8.13: Demographic Transition Model
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- 212721
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Demographic Transition Model
The evolution of the world’s economic system had implications for women’s reproductive rights, as well as population growth rates around the world. Geographers observed a regular pattern of reduction in average family size during the Industrial Revolution. Their observations have been described in a theory known as the Demographic Transition Model (DMT).
In stage one of the DMT, pre-industrial economic systems and traditional gender roles typically compelled women to bear numerous children, often more than 10. Families were large partly because children in pre-industrial economies tend to be economically beneficial, but also prone to pre-mature death. Keep in mind, that where (when) children could work around the house or on the farm and/or where they functioned as safeguards against starvation and violence for elderly parents, families with numerous children were considered wealthy. In places where a significant percentage of children die before they can become economically useful, it was (is) wise to have as many children as possible – just in case disease or some other misfortune takes some of them away. During stage one, the overall population growth rates are small and the population is reasonably stable, because births roughly equaled deaths. Occasional plagues and famines sometimes caused dramatic reductions in a population’s growth rate.
Figure Graphic representation of the Demographic Transition Model. The green line is the birth rate, the orange line represents the death rate and the dotted blue line represents the rate of natural increase or population growth rate.
During stage two of the DMT, advances in nutrition, sanitation, and medicine that accompany industrialization and urbanization greatly reduce the death rate as countries industrialize. Not only do far more children live to become adults, but women live far longer and therefore bear even more children. During stage two, the growth rate of countries increases rapidly because age-old cultural traditions continue to encourage parents to have numerous children, even though the economic value of children begins to falter. In Europe and the United States, stage two occurred roughly between 1800 and 1950.
Stage three occurs when people eventually realize that in an urban-industrial environment, children are no longer economic assets, especially once children are forbidden to work in factories and are forced to go to school. Children become expensive to house and feed. Once old-age insurance and other social security functions become available, children lose the last remaining economic value they bring to families. Overall, the population growth rate slows a good bit, as the birth rate falls dramatically.
During stage four of the DTM, the birth rate eventually drops to match the death rate, which fell during state two. At this point, population growth rates stabilize once again and countries experience modest population growth. Sometimes the demographic transition model includes a fifth stage in which large numbers of families decide to have only one, or no children. At this point, the growth rate becomes negative, and the overall population shrinks as the rate of natural increase (live births minus deaths per year) turns negative. Japan, Russia and some countries in Europe appear to have entered stage five. The United States appears to be transitioning to stage five slowly.
It is very important to keep in mind that the DTM describes an ideal evolution in national patterns of birth, death, and growth. Perhaps no country went through this process precisely as the theory predicts, but the model does help us understand the basic pattern of population change that most industrial countries witnessed over the last 200 years. It suggests that many developing countries, where populations are exploding (stage two) will eventually industrialize and enjoy manageable population growth rates, but there’s no guarantee that this will be the case. Women in many parts of the world, even where economic changes invite smaller families, have not managed to wrest control of their own reproductive choices. Free birth control devices are not enough to overcome economic logic, nor stubborn cultural norms that characterize parts of the world where very large families are valued. Until women in these cultures are free to leave the home to pursue education and work opportunities, the economic incentive to have fewer children will not emerge. In regions where women are prohibited from working outside the home, robust economic development is hindered. Without women workers to attract factories, the economic value of women remains muted, economic development stunted, and the prospects for smaller families, and slower, stable population growth rates are diminished. It’s a vicious circle.
Sex Ratio
Gender roles, economic decisions, and even government policies all influence where women and men live. Without the effect of culture, the sex ratio should be almost perfectly 1:1 – men to women. However, in many places around the world that ratio is skewed, sometimes dramatically, in favor of one sex or another. Skewing can occur because the infant mortality rate for boys may be higher or lower than it is for girls, or because women may outlive men by a few years on average. In the United States, about 105 boys are born for every 100 girls, but because more boys die as infants, through violence as young men, and from heart and lung disease as older men, women outnumber men in the United States slightly (97 males for every 100 females or .97). That ratio can be greatly exaggerated in some countries, and even in some US states. In the Lowland South, and parts of the Upper Midwest there are lots more women than men. The imbalance in the Deep South is related to the exaggerated infant mortality rate and the levels of deadly violence, especially among African Americans. In the Upper Midwest, where the average age is unusually high, elderly women skew the ratio toward women. On the other hand, in places where there are numerous economic opportunities for men in heavy industry and mining creates an excess male population.
Figure US map of counties by ratio of males to females expressed in standard deviation above or below the national average of 96.7 males to females.
Economic opportunities can also draw people across international borders as well. In many of the oil-producing countries of the Middle East, there are extremely lopsided sex ratios favoring men, while women far outnumber men in the countries where the migrants left. Sexist cultural attitudes, wars and government policies can also create imbalanced sex ratios. In parts of India, where families often pay a large dowry in order to secure a husband for a daughter and stand to receive a sizable dowry for each married son, it appears that boys are favored enough over girls that the sex ratio skews heavily toward boys. Similarly, in China where a national One-Child Policy (1979-2015) designed to reduce China’s population growth rate combined with traditional notions about gender greatly diminished the number of girls born there for nearly two generations. As a result, by 2020, China is expected to have nearly 30 million more men than women. Even small imbalances in the sex ratio have numerous, often significant, consequences for societies, including increases in crime and, ironically, an increase in the population growth rate.
Figure Sichuan, China. This roadside sign reminds passers-by that “It is forbidden to discriminate against, mistreat or abandon baby girls”, a horrifying indication of the human cost that traditional gender roles can have. Source: Wikimedia
Gendered Landscapes
Landscapes are given meaning by the way they are used. Landscapes also create meaning because they simultaneously affect the way we behave. Feminist geographers have explored the role of landscape both in terms of how the landscape is affected by gender roles and how landscapes influence evolving gender roles. The role of housing and neighborhood design offers a glimpse into these processes.
Figure : Burbank, CA - Barber shops were for many years a space reserved almost exclusively for men. Women had beauty salons that functioned similarly to reinforce gender roles.