for fertility mortality, migration, and population are instrumental to guide investment
in infrastructure to accommodate population shifts. This study uses other statistical models and the researchers own propriety model to demonstrate the effect of education and contraception on the completed cohort fertility at age 50 years (CCD50), which is the average number of children born to an female from a specific age (15-49 years). This study uses the length of time to reach universal contraceptive and education access to produce varying models to more accurate represent reality. Generally, this study shows that if the pattern of
female education and access to contraceptives continues, this will further the decline
in fertility and thus population growth.
need short and long-term population forecasts to estimate needs for important institutions
such as hospitals, business, schools, and public services. The largest
population forecasts depend on two key factors: fertility decline in sub-Saharan
Africa and countries that have a total fertility rate (TFR) lower than their replacement rate. FTR
is the number of children born per woman if she passes through her childbearing
years successfully. This study demonstrates limitations in previous forecasting models and
demonstrates their own model on fertility, mortality, and migration in relation
to changes in educational attainment and if contraceptive needs are met.
The population from
2018-2100 for 195 countries and territories was projected.
The researchers took a model
on mortality established by Foremann and colleagues, made minor changes, and
extended it to 2100.
This study looks at the less
obvious trends that appear in countries/territories with a lower total
fertility rate (TFR) than the replacement rate of the population. This essentially
means that when less individuals are being born than are able to replace the portion
of the population. The TFR trends in relation to other variables such as education increase in complexity. Due to
this complexity the researchers modeled fertility with the use of CCF50. This has
already been defined as the average number of children born to an individual female
from ages (15-49 years). This rate is more stable than the TFR and permits the researchers a less volatile variable to increase reliability of their model. The researchers also used urbanicity as an additional
variable to help explain inaccuracy in the data, but it did not explain their findings.
Migration was measured as a
result of SDI. SDI is death due to conflict, natural disaster, differences
between death and birth rate, and a random chance variable. 2017 UN data on
past migration patterns were used. However, the researchers note that the
migration patterns are incredibly subject to enormous changes and are therefore
are very uncertain in their quantification.
population was calculated by keeping the mortality, fertility, and migration
rate constant for each age, sex, and location during a specific calendar year. Two
other independent scenarios were created to account for total secondary
education and universal coverage in contraceptive needs by the year 2030 (as
indicated by the UN’s Sustainable Development Goal (SDG)). Gross domestic product (GPD) was also brought
into consideration during the construction of their models. Similarly, other
models were referenced for comparison.
inequalities still persist in 2100. With life expectancies ranges from 69.4
years to 88.9 years for both sexes. The difference in life expectancy decreased
from 6.9 years in 2017 to 3.6 years in 2100.
Global Fertility Scenarios
of fertility was highly dependent upon if the model was fast or slow with respect
to access to contraceptives and educational attainment by the year 2030 as indicated by the SDG.
Global Population Scenarios
researchers combined the models on fertility, mortality, and migration to forecast
a population. If the educational and contraceptive needs are met quickly than
the population as seen in the SDG model, then the population in 2100 is
forecasted to be 6.29 billion. If the educational and contraceptive goals are
met much slower, then the global population is estimated to be as much as 13.6 billion. The
peak population in the SDG scenario was estimated to occur in 2045. All scenarios
noted a large shit in the age structure of the global population from a mean
age of 32.6 years in 2017 to 46.2 years in 2100.
Country-level migration scenarios
USA, India, and China will have the largest absolute number of immigrations.
While Somalia, Philippines, and Afghanistan
are predicted to have the largest absolute number of emigrations. Canada,
Turkey, and Sweden will have the largest immigration rate while El Salvador, Samoa
and Jamaica have the largest emigration rates. The countries with the expected
largest life expectancies populations in 2100 are China, Bangladesh, Brazil,
Ethiopia, the USA, and Nigeria
Regional and Country-level fertility and
variations were observed for the TFR by country and territory across the
various scenarios. The top 10 countries reached peak population before 2100, except
Nigeria is expected to peak near the year 2100.
In all scenarios
population declines caused a reduce in economic growth.
on the researcher’s models on fertility, mortality, and migration the global
population will peak in 2064 at 9.73 billion and decline to 8.79 billion in 2100.
The access the education and contraception severely influenced the population particularly
in high fertility countries like the sub-Sharan. The decline in population in the
latter half of the century is good news for the global environment. However,
climate change and environment issues will still have serious consequences in
the coming years unless action is taken and passionately pursued. The large changes
in population will have dramatic effects on the GDP of countries as the ratio
of working age adults will shift wildly. This effect could be minimized by an
increase in robotic autonomy. However, these effects are difficult to model. Labor
force issues can be correct by a variety of methods including increasing the
TFR or the age during which adults participates in the labor force. However, only increasing TFR is effective for long-term protection of the labor force.
Countries that rely on immigration to maintain and grow their labor force such
as the USA may be in danger in the future as the amount of willing emigrants could
potentially decrease sharply.
uses imperfect data, therefore this will lead to imperfect results. Past trends
are not always predicative of future trends. This study does not account for
large global changes induced by disease, war, climate change, economic
collapse, or changes to migration policies. Future work is required for better
quantification to improve estimation in modeling pathways.
population will likely peak before the end of century. Some countries will have
rapidly growing or decreasing populations. Meanwhile, others will struggle or succeed
to maintain their population with liberal immigration and social policies. Most
importantly nothing is set in stone, so predications made in this paper could
be obsolete if large global changes occur.