Center-Periphery Dissonance as a Possible Factor of the Revolutionary Wave of 2013-2014

Abstract

Jack Goldstone proposes three predictors for acute social and political destabilization during the revolutionary wave of 2013-2014: (a) an intermediate level of per capita GDP, (b) a high level of corruption, and (c) a transitional type of political regime. After testing this theory on a broader sample, this study suggests and finds support for another predictor—“center-periphery dissonance” for the destabilization of the 2013-2014 wave. The emergence of this factor is common in the process of modernization, and is due to the heterogeneity of modernization processes, when a system’s central elements (“capitals”) are almost always modernized faster than its periphery. Identification of this factor is of considerable interest because accounting for this factor could significantly improve our capability to predict risks of sociopolitical destabilization of modernizing social systems.

Keywords

political regimes revolution central collapse risks of sociopolitical destabilization modernization middle-income countries elections capital city

Introduction

In 2013-2014, the world experienced a new revolutionary wave of rather weak, but very specific nature. Protest upsurges in Cairo, Kiev, and Bangkok led to the collapse of regimes (in the first and the third cases—with direct participation of military forces); protests in Tunis, Caracas, Istanbul–Ankara, and Sarajevo seriously challenged the corresponding regimes, though did not result in their actual collapse. Are there any common features between these major destabilization cases, which occurred synchronically in such distant (both from the geographical and civilizational point of view) countries as, say, Venezuela, Ukraine, and Thailand? Our analysis reveals that such common features exist and are surprisingly numerous.

Awareness of forces and factors acting behind such upsurges is an indispensable basis for developing forecasts of sociopolitical dynamics. In turn, those forecasts serve as a basis for understanding the looming strategic political risks and threats for the World System periphery (and the world as a whole) in the nearest and midterm future. However, the World System periphery (and, especially, semiperiphery) has recently experienced a series of developmental changes so dramatic in their speed, depth, and versatility, that analytical risk-forecasting systems based on the materials of the last decades of the 20th century proved unable to adapt to the new reality—indeed, none of these systems managed to predict in 2012 the major sociopolitical destabilization and upheavals of 2013-2014 in Ukraine, Thailand, Venezuela, or Bosnia. This makes the development of new effective systems for sociopolitical instability forecasting an especially urgent and high-priority task.

Literature Review

An interesting attempt at searching for the common features in the recent protest waves has been undertaken by a well-known American sociologist Jack Goldstone. He looks into four country cases, Thailand, Ukraine, Bosnia, and Venezuela, to specify the following common characteristics observed:

First, all four are middle-income countries, ranging in terms of per capita GDP (at purchasing power parity) from 73rd (Venezuela) to 106th (Ukraine) out of the 187 countries ranked by the International Monetary Fund (IMF).

Second, all four countries are rated as “partly free” by Freedom House. Note that Goldstone and his colleagues have presented substantial evidence demonstrating that these are, namely, intermediate political regimes (between consistently authoritarian regimes and consolidated democracies) that are the most prone to sociopolitical destabilization risks (Goldstone et al., 2010; Goldstone et al., 2003), whereas Freedom House “partly free” rating appears to indicate just this type of regimes.

Third, all four are rated as highly corrupt: In 2013, according to Transparency International’s (TI) corruption perception index (CPI), Thailand was 102nd, Ukraine was 144th, and Venezuela was 160th in level of perceived corruption¹ (note that the lower is the TI CPI rating, the worse is the corruption in the respective country).

They have just arrived at the point where the vast majority of the population is literate, expects a government to provide a sound economy, jobs, and decent public services. Yet they are not yet economically comfortable and secure. That security, and a better future for themselves and their children, depends very heavily on whether government leaders will work to provide greater opportunities and progress for the nation as a whole, or only to enrich and protect themselves and their cronies. They are at a point where limiting corruption and increasing accountability are crucial to whether their country will continue to catch up to the living standards of richer countries, or fall back to the standards of poorer ones. (Goldstone, 2014a)

Let us note here that everything quoted above fully pertains to the three other countries encompassed by the 2013-2014 revolutionary wave—Egypt, Tunisia, and Turkey—which were not investigated by Goldstone. Similar to Thailand, Ukraine, Bosnia, and Venezuela, they are middle-income countries (International Monetary Fund, 2014), rated as “partly free” by Freedom House for the time when the protests started (Freedom House, 2013) and characterized by a high level of corruption² (Transparency International, 2013).

To the phenomena described by Goldstone, we can add one more common feature shared by all seven above-mentioned countries which experienced destabilization in 2013-2014 and which dramatically differentiates the 2013-2014 destabilization wave from the one which occurred in 2011 (the Arab Spring). Indeed, all (in some cases successful) attempts at regime overthrowing during the Arab Spring were targeting the authoritarian rulers, while within the few nonconsolidated democracies of the Arab World (Lebanon, Palestine Autonomy, and Iraq), no crowds demanding for the rulers to step down (al-sha’b yurid isqat al-nizam!), could be observed (Korotayev, Issaev, Malkov, & Shishkina, 2013; Korotayev, Issaev, & Shishkina, 2013; Korotayev, Issaev et al., 2014). On the contrary, in the 2013-2014 destabilization wave, all antiregime protests targeted democratically elected powers.³

Here, we should emphasize one important circumstance. Goldstone relates high risk of destabilization in the middle-income countries encompassed by the latest revolutionary wave to the low quality of state administration in these countries. However, as we are dealing with nonconsolidated democracies, we should bear in mind the low quality of not only the state administration but of the citizens themselves as well. Indeed, a high percentage of citizens in such regimes (compared with the populations of consolidated democracies) have not sufficiently internalized the democratic values yet and think it normal not to wait until the next elections for bringing down the unwanted ruler, but rather take immediate revolutionary action to overthrow this ruler (Malkov, Korotayev, Issaev, & Kouzminova, 2013; Truevtsev, 2011; Tsirel, 2012).

Second, to all the common features of the 2013-2014 revolutionary events listed above, we can add one more feature—All these cases belong to the “central collapse” type.

Huntington (1968) pointed out that major revolutions show at least two distinct patterns of mobilization and development. If military and most civilian elites initially are actively supportive of the government, popular mobilization must take place from a secure, often remote, base. In the course of a guerrilla or civil war in which revolutionary leaders gradually extend their control of the countryside, they need to build popular support while waiting for the regime to be weakened by events—such as military defeats, affronts to national pride and identity, or its own ill-directed repression or acts of corruption—that cost it domestic elite and foreign support. Eventually, if the regime suffers elite or military defections, the revolutionary movement can advance or begin urban insurrections and seize the national capital. Revolutions of this type, which we may call peripheral revolutions, occurred in Cuba, Vietnam, Nicaragua, Zaire, Afghanistan, and Mozambique. (Goldstone, 2001, p. 143)

Clearly, this description does not fit the scenarios of revolutionary destabilization of 2013–2014 in Bosnia, Thailand, Ukraine, Egypt, Venezuela, Tunisia, and Turkey at all (though it fits the destabilization pattern of 2014–2015 in Yemen, Syria, Iraq, Libya, and Nigeria whose analysis goes out of the scope of this article).

In contrast, revolutions may start with the dramatic collapse of the regime at the center (Huntington, 1968). If domestic elites are seeking to reform or replace the regime, they may encourage or tolerate large popular demonstrations in the capital and other cities, and then withdraw their support from the government, leading to a sudden collapse of the old regime’s authority. In such cases, although the revolutionaries take power quickly, they then need to spread their revolution to the rest of the country, often through a reign of terror or civil war against new regional and national rivals or remnants of the old regime. Revolutions of this type, which we may call central revolutions, occurred in France, Russia, Iran, the Philippines, and Indonesia. (Goldstone, 2001, p. 143)

“The central collapse,” according to Goldstone (2014b),

may be precipitated by a short-term economic downturn or price spike, a military defeat, a manipulated election, or new and resented actions by the government.⁴ Whatever the initial impetus, it is swiftly followed by a major demonstration in the capital city. The government tries to disperse the demonstration but encounters surprising difficulty in doing so; initial efforts by the government are followed by expanding demonstrations. Police forces are unable to cope with the urban disorders, and the government faces a situation where the military has to be called in. Yet the military refuses to act decisively to clear the streets; key units may stand aside while others may even defect and go over to the opposition. The inaction of the military acts as a signal to the ruler, elites, and the population that the regime is defenseless. Crowds surge and take over the capital; similar mass demonstrations spread to other cities and the countryside. All of this generally unfolds over a few weeks or at most a few months. The ruler may then flee or be captured, while elites supported by the crowds or the military take over government buildings and set up a provisional government. (p. 27)

Clearly, this description suits the scenarios of revolutionary destabilization of 2013–2014 in Bosnia, Thailand, Ukraine, Egypt, Venezuela, Tunisia, and Turkeyvery well indeed.⁵ Further on, we will regard only the “central collapse” type, leaving the “peripheral advance” scenario largely out of attention.⁶

Central Collapse Scenario and “Center-Periphery Dissonance” (CPD): Historical Examples and 2013-2014 Revolutionary Wave

It is widely known that modernization processes, which are highly correlated with Westernization processes in the contemporary world (Huntington, 1998; Polyakov, 1997), proceed unevenly in different parts of a single country. Generally, modernization proceeds much faster in the capital than in peripheral regions, which can cause a rapidly deepening difference in the moods and attitudes of the population residing in the capital and in the periphery. To put in a crudely reductionist way, more “liberal”/“Westernized” views tend to prevail in the capital cities of the modernizing countries, while more “conservative”/less “Westernized” (more “Islamist” in the Islamic countries) attitudes prevail in the periphery.

In such a situation, the instatement of democracy in such countries systematically engenders a pattern where democratic elections bring to power a party supported by the majority of a country’s population, but very unpopular among the population of the capital city. This phenomenon is denoted by us as the “center-periphery dissonance.”

One of the most characteristic historical examples here can be found in France in 1848-1871. In 1848, the Parisians overthrew the French monarchy, and the first direct presidential elections took place on December 10 the same year.⁷ Much to the surprise of Parisian liberals, Charles-Louis-Napoleon Bonaparte won the elections. Next, the all-France referendum of December 21, 1851, prolonged his presidential term from 4 to 10 years. Another all-France referendum of November 21, 1852, authorized turning France from a republic into an empire, thus opening the democratic way to proclaiming Charles-Louis-Napoleon Bonaparte the Emperor of the Second French Empire, Napoleon III.

On September 3-4, 1870, the Parisians once more overthrew the French monarchy and proclaimed a republic again. At the following elections to the first National Assembly of the Third Republic on February 8, 1871, the Republicans won in Paris. However, across France as a whole, the majority of seats in the new Parisian Parliament were obtained by conservative monarchist parties (Lejeune, 1994), which probably served as one of the main factors that triggered the start of Parisian uprising, known as the Paris Commune.

We present evidence that CPD played an important role in generating the 2013-2014 destabilization wave. This thesis can be inferred from our analysis of the electoral statistics on the distribution of votes in the countries which experienced this destabilization. Let us view the events in the seven countries—Thailand, Tunisia, Venezuela, Turkey, Bosnia and Herzegovina, Egypt, and Ukraine—in more detail.

In Thailand, the ruling (until the 2013-2014 events) Phak Phuea Thai (“For Thais Party” = the Pheu Thai Party) received almost half of the votes in the general election in 2011, which allowed it to get 265 seats in Parliament out of 500. Its main rival, the Democratic Party, received 35% of the votes and 159 seats in the Parliament. However, in Bangkok, the Pheu Thai Party received only 30% of the votes, much less than the Democratic Party; as a result, Bangkok got represented in the Parliament by 23 deputies of the Democratic Party and only 10 deputies from the Pheu Thai Party (“Elections,” 2011). The opposition won in almost all districts of central Bangkok. The fact that majority of the Thai capital residents supported the opposition and not the ruling party was once more convincingly demonstrated during the elections of the Bangkok mayor in 2013. An indisputable victory was held by Suhumhand Paribatra of the opposition Democratic Party, who replaced Pongsapat Pongcharoen as the mayor of Bangkok (Bangkok Metropolitan Administration, 2013). Note that Bangkok became the epicenter of protest wave that started in November 2013 and ended in 2014 as the regime was taken down by the military.

In October 2011, the elections to the Constituent Assembly of Tunisia were clearly won by Ennahda (Revival), a rather moderate Islamist party which far outpaced its main secularist rivals, gaining 37% of votes. However, in the capital, Ennahda received only 29.9% of votes, which is one of the lowest results in the country. It was in the city of Tunis that the anti-Islamist protest wave began in February 2013, seriously jeopardizing the survival of the ruling moderate Islamist regime (Dolgov, 2014; Issaev, 2013).

In Venezuela, Nicolas Maduro, successor to Hugo Chavez and leader of the United Socialist Party, scored more than half of the votes at the country level in the presidential election in 2013. However, in the very important central regions of Caracas, Maduro only received a minority of votes, while the majority supported his opponent Henrique Capriles, the leader of Democratic Unity Roundtable (National Electoral Council of Venezuela, 2013). Note that these areas of Caracas became the major base of the protest wave starting in January to February 2014.

In Turkey, in 2011, the ruling Justice and Development Party led by Recep Tayyip Erdogan won a quite convincing victory in parliamentary elections both across the whole country, and in Istanbul. However, a Pew Research Survey conducted in March 2013 (2 months before the start of a powerful protest wave at the Taksim Square in Istanbul) showed that although across the whole country almost two thirds of population supported Erdogan, in Istanbul he enjoyed the support of only a minority of its inhabitants (Fisher, 2013).⁸

The situation in Bosnia and Herzegovina is especially difficult to analyze because of the extremely complex administrative system of the country. The head of state is not an individual, but the Presidency is a collective body comprising representatives of the country’s three main ethnic groups—Croats, Serbs, and Bosnian Muslims. At the same time, the country is divided into the Croatian-Bosnian Federation of Bosnia and Herzegovina, Republika Srpska, and the de facto controlled by the latter District Brcko (Torkunov, 2009). The most large-scale protests in 2014 in Bosnia were observed in the capital, Sarajevo, but they primarily affected the Croatian-Muslim Federation of Bosnia and Herzegovina (73% of its population are Bosnian Muslims). Against this background, it is noteworthy that the leader of the Bosnian community, Bakir Izetbegovic, received the majority of Bosnian Muslim votes across the whole country in the previous presidential election and only a meager minority in the capital (Central Electoral Commission of Bosnia and Herzegovina, 2010).

At the Egyptian 2012 constitutional referendum, the Muslim Brotherhood obtained rather substantial support for the constitution which they had been pushing forward, getting 63.8% of votes. However, in Cairo, the constitution got supported only by a minority—43.2%—of those who took part in the referendum (Egyptian Supreme Election Committee, 2012). Half a year later, Cairo became the epicenter of protests which ended on July 3, 2013, when the military forcibly removed the administration of the “Muslim Brotherhood” headed by President Mohamed Morsi with the mass support of Cairo residents (Issaev, 2014; Vasilyev & Vinnitsky, 2013). However, a mathematical analysis of the last presidential election showed that in Middle Egypt, the Muslim Brotherhood is still supported by the vast majority of population (Korotayev & Issaev, 2014).

A similar situation was observed in Ukraine. In the second round of 2010 presidential elections, Victor Yanukovych took the first place with 48.95% of the vote (Central Electoral Commission of Ukraine, 2010). In Kiev, however, he only received about quarter of the vote. In the parliamentary election of 2012, the Party of Regions (led by Yanukovych) won significantly more votes than any other party—about 30%. But that very election showed that the ruling party was supported by only a small minority (12.6%) of Kiev residents (Central Electoral Commission of Ukraine, 2012). In November 2013, Kiev became the epicenter of a wave of protests that culminated in February 2014 with an overthrow of the administration of President Yanukovych.

Hypotheses and Tests

Goldstone’s analysis can well be presented as a formal “politometric model” largely based on the following hypothesis liable to formal empirical quantitative tests:

In 2013-2014, sociopolitical destabilization following the “central collapse” scenario was strongly predicted by the combination of middle-level GDP per capita with high level of corruption and a political regime intermediate between the consistently authoritarian type and the consolidated democracy.

Bivariate tests of the correlation between the three above-mentioned independent variables and the dependent variable (sociopolitical destabilization following the “central collapse” scenario) yield the following results:

To test the correlation between the middle-level per capita GDP and “central collapse,” the middle-income countries have been operationalized as those belonging to the third quintile according to the IMF rating as regards per capita GDP (at purchasing power parity; IMF, 2014). This has turned out to be a rather weak, but still marginally statistically significant predictor of sociopolitical destabilization following the “central collapse” scenario in 2013-2014 (see Table 1).

To test the correlation between the high level of corruption and “central collapse,” the countries with high level of corruption have been operationalized as those having 2013 CPI values of 50 points and lower (let us recollect that the CPI ranges from 0 to 100 where “0” denotes the highest level of corruption and “100” denotes its lowest level—a total absence of corruption). This is also a rather weak predictor of sociopolitical destabilization following the “central collapse” scenario, though with an unequivocal statistical significance (see Table 2).

To test the correlation between “intermediate” political regime and “central collapse,” political regimes intermediate between the consistently authoritarian type and the consolidated democracy have been operationalized as those indexed by Freedom House for 2013 as “partly free” and coded as “1,” the countries indexed as “free” or “not free” were coded as “0.” With this operationalization, the “intermediate political regime” (“nonconsolidated democracy”) is significant as well as the strongest among the three predictors, but still, with a Rho of .33, it is a rather weak predictor of sociopolitical destabilization following the “central collapse” scenario (see Table 3).

Table 1.

Middle-Level GDP per Capita as a Predictor of the Level of Sociopolitical Destabilization Following the “Central Collapse” Scenario in 2013-2014.

Quintiles of the IMF rating as regards per capita GDP (at purchasing power parity; dichotomized)	Index of sociopolitical destabilization level following the “central collapse” model				Total
	0	0.25	0.5	1	Total
0 (the other quintiles)	96	28	23	2	149
0 (the other quintiles)	64.4%	18.8%	15.4%	1.3%	100%
1 (the third quintile)	21	5	8	3	37
1 (the third quintile)	56.8%	13.5%	21.6%	8.1%	100%
Total	117	33	31	5	186
Total	62.9%	17.7%	16.7%	2.7%	100%

Note. The values set for the sociopolitical destabilization index based on the “central collapse” model are as follows: 1.0—forcible overthrow of the government in the presence of mass revolutionary mobilization of the capital city population in accordance with to the “central collapse” model; 0.5—attempt at forcible overthrow of the government in the presence of mass revolutionary mobilization of the capital city population according to the “central collapse” model; 0—absence of forcible overthrow of the government or any attempts at such overthrow in accordance with the “central collapse” model; and 0.25—intermediary situation between 0 and 0.5. Only the latest period of the global political process is viewed—the countries are viewed at the period between the latest elections (if these occurred no later than March 15, 2014) and July 1, 2014. Rho = .1, p = .096 (one-tailed). IMF = International Monetary Fund.

Table 2.

High Level of Corruption as a Predictor of the Level of Sociopolitical Destabilization Following the “Central Collapse” Scenario in 2013-2014.

Corruption level in 2013 according to Transparency International (dichotomized)	Index of sociopolitical destabilization level following the “central collapse” model				Total
	0	0.25	0.5	1	Total
0 (low)	96	28	23	2	149
0 (low)	64.4%	18.8%	15.4%	1.3%	100%
1 (high)	21	5	8	3	37
1 (high)	56.8%	13.5%	21.6%	8.1%	100%
Total	117	33	31	5	186
Total	62.9%	17.7%	16.7%	2.7%	100%

Note. States with 2013 CPI scores of 50 and lower have been coded as “1” (states with higher corruption levels), whereas states in the range between 50 and 100 have been coded as “0” (states with lower corruption levels). CPI = corruption perception index. Rho = .27, p< .001.

Table 3.

Intermediate Political Regime as a Predictor of the Level of Sociopolitical Destabilization Following the “Central Collapse” Scenario in 2013-2014.

Freedom House Index (dichotomized)	Index of sociopolitical destabilization level following the “central collapse” model				Total
Freedom House Index (dichotomized)	0	0.25	0.5	1	Total
0 (other values ≈ consistently authoritarian regimes and consolidated democracies)	92	20	13	1	126
	73.0%	15.9%	10.3%	0.8%	100%
1 (partly free≈ nonconsolidated/partial democracies)	25	13	18	4	60
1 (partly free≈ nonconsolidated/partial democracies)	41.7%	21.7%	30.6%	6.7%	100%
Total	117	33	31	5	186
Total	62.9%	17.7%	16.7%	2.7%	100%

Note. Rho = .33, p<< .0001.

However, the analysis above suggests that the combination of all the three factors (middle income, high corruption, and intermediate political regime) might predict more strongly as regards the sociopolitical destabilization following the “central collapse” scenario in 2013-2014.

This hypothesis can be operationalized in the following way:

In 2013-2014, among the countries with per capita GDP within the middle quintile and with high level of corruption (indicated by the Transparency International CPI as being with CPI below 50), within the states indexed as “partly free” by Freedom House, the revolutionary destabilization following the “central collapse” scenario could be expected with significantly higher frequency than in the countries indexed as “free” or “not free.”⁹

A formal test of this hypothesis is presented in Table 4.

Table 4.

Nonconsolidated Democracy as a Predictor of the Level of Sociopolitical Destabilization Following the “Central Collapse” Scenario in Highly Corrupted Middle-Income Countries in 2013-2014.

Freedom House Index (dichotomized)	Index of sociopolitical destabilization level following the “central collapse” model				Total
Freedom House Index (dichotomized)	0	0.25	0.5	1	Total
0 (other values ≈ consistently authoritarian regimes and consolidated democracies)	12	3			15
	Algeria, Azerbaijan, China, El Salvador, Guyana, Iran, Jamaica, Serbia, South Africa, Suriname, Tonga, Turkmenistan	Brazil, Iraq, Peru
	80%	20%			100%
1 (partly free≈ nonconsolidated/partial democracies)	5	2	4	3	14
	Albania, Dominican Republic, Ecuador, Maldives, Paraguay	Columbia, Libya	Bosnia, Tunisia, Turkey, Venezuela	Egypt, Thailand, Ukraine
	35.7%	14.3%	28.6%	21.4%	100%
Total	17	5	3	3	29
Total	60.7%	17.9%	10.7%	10.7%	100%

Note. The values set for the sociopolitical destabilization index based on the “central collapse” model are as follows: 1.0—forcible overthrow of the government in the presence of mass revolutionary mobilization of the capital city population according to the “central collapse” model; 0.5—attempt at forcible overthrow of the government in the presence of mass revolutionary mobilization of the capital city population according to the “central collapse” model; 0—absence of forcible overthrow of the government or any attempts at such overthrow according to the “central collapse” model; and 0.25—intermediary situation between 0 and 0.5. Only the latest period of the global political process is viewed—The countries are viewed at the period between the latest elections (if these occurred no later than March 15, 2014) and July 1, 2014. Rho = .53, p = .003 (with Turkey); Rho = .50, p = .007 (without Turkey).

On the whole, for the recent years, the presence of a nonconsolidated democratic regime turns out to be a rather good predictor of sociopolitical destabilization following the “central collapse” model (see Figures 1 and 2).

Figure 1.

Risks of sociopolitical destabilization following the “central collapse” scenario in 2013-2014 in highly corrupted middle-income countries with consistently authoritarian or consolidated democratic regimes.

Figure 2.

Risks of sociopolitical destabilization following the “central collapse” scenario in 2013-2014 in highly corrupted middle-income countries with partly democratic regimes.

One can see quite clearly that in the latest years both consistent authoritarianism and consolidated democracy served as powerful inhibitors of sociopolitical destabilization following the “central collapse” scenario in highly corrupted middle-income countries. Indeed, only a small minority of such countries having a Freedom House rating different from partly free had any incidence of such destabilization—and its scale was minimal. Meanwhile, sociopolitical destabilization following the “central collapse” scenario was observed in the absolute majority of highly corrupted middle-income countries rated as partly free—and, moreover, such destabilization was commonly large scale, up to the collapse of regimes.

We should note, however, that for Table 4, the correlation between two variables is not particularly high yet (with Spearman correlation coefficient ranging between .50 and .53).

Let us now test another possible predictor of sociopolitical destabilization in highly corrupted middle-income countries, which we have denoted above as “center-periphery dissonance.” In general, the respective hypothesis may be formulated in the following way: “In 2013-2014, sociopolitical destabilization following the ‘central collapse’ scenario was strongly predicted by the combination of middle-level GDP per capita with high level of corruption and a substantial level of the CPD.” This hypothesis may be further operationalized in the following way:

In 2013-2014, for countries with per capita GDP within the middle quintile, with high level of corruption (indicated by the TI CPI as being with CPI below 50), and with higher value of center-periphery dissonance index (CPDI), one could expect a revolutionary destabilization following the “central collapse” scenario with a significantly higher frequency than in countries with lower value of this index.¹⁰

To test this hypothesis, we use the following CPDI:

1—level of support for the regime is significantly lower in the center (“capitals”) than across the country as a whole;

0.5—intermediate value; and

0—level of support for the regime is the same or higher in the center (“capitals”) compared with that across the country as a whole.

Out of seven cases viewed above, the value “1” was given to Thailand, Ukraine, Bosnia and Herzegovina, Tunisia, and Egypt, while Venezuela and Turkey were given the value “0.5.” We gave the value 0.5, for example, to Brazil as well, as during the presidential elections of 2010 the ruling party leader Dilma Rousseff got the majority of votes both across the country in general and in two out of three Brazilian “capitals,” Brasilia and Rio-de-Janeiro. However, in the third “capital,” Sao Paulo, the largest city of the country, she only got a minority of votes (Superior Electoral Tribunal of Brasil, 2010).¹¹

Let us now formally test the hypothesis formulated above (see Table 5).

Table 5.

Center-Periphery Dissonance as a Predictor of the Level of Sociopolitical Destabilization Following the “Central Collapse” Model in Highly Corrupted Middle-Income Countries, 2013-2014.

CPDI	Index of sociopolitical destabilization level following the “central collapse” model				Total
CPDI	0	0.25	0.5	1	Total
0 (level of support for the regime in the center [“capitals”] is the same or higher than across the country as a whole)	13	2			15
	Albania, Dominican Republic, Paraguay, Algeria, Azerbaijan, China, El Salvador, Guyana, Iran, Serbia, Suriname, Tonga, Turkmenistan	Libya, Iraq
	86.7%	13.3%			100%
0,5 (intermediary value)	2	1	2		5
	Jamaica, South Africa	Brazil	Turkey
	40%	20%	40%		100%
1 (level of support for the regime in the center [“capitals”] is significantly lower than across the country as a whole)	1	2	2	3	8
	Ecuador	Columbia	Bosnia, Tunisia	Egypt, Thailand, Ukraine
	12.5%	25%	25%	37.5%	100%
Total	16	5	4	3	28
Total	57.1%	17.9%	14.3%	10.7%	100%

Note. Rho = .722, p< .001 (with Turkey included in the sample); Rho = .721, p< .001 (with Turkey excluded from the sample). CPDI = center-periphery dissonance index.

As we can see, for the last few years, the presence of CPD is an even more powerful predictor of sociopolitical instability following the “centralcollapse” model than the presence of a partly democratic regime. Thus, the correlation coefficient (.72) for the CPD is appreciably higher than for nonconsolidated democracy (.50-.53). It is amply evident when using dichotomized indices (see Figures 3 and 4).

Figure 3.

Risks of sociopolitical destabilization following the “central collapse” scenario in 2013-2014 in highly corrupted middle-income countries without center-periphery dissonance.

Figure 4.

Risks of sociopolitical destabilization following the “central collapse” scenario in 2013-2014 in highly corrupted middle-income countries with center-periphery dissonance.

Let us remember that in 2013 and the first half of 2014, the presence of unconsolidated democracy in highly corrupt middle-income countries is an indicator of 64% risk of sociopolitical instability of the “central collapse” model. However, the presence of CPD indicated a much higher risk—87%.

Discussion and Conclusion

We were able to identify a very strong predictor of destabilization waves of the “central collapse” type in 2013-2014. We name this factor “center-periphery dissonance.” We emphasize again that this factor does not emerge randomly, but is rather logically generated in the process of modernization, and is associated with the natural heterogeneity and asynchrony of modernization processes, wherein the central elements (“capitals”) of the state almost always modernize faster than its periphery.

The greatest forecasting effect will likely be achieved by a method which will account for all the above-mentioned factors, including the level of economic development, corruption level, the type of political regime, and CPD.¹²

Identification of the CPD factor is of considerable interest because taking this factor into account can significantly improve the quality of forecasting the sociopolitical destabilization risks in modernizing social systems. Let us emphasize here that in no way do we try to deny the importance of the type of political regime as a destabilization predictor in modernizing sociopolitical systems. We agree that unconsolidated democracies are characterized by a significantly higher risk of destabilizing than consistently authoritarian regimes, as well as consolidated democracies. We propose not to replace the “type of political regime” factor with the “center-periphery dissonance” factor, but rather to complement it. The greatest forecasting effect will likely be achieved by a method which would take into account the effect of all the above-mentioned factors—the level of economic development, the level of corruption, the type of political regime, and the CPD.

It should be emphasized that the destabilizing role of this factor can be very strong and dangerous. Indeed, the events of recent years have shown that this factor can destabilize a society which has completed its demographic transition and no longer has such traditionally important factors of political destabilization as population pressure or “the youth bulge” (see, for example, Goldstone, 2002; Grinin & Korotayev, 2010, 2012; Hagesteijn, 2008; Korotayev, Issaev, Malkov et al., 2013; Korotayev, Issaev et al., 2014; Korotayev, Malkov, & Grinin, 2014; Korotayev & Zinkina, 2011; Korotayev et al., 2011; Turchin & Korotayev, 2006). At the same time, there are reasons to believe that the CPD may trigger not only relatively bloodless “central collapse” but also civil wars with considerably high death toll. Indeed, if the central collapse occurs under the influence of CPD, it almost by definition means that at least a very large part (if not all) of the politically active population in the periphery considers the forces seizing power in the capital to be illegitimate, thus justifying their own right to use violence to overthrow these forces. Let us note here that CPD played its role in the generation of a full-scale civil war in Russia in 1917-1918. Indeed, in November 1917, the elections to the All-Russian Constituent Assembly were won by the Socialist-Revolutionaries (SR), who received more than 40% of the vote and 347 seats in the Constituent Assembly—that is, almost twice more than the next most popular party, Russian Social Democratic Labour Party (bolsheviks) (RSDLP(b)) headed by Vladimir Lenin, which received 24% of the vote and only 168 seats in the Constituent Assembly (Znamenskii, 1976).

However, in the capitals, the Bolsheviks received more votes than the SR—indeed, a higher percentage of votes than the SR received across the country. Thus, in Petrograd, Bolsheviks received 45% of votes, while SR got only 17%. In Moscow, the Bolsheviks got an even greater share of the vote—48%. The fact that the majority of the Russian capitals’ population supported the Bolsheviks (and Left SRs) exerted strong influence on their decision in January 1918 to forcibly eliminate the most legitimate authority represented by the Constituent Assembly on January 6 (19th) the same year, thus triggering a full-scale civil war.

Afterword

One of the anonymous referees of this article has made the following suggestion: “It would be interesting to see what cases in 2015 now fit the pattern, so that we can see if any of these countries experience political crises.” We find this suggestion very interesting indeed. Indeed, in the main text, we have taken into account the political destabilization events (of the central collapse type) that took place before July 1, 2014. So, we have considered how well those political destabilization events that have taken place between July 1, 2014, and the current moment (June 1, 2015) would fit the pattern that we have detected.

To start with, it is worth noting that in the second half of 2014, the destabilization wave following the central collapse pattern (that was so characteristic of 2013 and early 2014) began to subside. The second half of 2014, as well as the first half of 2015, has evidenced a lot of turmoil, but this mostly followed the peripheral advance model. This includes the September revolution in Yemen in 2014, when the Ansar Allah movement started to spread its influence from the northern peripheral province of Saada, and eventually got control of the capital—Sana’a, and by 2015, it controlled most of the country. The situation in connection with the emergence and spread of the Islamic State in the Middle East (as well as Boko Haram in West Africa) can be also regarded as a straightforward peripheral advance. The same can be said about the conflict in the Central African Republic, where the rebels, mainly Muslim, being dissatisfied with harassment from the Christian leadership, headed by President François Bozizé in December 2012 seized a number of towns in the central and eastern parts of the country, and by March 2013 entered the capital Bangui, overthrowing the previous leadership. Armed conflicts in Bangladesh and Pakistan have deep roots, last for decades and have nothing to do with the subject of our study, going beyond it. The same, incidentally, applies to the civil war in Libya, where the country since 2011 has been gradually losing traits of a coherent state.

In the period in question, there have still been three cases of political destabilization following the central collapse model—in Macedonia (in May 2015), Burkina Faso (in October 2014), and Burundi (in April 2015). Note that only one of them (Macedonia) is a middle-income country, while two other should be rather classified as “low income.” On the contrary, that all the three countries were rated by Freedom House as “partly free” in recent years (Freedom House, 2015; though last year Freedom House rated Burundi as “not free”). And all of them are rated as highly corrupt by TI (2014). As regards the CPD, it does not appear to be observed in Burundi, but it appears to be observed both for Burkina Faso and Macedonia.

In Burkina Faso, at the last parliamentary elections in 2012, the ruling party—Congress for Democracy and Progress (CDP)—took a total of 48.66% of the vote in the country as a whole, but in the capital Ouagadougou, the percentage of votes cast for the CDP was significantly lower—36.48% (Commission Électorale Nationale Indépendante, 2014). Against this background, it does not appear coincidental that this was the capital, Ouagadougou, where in October 2014, demonstrations began against the government; moreover, the demonstrations grew into riots whose participants captured the Parliament building and burnt headquarters of the ruling party. As a result, President Blaise Compaoré announced his resignation from the post of the head of the state and fled to Senegal.

As for Macedonia, the protests began there in its capital Skopje, on May 5, 2015, and grew into large-scale demonstrations against the current government. The last election held in Macedonia in 2014 showed a low level of support for President Gjorge Ivanov in Skopje compared with other regions of the country. In the presidential election in 2014, Gjorge Ivanov won, gaining 55.28% of the vote; however, the majority of residents of the capital Skopje voted for his opponent, Stevo Pendarovski (57.8% against 41.14%; State Election Commission, 2014).

Another suggestion made by the same anonymous referee is to offer (on the basis of the proposed methodology) a prediction of what countries will experience destabilization waves in the near future.

We find this suggestion rather appropriate and below we will apply the proposed methodology to the most recent data to identify countries with a high risk of sociopolitical destabilization following the central collapse pattern.

According to the most recent version of the IMF (2015) database, the middle quintile of the countries of the world as regards per capita GDP (at purchasing power parity) looks as follows: Albania, Algeria, Barbados, Bosnia and Herzegovina, Brazil, China, Colombia, Costa Rica, Dominica, Dominican Republic, Ecuador, Egypt, FYR Macedonia, Grenada. Indonesia, Iraq, Jamaica, Jordan, Libya, Maldives, Mongolia, Montenegro, Namibia, Palau, Peru, Serbia, South Africa, Sri Lanka, St. Lucia, St. Vincent and the Grenadines, Thailand, Tunisia, Turkmenistan, and Venezuela.

Out of them, 26 fall into the group of the most corrupt states (50 points or less) according to the most recent rating of TI (2014): Albania, Algeria, Bosnia and Herzegovina, Brazil, China, Colombia, Dominican Republic, Ecuador, Egypt, Macedonia, Indonesia, Iraq, Jamaica, Jordan, Libya, Mongolia, Montenegro, Namibia, Peru, Serbia, South Africa, Sri Lanka, Thailand, Tunisia, Turkmenistan, and Venezuela.

Within this group, the most recent election results indicate the presence of the CPD in the following countries:

Bosnia and Herzegovina: In the Bosnian General Elections of 2014, the pattern observed in 2010 was reproduced—Although Bakir Izetbegovic’s Party of Democratic Action got the largest share of votes in the country (32.87%), it only got 19.5% in the capital Sarajevo (Central Electoral Commission of Bosnia and Herzegovina, 2014). Thus, the CPD continues to be observed in Bosnia and Herzegovina, which indicates the possibility of new destabilization waves following the central collapse pattern in the near future. This possibility is further amplified by the point that Freedom House still rates Bosnia and Herzegovina as “partly free” (Freedom House, 2015) indicating an intermediate type of political regime (most liable for political destabilization).

Macedonia: See above. Note that in this country, the combination of middle level of economic development, high corruption, and CPD (in conjunction with an intermediate type of political regime) has already led to a destabilization wave following the central collapse pattern. The point that the country continues to have this combination of traits suggests a high risk of new waves of this type of destabilization waves in the near future.

Peru: At the most recent general elections, the Gana Peru party led by President Ollanta Humala won the election at the national level, but it only got 19.03% of vote in the capital (Lima; Oficina Nacional de Procesos Electorales, 2011). However, the risk of destabilization here appears to be somehow mitigated by a higher (than in the previous cases) level of the democratic development (indicated by the “free” rating of the Freedom House) which appears to be capable to serve to a certain extent as an inhibitor of the sociopolitical destabilization.

Colombia: In the last parliamentary elections, the ruling “Party of the U” got twice as small share of votes in the capital (Bogota) as in the country as a whole (Registraduria Nacional del Estado Civil, 2014). This possibility is further amplified by the point that Freedom House rates Colombia as “partly free” (Freedom House, 2015) indicating an intermediate type of political regime (most liable for political destabilization).

South Africa: In the general elections of 2014, the ruling African National Congress got a sizable majority of votes (59.38%) in the country as a whole, but it only got a minority of votes (32.89%) in one of the South African “capitals”—Cape Town (Electoral Commission of South Africa, 2014). However, as in Peru, the risk of destabilization here appears to be somehow mitigated by a higher level of the democratic development (indicated by the “free” rating of the Freedom House) which appears to be capable to serve to a certain extent as an inhibitor of the sociopolitical destabilization. The risk appears to be further lowered by the point that the CPD is only observed here for one of the capitals, whereas in two other capitals (Pretoria and Johannesburg), the African National Congress got the majority of votes.

Thus, our analysis has identified five countries, where at present, we observe the combination of middle level of economic development, high corruption, and CPD that indicates a high risk of sociopolitical destabilization following the central collapse pattern: Colombia, Bosnia and Herzegovina, Macedonia, Peru, and South Africa. Out of them, the risk appears to be higher for Macedonia, Colombia, and Bosnia where the above-mentioned combination of traits is complicated by the intermediate type of political regime (indicated by “partly free” rating of the Freedom House). It appears to be lower in South Africa and Peru where more democratic political regimes (indicated by the “free” rating of the Freedom House) could to a certain extent inhibit such a destabilization.

Footnotes

Appendix

Results of Estimation of Multinomial Logistic Model, Explaining RCI by CPDI and Freedom House Index (Dichotomized).

	Multinomial logit (Model 1)
	Coefficient	(SE)
Probability of “absence of forcible overthrow of government” (RCI = 0) comparing with probability of “forcible overthrow of government” (RCI = 1)
Independent variables
Constant	−1,566	(1,287)
CPDI = .00	21,268	(10,335)
CPDI = .50	4,201	(26,634)
Freedom House Index (= 0)	18,969***	(1,389)
Probability of “minor attempt of forcible overthrow of government” (RCI = 0.25) comparing with probability of “forcible overthrow of government” (RCI = 1)
Independent variables
Constant	−0.782	(1,002)
CPDI = .00	18,801	(10,335)
CPDI = .50	2,989	(26,634)
Freedom House Index (= 0)	18,704***	(0.000)
Probability of “attempt of forcible overthrow of government” (RCI = 0.5) comparing with probability of “forcible overthrow of government” (RCI = 1)
Independent variables
Constant	−0.405	(913)
CPDI = .00	1,079	(12,699)
CPDI = .50	22,322	(26,836)
Freedom House Index (= 0)	−18,760	(11,007)
Model information and diagnostics
Number of observations	28
Likelihood ratio χ² (overall model)	29,616 (df = 9) p = .001
Likelihood ratio χ² (CPDI)	15,667 (df = 6) p = .016
Likelihood ratio χ² (Freedom House Index)	8,975 (df = 3) p = .030

Note. Baseline category “(Forcible overthrow of government with mass mobilization of the population of the center/“capitals”). (RCI = 1).” RCI = regime change index; CPDI = center-periphery dissonance index.

.01 ≤p< .05. **.001 ≤p< .01. ***p< .001.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article is an output of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (HSE) in 2015 with support by Russian Science Foundation (Project No. 14-11-00634).

Notes

Author Biographies

Andrey Korotayev is the head of the Laboratory of Monitoring of the Risks of Sociopolitical Destabilization of the National Research University Higher School of Economics. Together with Askar Akaev, he is a coordinator of the Russian Academy of Sciences Program “System Analysis and Mathematical Modeling of World Dynamics.” His main research interests include cross-cultural research, quantitative analysis of sociopolitical destabilization and its factors, as well as mathematical modeling of the World System development. He is an author of more than 200 academic articles and 20 monographs, including the recently published Great Divergence and Great Convergence. A Global Perspective (New York, NY: Springer, 2015; with Leonid Grinin). His monograph Introduction to Social Macrodynamics (Moscow: URSS, 2006; in collaboration with Artemy Malkov and Daria Khaltourina) has brought him an award of the Russian Science Support Foundation in “The Best Economists of the Russian Academy of Sciences” nomination. In 2012, he was awarded with the Gold Kondratieff Medal by the International N. D. Kondratieff Foundation.

Leonid Issaev is a senior lecturer of the general politics department at the National Research University Higher School of Economics, vice-head of the Laboratory for Sociopolitical Destabilization Risks Monitoring (National Research University Higher School of Economics), and Member of Scientific Council of the Russian Association of Political Science. He graduated from the Faculty of Applied Political Science, National Research University Higher School of Economics, and the University of Cairo. He had internship at the headquarters of the League of Arab States in Cairo. He is an author of more than 80 scientific publications, including the monographs such as Egyptian Turmoil of the 21st Century (Moscow: Librokom, 2012, coauthored with A. R. Shishkina), Syria and Yemen: Unfinished Revolutions (Moscow: Librokom, 2012, in collaboration with A. R. Shishkina), System Monitoring of Global and Regional Risks: The Arab World After the Arab Spring (Moscow: Librokom/URSS, 2013; Ed., together with A. V. Korotayev and A. R. Shishkina), and System Monitoring of Global and Regional Risks. Central Asia: New Challenges (Moscow: Librokom/URSS, 2013, Ed., together with B. A. Akayeva, A. V. Korotayev, and A. R. Shishkina). His sphere of academic interests includes political process and political systems in Middle East and Central Asia, as well as Islamic political philosophy.

Julia Zinkina is a research professor at the Laboratory for Sociopolitical Destabilization Risks Monitoring, National Research University Higher School of Economics. She is an author of more than 100 scholarly publications, including articles such as “Kondratieff Waves in Global Invention Activity” (Technological Forecasting & Social Change, 2011, 78, pp. 1280-1284); “The Origins of Dragon-Kings and Their Occurrence in Society” (Physica A, 2012, 391(21), pp. 5215-5229); “Urbanization Dynamics in Egypt: Factors, Trends, Perspectives” (Arab Studies Quarterly, 2013, 35(1), pp. 20-38); “Measuring Globalization: Existing Methods and Their Implications for Teaching Global Studies and Forecasting” (Campus-Wide Information Systems, 2013, 30(5), pp. 11-25); “Projecting Mozambique’s Demographic Futures” (Journal of Futures Studies, December 2014, 19(2), pp. 21-40); “Explosive Population Growth in Tropical Africa: Crucial Omission in Development Forecasts” (World Futures, 2014, 70(2), pp. 120-139); and “On the Structure of the Present-Day Convergence” (Campus-Wide Information Systems, 2014, 31(2-3), pp. 139-152). Her sphere of academic interests includes political demography, global history and global networks, as well as social processes in Tropical Africa and Middle East.

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