for the Cold War sample when V-Dem is used, the finding is robust. The remarkably consistent results corroborate the widely held notion that democracy is less prone to collapse in rich than in poor countries.

Fig. 3 Fatal variables for GDP pc on democratic stability. Black = insignificant. Dark grey = significant at .10 level. Grey = significant at .05 level Full size image

However, particular controls could be “fatal” to the income–democratic survival result. Figure 3 shows, first, that when democracy is measured by PolityIV and DD/ACLP, income has a large, positive coefficient on democratic survival no matter which control enters the model. In contrast, \({\bar{\beta }}\) declines and significance levels drop when controlling for two and six variables if BMR and V-Dem, respectively, are used. Second, \({\bar{\beta }}\) is smaller and more often insignificant when controlling for concepts that tap into other features of development (communications technology proliferation, industrialization and urbanization, education, health, administrative capacity). Thus, as for the income-democratization link, the evidence is (even) clearer for the notion that broader development processes, rather than only income more narrowly, stabilize democracy.

Is democratization less likely in countries that are predominantly Muslim?

The aggregate results, shown in Fig. 4, support the proposition that democratization is less likely in predominantly Muslim countries: \({\bar{\beta }}\) is negative across all democracy measures and time periods; it is significant at .10 in ten of 16 samples. The estimated effect also is quite large: A standard deviation change in the Muslim population percentage reduces the odds of democratization by 27%. In the Third Wave sample, the negative relationship is remarkably consistent across democracy measures, while more uncertainty exists surrounding the effect in the Cold War sample.

Fig. 4 Coefficient plots of \({\bar{\beta }}\) with 95% CI across all specifications for Muslim share of population on democratization. Black = significant at .05 level. Dark grey = significant at .10 level. Grey = insignificant Full size image

To inspect the negative aggregate relationship more closely, Fig. 5 plots fatal variables for the Muslim population share effect on democratization. As anticipated from the literature review, the aggregate result is sensitive to the inclusion of specific controls. First, \({\bar{\beta }}\) shifts toward zero and loses significance—consistently across democracy measures—in models controlling for resource curse variables, suggesting that the aggregate relationship is inflated by abundant natural resources in Muslim countries. The odds of finding that the Muslim share has a significant negative relationship with democratization drops by 82% when controlling for resource curse variables, and \({\bar{\beta }}\) drops from a median of − 0.33 to − 0.21. Second, controlling for neighborhood democracy levels reduces the odds of finding a significant result by 96%. Here, \({\bar{\beta }}\) also drops substantially, from − 0.32 to − 0.17. Third, the significance of the Muslim variable drops across democracy measures when controlling for education. However, if having a Muslim population affects educational outcomes, for instance by depressing female school enrollment (Fish 2011), the estimates are afflicted by post-treatment bias. Other, more complex stories may underlie the finding, as dependence on natural resources reduces the need for an educated, specialized workforce. In regressions from which natural resource measures are excluded, education might pick up such variation and contribute to reducing omitted variable bias.

Fig. 5 Fatal variables for Muslim share of population on democratization. Black = insignificant. Dark grey = significant at .10 level. Grey = significant at .05 level Full size image

In sum, closer scrutiny reveals that the robust aggregate negative effect of Muslim population on democratization is weakened when theoretically relevant controls are entered. When controlling for resource abundance, education, and neighboring regime type, the relationship is attenuated and most specifications fail to yield a significant result. At the same time, we refrain from concluding too forcefully since \({\bar{\beta }}\) for the Muslim-share variable consistently is negative and relatively large even when the above-discussed “fatal” controls are entered.

“Robust” determinants

Table 1 List of highly robust determinants for all democracy measures, results for democratization and democratic stability. Highly robust = significant at .05 for all four democracy measures in regressions run on the full time-series (1960–2015) Full size table

We will now discuss “robust” determinants of democratization and democratic survival. Our criterion for applying that label is that the variable is significant at .05 for all four democracy measures in regressions run on the full time series (1960–2015). All robust variables are listed in Table 1 (democratization or democratic stability), and ranked according to \({\bar{t}}\)-values for regressions using PolityIV. The tables also contain information on the number of fatal variables for each robust determinant ('Nr. fat'). Overall, we identify many more robust determinants of democratization than of democratic survival (20 versus two). That conclusion reflects, in part, less variation and, thus, greater uncertainty in estimates associated with the fewer transitions to dictatorship from democracy than vice versa. Yet, the large difference in robust determinants suggests additional reasons for the pattern. The large difference could reflect that previous theoretical efforts and empirical studies, which have informed our variable selection, are more attuned to explaining democratization (see Knutsen and Nygård 2015). But, democratic breakdowns also could be processes that inherently are harder to explain than democratization episodes. Nonetheless, our analysis highlights that, in addition to GDP per capita, only the indicators for political corruption and impartial public administration are robust determinants of democratic survival. The results thus suggest that the most robust determinants of democratic survival relate to features of the political and institutional history of a country and, most notably, the extent to which a law-abiding bureaucracy has developed.

For democratization, Table 1 reveals that the length of executive tenure is robust and, moreover, that variable is not associated with any fatal controls. The measure of time since irregular regime change reveals a similar result. Our analysis thus leaves little doubt that autocratic incumbents who have entrenched their positions over longer periods of time are less likely to experience democratization. Furthermore, being located in a democratic neighborhood increases the likelihood of democratization (Gleditsch and Ward 2006); also that result is remarkably robust.

Other robust determinants of democratization include measures of domestic unrest, especially non-violent mass campaigns (corroborating Chenoweth and Stephan 2011). The domestic unrest variables reveal little sensitivity to including particular controls. In addition, we find that having a majoritarian electoral system is related negatively to democratization. Indeed, the literature on autocratic elections highlights how majoritarian systems may be easier to manipulate for autocrats, yield large regime-supporting parties seat premiums in most contexts, and mitigate the formation and growth of new opposition parties that later may challenge the regime (Higashijima 2019).

Regarding economic indicators, various measures of communication technologies, namely televisions, radios, or phones per capita, also are positive and robust. Similarly, variables capturing social development, in particular average years of education for women/men, are robust. Thus, evidence indeed exists favoring a more nuanced interpretation of the Lipset (1959) thesis; at least some forms of economic development are associated robustly with democratization. Moreover, we note that both the robust education measures, as well as less robust measures of communications technology (extension of radios, telephones, or televisions) display a stronger positive correlation with democratization in all other sample specifications than the post-Cold War sample. In other words, those aspects of development are more strongly related to democratization in the earlier than in later parts of our sample.

Regarding other economic determinants, our results lend support to the resource curse argument. All three of our variables related to resource wealth display negative and robust relationships with democratization (Ross 2001). One noteworthy omission from our list of most robust indicators is economic growth. Gassebner et al. (2013) find strong evidence that economic success stabilizes autocracies (but not democracies). Also in our analysis, growth is related negatively to democratization and significant at conventional levels for Polity and DD/ACLP, but not for BMR and V-Dem. Moreover, that coefficient is by far the largest in the Cold War period (− 0.28) and much smaller during the Third Wave (− 0.10) and the post-Cold War (0.00) periods. That reduction in the size of the growth coefficient is consistent across all democracy measures.

Additional tests

In this section, we discuss variations on our main analysis (see online Appendix E, F, G for results). First, we estimated our models excluding GDP per capita as a core variable in order to reduce concerns of post-treatment bias. Yet, for the democratization results, omitting income has only a minor impact on which of the indicators are robust. Only three variables drop off the robust list, namely energy consumption per capita, oil/gas per capita and global proportion of democracies. For democratic survival, however, several indicators of concepts associated with broader economic modernization become robust (industrialization and urbanization, education) once income is omitted.

The latter result provides additional evidence that broader economic development enhances democratic survival. However, when it comes to disentangling which features of development have a clear relationship with democratic stability, we run into issues of complex causality. For instance, it is plausible that richer countries can afford to build better educational systems, suggesting that income should be controlled for. Simultaneously, improved education may enhance economic efficiency and, thus, income (Bils and Klenow 2000). Controlling for GDP per capita therefore also could produce downward post-treatment bias. What we can say, however, is that development is related to democratic durability—be it through increased incomes or other channels. And, the sensitivity analysis with and without GDP per capita may be considered to represent, respectively, lower and upper bounds for the direct relationship between the different features of development and democratic survival.

Second, we re-ran the analysis using GDP data from the Maddison project (Bolt et al. 2018) rather than World Bank (2019). Changing the data source for GDP has some effects on the estimates, for instance on the coefficients on GDP per capital itself for democratization. These results are reported in online Appendix G. While the democratic survival results are robust, the democratization results are weakened. That evidence adds to the cautionary note that the relationship between income and democratization is not robust across specifications.

Third, we assess the extent to which between- or within-country differences drive the results by applying the Mundlak (1978) estimator, which calculates the mean value for each country (between-variation) and subtracts country-year values from this mean (within-variation). Both the between- and within-variables subsequently are entered into our original setup. We focus our discussion on the effects of income on democracy since the effect of Muslim population on democratization—unsurprisingly owing to its stability over time—exists only between countries. The Mundlak estimation reveals that the income–democratic survival relationship is driven mainly by variation between countries. A relatively large average estimated within-country effect also is found (with considerable uncertainty) when using Polity, DD/ACLP and BMR, but not when using V-Dem. We note that the uncertainty for the within-country findings can stem from relatively few transitions from democracy to autocracy in the post-1960 data. Yet, in combination, the results add nuance the findings above; it is not clear that a democratic country becomes less prone to breaking down as it becomes wealthier. But, when comparing across countries, richer democracies are more stable than poorer ones.

For income and democratization, between-country estimates do not suggest a relationship; \({\bar{\beta }}\) hovers around 0. But we find evidence of a positive within-country relationship; the income coefficient is positive and statistically significant across all democracy measures. If we consider only models controlling for natural resources, a further increase in \({\bar{\beta }}\) is evident across all democracy measures. Thus, despite the overall sensitivity of the relationship, the latter results point in the direction that higher incomes are related to higher probabilities of democratization.