Majority – Minority Ethnodemographic Differences

Minority Youth Bulges and State Stability

 

Read about Ethnodemographic Differences and majority-minority relations (first posted in The New Security Beat, 2012).  Since its appearance, this two-by-two model of sub-state demographic differences has been increasingly used as a means of spotting escalating ethnic tensions and warning of future armed conflicts.

Read an application of the model (Barnhart et al., 2015, “The Refugee Crisis in the Levant”); and others by Rachel Blomquist on Myanmar’s Rohingya conflict (Fall, 2016; Spring, 2016).

 

 

Figure 1. Two-by-two sub-state model of majority-minority relations, based on the age structural configurations of the majority and a politically organized minority population. Where there is no external interference, the “demographic integration” condition is hypothesized to be the most politically stable.

 

 

Attachments

Population Aging: A Demographic and Geographic Overview

Read “Population Aging: A Demographic and Geographic Overview” by Richard Cincotta, published in the National Intelligence Council’s Global Trends 2030 blog, and in the New Security Beat, July, 2012. Download “Population Aging: A Demographic and Geographic Overview” here …

This GT2030 blog, focused on population aging, begins with this introductory essay aimed at familiarizing readers with some of the demographic and geographic particulars of this phenomenon, and with several key demographic terms. The term most in need of definition is, of course, population aging. Strictly speaking, aging is any shift in the population’s age structure (the distribution of individuals, by age) that produces an increase in the median age (the age of the individual for whom one-half of the population is younger). Generally, advances in a population’s median age are associated with increases in the proportion of seniors (aged 65 years and older), and declines in the proportion of children (younger than 15). Sustained population aging leads to a relatively older workforce, slowed workforce growth and slowed growth among school-age children.
While various age-specific patterns of birth, death and migration can induce change in the median age, over the past century two demographic processes have contributed most powerfully to country-level population aging. First and foremost is declining fertility (fertility is usually measured by computing the total fertility rate (TFR), an immediate estimate of the number of children that women are bearing over their reproductive lifetime). The second most influential factor has been increasing longevity. Not all trends associated with modernization, however, contribute to aging. Declines in childhood mortality have served to slow aging’s pace or make it retreat, as have waves of youthful immigrants (until the immigrants themselves age) and occasio
nal baby booms.

Is an advance in the median age bad news? That depends on “where you are” the broad diversity of age structures suggested by today’s lengthy spectrum of median ages—which in 2012 stretches from around 16 years (Niger, Uganda, Mali) to around 45 (Japan, Germany). For states in the youthful phase of the age-structural transition (median age 25.4 years or less; see Figure 1), the near-term net economic, social, political outcomes of aging are overwhelmingly positive. Getting to the next next age-structural phase— the intermediate phase (25.5 to 35.4)—is crucial; it is associated with very high support ratios (working-age adults per child), diminished risk of intra-state conflict, the accumulation of human capital, and higher savings (among “saver” societies).
There are growing indications that states might develop more quickly by sustaining their intermediate phase—which, for very-low-fertility states, has been rather fleeting (for example, China recently departed the intermediate phase after entering 25 years ago). In fact, states that have achieved near-universal secondary education and sustained a lengthy period of economic prosperity and liberal-democratic stability, including the US, have done so during their population’s presence within the so-called age-structural sweet spot: starting in the their intermediate phase and finishing during the first half of the mature phase (the mature phase ranges from 35.5 to 45.4 years).

The forthcoming essays in this blog are focused “beyond the sweet spot.” It is concerned with the challenges and possible outcomes of “advanced aging”—a condition never before encountered—that will evolve in the so-called post-mature phase (median age >45.5 years) of the age structural transition. Countries approaching the end of the mature phase, most in Europe and East Asia, are accumulating large proportions of seniors, most of whom are moving out of the workforce, drawing on pensions, drawing down personal savings and other accumulated assets, and accepting transfers from their children, other relatives, and other public and non-profit sources. As they age, seniors face an increasing risk of morbidity due to chronic illness and declining physical mobility, as well as an increasing risk of poverty.

While improvements in healthcare and nutrition promise to compress the late-in-life period of high morbidity and permit the extension of workforce participation, the projected declines in the number of working-age adults per retiree (the old-age support ratio) in European and East Asian states over the coming two decades is unprecedented. These projections suggest that those states heading for a post-mature future need to deftly manipulate a full range of social and fiscal policy levers in order to mediate, and adapt to, the cost burdens that are poised to descend upon their pension and healthcare systems. Simultaneously, most of these states will likely wrestle with the challenging and politically delicate task of encouraging the reestablishment of near-replacement-level TFR.

The four age-structural phases experienced by Japan (1935, 1970, 1990, 2025 (projected).

As of 2012, only Japan and German have attained the 45-year median-age mark—and just within the past year or two. Significantly, both countries face “negative momentum”; in other words, because of several decades of annual TFRs below 1.5 children per woman and steadily increasing life expectancies, these and other very-low-fertility states are projected to continue to age for the foreseeable future—until old-age mortality dissipates their populations’ currently broad bulges of seniors and middle-agers, and fertility or migration significantly enlarges their childhood and young adult cohorts. In other words, advanced aging is not a momentary inconvenience.

By 2030, advanced aging will have spread widely through Europe (see figure 2: world maps, 2015 and 2030). Current projections by demographers at the US Census Bureau’s International Program Center (International Data Base, June 2011) suggest that the populations of 29 states (each over 1 million residents) will experience a median age over 45.0 years by 2030. Of these, the Census Bureau indicates that 26 will be located in Europe and 3 in East Asia (Japan, Taiwan and South Korea). Despite China’s rapid pace of aging, US Census Bureau projections place its 2030 median age at 43 years, very similar to the UN Population Division’s medium fertility-variant projection for the PRC. The UN Population Division, using a somewhat different set of projection assumptions to produce its medium fertility variant, projects that by 2030 this post-mature group of countries (median age >45.0 years) will consist of 19 states: 14 European, 4 East Asian (including Singapore), and Cuba.

Richard Cincotta is Demographer-in-residence at the Stimson Center in Washington, DC, and a consultant on political demography for the Woodrow Wilson Center’s Environmental Change and Security Program. From 2006-09, he served as a long-range analyst for the National Intelligence Council.

 

Minority Youth Bulges and the Future of Intrastate Conflict

Read “Minority Youth Bulges and the Future of Intrastate Conflict,” by Richard Cincotta, posted in the New Security Beat on October 13, 2011. The sub-state demographic theory of the risk of ethnoreligious conflict described in this essay has been applied to several countries. See “The Refugee Crisis in the Levant” (Barnhart et al., 2015, American U.), and The Demography of the Rohingya Conflict (Blomquist & Cincotta, 2015), and the Ethno-demographic Dyanmics of the Rohingya Conflict.

From a demographic perspective, the global distribution of intrastate conflicts is not what it used to be. During the latter half of the 20th century, the states with the most youthful populations (median age of 25.0 years or less) were consistently the most at risk of being engaged in civil or ethnoreligious conflict (circumstances where either ethnic or religious factors, or both, come into play). However, this tight relationship has loosened over the past decade, with the propensity of conflict rising significantly for countries with intermediate age structures (median age 25.1 to 35.0 years) and actually dipping for those with youthful age structures (see Figure 1 below).

Why has this relationship changed? At least two underlying trends help explain the shift:

  1. Over the last two decades, the deployment of peace support operations to countries with youthful populations has surged (described in a previous post on New Security Beat); and
  2. Ethnoreligious conflicts have gradually, though noticeably, increased among a group of states with a median age greater than 25.0 years (including Thailand, Turkey, and Russia).

Read the rest at …

Three Classes of Age-structural Functions

Age-structural Functions: Classes I, II, and III

 

A Class I Function: The World Bank’s Income Categories.

The World Bank Income Category Model (ASM-GNI) is composed of four class I age-structural functions that generate expectations of the age-structural timing of each of the World Bank’s standard income categories (Fig. 1). These categories are based on gross national income per capita (GNI per capita), calculated in current-year (or other standard year) US dollars using the World Bank’s Atlas Method (WB, 2016). States rarely slip from a higher to lower category.

Figure 1. Examples of Class I age-structural functions: the World Bank’s Income Categories. Class I functions depict the state’s attainment of a discrete level that is irreversible or nearly so. 

GNI per capita (Atlas Method) data were transformed into four dependent variable data sets, each composed of presence of absence data (0,1). For the World Bank’s Low Income Category was transformed to indicate whether it was in the category (1), or not (0).  For the following three higher categories (Lower-middle Income, Upper-middle Income, and High Income), GNI per capita data were transformed to identify whether the state was in the chosen category or a higher category (1), or in a lower category (0).

The functions displayed in this graph are the product of Model 2ac, which uses two statistically significant controls (p<0.05): small population size (<5.0 million), and reliance on oil or mineral resources (>15.0 percent of GDP). Thus controlled, relatively narrow 0.95 confidence intervals, reaching a maximum of +0.9 years on the median age axis at low median ages, surrounds each of the logistic functions.

While still untested by forecasting and experimentation, or examined in terms of the behavior of its exceptional states, the model reveals fresh aspects of the relationship between age structure and income. While it appears that states routinely achieve the World Bank’s Lower-middle Income category in the youthful phase of the age-structural transition (median age, <25 years), the results of modeling suggest that states must be well into the intermediate phase of the age-structural transition (thus attain fertility levels below 2.5 children per woman) to attain Upper-middle Income status—a milestone on the pathway to economic development at which development donors graduate countries from basic sectors of development.

Notably, the demographic window of opportunity—introduced by UNPD (2004) to estimate the period of greatest potential for economic development—coincides closely with the period when most states attain Upper-middle Income status. In its original formulation, the demographic window was calculated open when proportions of children, 0 to 14 years of age dipped below 30 percent of the total population, and seniors, 65 years and older, remained below 15 percent of the population. In the age-structural domain, that ranges from a median age of about 26 years to about 41 years.

 

A Class II Function: The Presence of Liberal Democracy.

The age-structural model of liberal democracy (ASM-LD) generates timed expectations of the likelihood of being assessed at a high level of democracy across the age-structural axis (Fig. 2). The ASM-LD is the most well-studied of all age-structural functions, having been investigated by three independent research efforts, each using different measures of age structure (several variations of “youth bulge” measures, median age), and various indicators of democracy. Indications of democracy include Freedom House’s Free status (Cincotta 2008, 2008-09), high levels (8 to 10) of Polity IV regime scores (Cincotta & Doces, 2012; Weber, 2012), and high levels of voting as a proportion of eligible voters (Dyson, 2013). The conclusions were similar. Moreover, the ASM-LD is the subject of several successful forecasts and statistical experiments, which in turn have inspired additional hypotheses and modeling (Cincotta, 2008-09; Cincotta & Doces, 2012; Cincotta, 2015a, 2015b).

Figure 2. An example of a Class II function: the age-structural function depicting the probability of being assessed as FREE in Freedom House’s annual survey.

The functional form of the ASM-LD, shown here (Fig. 2), plots the timed expectation of attaining Free in Freedom House’s annual survey (Model 2ac, Table 1), as a probability calculated across the age-structural domain (Cincotta, 2015b). The most rapid pace of shifts to Free from lower categories should be expected to occur around the theoretical infliction point, where the probability of being assessed as Free is 0.50. This point, called Free50, is at about 29.5 (+0.5) years of median age.

 

A Class III Function: The Presence of Intra-state Peace.

The Age-structural Model of Intra-state Peace (ASM-ISP) predicts the probability of the absence of intra-state conflict across the age-structural domain (Fig. 3). The model draws its data on the presence or absence of intra-state conflict (>25 battle-related deaths per year) from the UCDP-PRIO Conflict Database, maintained and published cooperatively by the Uppsala Conflict Database Project (UCDP) and Peace Research Institute of Oslo (PRIO) (UCDP/PRIO, 2016; Gleiditsch et al., 2002; Themnér & Wallensteen, 2013). Its function (Fig. 3) is a class III age-structural function, based upon the ASM-ISP, with controls for small population (<5.0 million), and natural resource reliance (resource rents <15.0 percent of GDP).

Figure 3. An example of a Class III function: the age-structural function of intra-state peace (absence of an intra-state conflict).

The ASM-ISP is neither a tightly fit nor strongly predictive model—its gradual slope is not conducive to forecasting. It is nonetheless useful in mapping states, now and over the next two decades, that are generally vulnerable to the outbreak of intra-state conflict and other forms of political violence. It is worth noting that, according to the ASM-IP, at a median age of 15.0 years, roughly 60 percent of all states are unlikely to be experiencing an intra-state conflict.  Further investigations of the function indicate that while civil conflicts appear almost exclusively in the youthful portion of the age-structural domain (median age of 25 years or less), ethnoreligious conflicts extend throughout the domain (see Yair, 2016).

Attachments

Getting “Unstuck” from Chronological Time

State Behavior in Age-structural Time

 

The most discussed breakthrough in international political theory over the past decade appears to be Nassim Taleb’s formulation of the “Black Swan hypothesis. Taleb argues that the dynamics of state behavior are so complex and hidden that political outcomes and their timing are generally impossible to forecast. Others (including myself) read Taleb’s thesis as an exhaustingly lengthy excuse–a disheartening admission that the endeavor of forecasting state political behavior is beyond the capacity of current social science. Taleb’s treatise follows on a study published by Philip Tetlock in 2005, which reported on a 15-year experiment that the author ran with the consent of a group of highly qualified political scientists. Tetlock found that when these seasoned academics chose among predicted futures based on current political theories and the full wealth of their experience, they could perform no better than the guessing average.

Table 1.  Logistic regression statistical table for the age-structural model of liberal democracy (likelihood of being assessed as FREE in Freedom House’s annual survey of civil liberties and political rights).

Whereas political analysts expect political outcomes to be generated, over time, by the activities and evolving relationships of political actors, political demographers find that most states that have populations with similar age structures (distributions of residents, by age) behave similarly. Rather than evolving over time, these state behaviors appear to be timed by the movement of country-level populations through the age-structural transition–the position and pace of which has been called “age-structural time.” Thus, political demographers tend to analyze state behaviors over the course of the age-structural domain–an X axis measured in median age (a indicator of the maturity of a country’s age structure).

Why has this form of analysis yielded new insights? Over the past two decades, economic and political demographers have proposed and tested theories that identify demographic changes as key factors in a range of economic and political transitions (reviewed by Cincotta, 2012, and by Goldstone, 2012). Whether or not these are causally related, either directly or complexly, is a contentious topic that is the subject of debate among economic demographers and economists, and political demographers and political scientists.

The list of state-level effects that are associated with fertility decline and age-structural change is surprisingly long, and the effects are politically consequential. These country-level effects include: the onset of intrastate conflict (Möller, 1968; Mesquida and Weiner, 1999; Goldstone, 2002; Urdal, 2006; Cincotta and Leahy 2007); employment (Easterlin, 1968); women’s participation in the workforce (Bauer, 2001); democratization and democratic stability (Cincotta, 2008, 2009, 2013; Weber 2012; Cincotta and Doces, 2012; Dyson, 2013); the accumulation of government and household savings (Higgins and Williamson, 1997; Lee and Mason, 2011); economic development (Williamson, 2001; Bloom et al., 2002); societal investments in education (Lee and Mason, 2011); and the accumulation of public debt (Eberstadt and Groth, 2010; Lee and Mason, 2011).

For defense and foreign policy analysts, the implications of these findings are noteworthy. They indicate that, for a number political and economic transitions, modern states appear to perform more predictably when these variables are monitored as a response to changes in the configurations of their age-structure, than they do when monitored in chronological time. Therefore, analysts should expect to improve aspects of their analyses by shifting countries onto the age-structural time domain—an X-axis measured in years of median age (the age of the “middle person,” for whom 50 percent of the population is younger, and the other 50 percent is older).

For analysts tasked with early warning, shifting to age-structural time has a substantial advantage. Because UN demographers biennially generate demographic projections (demographic scenarios of the future) for each currently extant state, the future ceases to be a barrier to analysis. In other words, age-structural models that were originally fit to historic data—observations drawn from the demographic and political outcomes of countries that have already advanced through the age-structural transition—can statistically predict future trends by using projected (future) median ages as their inputs.

Unlike conventional historians and political scientists, analysts using age-structural methods need not be “stuck” in the chronological time domain. They can move back and forth, shifting from chronological time (the year) into age-structural time (the median age), in order to make a statistical prediction. And then they can re-transform their predictions, returning to chronological time—the domain in which intelligence consumers operate—to report their timed early warnings.