Topic: Theory

What Future for the Western Sahel?

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The Region’s Demography and Its Implications by 2045 (Atlantic Council, 2021)

By Richard Cincotta and Stephen Smith

The 6 states of the Western Sahel, northern Nigeria, and the Sahel climatic zone.

The Western Sahel—a region stretching from Senegal and Mauritania to Mali, Burkina Faso, Niger, and Chad, and including the twelve sharia law states of northern Nigeria—is in a demographic impasse. Rather than yielding an economic dividend, the conditions spawned by the region’s persistently youthful, rapidly growing, high-fertility populations overwhelm the capabilities of state-run services, generate extensive urban slum conditions, slow if not stall economic and social progress, and aggravate ethnic tensions. Decades of exposure to these mutually reinforcing conditions have undermined the legitimacy of central governments and rendered the region’s states vulnerable to the spread of Islamic populism and regime instability. For more …..

To view the report online: go here.  

To view the Executive Summary, go to the Atlantic Council website

To download the report in .pdf format: download here

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Emulating Botswana’s Fertility Transition

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“Emulating Botswana’s Approach to Reproductive Health Services Could Speed Development in the Sahel”

This new web essay (on the NSB website <click here>) reviews research that uses the relatively rapid changes in Botswana’s age-specific fertility rates to produce a “Botswana historic fertility variant” and applies this projection to the populations of countries in the Western Sahel—a contiguous cluster of states with populations that remain at the early stages of the fertility transition. For these high-fertility countries, projections of this variant (up to the year 2050) fall roughly around the TFR trajectory the UN low fertility variant.


Figure.  Declines in Fertility Across Women’s Age Groups in Botswana, Niger, and Mali from 1980–85 to 2015–20. In Botswana, girls’ education and a quality family planning program helped adolescents postpone childbearing and helped older women avoid the risks associated with unwanted middle-age pregnancy (Data source: UN Pop. Div., 2019 Rev.)

This exercise provides a glimpse of what could possibly be achieved in some states in tropical Africa by emulating Botswana’s efforts to ramp up access to family planning, to decrease the frequency of teen pregnancies, and to increase girls’ educational attainment. However, the essay notes how different the Sahelian countries are from Botswana—a well-governed, resource-endowed southern African country with a relatively small, urbanized (>65%) population (2.3 million)—and thus the formidable challenges those differences present in attempting to replicate the pace of Botswana’s fertility transition.



Figure.  UN Population Division’s High, Medium, and Low Fertility Variants for Niger and Mali vs. Botswana Historical Fertility Variant (BHFV). In both cases, applying Botswana’s age-specific pattern of fertility declines would produce a projection roughly similar to the current UN low fertility variant. (Data Source: UN Pop. Div., 2019 Rev. & author’s research)

For the full essay, go to the NSB website <click here>

Or, download the essay <click here to download>

Which Demographic “End of History”?

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Dec. 9, 2019
[Among the top 5 New Security Beat Posts, December 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.


Maps.  The 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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Defining the Demographic Window

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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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Discussion: Does Demographic Change Set the Pace of Development? (with Jane O’Sullivan, U. Queensland)

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Original Essay: Does Demographic Change Set the Pace of Development? 

Comment:  Jane O’Sullivan, U. Queensland.

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.    [Note: see Dr. O’Sullivan’s research on population impacts, demographic ageing, food security, and climate change issues]

 

Reply:  Richard Cincotta, Woodrow Wilson Center/Stimson Center

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