What explains the historic experience of low initial population growth, followed by an explosive increase and finally by a steady decline in growth? The clas- sic theory of demographic transition proposed by Notestein (1953) and others postulated that all societ- ies initially start off with high fertility and high mor- tality levels. At some point in societal development, mortality rates fall due to public health advances, while fertility rates remain high. This combination results in explosive population growth, with birth rates far exceeding death rates, until at some point birth rates also start to decline and a new equilibrium is reached at low fertility and low mortality levels.
Until fairly recently, the classic theory of demo- graphic transition held sway, and all societies were supposed to go through it in a lock-step manner. In the early to mid-1970s, however, an international team of researchers participated in the Princeton Uni- versity European Fertility Project and carefully exam- ined historical fertility declines in Europe. They came to a somewhat surprising conclusion: The process of demographic transition is quite varied and does not always follow the path suggested by classic theory (Coale & Watkins, 1986). Under that scenario, a cer- tain level of socioeconomic development was required for the initial mortality decline, which was followed, at some later point, by fertility decline. Researchers in the European Fertility Project, however, found that mortality decline took place in different societies at different levels of development and that there was no magic threshold of mortality above which fertility decline would not take place.
The current consensus about the demographic transition is that there is no specific sequence in which fertility and mortality decline. They can decline together, or one can decline before the other. Fur- thermore, no specific thresholds of development are required for either process to start. Moreover, the inter- vals between a high-fertility and high- mortality regime and a subsequent low-fertility and low- mortality regime FIGURE 5-3 World population growth rate, 1950–2050,
projected in 2008 (1950–2050) and 2015 (2010–2050).
Data from United Nations, Department of Economics and Social Affairs, Population Division (2009). World population prospects: The 2008 revision, volume I: Comprehensive tables (ST/ESA/SER.A/287). Accessed on November 10, 2016 from http://kczx.shupl.edu.cn /download/786444c9-20c1-4b5a-b0d6-d7544569a2ee.pdf and United Nations, Department of Economics and Social Affairs, Population Division (2015). World population prospects: The 2015 revision, volume I: Comprehensive tables (ST/ESA/SER.A/379). Accessed on November 10, 2016 from https://esa.un.org/unpd/wpp/publications/Files/WPP2015_Volume-I_Comprehensive-Tables.pdf
1.11 0.97
0.71 0.57 1.77 1.94 1.94
1.76 1.54
1.26 1.11 0.86
0.44 0.34 0.00
0.50 1.00 1.50 2.00 2.50
1950 1960 1970 1980 1990 2000 2010 2020 2040 2050
Growth rate (%)
Year 2015 projection 2008 projection
are also not fixed and can vary considerably. The expe- rience of the LMICs has borne out this new consensus.
Demographic transitions have taken place at different rates in different places and with different sequences.
A common thread, however, is that the transition has often been considerably more rapid than those seen in the European or North American historical record.
Population growth rates and fertility and mortality for different parts of the world since the mid- twentieth century to the mid-twenty-first century are shown in TABLE 5-1, and for the specific cases of Bangladesh and Kenya, in EXHIBIT 5-1. Growth rates are given as the per- cent change in population per year for the 5 years sub- sequent to the specified date. When a population stops changing in size, it becomes stationary and its growth rate is zero. The total fertility rate (TFR) is the number
of children a woman would bear, on average, if they lived to the end of the reproductive period under the childbearing pattern of a particular year—for example, if they had, at age 15, the birth rate of 15-year-olds in 1970; at age 16, the birth rate of 16-year-olds in 1970;
and so on. Life expectancy at birth is the average num- ber of years people would live if their entire life were spent under the age-specific mortality conditions of a particular year—for example, if they experienced the infant mortality of 1970, the death rate at age 1 of 1970, and so on. The numbers resulting from these exam- ples would be the 1970 TFR and the 1970 life expec- tancy. A TFR of approximately 2.1 is usually referred to as replacement-level fertility. If, over the long run, women have that number of children on average, the population will become stationary, neither growing
TABLE 5-1 Growth Rate, Total Fertility Rate, and Life Expectancy for Regions of the World by Time Period
Region 1950 1970 1990 2000 2010 2020 2050
World
Growth rate 1.77 1.96 1.54 1.24 1.18 0.97 0.57
Total fertility rate 4.96 4.48 3.04 2.62 2.51 2.43 2.25
Life expectancy 46.8 58.0 64.5 67.1 70.5 72.7 77.1
Low- and Middle-Income Countries
Growth rate 2.03 2.39 1.84 1.46 1.36 1.12 0.66
Total fertility rate 6.08 5.42 3.39 2.83 2.65 2.52 2.30
Life expectancy 41.5 54.8 62.5 65.3 68.8 71.2 76.0
Higher-Income Countries
Growth rate 1.19 0.77 0.44 0.34 0.29 0.17 −0.02
Total fertility rate 2.82 2.15 1.67 1.58 1.67 1.72 1.82
Life expectancy 64.7 71.1 74.1 75.6 78.3 79.9 83.5
Asia
Growth rate 1.91 2.29 1.63 1.20 1.04 0.75 0.19
Total fertility rate 5.82 5.06 2.96 2.39 2.20 2.09 1.92
Life expectancy 42.1 56.4 65.1 68.5 71.6 73.9 78.3
(continues)
nor declining. Women will be contributing to the next generation one child for themselves and one for their partner, and a bit more for girls who were born but did not survive to reproduce.
As shown in Table 5-1, life expectancy rose con- tinuously over the latter half of the twentieth century.
The exceptions (not shown) occurred in the countries of Africa hardest hit by AIDS, where, by 2000, this dis- ease had wiped out much, if not all, earlier gains (see Exhibit 5-1 for the specific case of Kenya). Accord- ing to the United Nations, nine countries had adult HIV prevalence of 10% or more in 2009: Swaziland,
Region 1950 1970 1990 2000 2010 2020 2050
Africa
Growth rate 2.08 2.61 2.63 2.45 2.55 2.31 1.77
Total fertility rate 6.60 6.67 5.73 5.10 4.71 4.14 3.11
Life expectancy 37.3 46.4 51.7 53.3 59.5 62.9 69.9
Latin America and Caribbean
Growth rate 2.69 2.43 1.73 1.36 1.12 0.85 0.26
Total fertility rate 5.89 5.03 3.01 2.52 2.15 1.96 1.78
Life expectancy 51.2 61.2 68.4 72.1 74.5 76.8 81.7
Oceania
Growth rate 2.23 1.76 1.49 1.43 1.54 1.23 0.79
Total fertility rate 3.84 3.23 2.49 2.43 2.42 2.29 2.06
Life expectancy 60.4 66.4 72.5 75.1 77.5 79.2 82.1
North America
Growth rate 1.67 0.95 1.05 0.92 0.78 0.69 0.38
Total fertility rate 3.35 2.01 2.00 1.99 1.86 1.87 1.90
Life expectancy 68.6 71.4 75.8 77.4 79.2 80.6 84.3
Europe
Growth rate 0.98 0.60 0.19 0.07 0.08 −0.04 −0.21
Total fertility rate 2.66 2.17 1.57 1.43 1.60 1.66 1.79
Life expectancy 63.6 70.6 72.6 73.8 77.0 78.6 82.2
Note: Growth rate (% per year), total fertility rate, and life expectancy are for the subsequent 5-year period (e.g., 1950–1955). Life expectancy is for both sexes combined. All estimates are from the medium variant projections.
Data from United Nations, Department of Economics and Social Affairs, Population Division. (2015). World population prospects: The 2015 revision, volume I: Comprehensive tables (ST/ESA/SER.A/379).
Accessed on November 10, 2016 from https://esa.un.org/unpd/wpp/publications/Files/WPP2015_Volume-I_Comprehensive-Tables.pdf
TABLE 5-1 Growth Rate, Total Fertility Rate, and Life Expectancy for Regions of the World by Time Period
(continued)Botswana, Lesotho, Zimbabwe, South Africa, Namibia, Zambia, Malawi, and Mozambique (UNAIDS Fact Sheets, 2009). The U.S. Census Bureau (2004) esti- mated that, on average, life expectancy in 2010 will be significantly less in these countries than it would have been in the absence of AIDS. Much of the increase in every country in life expectancy is due to improvements in infant and child survival; by con- trast, the HIV-related declines are primarily the result of increased adult mortality. However, the latest WHO (2016) estimate shows that African countries have
overcome the setbacks posed by AIDS epidemic and that life expectancy dramatically improved between 2000 and 2015. With increasing access to antiretro- viral treatment (ART), the number of AIDS-related deaths declined by 39% between 2005 and 2013 in sub- Saharan Africa, increasing life expectancy in that area by 10 years (Kharsany & Karim, 2016).
Total fertility rate had declined by 1970 in all parts of the world, with the exception of Africa. That decline was, in low- and middle-income regions, over- whelmed by increases in life expectancy, so that these
EXHIBIT 5-1 Demographic Change in Kenya and Bangladesh
Kenya (population = 46 million) and Bangladesh (population = 161 million) will be used as case studies to illustrate demographic change in this chapter for a number of reasons. There are both similarities and differences in their experiences.
First, similar to Bangladesh, Kenya experienced high population growth rates that were the result of continuing high fertility during a period when mortality was declining quite sharply. Thus, in the period 1950–1955, life expectancy was 40.5 years for males and 44.2 years for females. By 1980–1985, almost 16.5 years had been added to life expectancy for both males and females in Kenya, raising life expectancy there to 57 years for males and 60.7 years for females. In comparison, although life expectancy also rose sharply during the same period for Bangladesh, the increase was notably less than in Kenya (10 years for males and 14 years for females). Because Bangladesh started from a lower base in 1980–1985, life expectancy in Bangladesh was actually significantly (almost 10 years) lower than that in Kenya (male = 48.9 years; female = 50.2 years). As a result of the sharp decline in mortality coupled with continuing high fertility rates (during that period, the total fertility rate in Kenya exceeded 7 births per woman), population growth rates in Kenya increased from 2.77% per year in 1950–1955 to 3.78% per year in 1980–1985. Similarly, for Bangladesh, due to the sharp drop in mortality and the continued high fertility (TFR remained between 6 and 7 births per woman), there was also a sharp increase in population growth rates, from 2.11% in 1950–1955 to 2.61% in 1980–1985.
Second, as was the case in Bangladesh, a successful family planning program in Kenya managed to bring about a fertility decline despite relatively little improvement in socioeconomic indicators (Toroitich-Ruto, 2001). Fertility dropped sharply over the next 15 years, from 7.22 births per woman in 1980–1985 to 5.07 births per woman in 1995–2000. Fertility subsequently appears to have declined slowly; it was approximately estimated at 4.96 births per woman in 2005–2010 and 4.54 births per woman in 2010–2015. Both the initial sharp decline and the subsequent slower decline in total fertility rates are similar to the experience of Bangladesh, although TFR in Bangladesh remained at a significantly lower level—
approximately 3.3 births per woman in 1995–2000—and subsequently dropped very slowly to an estimated 2.36 births per woman in 2005–2010 and 2.2 births per woman in 2010–2015.
Kenya, like other countries severely affected by AIDS, saw its life expectancy fall in the latter part of the twentieth century, from 57 to 51 years for males and from 62 to 52.3 years for females in 2000. Since then, the situation in Kenya has improved due to a significantly faster than expected morality decline resulting from more effective control of HIV in the post-2008 period. Additional gains of 4 to 5 years in life expectancies have been observed in both males and females for Kenya. However, unlike mortality reductions, fertility rates in Kenya declined much less than expected as per the 2008 projections. Instead of declining from 4.9 births per woman in 2010 to 2.39 births per woman in 2050, TFR will decline to only a projected 2.85 births per woman in 2050 as per 2015 projections. Thus, the annual population growth rate in Kenya was predicted to be 1.27% in 2050 by the 2008 projections and is now 1.69% according to the 2015 projections.
In the case of Bangladesh, since 2008, mortality reductions have been slightly greater than expected (an additional 2 years of life expectancy is projected through 2050) and fertility reductions have also been greater than expected, thus overriding the mortality decline. TFR, instead of declining from 2.36 births per woman in 2010 to 1.85 births per woman in 2050, is now expected to decline to a projected 1.67 births per woman in 2050. Thus, the predicted growth rate in 2050 for Bangladesh was 0.26% as per the 2008 projections and is now 0.18% according to the 2015 projections.
In conclusion, Kenya experienced significantly lower than expected declines in population growth rates in the post- 2008 period due to a combination of higher than expected mortality decline along with significantly lower than expected fertility decline. Bangladesh, by contrast, experienced basically a flat picture, with a slightly higher than expected fertility decline matching the slightly higher than expected mortality decline.
(continues)
EXHIBIT 5-1 Demographic Change in Kenya and Bangladesh
(continued)Population growth rates for Bangladesh and Kenya, 1950–2050, projected in 2008 (1950–2050) and in 2015 (2010–2050). All estimates are from the medium variant projection.
0 0.5 1 1.5 2 2.5 3 3.5 4
1950–1955 1960–1965 1970–1975 1980–1985 1985–1990 1990–1995 1995–2000 2000–2005 2005–2010 2010–2015 2015–2020 2020–2025 2025–2030 2035–2040 2045–2050
Annual growth rate
Period Bangladesh 2008
Population growth rates for Kenya and Bangladesh
Bangladesh 2015 Kenya 2008 Kenya 2015
Data from United Nations, Department of Economics and Social Affairs, Population Division (2009). World population prospects: The 2008 revision, volume I: Comprehensive tables (ST/ESA/SER.A/287). Accessed on November 10, 2016 from http://kczx.shupl.edu.cn /download/786444c9-20c1-4b5a-b0d6-d7544569a2ee.pdf. United Nations, Department of Economics and Social Affairs, Population Division (2015). World population prospects: The 2015 revision, volume I: Comprehensive tables (ST/ESA/SER.A/379). Accessed on November 10, 2016 from https://esa.un.org/unpd/wpp/publications/Files/WPP2015_Volume-I_Comprehensive-Tables.pdf
Total fertility rate (per woman) for Bangladesh and Kenya, 1950–2050, projected in 2008 (1950–2050) and 2015 (2010–2050). All estimates are from the medium variant projection.
Data from United Nations, Department of Economics and Social Affairs, Population Division (2009). World population prospects: The 2008 revision, volume I: Comprehensive tables (ST/ESA/SER.A/287). Accessed on November 10, 2016 from http://kczx.shupl.edu.cn/download/786444c9-20c1-4b5a-b0d6-d7544569a2ee.pdf. United Nations, Department of Economics and Social Affairs, Population Division (2015). World population prospects: The 2015 revision, volume I: Comprehensive tables (ST/ESA/SER.A/379). Accessed on November 10, 2016 from https://esa .un.org/unpd/wpp/publications/Files/WPP2015_Volume-I_Comprehensive-Tables.pdf
Bangladesh 2008
Total fertility rates (per woman) for Kenya and Bangladesh
Bangladesh 2015 Kenya 2008 0
1 2 3 4 5 6 7 8 9
1950–1955 1960–1965 1970–1975 1980–1985 1985–1990 1990–1995 1995–2000 2000–2005 2005–2010 2010–2015 2015–2020 2020–2025 2025–2030 2035–2040 2045–2050
Total fertility rate
Period
Life expectancy at birth in years for males in Bangladesh and Kenya, 1950–2050, projected in 2008 (1950–2050) and 2015 (2010–2050). All estimates are from the medium variant projection.
Data from United Nations, Department of Economics and Social Affairs, Population Division (2009). World population prospects: The 2008 revision, volume I: Comprehensive tables (ST/ESA/SER.A/287). Accessed on November 10, 2016 from http://kczx.shupl.edu.cn /download/786444c9-20c1-4b5a-b0d6-d7544569a2ee.pdf. United Nations, Department of Economics and Social Affairs, Population Division (2015). World population prospects: The 2015 revision, volume I: Comprehensive tables (ST/ESA/SER.A/379). Accessed on November 10, 2016 from https://esa.un.org/unpd/wpp/publications/Files/WPP2015_Volume-I_Comprehensive-Tables.pdf
Life expectancy at birth in years for males in Kenya and Bangladesh
0 10 20 30 40 50 60 70 80 90
1950–1955 1960–1965 1970–1975 1980–1985 1985–1990 1990–1995 1995–2000 2000–2005 2005–2010 2010–2015 2015–2020 2020–2025 2025–2030 2035–2040 2045–2050
Life expectancy in years
Period
Bangladesh male 2015 Bangladesh male 2008 Kenya male 2015 Kenya male 2008
regions’ growth rates—and their population growth—
increased. Fertility continued to fall, however, and at sufficient rates to counteract the continuing increased in life expectancy. This fertility transition is serving to bring growth rates down in all low- and middle- income regions today. HIV/AIDS may be contributing to recent declines in fertility in the parts of the world hardest hit by this disease; a mounting body of evidence indicates that infected women have reduced fecundity (Lewis, Ronsmans, Ezeh, & Gregson, 2004; United Nations Population Division, 2002). The changes in population growth rates, total fertility rates, and life expectancy are illustrated in Exhibit 5-1, which pro- vides information for Bangladesh and Kenya from 1950 to 2050 using the medium-variant UN popula- tion estimates (United Nations Population Division, 2015b).
To understand the different types of fertility tran- sitions that have taken place, we need an understand- ing of the determinants of fertility and fertility change in different contexts. An extensive body of literature has examined the impact of socioeconomic factors on desired family size (Bankole & Westoff, 1995; Bulatao &
Lee, 1983; Rutstein, 1998). Much of this discussion
centers on the costs and benefits of children and the notion that couples desire additional children as long as the benefits are greater than the costs. These ben- efits and costs are, in turn, determined by a range of factors, some of which are structural (e.g., wages, rates of return on investments, opportunity costs), and some of which are attitudinal (i.e., changes in values and expectations). Improvements in the educational status of women, for example, are thought to decrease desired family size because such trends increase the potential wages that women can earn and, therefore, raise the opportunity costs of childbearing. Educa- tion may, in addition, lead to attitudinal change about quantity–quality tradeoffs in numbers of children—
for example, having fewer children so that greater investment in the education of each child is feasible.
Implicit in this theoretical framework is the idea that couples weigh a variety of alternatives, with childbearing being just one of the possible behavioral choices available. Other structural factors affecting fertility rates include trends such as increasing land- lessness, which decreases the benefits of the labor pro- vided by children and thereby tends to reduce family sizes. More recent research emphasizes attitudinal Life expectancy at birth in years for females in Bangladesh and Kenya, 1950–2050, projected in 2008 (1950–2050) and 2015 (2010–2050). All estimates are from the medium variant projection.
Data from United Nations, Department of Economics and Social Affairs, Population Division (2009). World population prospects: The 2008 revision, volume I: Comprehensive tables (ST/ESA/SER.A/287). Accessed on November 10, 2016 from http://kczx.shupl.edu.cn /download/786444c9-20c1-4b5a-b0d6-d7544569a2ee.pdf. United Nations, Department of Economics and Social Affairs, Population Division (2015). World population prospects: The 2015 revision, volume I: Comprehensive tables (ST/ESA/SER.A/379). Accessed on November 10, 2016 from https://esa.un.org/unpd/wpp/publications/Files/WPP2015_Volume-I_Comprehensive-Tables.pdf
Life expectancy at birth in years for females in Kenya and Bangladesh
0 10 20 30 40 50 60 70 80 90
1950–1955 1960–1965 1970–1975 1980–1985 1985–1990 1990–1995 1995–2000 2000–2005 2005–2010 2010–2015 2015–2020 2020–2025 2025–2030 2035–2040 2045–2050
Life expectancy in years
Period
Bangladesh female 2015 Bangladesh female 2008 Kenya female 2015 Kenya female 2008
change as affecting fertility rate. It posits that values and expectations can change as a result of outside influences. Thus, exposure to messages in which small families are treated as a marker for modernity may motivate couples to reduce their desired family sizes even in the absence of any changes in the structural costs and benefits of children.
Although this chapter focuses on LMICs, in almost none of which has fertility declined to replace- ment level, it is worth noting that high-income coun- tries, especially those in Europe, are concerned about their very low fertility rates and population declines.
For Europe as a whole, TFR fell to less than 1.9 births per woman before 1980 and has continued to decline.
It is expected that the entire continent will have a negative growth rate for 2000–2020 (United Nations Population Division, 2009). Understanding which fac- tors maintain below-replacement fertility and which factors cause it to increase is an important issue for high-income countries.