State international income position,trade openness,and mass antigovernment protests

Abstract

This research provides a state-level systemic explanation for antigovernment insurgencies. We posit that state international income position influences antigovernment movements, which effect, however, hinges on the level of trade openness. By analyzing 157 states from 1990 through 2018, it is found that for a state having an unclosed market, its internationally relative income level is negatively associated with mass antigovernment protests. This study implies that trade liberalization may exert a context-specific impact on domestic political insurgency, which helps understand why trade liberalization seems to be a reason for mass anti-government movements in some contexts while enhancing a regime in other cases.

Keywords

Mass protests trade openness international relative deprivation international comparison political confidence

Introduction

This research provides a state-level, systemic explanation for the rising of regime-challenging movements. Specifically, we find that, with a normal level of trade openness, a state’s low position in global or regional income distribution has a bearing on antigovernment uprisings. So far, a plethora of research endeavors to explain large-scale protests through economic lenses. Primarily, economic downturns are supposed to instigate political protests (e.g. Bermeo and Bartels, 2014; Grasso and Giugni, 2016; Lehman-Wilzig and Ungar, 1985; Yagci, 2017), given that a plummeting economy tends to provoke people's sense of relative deprivation and suggest the failure of some policy or/and incompetence of political incumbents. And economic failure threatens the survival of democratic regimes (Andersen and Krishnarajan, 2019). By contrast, theorists like Lipset (1959) posit that economic development drives civic movements because public demands for political participation are aroused as citizens’ material possessions grow. The existence of negative cases against the initial proposition, however, led to several modifications. Przeworski et al. (2000) find that democracy seems more likely to survive at a higher income level, but economic development needs not cause democratization. Relatedly, resource curse theorists point out that preying on domestic natural resources and thereby gaining financial autonomy probably largely accounts for the viability of some wealthy dictatorships (e.g. Ross, 1999). Likewise, other economic factors, such as inflation, income inequality, trade, foreign investment, and sanctions, are supposed to be linked with political rages and protests, though some claimed causations remain contentious. That being said, there still exist puzzling cases which have not yet been satisfactorily explained by economic recessions or disruptions. For instance, despite Belarus's steady economic growth in terms of GDP per capita by 2020, mass protests spread across this country right after the presidential election of 2020, venting severe public grievance against Lukashenko's regime. Nor can this insurgence be accounted for by economic sanctions or income inequality, since Belarus had not received effective sanctions until it made blatant right breaches during the post-election protests, and its income inequality had been kept at a low level and even falling.¹

This study shifts attention from the conventionally focused economic factors to state international economic position and investigates whether and how it affects political turbulence. Specifically, we propose that large-scale antigovernment protests are more likely in a state of a greater international economic welfare lag, and this effect hinges on the level of market openness. This theoretical model sheds light on cases like Belarus from a novel lens. As Eastern European neighbors economically had grown faster than had Belarus, its regionally relative income had been declining and appeared sluggish, despite its absolute rise in terms of standard income indexes. People in autocracies may consider material gains as compensation for the deprivation of political liberty. But Lukashenko's regime had appeared even incapable of bringing as much welfare as its democratic neighbors had (see Supplemental Appendix A.1), which had probably bred political disappointment and opposition. More broadly, this study has the potential to decipher a wide range of political phenomena or patterns like the following. In a later section, we elaborate on how these seemingly disparate questions can be approached using our model.

Why did Gorbachev's economic liberalization reforms in the 1980s catalyze the falling of rather than bail out the Eastern European economically troubled Soviets?

Why did Deng Xiaoping's course of Reform and Opening seem to have consolidated the Chinese Communist Party's political dominance rather than provoked democratic movements since the 1989 protests?

Why did the so-called “fourth wave” of regime-targeting protests in contemporary major nondemocracies like China (the A4 movement), Iran (the Mahsa Amini protests), and Kazakhstan (the Kazakh unrest) rise recently?

Why would there be protests in support of autocracy in countries like Tunisia (Ash, 2023)? And how could autocracy stand out as a better alternative for the grieved than a democratic opposition?

Why have democratic regimes been more likely to survive at a high level of individual income (Przeworski et al., 2000)?

The contribution of this research is two-fold. First, it advances the study of trade-protest relations by offering a novel explanation for why trade liberalization seems to contribute to mass anti-government movements in some contexts while enhancing a regime in other cases. Through highlighting trade's role in disseminating information and thereby facilitating between-state comparison, this study suggests that the impact of exposure to the global market on political uprisings is context-specific—depending on state position in international welfare distribution. Secondly, this research helps extend the application of the relative deprivation approach to the state level.

Systemic factors matter: Comparison, inference, and learning

The institutional diffusion school contends that external environments and international networks play crucial roles in the transboundary diffusion of institutions (e.g. Cranmer et al., 1991; Starr, 1991). States adopt certain political institutions probably because of foreign coercion, competition for investment, or learning (Dobbin et al., 2007; Simmons et al., 2006). Particularly, the learning approach asserts that if liberal institutions appear more economically successful in foreign states, they become more attractive to domestic citizens, elites, and politicians, and encourage their pursuit of the institutions (Miller, 2016; Simmons et al., 2006). One corollary of this perspective is that people under illiberal institutions are more likely to protest the regime and advocate liberal reforms if they perceive a greater correlation between liberal institutions and economic achievement among other states.

Existing empirical evidence supports such learning-driven democratization on the global scale, whereas it fails to hold at the regional level (Miller, 2016). In effect, the approach probably underestimates the reality that though people may make comparisons between other states whereby they form an inference about the correlation between institutions and economic outcomes, they are concerned primarily about variations between themselves and others, especially for identifying issues before seeking a solution. Suppose there is an authoritarian state which has an income level much higher than an average democracy. Its economic success should unnecessitate and dissuade political transition due to attendant costs and uncertainty of this attempt; yet this state faces a greater democracy-welfare correlation based on the state-level observations (excluding the observer itself) than do average others, which, according to the learning approach, forecasts a great likelihood of institutional transformation. That is, the common wisdom appears at odds with the theory's anticipation.² As a resolution to this contradiction, this research regards people's sense of between-sovereignty relative deprivation as the basis of their polity-welfare inference, and then the learning mechanism approves applicable on the regional scale.

Interstate relative deprivation

The initial analytical framework for social movements emphasizes the role of shared grievances in triggering collective actions (Finkel and Rule, 1986; Gurr, 1970; Smelser, 2011). Gurr (1970) traces social insurrections back to collective grievances and the so-called “relative deprivation” in participants’ minds. People are innately comparativists, and “persons may feel deprived of some desirable thing relative to their own past, another person, persons, group, ideal, or some other social category” (Walker and Pettigrew, 1984: 302), and such relative loss causes social movements.³ The grievance-aggression approach asserts that people with an awareness of relative deprivation tend to develop discontent and seek policy remedies through informal political engagement (Finkel and Rule, 1986; Gurney and Tierney, 1982; Jo and Choi, 2019).⁴ Existing research on intergroup relative deprivation mostly focuses on domestic cross-group comparison and grievance against out-groups. Focus has been on major social categories such as ethnic groups (e.g. Caplan and Paige, 1968), employed versus unemployed groups (e.g. Walker and Mann, 1987), job positions (Martin, 1982), and so forth; they are under an overarching power, the state, local government, or company, that determines policy and serves as the major target of complaints. So, transnational relative deprivation as well as its relations with public attitudes toward or/and actions against the state remain under-investigated. This research bridges this gap by checking relative deprivation's application at the state level. We assert that citizens, elites, and social segments resent significant interstate income lags, and the widely shared grievance can be transformed into political upheavals.

Importantly, some social psychologists stress that people select comparison targets. Gartrell (2002) contends that an individual tends to compare himself to those whom he frequently contacts, has multiplex relationships with, and has a strong tie with, as well as those who are similar to himself.⁵ Similarly, as one of the premier relative deprivation theorists, Davis (1959) finds that “comparisons with people in the same logical classes produce relative deprivation or gratification” (p. 290). The First Law of Spatial Correlation assumes that “everything is related to everything else, but near things are more related than distant things” (Tobler, 1970: 234). Thus, adjacent places tend to share identical physical, and natural conditions. Because contiguity as well facilitates communication and transportation, people are more likely to be susceptible to the influence of nearby nations, and neighboring states tend to share homogenous cultures. Relatedly, Albrecht and Koehler (2020) find that revolutions are contagious. So, according to the socialized comparison theory, people are inclined to conceive of intraregional states as comparable. A strength of intraregional comparison is that it guides people to find presumably feasible solutions to their national problems. People are supposed to have substantial motivations to imitate economically successful states, but they are less likely to develop sufficient confidence in duplicating economic success created by a remote nation—their far different natural and social settings make them nearly incomparable. It is reasonable for people to reckon that anything workable on neighboring, similar nations is likely to function the same way on themselves.

The cognitive process of responsibility attribution

A few relative deprivation theorists stress that the cognitive process of attribution (i.e. direction of blame) is crucial in mobilizing social movements (Crosby, 1976; Ferree and Miller, 1985; Walker and Smith, 2002). People may have three kinds of response to relative deprivation: self-, object-, and system-directed responses (Mark and Folger, 1984), and the last directly relates to social movement participation (Walker and Smith, 2002). Olson and Roese (2002) integrate relative deprivation with counterfactual comparison and posit that people make comparisons by imagining “what their outcomes might have been if circumstances had been different” (p. 266). Building on the former studies, we summarize the cognitive process of judging the regime in face of state-level relative deprivation: (1) Individual A finds people in some other states have a higher income level; (2) A wants such a level of income; (3) A finds her state have most of the things that the richer states have, except for certain political institutions; (4) A believes that she will have as high income as in the richer states if her state adopts the institutions. Simply put, juxtaposing sovereignty with its wealthier counterparts which have conspicuously different institutions can assure citizens of their institution being responsible for well-being backwardness; the government-targeting blaming drives them to join the opposition and engage in anti-regime actions.

For certain scenarios, people may find wealthier states having disparate institutions. For instance, for most of the last century, pro-Communism and pro-West political factions coexisted within most developing countries. Though aligned with contrasting political factions or ideologies, people were sharing the consensus that advancing political institutions after a successful model was a prerequisite for achieving post-colonial development and catching up toward the advanced circle. Therefore, whether wealthier comparison targets have homogeneous or conflicting institutions does not matter much—different models can appeal to certain citizens, elites, activists, or social segments by fitting their personal mindset or socioeconomic position and offer a theory to justify their aversion to the status quo and regime-challenging action.

Government's diversionary efforts

Political leaders always have incentives to frame and disseminate information favoring themselves. Media have been widely used as a means of shaping political perceptions of recipients (e.g. Doob, 1948; Ellul, 1966). A state's significant welfare lagging behind other states tends to pressure the leadership into prohibiting unfavorable between-sovereignty comparisons from emerging among the populace and reinforcing pro-regime opinions. To this end, they are likely to intensively produce pro-regime propaganda to trumpet its economic and social achievements, as well as to release fake, biased, or distorted information on wealthier countries, especially those having competing institutions. Likewise, the theory of diversionary foreign policy contends that, when domestic political situations are deteriorating for the leadership, political leaders are incentivized to use force abroad or exaggerate external threats, as a way to divert the public attention outward and create leadership-supporting rallies (e.g. Brody, 1991; Mueller, 1970). In all, through driving the leadership to concentrate on manipulating people, international income lags have the potential to reduce political opposition and anti-regime movements.

Nevertheless, mass media are less influential when the populace possesses alternative information sources than when they are occupied by one voice (McCombs, 2004). External information that falls outside governmental control sometimes permeates borders and sneaks in, which can compromise governmental efforts to propagandize and render cross-sovereignty comparison a reality. There exist a variety of activities bringing about cross-border information diffusion, such as foreign travel, abroad study, investment inflows, and international trade.

Cross-border trade: The medium of accessible knowledge for the masses

Relations between trade and protests are still vigorously contested. International trade creates unevenly distributed benefits, and losers from it are likely to protest a certain trade treaty or/and advocate protection or compensation (e.g. Milner, 1999; Rogowski, 1987). There is empirical evidence in support of this postulation (e.g. Karakaya, 2018; Palmtag et al., 2020), whereas some research finds that trade openness can reduce social unrest (e.g. Dodson, 2015). Some other investigations do not find a significant influence of trade openness on domestic insurgencies. These studies mainly focus on sector- or industry-based grievance and policy-specific protests, and less attention has been given to trade's contribution, if any, to mass, regime-challenging protests. In this regard, some theorists of modernization explain that economic liberalization raises the middle class and thereby leads to civic movements (e.g. Lipset, 1959). But this idea falls short of explaining cases like the collapse of the European Soviets soon after the liberal reforms—no capital-based middle classes had a chance to arise therebefore. To bridge the gap, this study, by emphasizing the spillover effects of trade in terms of knowledge, offers an alternative theoretical model to understand the relation between trade liberalization and political insurrections.

Trade informs people of interstate income gaps and institutional options and thereby impacts their assessment of the regime's effectiveness. Apparently, people by no means develop interstate relative deprivation if they know nothing about foreign states or/and lack institutional knowledge. Trade serves as one of the main channels through which external information comes in, whereby it makes it possible for individuals to compare sovereignties. A plethora of trade research attests to spillover effects of trade in terms of knowledge (e.g. Grossman and Helpman, 1991; Spencer, 2003), but they focus largely on industrial technology rather than social knowledge. In contrast, we stress that trade is a medium of accessible knowledge for the masses. For garnering global profits, merchants are incentivized to seek and master foreign knowledge, relying on which they are able to design strategies fit for the political and economic context of overseas destinations for their investment or exports. They keep traveling abroad and even spend years in foreign countries for business. Subsequently, informed entrepreneurs, who are usually social elites, tend to wield their domestic influence and pass to folks their updated knowledge gained from their transborder activities. Even though many individuals probably lack interest in or/and spend little time on searching for external political and economic information, they unconsciously attain it from contacting foreign consumer goods, which convey information about foreign political practices and thoughts as well as exhibit exporters’ living standards, productivity, and technology. These real-life, tangible experiences can greatly assure them of what they are being taught by reform advocates or political oppositions consisting of intellectuals, activists, or other elites. That is, people in a highly autarkic state are likely to be immersed in and susceptible to pro-regime propaganda, but this situation becomes less sustainable if the market is open. Considering the preceding reasonings, we put forth the following hypothesis.

Hypothesis (Global level). If a state has an open market, its globally relative income level is negatively associated with likelihood of mass antigovernment protests, ceteris paribus.

Hypothesis (Regional level). If a state has an open market, its regionally relative income level is negatively associated with likelihood of mass antigovernment protests, ceteris paribus.

This theoretical model helps to answer the series of questions posed in the introduction section. In the late 1980s, in the context of the Soviet regimes’ economic failure in comparison to their Western counterparts, people's sense of interstate relative deprivation was aroused by the opening of the market. The burst of political distrust and outrage brought out the wave of anti-regime uprisings. After the 1989 protest, Deng Xiaoping's open policy greatly elevated China's economic welfare level, and the fast income growth reduced people's sense of internationally relative deprivation and raised their content with the Leninist party-state system. Nevertheless, trade-driven economic growth in these authoritarian regimes tends to lose momentum at some point due to the inefficiency of a state-controlled domestic economy, a less creative society under grave repression, economic sanctions, or other kinds of policy failure. As interstate economic lags regrow, authoritarian regimes have to be confronted with another surge of mass protests. This probably is what is going on in Belarus, Iran, and China where mass protests emerged recently. This logic is also applied to anti-democracy sentiments and insurgencies, especially in unconsolidated democracies. Tunisia has been one of the most democratic countries in the Middle East and North Africa region, but its income level has been among the least (see Supplemental Appendix A.1). This particular regional environment facing Tunisia probably accounts for the active protests in support of autocracy rather than a democratic opposition in there (Ash, 2023). Lastly, through testing the hypothesis, we can check a plausible state-level explanation for the survival model of economy–democracy correlation (i.e. democratic institutions are more likely to be retained at a higher income level)—internationally relative high income can consolidate democracy by making people feel that autocracy is by no means a better exit option.

Large-N analysis

We conduct an empirical analysis based on a time-series cross-state data for 157 countries covering the period from 1990 through 2018. This study uses the mass protest count as the dependent variable, indicating the likelihood of a mass antigovernment protest. In addition to the key explanatory variables, global and regional income lags as well as the trade openness level, we include polity, GDP per capita, population, and external conflict as potential confounders to control for.

Dependent variable

The dependent variable, mass protest counts, is the total number of mass protests arising in a state-year. The count data are calculated based on the Mass Mobilization (MM) Data Project, which collects social movements against the government (Clark and Regan, 2016).⁶ We set a criterion for selecting mass protests—those protests that have at least 1000 protesters are identified as a mass protest.⁷ Setting a threshold for participant numbers can also ensure a high level of accurateness and representativeness of protest sampling, considering that small-scale protests are difficult to document, and many tend to be missed. A weakness of the MM data set is that it does not include protests in several important countries like the United States of America, Australia, and New Zealand.

Explanatory variables

We follow the conventional way to compute the variable, trade openness—it equals the summation of imports (%GDP) and exports (%GDP). The trade and GDP data come from the World Bank. We designate the international income lag facing state i in year t with ρ_i_,t, and a state has a greater income lag as it has a higher ρ. Let gdppc_i,t denote the GDP per capita (PPP) (from the World Bank database) of the state i in year t. The income gap facing state i relative to state j in year t is computed through log(gdppc_j,t)–log(gdppc_i,t), and if it is negative, it means state i has a higher individual income. As aforementioned, inferring the accountability of government for an international welfare lag entails information on both economy and institutions across sovereignties, we introduce between-state polity distance as a weight to their income gap. We adopt the Polity2 data of the Polity V project (Marshall et al., 2017) to compute the polity distance. The scale of Polity2 index ranges from − 10 (the most autocratic) to 10 (the most democratic). Let polity_i,t stand for the Polity2 index of the state i in year t, and the institutional distance (absolute value of the difference) between states i and j in year t is used to weight their income gap. Then we compute ρ_i,t using the following formula⁸:

ρ_{i, t} = \frac{\sum_{j = 1, i \neq j}^{n - 1} [\log (g d p p c_{j, t}) - \log (g d p p c_{i, t})] \times | p o l i t y_{i, t} - p o l i t y_{j, t} |}{(n - 1) \cdot \log (g d p p c_{i, t})}

Considering that data missing for some states in some years would distort the measurement and undermine the comparability of state relative incomes, we divide the sum of income lags by n−1 to standardize it. Likewise, we incorporate the state’s own GDP per capita into the denominator because wealthier states are assumed less sensitive than lower-income states to the same income gap. The formula is applied to evaluate both the state’s global position, ρ^globe, and its intraregional position, ρ^region. With respect to ρ^region, we adopt the region classification of states used by the Integrated Model to Assess the Global Environment (IMAGE), which considers both geographical and cultural proximity between states.⁹ We make some modifications to it to achieve a more reasonable categorization.¹⁰

Figure 1 displays the plots of the data. The upper two subfigures suggest no clear monotonic, linear association between either global or regional ρ and mass protests. However, the lower three-dimensional plots of the sample with respect to trade openness, ρ, and protest counts indicate an interactive effect—as both ρ and trade openness increase, the protest count has a rising tendency.

Figure 1.

The plots of the sample. Note: To mitigate the effects of several extremely high regional ρ cases and make an informative visualization, the plots display cases with a regional ρ less than 100. Similarly, the range of the trade openness for the three-dimensional plots is [0, 200).

Alternative Rho's for robustness checks

To demonstrate the robustness of this analysis, we will test the following alternative ρ's. First, we examine international income lags without being weighted by polity distances. The unweighted ρ is computed by the following formula:

ρ_{i, t}^{(u n w t)} = \frac{\sum_{j = 1, i \neq j}^{n - 1} [\log (g d p p c_{j, t}) - \log (g d p p c_{i, t})]}{(n - 1) \cdot \log (g d p p c_{i, t})}

Second, we use an alternative region categorization from the UN project of “Standard Country or Area Codes for Statistical Use” to calculate the regional ρ, which is denoted by ρ^region_2, for the purpose of making a robustness check on regional ρ. We adopt nineteen geographic regions from this project.¹¹

Third, we compute ρ based on an alternative measurement of democratic institutions: the v2x_polyarchy index of the V-Dem data set, which mainly indicates to what extent the state has free, contentious political elections (Pemstein et al., 2021). The explanatory variables, ρ^(poly)globe and ρ^(poly)region, indicate global and regional negative income lags weighted by polity distance based on the v2x_polyarchy index.

Control variables

Domestic economic performance is a major factor that influences public attitudes toward the government. Public grievance increases as people perceive economic recession (e.g. Eichengreen, 2018; Piazza, 2017). Empirical findings show that the growth rate of the economy is associated with protest events (e.g. Bermeo and Bartels, 2014; Grasso and Giugni, 2016). The correlation matrix in Supplemental Appendix A.3 shows that global ρ appears greatly correlated with GDP per capita, whereas regional ρ's correlation with state income levels is not strong. Hence, the model specification includes GDP per capita as a confounder, and the data are drawn from the World Bank database. A state's domestic institutions impact its economic performance (e.g. Helliwell, 1994) and social movement participation (e.g. Tilly and Tarrow, 2007) simultaneously, so regime types should be controlled for; the control variable, polity, is the polity2 score index. A diversionary interstate conflict can be utilized by political leaders as a means of distracting people's grievances over domestic affairs and thereby securing their position (e.g., Richards et al., 1993; Smith, 1996). That is, external conflict can be used to retard political opposition and protests. Meanwhile, interstate conflict tends to negatively impact international welfare positions by damaging the economy. This research draws on the External Conflict Index from the International Country Risk Guide's (ICRG) data set as the measurement of external conflict facing a state; and the variable, external conflict, ranges from 0 (extremely high risk) to 12 (extremely low risk) (The PRS Group, 2018).¹² The last control variable is population, which data are drawn from the World Bank's development indicators.

Models

Several maximum likelihood estimation regression models are employed in our analysis. Poisson and negative binomial (NB) regression models are established to examine the relationship between ρ and mass protest counts. To control for probable autocorrelation, a one-year-lagged dependent variable is included in the specification. The presence of excess of zero is an issue in the sample, so we also run zero-inflated negative binomial (ZINB) regressions to test the robustness of the analysis, with population being the key predictor of the excessive zeros (Silva, Tenreyro and Windmeijer, 2015). To deal with potential error covariance, fixed effects and random effects models will be tested. The Poisson regression model is as follows.

Pr (p r o t e s t_c o u n t_{i, t} | e^{x_{i, t} β}) = \frac{e^{- e^{x_{i, t} β}} e^{(x_{i, t} β) \cdot p r o t e s t_c o u n t_{i, t}}}{p r o t e s t_c o u n t_{i, t}!}

\begin{aligned} x_{i, t} β = & β_{0} + β_{1} \cdot p r o t e s t_c o u n t_{i, t - 1} + β_{2} \cdot ρ_{i, t} + β_{3} \cdot ρ_{i, t} \times t r a d e_o p e n_{i, t} \\ + β_{4} \cdot t r a d e_o p e n_{i, t} + β_{5} \cdot \log (g d p_p e r_c a p i t a_{i, t}) + β_{6} \cdot p o l i t y_{i, t} \\ + β_{7} \cdot e x t e r n a l_c o n f l i c t_{i, t} + β_{8} \cdot \log (p o p u l a t i o n_{i, t}) \end{aligned}

Regression results

Table 1 reports the statistics of regressing annual mass protest counts on trade openness as well as global and regional ρ, respectively. When the interaction term is excluded from the model, neither regional nor global income lags appear associated with mass protests. In the models, the interaction term has a positive coefficient which is statistically significant at the 99% level, while the coefficient of ρ keeps significantly negative at the 95% level except for in Models 1.4 and 2.5. The results are basically consistent with the hypothesis. Considering their different scales, global and regional ρ's standardized effects on protests are conditioned by trade openness to a similar degree. The models including the one-year lagged explanatory variable are tested, and the hypothesis holds especially for regional ρ (see Supplemental Appendix A.5). The regression models that include year dummies from 1990 through 2018 support the hypothesis as well (see Supplemental Appendix A.6). Moreover, we have special interests in the questions about whether and how well this model is applied to mass antigovernment insurgencies in autocracies. Supplemental Appendix A.7 displays the regression models for a sub-population of the states with a Polity2 score equal to or less than 5,¹³ and the results suggest that the hypothesis holds well for nondemocracies.

Table 1.

The regression of mass protest counts on global or regional Rho and trade openness from 1990 through 2018.

	Model 1.1	Model 1.2	Model 1.3	Model 1.4	Model 1.5	Model 1.6	Model 1.7
ρ ^globe	−0.594***	0.00777	−0.201***	−0.145*	−0.198**	−0.202***	−0.194***
ρ^globe × Trade openness	0.00526***		0.00363***	0.00368***	0.00369***	0.00367***	0.00292***
Trade openness	−0.00747***	−0.000569	0.000721	0.00105	−0.00317***	0.00287***	0.00378***
Controls	N	Y	Y	Y	Y	Y	Y
Lagged DV	N	Y	Y	Y	N	N	N
N	3683	2973	3973	2973	3061	3061	3042
	Poisson	Poisson	Poisson	NB	ZINB	RE	FE
	Model 2.1	Model 2.2	Model 2.3	Model 2.4	Model 2.5	Model 2.6	Model 2.7
ρ ^region	−0.0198***	0.00204	−0.00637***	−0.0107***	−0.00320	−0.0202***	−0.0195***
ρ^region × Trade openness	0.000142***		0.000153***	0.000202***	0.000186***	0.000236***	0.000181***
Trade openness	−0.00728***	−0.000631	0.000450	0.000934	−0.00335***	0.00338***	0.00430***
Controls	N	Y	Y	Y	Y	Y	Y
Lagged DV	N	Y	Y	Y	N	N	N
N	3683	2973	3973	2973	3061	3061	3042
	Poisson	Poisson	Poisson	NB	ZINB	RE	FE

Note: See the regression results for the whole specification in Supplemental Appendix A.4; ZINB: zero-inflated negative binomial; NB: negative binomial; RE: random effects; FE: fixed effects. t statistics in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

The marginal effects of global and regional ρ on protest counts with respect to trade openness levels, with 95% confidence intervals, are presented in Figure 2. They are calculated based on Models 1.3 and 2.3. It shows that the effect of global ρ becomes positive when trade openness is greater than 55, whereas the effect of regional ρ on protests becomes positive at 40 of trade openness. It suggests that when trade openness crosses a threshold, ρ is positively associated with mass protests, and particularly, intraregional income lags’ political effect is more likely to be triggered, implying that people are more susceptible to intraregional information. Figure 3 visually illustrates the predicted protest counts with respect to trade openness (ranging from 0 to 180) and the income lag. The control variables have their mean values in the prediction.

Figure 2.

The marginal effects of global and regional Rho’s on annual mass protest counts with respect to trade openness levels.

Figure 3.

The predicted mass protest count.

Robustness checks

Supplemental Appendix A.8 presents regressions of protests on another ρ, in which two states’ income gaps are not weighted by their polity distance. The results support the hypothesis. Yet, triggering the positive effect of unweighted ρ, compared to the weighted, on protests requires a higher level of trade openness; moreover, the estimated effect of unweighted ρ on protests, if standardized, is generally smaller than that of the weighted. The findings suggest that people are more sensitive to polity-distance weighted welfare backwardness than the income lag solely. We also regress protests on an alternative measurement of regional ρ—we compute it based on the UN regional categorization. The coefficients of trade openness and ρ are statistically significant at the 95% confidence level (see Supplemental Appendix A.9). Furthermore, we adopt the polyarchy index of the V-Dem data set and compute ρ by substituting this index for the Polity2 score to check the robustness of the analysis. The regression results appear not challenged by the change in the measurement of political institutions (see Supplemental Appendix A.10).

Empirics for the causal chain

This section is dedicated to making a brief empirical review of the supposed causal mechanism that underpins our proposition. First, we check on the joint influence of trade openness and state international income position on individual confidence in government. Prior studies have testified to a close correlation between public trust in government and confidence in institutions (e.g. Hetherington, 1998). We adopt the index of individual confidence in state government from the World Value Survey project (Inglehart et al., 2014), with a higher value indicating lower confidence. We regress the confidence indexes ranging from 1990 through 2018 on the same model specification for protests except for the dropping of population. The regression results strongly suggest that, with an unclosed market, state internationally relative low income is associated with low government confidence (see Supplemental Appendix A.1 1).

Then we introduce the existing studies concerning relations between individual confidence in the regime and antigovernment protest participation. Katsanidou and Eder (2018) examine individual data from the European Values Study from 1999 to 2001 and 2008 to 2010 and find that low confidence in political institutions correlates to a high probability of protesting. Likewise, Hansen and Ford’s (2023) research reveals that Belarusians of low satisfaction with the political system are more likely to protest. And there is also earlier important research like Kaase (1999) and Hooghe and Marien (2013) empirically testifying to the linkage between distrust of government or institutions and informal political participation.

Conclusion

This study contends that state internationally relative income levels can exert an influence on domestic mass antigovernment protests, while the effect hinges on the degree of market openness. We assume that people develop grievances when detecting international welfare deprivation; meanwhile, an income lag, especially with a palpable institutional difference, can drive people to ascribe their relative loss to governmental or institutional failure. Such regime-targeting outrage is likely to be transformed into mass antigovernment protests. Political leaders tend to respond to internationally comparative income disadvantages by immersing people in pro-regime propaganda so that they can impede between-sovereignty comparisons among citizens through hiding and distorting information. However, such efforts can be counteracted by transnational economic activities, which offer access to external information to domestic citizens and thereby enable people to economically and politically juxtapose states and formulate evaluations. So, we posit that a state’s lower position in global or regional welfare distribution, especially accompanied by palpable regime divergence, causes a higher likelihood of large-scale insurgencies, and the level of state integration into the global economy conditions this process. Our large-N analysis shows that as long as a state has a moderately open market, international income lags are positively associated with mass protests. And our empirical findings also suggest that regional income lags’ effect on antigovernment protests is more likely to be triggered by free market policy than the global one, implying that people are especially susceptible to intraregional information.

This study provides a state-level, systemic explanation for a number of political and economic phenomena of importance. First, this study suggests that trade liberalization's effects on public grievance, political opposition, and antigovernment movements are context-specific, depending on the state’s international income position. A free trade policy implemented by an underdeveloped authoritarian state, in the short run, can trigger inter-sovereignty comparison and raise the probability of uprisings. Examples include the collapse of the Soviet Union and the wave of nationwide protests in other socialist regimes. However, if authoritarian regimes can survive the difficulties in the first phase, they will enjoy great regime safety during the next, in which the open market brings rapid, persistent economic growth, and people's sense of internationally relative deprivation therefore falls. Nevertheless, trade-driven economic growth may have to decline at some point for reasons like sanctions or mismanagement of the economy. As a significant international economic welfare lag recurs, the regime has to be confronted with another surge of mass protests. Second, this theoretical framework approaches the question about why democracies are more likely to survive at a higher income level by referring to the fact that relative affluence helps people maintain confidence in democratic institutions. Likewise, this logic is also applicable to explain authoritarian stability in oil states as well as political movements in support of autocracy in some unconsolidated democracies.

Supplemental Material

sj-pdf-1-ias-10.1177_22338659231194923 - Supplemental material for State international income position, trade openness, and mass antigovernment protests

Supplemental material, sj-pdf-1-ias-10.1177_22338659231194923 for State international income position, trade openness, and mass antigovernment protests by Tianjing Liao in International Area Studies Review

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Tianjing Liao

Supplemental material

Supplemental material for this article is available online.

Notes

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