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  • Guest Contributor

    Does Demographic Change Set the Pace of Development?

    December 3, 2018 By Richard Cincotta
    KOCIS_Ban_KiMoon_Lecture_in_Korea_04_(9620811088)

    This year, 2018, marks the 60th anniversary of a landmark publication by a pair of academic social scientists who first recognized the close relationship between population age structure (the distribution of a country’s population, by age) and development. In Population Growth and Development in Low Income Countries (Princeton U. Press, 1958), demographer Ansley Coale (1917-2002) and economist Edgar M. Hoover (1907-1992) theorized that eventual declines in fertility would transform developing-country age structures. Coale and Hoover demonstrated that these newly transformed age structures would exhibit larger shares of citizens in the working ages, and smaller shares of dependent children and seniors (Fig. 1). This transition, they argued, would someday help lift countries with youthful populations in Asia, Latin America, and Africa out of the low-income bracket.

    Sixty years later, data are abundant and evidence supporting Coale and Hoover’s theory is strong. Yet, academic economists and political scientists—whose theories exert a great deal of influence on foreign assistance priorities and development goals—have been slow to accept that progress in the age-structural transition (the shift from very youthful to more mature population distributions) influences the pace of other, more basic development transitions.

    Figure 1. Age-structural profiles of Colombia and Tunisia, 1980 and 2015.

    As their populations’ fertility declined, Colombia’s and Tunisia’s youthful profiles were gradually replaced by more mature profiles over the 35-year period. The 2015 profiles show a bulge in the working age population and relatively small childhood and old-age cohorts.

    Fig1_Col-Tun-80-15

    Data source: UN Population Division, 2017.

    Age-structural Timelines

    To illustrate how changes in age structure set the pace for other development transitions, we borrowed a model that was developed to forecast political trends for the National Intelligence Council’s (NIC’s) Global Trends publications. By measuring the maturity of age structures using median age (the age of the person for whom one-half of the population is younger) we produced “age-structural timelines” that plot the pace—in terms of median age—of development’s three most basic transitions: child survival, educational attainment, and per-capita income.

    Age structure timelines differ markedly from historic timelines. On historic timelines, chronological time is generally depicted along a horizontal line (horizontal axis) upon which points and bars mark when an historic event occurred, and how long it endured. Instead of chronological time, age-structural timelines use a horizontal axis that represents the age-structural transition (referred to as “age-structural time”), which ranges from a median age of 15 to 55 years. Currently, the country with the most youthful age structure is Niger, with a median age of about 15 years. Japan, with a median age of 47 years, has the most mature age structure.

    The statistical method used to produce age-structural timelines (logistic regression) works with categorical data, rather than individual data points. So, to show the pace of development on age-structural timelines, each of the three basic transitions—child survival, educational attainment, and income—are divided into a series of consecutive categories. For example, the income transition is represented by the World Bank’s low, lower-medium, upper-medium, and high-income categories. Similarly, the child survival transition is divided into five survival categories, and the educational attainment transition into five attainment categories. 

    Unlike historic timelines, each age-structural timeline is spanned by a series of curves. Each curve shows when—in terms of median age—countries are likely to achieve that category. The next curve (to the right in the series) shows how rapidly, in terms of progress in median age, countries are likely to move into the next higher category (Fig. 2).

    Figure 2. Age-structural timelines for three basic development transitions: a) child survival, b) educational attainment, and c) income.

    Curves show the probability of entering higher categories as median age rises. The NIC’s intermediate phase of the age-structural transition (shaded) is used here to estimate the period with highly favorable age structures (the demographic window).

    Fig2_3Graphs

    Data sources: UN Population Division, Wittgenstein Centre, World Development Indicators.

    Scheduling Development

    If age structure did not somehow relate to the pace of any of these three development transitions, no ordered sequence of curves would be apparent. However, each transition’s set of curves does reflect an orderly pattern. Although each pattern differs in slope and spacing, the three transitions share a common feature: As median age advances, countries predictably progress through each transition’s progressive sequence of categories—much like a demographic schedule for development.

    Another key observation—it’s difficult to behave like a modern state when the age structure is youthful. The graphs show that with a median population age below 25 years, countries are most likely to be in the low or lower-middle levels of development in child survival, educational attainment, and income. For most countries—with the exception of oil and mineral exporters and the least populous countries (mostly island states under 5 million residents)—attaining moderate levels of development (the upper-middle categories) occurs between a median age of 26 and 35 years. During this 10-year slice of age-structural time, countries traverse a “demographic window” of socially and economically favorable age structures that the NIC’s Global Trends has called the intermediate phase of the age-structural transition (shaded in Fig. 2).

    Development’s Neglected Demand Side

    Does the close relationship between age structure and other key development transitions suggest that policies and investments in education and job growth aren’t important? Or that fertility decline matters the most? Not at all. These results simply echo a conclusion that is common throughout the public health literature: that service-driven achievements in any of the basic transitions—whether child survival, fertility decline, educational attainment, or income—tend to drive demand for other services, spurring progress across several development transitions (see Fig. 3).

    These results also suggest that demography—the demand side of development—is as essential as the infrastructure that constitutes development’s supply side. Declines in family size produce declines in the size of school-age cohorts, which helps governments, as well as parents, invest more in each child. Moreover, slowed growth among young-adult cohorts reduces job competition and tends to boost the proportion of fully employed.

    Figure 3. Links between the age-structural transition (left side) and the four basic development transitions (right side).

    Fig3_Development-b

    Source: Author review of literature.

    Reconsidering Demography’s Role

    How did Coale and Hoover’s insights into development get lost? Over the six decades since they published their conclusions, development economists and political researchers have focused almost exclusively on development’s supply side: on the institutions, policies, and investments in infrastructure that are key to economic development. That’s not surprising; these have been the central topics of their fields since the end of World War II. However, demography—the demand side of development—was largely ignored by development theorists, relegated to a role of “supporting statistics,” and sidelined even in social science curricula.

    Then came the Asian tigers and lots more data. In the late1960s and 1970s, fertility declined in a handful of East Asian countries (South Korea, Singapore, Taiwan, Thailand, Hong Kong, and Indonesia), launching them through the age-structural transition. By the mid-1980s, these countries exhibited the favorable characteristics that Coale and Hoover had foreseen: a large worker bulge, and relatively small cohorts of dependent children and seniors—what demographers now call a “demographic dividend.” As those same states began their economic takeoff, demographers turned to reprising (and expanding) Coale and Hoover’s theory.

    Most demographers believe that international development agencies are long overdue in acknowledging the role that shifts in population age structure play in social, economic, and political development. Based on current evidence, a realistic reconceptualization would place fertility decline at a key juncture (Fig. 3) linked to improvements in educational attainment and child health and driving the shift to the set of favorable age structures that are associated with the demographic dividend.

    Richard Cincotta is a global fellow at the Wilson Center and non-resident fellow at the Stimson Center.

    Sources: U.S. Department of National Intelligence, UN Population Division, Wittgenstein Centre, World Bank

    Photo Credit: UN Secretary General Ban Ki-moon’s Special Lecture for Korean youth. Chungju City Hall, Chungcheong Province, Korea, August 2013. Courtesy of the Korean Culture and Information Service.

    Topics: aging, demography, development, economics, featured, Guest Contributor, Infrastructure, security, youth
    • Jane O’Sullivan

      A good title but a disappointing treatment. In the context of the fertility transition, age structure and population growth rate are confounded. There is little evidence that age structure per se delivers these benefits – it is the slowing of population growth that affects “demand side”. Of course, age structure affects specifically which areas of demand are first affected, but that’s not what matters. The problem is that the “demographic dividend” discourse is used as a means to not have to talk about population growth, but in doing this, it undermines the motivation to get fertility down below replacement-level, which is the urgent need. Indeed, by fuelling the “aging crisis” myth, the DD message actively discourages efforts to complete the transition to low fertility. Also, the role of family planning programs is missing from the flow diagram. Education, income and child survival don’t have strong effects on fertility without explicit behaviour-change programs.

      • http://www.stimson.org/experts/rcincotta Richard Cincotta

        Thank you for your interesting comments, Dr. O’Sullivan. And, welcome to the growing ranks of the disappointed.

        The curves featured in this essay were developed as part of the (U.S.) National Intelligence Council’s (NIC) long-range effort (the Global Trends series of publications) solely for the purpose of forecasting changes in political, social, and economic indicators from 2 to 20 years into the future.

        For forecasting, the method has worked surprisingly well (you can read some of its forecasts in my other NSB posts). Analysts have used the method’s “eight rules of political demography” (also on NSB) to successfully forecast the rise of a liberal democracy in North Africa two years before the Arab Spring, to identify the remaining clusters of countries most at risk of intra-state conflicts, and to predict declines from liberal democracy among youthful countries.

        Along the way, however, its findings have disappointed (or even angered) diplomats, advocates, and political scientists who have deeply-held views of how the world works.

        One source of disappointment has to do with population growth. While states with a population under 5 million (particularly small island states) do, indeed, appear to develop politically and socioeconomically more quickly than expected, we have found no additional statistical evidence suggesting that larger population sizes or densities are—so far, at least—a “net impediment” to political and socio-economic development.

        For example, population size and density may depress economic productivity by limiting per-capita freshwater supplies, exacerbating pollution, and forcing agriculture into marginally productive land. At the same time, however, population growth drives urbanization, which positively affects per-capita income and speeds other development transitions (including fertility decline).

        My own experience with assessing population’s influence leads me to conclude that, in general, increasing human density disrupts and alters natural ecosystem processes (i.e., nature). Unfavorable age structures disrupt development processes (i.e., the state). States are, indeed, affected by ecosystem disruption, but our species has become very good at making and remaking its own highly productive ecosystems—most of which create additional long-term disruption (e.g., climate change, species loss, high nitrogen loading in soils, etc.–a point that you know well from your own research).

        As for your critique of Fig. 3: I have indicated (in the diagram) that income and child survival’s effects on fertility are (as you suggest) weak or highly variable. However, I believe that most development analysts would disagree with your assessment of education’s impact on fertility—particularly the impact of women’s educational attainment, which in the past has been statistically strong. Nonetheless (consistent with your assertion), the effect of women’s educational attainment on fertility in tropical sub-Saharan African countries seems, so far, to be weaker than expected (when compared to the Asian and Latin American fertility transitions).

        While you insist that getting fertility “down below replacement-level” is an urgent need, our analysis differs somewhat. To achieve a median age of 26 years—the beginning of the demographic window—fertility must decline below 2.8 children per woman. However, continued progress toward a median age of 30 years has generally led to additional fertility decline to near-replacement (close to TFR 2.1) or below-replacement levels.

        On population aging: A myth? Perhaps you are referring to the mistaken beliefs/rhetoric of some political leaders of tropical African states who fear an aging population—seemingly unaware that the current UN Medium Fertility Variant scenario projects that such large proportions of elderly, for most tropical African states, will likely show up on the far side of 2100 (probably a century into the future). I believe that, to achieve a median age of 26 years, the leadership of those states must be prepared to dismantle the traditional and religious constraints on women’s lives (much like Habib Bourguiba did shortly following Tunisia’s independence), in addition to supporting quality family planning programs, girls’ education, and elevating women into positions of political power.

        However, for the group of very low fertility European and East Asian countries that are rapidly advancing into the post-mature phase of the transition (median age 46+ years), the challenges of population aging are hardly mythical. Does population aging qualify as a crisis for economic and political liberalism? No one knows. However, the most credible research foresees substantial fiscal strains on retirement and healthcare systems (see Lee & Mason’s National Transfer Accounts review), and the admixture of population aging and immigration seems (to me, at least) to be yielding some unfavorable political byproducts.

        Thanks again for your comments, Dr. O’Sullivan. // Richard Cincotta

        • Jane O’Sullivan

          Thank you Dr Cincotta for an interesting response. However, you failed to address any points in my post. I do not challenge any of the relationships you present, only the implied causation. For forecasting purposes, correlation is perfectly adequate, as long as it doesn’t extrapolate far from empirical patterns. Its predictive power still doesn’t demonstrate causation. As I said, in this context, age structure and population growth rate are confounded. If you are not even investigating the impacts of population growth rate, you can’t attribute impacts to age structure. As I explained, my concern is not a trivial matter of semantics, it is a deep concern for perverse impacts of ‘demographic dividend theory’ in fuelling ‘ageing crisis’ myths – myths which are already generating fertility-boosting policies in many countries..

          • http://www.stimson.org/experts/rcincotta Richard Cincotta

            Dr. Sullivan, I took the time to read your (and co-authors’) papers on your website. I agree with much of your critique of the popular assumptions concerning the economic and social costs of population aging (also, see the work of Jennifer Sciubba, who has argued similarly).

            Some aspects of population aging will be challenging, but–as the curves in my research show–states that have passed through the demographic window are likely to be endowed with sufficient human capital and institutional capacity to adapt, in some ways, to the next transitional phases (unlike states in the youthful phase of the transition, which have very little institutional capacity or human capital).

            As your papers indicated, some European and East Asian states are already in the process of reforming their pension and healthcare systems to accommodate a larger proportion of seniors. Over the next two decades, I suspect that many of today’s fears concerning population aging will crumble. But, I also suspect there will be a few trends that will be difficult to alter — particularly slowing economic growth among most post-mature populations, and some political instability associated with political actors who, in low-fertility countries, capitalize on in-migration and ongoing shifts in ethnoreligious proportions. I’m not confident that liberal democracy is as secure as most political scientists believe it to be (in other words, it’s NOT the end of history).

            Youthful age structures are, indeed, rapidly growing populations — and, as you suggest, it is difficult (and, I think, probably unnecessary) to uncouple those effects. After more than 60 years of international development assistance, it should be more obvious to economists and political scientists that those conditions (youthfulness/rapid growth) are synonymous with chronically low levels of development. Among all countries currently listed in the World Bank’s low-income category, all are in the youthful (fast-growing) phase, except one: North Korea – where the political leadership has worked over-time thwarting development.

            That said, it is also hard to ignore that moderate and high levels of socioeconomic and political development (stable liberal democracy, low risk of revolutionary conflict) have generally been achieved within the demographic window (median age 26 to 35 years), when the human population is still growing–but slowly (typically around 1%). Because of the post-War baby boom (~1946-58) the USA, Canada, and some Western European states remained in the demographic window for an extended period of time—which (I argue) shaped the world order in the latter half of the 20th century.

            Does an age-structural approach to predicting socioeconomic and political development discourage political leaders from accepting their population’s eventual progress toward an older median age? Perhaps it does. But that shouldn’t stop researchers from trying to predict/model developmental progress. Part of the reason that leaders fear population aging is that they fear “the unknown” — we really don’t know much about population aging; most is conjecture. Therefore, as European and East Asian states age, more research is needed to test current assumptions about this final phase (the post-mature phase) of the age-structural transition.

            Thanks again for your comments. /s/ Richard Cincotta

            • zovi

              Excellent discussion. Wouldn’t be the other way around?: That population growth and age structure be a consequence of economic development and that’s why you find correlations of age structure with the three measures you studied? So not necessarily age structure is a cause of the variation in those measures but is just correlated with them. Could be also that one feeds the other and viceversa (age structure/population growtheconomic development), but to discover which arrow of causation is stronger we need more analysis.

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