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    This website is dedicated to political demography, and to The Age-structural Theory of State Behavior (download article here). Read "The 8 Rules" for a quick summary of the theory. See sidebar (homepage) for key publications and web essays. Follow on Twitter at @rpCincotta or visit the New Security Beat.

  • Which Demographic “End of History”?

    Dec. 9, 2019

    Demography’s theoretical end-state is a set of hard-to-escape conditions typified by low and often sub-replacement levels of fertility, large proportions of retirees, and an aging workforce—an endpoint that the National Intelligence Council’s Global Trends reports refer to as post-maturity. The shift toward post-maturity is so unrelenting in parts of Europe and East Asia that some analysts imagine humanity plunging globally into post-maturity. However, this scenario, which I call “Post-mature World,” is looking much less likely than its non-endpoint alternative (see Figure 1), a chronically demographically “Polarized World.”

    To visit the complete essay on the New Security Beat website, click HERE. Or, download the essay HERE.


    MapsThe age-structural phases of countries in Europe and in parts of Africa and Asia, 2015 & 2035 (projected). Maps represent the UN Population Division’s current estimates of median ages for 2015 and projections for 2035 (UN medium fertility variant). By 2035, most of Europe’s states will likely have advanced into post-maturity, whereas in the Sahel and tropics of Africa, countries will probably still be in the youthful phase of the age-structural transition. 

    Figure 1.  Post-mature World and its alternative Polarized World, showing the Global Trends four-phase schema representing the path of the age-structural transition. Black arrows indicate the transition’s path and identify the two hard-to-escape demographic conditions, the youthful and post-mature phases. In a Post-mature World, countries age and their populations eventually decline. In a Polarized World, human population continues to grow in the youthful regions, stimulating increased migration to nearby states and to more mature regions.
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    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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    Statistical Forecasting Using Median Age

    Statistical predictions can be made from can be made from simple curves (see Figure below for Tunisia). This brief essay shows how drawing a line through a set of age structural functions (functions that describe the shift in a categorical probabilities over the age-structural transition) yields a set of probabilities for each category. To see how it’s done for child survival, per-capita income, and liberal democracy, and to see the predicted outcomes for Tunisia, Colombia, Bangladesh (this one is surprising!), Uzbekistan, Philippines, and Nigeria, click here or go to the “Individual State Forecasts” page in the Forecasts menu.


    Fig. 1. Tunisia’s path, from 1980 to 2015, to a higher probability of being assessed as Free in Freedom House’s annual survey of political rights and civil liberties. Once in the demographic window (the intermediate phase, shaded in dark grey), the chances of retaining Free are much higher than in the preceding youthful phase.

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    Presentation at World Affairs Council of Albuquerque, and at Sandia Natl Lab.

    Aug. 13, 2019

        Much thanks to the Albuquerque’s World Affairs Council for inviting me to speak and for the hospitality. Also, while I was in NM, I was asked to present a seminar to the Strategic Futures unit at Sandia National Laboratories. The presentation slides (in .pdf form) can be accessed here.

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    Defining the Demographic Window

    Introduced by UN demographers during the Population Division’s release of a series of long-range projections (UNPD 2004, 2, 70-73), the original intent of the demographic window was to call attention to the segment of the age-structural transition that tends to measurably favor economic development. In its original formulation, UN demographers assumed that this window occurs when both children (0 to 14 years) and seniors (65 and older)—age groups that are composed principally of dependents—are at a low ebb. However, unlike other measures of dependency, in the UNPD’s formulation, children and seniors are differentially weighted; each senior is assumed to represent twice the per-child burden on economic development. According to this method, the demographic window is deemed open when children comprise less than 30 percent of the total population, and simultaneously, seniors comprise less than 15 percent of the total population.

    Figure. Total fertility rate versus median age for all independent states (except the GCC states) in 2015, showing states in sub-Saharan Africa (SSA), and Europe. To enter the demographic window (the intermediate phase of the age-structural transition) appears to require a TFR near 2.8 children or below. Two exceptions, Israel (IS) and Algeria (AL) have experienced a rise in TFR. The position of some other states are shown: Niger (NG), South Sudan (SO), Timor Leste (TL), Nigeria (NI), the USA (US), China (CH), South Korea (RK), and Japan (JP).

    Despite its seemingly arbitrary rules and speculative boundaries, in practice the UNPD’s demographic window has done well identifying conditions during which states achieve the World Bank’s upper-middle income classification, a milestone in development. For example, among the World Bank’s 2018 list of upper-middle income states, all are either in the UNPD’s demographic window or have passed through it (World Bank, 2019). The remainder are either significant oil and/or mineral exporters or have a population under 5.0 million—a group that includes numerous island states with exceptional per-capita tourist revenue and/or remittances.

    Coupled with the UNPD’s demographic estimates and projections, the analytical usefulness of this simply defined window is obvious. To make the concept compatible with the conventions of age-structural modeling, Cincotta (2017) employed the UNPD (2004) metric to estimate the median ages of its assumed boundaries. From a sample comprising the 40 states that had entered that window since 1950 (excluding states with greater than 15 percent of GDP in oil or mineral rents). On this basis, the UNPD’s demographic window generally began at a median age between 26 and 27 years. Among most of the sample, the window ended at a median age between 39 and 42 years.

    Using the lower median age boundary, Cincotta (2017) noted that states are generally unable to enter the demographic window without declining below a total fertility rate (TFR) of 2.8 children per woman (Fig. 4). Nonetheless, among states that have sustained rapid rates of fertility decline, populations have often declined well below a TFR of 2.8 before achieving a median age of 26 years. As these states aged—as relatively large cohorts of children and adolescents were replaced by smaller cohorts—they usually crossed the window’s nominal threshold within a decade.

    This analysis noted that for most states, their trip through this demographic window of favorable age structures is likely to be a onetime opportunity—but with exceptions. The length of chronological time spent in the demographic window has occasionally been extended, at least temporarily, by: (a.) a baby boom, such as the post-war WWII rebound in fertility in the United States and in some Western European states; (b.) an influx of youthful immigrants; (c.) sustained rapid population growth among an ethnic minority with significantly higher-than-replacement fertility; or (d.) fertility settling near replacement levels, with very low child mortality.

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