Should Demography Weigh in on U.S. Responses to Coups d’État?

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February 14, 2023 By Richard Cincotta

When a military-led or military-influenced coup d’état occur in a foreign country, does evidence from demographic research merit consideration in the U.S. foreign policy response? It’s a question that U.S. policymakers should be asking as deteriorating political conditions in West Africa come increasingly into confluence with the limited tools available either to deter or respond to illegal and extra-legal forms of political succession.

A recent series of military-led takeovers in that region has led several authors to criticize the U.S. government’s inconsistent record of response to coups. The focus of these articles has been the State Department’s reluctance to trigger Section 7008 of the Department of State, Foreign Operations, and Related Programs Appropriations Act—a congressionally-mandated policy intended to encourage a swift return to civilian rule, to ultimately open a path to elections, and to discourage other would-be coup plotters. Once a post-coup regime is identified and Section 7008 is procedurally set in motion, the act restricts expenditures by USAID programs in that country. Currently, the only exceptions to 7008’s punitive restrictions apply to democracy assistance and some types of humanitarian aid.

Some critics argue that to avoid triggering Section 7008 is to flout a law that clearly intends to restrict post-coup regimes that have been installed using military power. Others point out that coups create complex international political situations, and argue in favor of the State Department retaining some flexibility.

Political demography offers an alternative criticism—not of the U.S. State Department’s reluctance to trigger Section 7008, but of its broad restrictions. This criticism argues that Section 7008’s restrictions may not always work in the best long-term interests of the citizens of the countries affected, of the region, or of the United States.

Unlike other foreign-policy perspectives, political demography’s recommendations are rooted in published research that uses models, based on 50 years of age-structural data (estimates of the numerical distribution of residents, by age), to predict various forms of political behavior and socioeconomic performance. A growing body of research demonstrates that countries in the youthful phase of the age-structural transition (Fig. 1), as a group, have been the most likely to be engaged in armed civil conflictto occupy the lowest levels of social and economic development, and to lose liberal democracy. Recent research on successful coups d’état also shows a similar demographic pattern of risk. In each case, the shift from a youthful to an intermediate age-structure—the result of a decline in fertility to below 3 children per woman—is associated with lower probabilities of political instability and higher probabilities of development.

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Figure 1: Principal Phases of the Age-structural Transition

The Golden Rule

From this research comes a general recommendation, or what could be called political demography’s golden rule: Avoid policies that keep persistently youthful populations demographically young. Their consequences can come back to bite you.

Rather than promoting long-term political stability, some of 7008’s restrictions run counter to this recommendation by disrupting social programs that have played critical roles in advancing the age-structural transition—specifically, programs that improve access and quality of family planning and other reproductive health services, efforts that augment girls’ educational attainment, and activities that advance women’s autonomy and rights.

 A closer look at the relationship between coups d’état and countries in the youthful phase of the age-structural transition suggests that political demography may offer valuable insights that could offer an occasion to rethink Section 7008 and broader U.S. policy.

Mind the Gap

Even during the Cold War, when the risk of a coup d’état was more than three-times as high as today, a wide and persistent disparity (Fig. 2) existed between youthful countries—as a group, the most vulnerable to coups, regardless of their geographical location—and countries in their intermediate and mature phases. Among youthful countries, those with a population in the so-called early segment of this phase (with a median age of 20 years or less) have consistently been the most vulnerable to a successful coup and the most likely sites of back-to-back coups (coups that occur within five years of a prior coup).

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Figure 2:  Vulnerability to Coups, by Age Structure

Shifts in population age structure, coupled with the close of the Cold War, provide a statistical explanation for most (but not all) of the recent downward trend in coups. Data compiled by the University of Illinois’s Cline Center indicate that this decline has been steep: from 39 coups in 24 countries (each with a population over 500,000 residents) during the 1990 to 1994 period to a post-World-War-II low of just 5 coups in 5 countries from 2015 to 2019.

Despite concerns raised by the recent uptick in coups (8 coups in 6 countries since the beginning of 2020), political demographers are more worried by the slow rate of age-structural change that the UN Population Division projects for the Greater Sahel (from Senegal and Mauritania, east to Somalia) and tropical Africa (south of the Sahel to Angola and Mozambique). If the predictions of age-structural models prove reasonably accurate, analysts should expect successful coups to occur in 4 to 9 countries every five years, from now to 2040. At least two-thirds are likely to occur in countries in the transition’s youthful phase. Because countries in the early segment of the youthful phase are typically most vulnerable, the Greater Sahel and tropical Africa are likely to remain hotspots for coups d’état (see Map, Fig. 3).

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Figure 3:  Population Age Structures, 2020 (estimates) and 2040 (projections)

Among countries in either the intermediate or mature phases, not only are coups substantially less frequent, they are also often qualitatively quite different. For example, coups in the intermediate and mature phases are nearly twice as likely as those in the youthful phase to be instigated by a large-scale popular uprising—a so-called color revolution, such as Georgia’s Rose Revolution in 2003, Tunisia’s Jasmine Revolution in 2011, or Algeria’s Revolution of Smiles in 2019. Similarly, later-phase coups have been twice as likely as those in youthful countries to be carried out by a faction within the ruling government (known as a palace coup).

The Constraints of a Youthful Age Structure

One explanation for a state’s vulnerability to a coup lies in the political regime’s inability to meet the demands of governance. Analyses of data from the World Bank’s World Governance Indicators database suggest that, as a group, countries with youthful populations have been scored as the least effectively governed, the least able to control corruption, the least likely to apply the rule of law, and the least politically stable (Fig. 4).

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Figure 4:  Youthfulness and Governance, 2018

Notably, each of these trends reaches its upper-middle scores (above zero), near the middle of the demographic window—a series of development-favorable age structures encountered midway through the age-structural transition (beginning at a median age of 26 years and ending at about 40 years).

With the exception of some oil and mineral-rich states and a few of the least populous states (less than 5 million residents), youthful countries rarely attain the World Bank’s upper-middle income category or achieve similarly high levels of child survival and educational attainment until they attain the demographic window. Moreover, youthful countries have been the least likely to end persistent and intermittent armed civil conflict—a factor that figures in coups led by junior military officers (for example, the recent back-to-back coups in Mali and Burkina Faso), who typically justify their actions with claims that political leaders and senior military officers were too inept and corrupt to resolve the ongoing insurgency.

Age Structure’s Conundrum of Causality

Age-structural models are not without their detractors. Critics have puzzled over causal connections that might strengthen their probabilistic predictions, or argued that age structure is merely a passive reflection of the current level of economic development. Yet this latter characterization falls short of explaining how the UN Population Division’s medium scenario—a projection that chooses the statistical middle-path from thousands of similar historical patterns of fertility and mortality change—has been used successfully to predict both the rise and demise of liberal democracy, years before other discernable economic or political signals were on the horizon.

When first presented with evidence of age structure’s predictive abilities more than a decade ago, a group of intelligence analysts and researchers broke this riddle of causality into three hypothetical possibilities:

  • First, the age-structural transition’s most development-favorable distributions contribute to, or facilitate, stronger state performance, higher tax revenues, higher levels of human capital, and more stable political behavior.
  • Alternatively, the transition’s least favorable age structures constrain socioeconomic and political progress, encourage non-productive spending, or result in shortfalls in services that undermine government legitimacy and promote state weakness.
  • And lastly, progress along the age-structural transition simply discloses poorly visible set of social and institutional transformations that precede more extensive, more observable socioeconomic and political changes.

The group concluded that the most appropriate choice was, “Probably all of the above.”

Opting for Transition

At the very least, this analysis should encourage policymakers to rethink a pair of basic policy questions: When are Section 7008’s broad set of restrictions most likely to succeed? And when, instead, might these restrictions inhibit the age-structural transition and prolong conditions that facilitate additional political instabilities?

According to political demography’s calculus, the answers to these basic questions hinge on a country’s position in the age-structural transition. Post-coup regimes that govern countries in the transition’s intermediate or mature phase (see Fig. 1)—including Tunisia, Myanmar, and Thailand—currently face low risk of experiencing an outbreak of a non-ethnic civil conflict and a relatively high probability of experiencing political liberalization. By applying Section 7008 restrictions, their governments might, indeed, be successfully coaxed back onto their former track of political development.

However, for post-coup regimes on the wrong side of the vulnerability gap, that same calculus suggests that Section 7008 be considered with much more caution, and that the U.S. should avoid actions (as political demography’s golden rule suggests) that might prolong a country’s persistence in the transition’s youthful phase. In many of today’s most youthful countries, it seems doubtful that the possible short-term payoffs from applying 7008’s restrictions could be worth the long-term costs of setbacks to women-centered programs that have labored for decades to train in-country staff and to overcome formidable traditional, religious, and bureaucratic barriers. 

Apparently, there are two ways to discourage coups d’état without breaking political demography’s golden rule. The most effective way (and the most difficult way in a divided Congress) would be to broaden Section’s 7008’s exemptions to include assistance to family planning and other reproductive health services, girls’ education, and women’s rights efforts. The alternative is to continue to avoid triggering Section 7008, while focusing on diplomacy and defense-cooperation to encourage civilian rule and discourage would-be coup perpetrators.

Richard Cincotta is a Wilson Center Global Fellow at the Environmental Change and Security Program. He wishes to thank the Population Institute for its support during this research.

Sources: UNDESA, Population Division, Population Prospects, the 2022 Revision, 2022; World Bank World Governance Indicators Database, 2020Peyton, B. et al. Coup D’état Project Dataset, v. 2.0.0, Univ. of Illinois, Cline Center for Advanced Social Research, 2020; National Intelligence Council, Global Trends 2030: Alternative Worlds, 2012.

Photo Credits: Woman speaking at the “Women, Peace and Security” event in Mali organized by the Government of Mali in partnership with UN Women, courtesy of Flickr user UN Women/Ryan Brown.

Review of “The Demographic Transition Theory of War” (Brooks et al., 2019, International Security)

This review, recently published on the
H- Diplo website (read it here), provides insights into the background, methods, and conclusions of a recent landmark paper by Deborah Jordan Brooks, Stephen G. Brooks, Brian D. Greenhill, and Mark L. Haas, entitled “The Demographic Transition Theory of War: Why Young Societies Are Conflict Prone and Old Societies Are the Most Peaceful.” published in International Security 43(3): 53-95.

To download a copy of the click here …

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Youthful Clusters of States: The Future of Revolutionary Conflict

Youthful Clusters

Have you ever noticed that shifts in the world political order are clustered in time and geographical location? For example, the late-1980s and 1990s, economists and political scientists were caught off guard by the economic rise and a series of political reforms among the East Asian Tigers (South Korea, Taiwan, Thailand, Singapore, and Indonesia), quickly setting them apart from the rest of the developing world. The Tigers’ successes were followed in the early 2000s by an unheralded burst of liberal democracy among Caribbean and Latin American states (including Brazil, Chile, Argentina, Guyana, Trinidad & Tobago).  Then, beginning in 2010, came the Arab Spring, during which Tunisia’s attainment of liberal democracy caught regional political experts completely off guard.

However,  demographers were less surprised (see 2008/09 article predicting the rise of a democracy in North Africa). The states of these regions were entering the demographic window–a period during the age-structural transition when states typically progress economically and often undergo political changes.

Similar temporal and geographical clustering arises where progress in the age-structural transition has not occurred; i.e., among youthful clusters of states that are “stuck in age-structural time.” Today, the vast majority of revolutionary conflicts (insurgencies that are focused on overthrowing the political system or altering the central government, as indicated in the latest (2017) version of the UCDP/PRIO Armed Conflict Data Set) occur in youthful states—those with a median age at or below 25.5 years.  Youthful states typically appear in clusters, and these conflicts tend to spread across their borders, typically stopping at the borders of non-youthful states.

In the following 6 regional tables (South Asia & the Pacific Rim; West & Central Africa; East & Southern Africa; the Middle East, North Africa & Central Asia; the Americas; and Europe), I’ve divided the world into 21 clusters of states. In 1975, 18 of those clusters were dominated by youthful states–countries with a median age of 25.5 years or less.

A lot has transpired since then. I track the cluster to 1990, and 2020 (a very short-term projection), and finally a projection to 2035 (using the UN Population Division’s medium fertility variant). As the years progress, states experiencing fertility decline advance to the next phase of the age-structural transition (the intermediate phase, between a median age of 25.6 and 35.5 years). These drop out of the cluster until, as in 3 cases, the youthful cluster disappears (see Table 1, below).

Table 1

However, 16 clusters still remain.  Four situated along the midriff of Africa (West Africa Coastal Cluster, the Sahelian Cluster, Central African Cluster, and South Central African Cluster), within the tropics, persist just as they did over 40 years ago. These are Western and Central African clusters likely to be significant sources of revolutionary conflict and other forms of political instability throughout the rest of the century.  Another 7 clusters (in the Middle East, other parts of Asia, and Africa) have gone through minor and major changes also generate ongoing as well as future risks. However, according to the UN Population Division’s demographic projections, these may gradually fade over the coming three decades.

The final 5 clusters of states are “in transition.” The states in these groups are experiencing fertility decline and shifting to the intermediate phase of that age-structural transition. These youthful clusters may disappear over the coming two decades.

Separatist Conflicts Persist, While Revolutions Just “Age Away”

Since the seminal research of Herbert Möller in the late 1960s and Jack Goldstone in the early 1990s, numerous political researchers have called attention to the tendency of countries with youthful populations—often called a “youth bulge”—to be more vulnerable to civil conflict than nearby states with a more mature population. Then a 2016 article upset political demography’s apple cart. When investigating countries with at least three consecutive years without an intra-state conflict, Omar Yair and Dan Miodownik found a fundamental difference between ethnic and non-ethnic warfare. The risk of a non-ethnic conflict was, indeed, higher under youth-bulge conditions. However, they found that youth bulges (measured at the country level, rather than at the ethnic level) were unrelated to the risk of an onset of an ethnic conflict.

La Différence Vit?

In research for a soon-to-be-published article, I set out to investigate a bit beyond Yair and Miodownik’s conclusions, asking two new questions:

  • Do the differences that Yair and Miodownik observed extend to countries with a history of recent of conflict?
  • How do these effects play out over the course of the age-structural transition—as populations age, shifting from a youthful population with a low median age, to a more mature population with a high median age?

For the rest of the article on New Security Beat … CLICK HERE.     Or download briefing: “Revolutions Just Age Away” in .pdf.

 

Fig. 1.  The patterns of 5-year risk of (a.) revolutionary conflict and (b.) separatist conflict  over the length of the age-structural transition. While the three types of conflict-history cases of revolutionary conflict (absent, intermittent, persistent) all respond to aging, the same classes of separatist conflict appear much less responsive.

Conflict history classes: Absent: without conflict during the prior four years; Intermittent: 1 or 2 years of conflict during the prior 4 years; Persistent: 3 or 4 conflict years during the prior 4 years.

 

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Age-structure and Intra-state Conflict: More or Less Than We Imagined?

Are younger countries at higher risk of civil conflict? The International Crisis Group’s 2018 list of 10 conflicts to watch suggests they might be: Like last year, intra-state conflicts (civil and ethnic conflicts within states, rather than wars between states) dominate the list, and among those, about 70 percent are within youthful countries, or states with a median age of 25.5 years or
younger. The only multi-state cluster mentioned in both 2017 and 2018 lists is the Sahel, the world’s most youthful region.

However, recent studies indicate that population youthfulness can be a less reliable and more unruly predictor than its proponents (including me) initially perceived. Three key factors complicate the relationship between age structure and intra-state conflict: conflict type (civil or territorial); conflict history; and conflict spillover (the cross-border spread of insurgencies among contiguous clusters of youthful countries).

Figure. The proportions of three age-structural groups (youthful, intermediate, mature) in intra-state conflict, 1972 to 2016.

 

 

To read the entire article, go to the New Security Beat version, or download from this site

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