Today, the future of world population growth looks more uncertain than it did a decade ago because of a controversial recent stalling of fertility decline in a number of African countries and a controversy over how low below replacement level fertility will fall, particularly in China. Probabilistic population projections try to quantify these uncertainties based on statistical extrapolation, expert judgement, or a blend of both. Although such projections published in 2008 gave a 95% prediction interval ranging from 5.2 to 12.7 billion for the global population in the year 2100, probabilistic projections published by the United Nations (UN) Population Division in 2015 based on a different approach give a much narrower 95% interval ranging from 9.5 to 13 billion in 2100. Another recent set of world population projections defined alternative global population scenarios in the context of the work of the Intergovernmental Panel on Climate Change (IPCC) and related integrated assessment models. In the medium scenario these Shared Socioeconomic Pathways (SSPs) show a peaking of world population around 2070 at 9.4 billion, followed by a decline to 9 billion by the end of the century with high and low scenarios reaching 12.8 and 7.1 billion, respectively. As discussed below, these differences in world population projections result from different approaches taken in terms of disaggregating national populations according to age, sex, and education structures and in combining statistical extrapolation with expert knowledge in specifying assumptions for the future.
One of the strongest and most consistent relationships in demography is between mothers’ education and infant mortality – the children of women with more years of schooling are much more likely to survive infancy. More-educated mothers have better health care, marry later and are significantly more likely to use contraception to space their children. They have better skills for obtaining and evaluating information on health care, disease prevention and nutrition. They also have better access to resources, through earning opportunities and marriage, and can manage them better. They are more likely to recognize the advantages of educating their children.
The relationship between women’s education and fertility is also complex, but the underlying pattern in most countries is that the more years of schooling a woman has, the fewer children she is likely to have. In a small number of countries, particularly in sub-Saharan Africa, fertility rises slightly with the first few years of women’s schooling, then falls with subsequent years of education. In all recent studies, additional secondary education for women correlates with lower fertility.
Educated women are more likely to use modern methods of contraception, and they tend to marry later. Educated parents of both sexes also generally desire smaller families than those with less education, and educated women tend to act on that reproductive preference for fewer children.
Infant mortality and high risk of death for adults increases the family size as well. Why is this ? Say you are a poor person in Africa. You would like a healthy 2 or 3 children when you plan for you personal family. But you know from people around you that at least 1 child is likely to at childbirth or before the age of 5 and another could die from disease or violence before being old enough to marry and have a family of their own. So you have 5 to 6 children in your family, because of the likelihood of losing two and smaller chance of losing 3. You actually end of up having 5 children and losing 1 and your neighbor had size and lost 2. So on average the surviving family size is 4 children.
In the developed world the chance of losing a child at childbirth and before the age of five is 0.1%. and the chance of children not making it the age when they have their own family is say 0.5%. Thus many have two children and lose none.
Educated mothers and fathers have healthier children and lose less of them to disease or other factors.
Higher education boosts income levels and lifts people out of poverty.
Poverty increases death rates as well.
Higher education across an entire country boosts overall income levels and enables the society to afford the cost of better medicine and hospitals and sanitation.
Researchers show the extent to which the expected world population growth could be lowered by successfully implementing the recently agreed-upon Sustainable Development Goals (SDGs). The SDGs include specific quantitative targets on mortality, reproductive health, and education for all girls by 2030, measures that will directly and indirectly affect future demographic trends. Based on a multidimensional model of population dynamics that stratifies national populations by age, sex, and level of education with educational fertility and mortality differentials, they translate these goals into SDG population scenarios, resulting in population sizes between 8.2 and 8.7 billion in 2100. Because these results lie outside the 95% prediction range given by the 2015 United Nations probabilistic population projections, we complement the study with sensitivity analyses of these projections that suggest that those prediction intervals are too narrow because of uncertainty in baseline data, conservative assumptions on correlations, and the possibility of new policies influencing these trends. Although the analysis presented here rests on several assumptions about the implementation of the SDGs and the persistence of educational, fertility, and mortality differentials, it quantitatively illustrates the view that demography is not destiny and that policies can make a decisive difference. In particular, advances in female education and reproductive health can contribute greatly to reducing world population growth.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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