Natural increase, doubling time, population momentum and projections
Natural increase
"Natural increase" is a term used in economics, geography, sociology and population studies. In simplest terms, it is the birth rate minus the death rate. Birth rate in this context almost always refers to the annual number of births per thousand in a given population.
Death rate is defined the same way, as the annual number of deaths per thousand in a given population.
Because the term is always defined in terms of a given rate of birth minus a given rate of death, "natural increase" is itself a rate, i. e., the rate of net increase in births over deaths. It is also a ratio, where the birth rate in a specified period is the numerator and the death rate in the same period is the denominator.
The term is often referred to by its acronym, RNI (Rate of Natural Increase). Note also that an RNI rate can be negative if a population is in decline, i. e., is actually a rate of natural decrease.
What is Natural?
How population increases acquired the qualification "natural" is information lost over time, but probably originated with Malthus, the early economist who first proposed a math-based theory of population growth in his Essay on the Principle of Population (1798).
Basing his conclusions on his studies of plants, Malthus proposed an alarming "natural" rate of population growth, proposing that human populations increased exponentially -- meaning that they double and redouble to infinity -- in contrast the arithmetic progression of food growth.
The difference between the two growth rates as Malthus proposed it, would inevitably end in disaster, a future where human populations would starve to death.
To avoid this disaster, Malthus proposed "moral restraint," that is, the humans marry late in life and only when they clearly have the economic resources to support a family.
Malthus study of natural population growth was a welcome investigation into a subject that had never before been systematically studied. Essay on the Principle of Population remains a valuable historic document. It turns out, however, that his conclusions were somewhere between "not exactly right," and "totally wrong." He predicted that within 200 years of his writings the world population would have increased to about 256 billion, but that increases in food supply would then support only nine billion. But in the year 2,000, the world population was only a little over six billion. A significant portion of that population was underfed and starvation remained and remains a significant world problem, but the starvation rate never approached the drastic 96 percent starvation rate Malthus proposed.
His conclusions "weren't exactly right" in the sense that the "natural increase" Malthus proposed could exist and actually might exist in the absence of factors he didn't take into account, the most significant of them being the phenomenon studied soon after by Darwin, who noted that populations are in competition with one another -- there is a battle for survival going on everywhere in the natural world (of which we are a part) and absent deliberate remedies, only the fittest survive.
Doubling time
Doubling time is the amount of time it takes for a given quantity to double in size or value at a constant growth rate. We can find the doubling time for a population undergoing exponential growth by using the Rule of 70. To do this, we divide 70 by the growth rate (r).
Note: growth rate (r) must be entered as a whole number and not a decimal. For example 5% must be entered as 5 instead of 0.05.
dt = 70/r
For example, a population with a 2% annual growth would have a doubling time of 35 years.
35 = 70/2
Key Properties of Doubling Time
The larger the rate of growth (r), the faster the doubling time.
Rate of growth varies considerably among organisms. For example, most small bodied organisms grow faster and have larger rates of population increase than larger organisms. Think about the difference in growth rate between bacteria and elephants.
Most populations cannot double forever. Resistance factors like natural resource constraints and disease contribute to a leveling off in population size over time. When this happens, we say the population has reached its carrying capacity. This type of growth is also referred to as logistic growth.
Resources for Teaching Students about Doubling Time
Double Trouble: A secondary activity (grades 9-12) exploring the concepts of exponential growth and doubling time through a lab activity with yeast and an analysis of the human population growth curve.
Population momentum
Population momentum is an effect which causes population growth. This phenomenon refers to the percentage of the population that are in their child bearing years who have not yet had children, and thus are scheduled to eventually have children which add to the population through reproduction. The higher the percentage of people aged, for example 18 and under, the larger the population growth will be because there is such a large percentage of the population capable of having children. This means the population will continue to grow, even if the fertility rate reaches replacement level. The reason is that population momentum would have an effect is that high fertility levels in the past caused a largely young population which still has to reach child-bearing years.
For example, consider country A, which has 50% of its population under 18, and country B, where only has 10% of its population is under 18. If both countries have a present population of 1 million, and a fertility rate of 2 children per woman, the effect of population momentum can be illustrated.
Country A:
500,000 people in child bearing years = 250,000 couples, who each have 2 children - 500,000 new babies.
Country B:
100,000 people in child bearing years = 50,000 couples who each have 2 children - 100,000 new babies.
It can be seen that whilst both countries have the same starting population in terms of numbers and the same fertility rate, because such a high percentage of country A's people are in their child bearing years, their population growth is 5 times that of Country B.
Countries with a Population momentum (Growth in spite of a fertility rate under 2.1):
Canada, Brazil, Turkey, Algeria, Tunisia, China, South Korea, Taiwan, Vietnam, Thailand, Iran.
Population projection
Government policymakers and planners around the world use population projections to gauge future demand for food, water, energy, and services, and to forecast future demographic characteristics. Population projections can alert policymakers to major trends that may affect economic development and help policymakers craft policies that can be adapted for various projection scenarios.
The accuracy of population projections has been attracting more attention, driven by concerns about the possible long-term effects of aging, HIV/AIDS, and other demographic trends. The National Research Council of the U.S. National Academy of Sciences (NAS) convened a panel of experts in 1998 to examine the assumptions, accuracy, and uncertainty related to the most widely used population projections and to recommend ways to improve these projections. After extensive review, the panel concluded in July 2000 that current world population projections to the year 2050 are based on sound scientific evidence and provide plausible forecasts of demographic trends for the world. The panel cautioned, however, that projections for specific countries, for certain population groups, or for longer periods in the future are less certain than global and shorter-range projections.
Users of population projections need to understand the reliability and the limitations of projection series. Awareness of how projections are prepared and the possible sources of uncertainty in the numbers can help policymakers more effectively incorporate projections in their planning process.
Who Makes Population Projections?
Most national governments make population projections for their own countries. In addition, a few international organizations prepare population projections for the world, regions, and individual countries. The United Nations (UN) and the U.S. Census Bureau issue revised global and national projections on a regular basis. The UN projections are the most widely used worldwide. Many national governments, international agencies, the media, researchers, and academic institutions rely on UN projections. The World Bank and the International Institute for Applied Systems Analysis (IIASA) also prepare population projections for the world, major regions, and (especially the World Bank) for individual countries. World Bank projections generally are used for planning and for managing projects, while IIASA projections have been used primarily to assess various projection assumptions and methods. Each of these international organizations uses slightly different methodologies, makes varying assumptions about future demographic trends, and begins with slightly different estimates of current population size. Nevertheless, their results fall within a relatively small band for the next 50 years.
Source: http://www.prb.org/Publications/Reports/2001/UnderstandingandUsingPopulationProjections.aspx
The demographic transition model
Stage 1 - High Fluctuating
Birth Rate and Death rate are both high. Population growth is slow and fluctuating.
Reasons
Birth Rate is high as a result of:
Lack of family planning
High Infant Mortality Rate: putting babies in the 'bank'
Need for workers in agriculture
Religious beliefs
Children as economic assets
Death Rate is high because of:
High levels of disease
Famine
Lack of clean water and sanitation
Lack of health care
War
Competition for food from predators such as rats
Lack of education
Typical of Britain in the 18th century and the Least Economically Developed Countries (LEDC's) today.
Stage 2 - Early Expanding
Birth Rate remains high. Death Rate is falling. Population begins to rise steadily.
Reasons
Death Rate is falling as a result of:
Improved health care (e.g. Smallpox Vaccine)
Improved Hygiene (Water for drinking boiled)
Improved sanitation
Improved food production and storage
Improved transport for food
Decreased Infant Mortality Rates
Typical of Britain in 19th century; Bangladesh; Nigeria
Stage 3 - Late Expanding
Birth Rate starts to fall. Death Rate continues to fall. Population rising.
Reasons
Family planning available
Lower Infant Mortality Rate
Increased mechanization reduces need for workers
Increased standard of living
Changing status of women
Typical of Britain in late 19th and early 20th century; China; Brazil
Stage 4 - Low Fluctuating
Birth Rate and Death Rate both low. Population steady.
Typical of USA; Sweden; Japan; Britain
Stage 5 - Decline?
In Stage 5 of the DTM a country experiences loss to the overall population as the death rate becomes higher than the birth rate. The negative population growth rate is not an immediate effect however. Based on demographic momentum, in which total population growth increases even while birth rates decline, it will take a generation or two before a negative population growth rate is observed.
In recent years a few countries, primarily in Eastern and Southern Europe, have reached a negative rate of natural increase as their death rates are higher than their birth rates. Possible examples of Stage 5 countries are Croatia, Estonia, Germany, Greece, Japan, Portugal and Ukraine. According to the DTM each of these countries should have negative population growth but this has not necessarily been the case. Complicating the Demographic Transition Model’s framework is the impact of migration across national borders. Even with smaller birth rates countries are still growing because of positive net migration rates. This demographic phenomenon has muddled the expected progression of countries along the DTM. Does a country belong in Stage 5 if it has a higher death than birth rate but does not have negative total population growth? The debate begins here.
Is the model universally applicable?
Like all models, the demographic transition model has its limitations. It failed to consider, or to predict, several factors and events:
1 Birth rates in several MEDCs have fallen below death rates (Germany, Sweden). This has caused, for the first time, a population decline which suggests that perhaps the model should have a fifth stage added to it.
2 The model assumes that in time all countries pass through the same four stages. It now seems unlikely, however, that many LEDCs, especially in Africa, will ever become industrialized.
3 The model assumes that the fall in the death rate in Stage 2 was the consequence of industrialization. Initially, the death rate in many British cities rose, due to the insanitary conditions which resulted from rapid urban growth, and it only began to fall after advances were made in medicine. The delayed fall in the death rate in many developing countries has been due mainly to their inability to afford medical facilities. In many countries, the fall in the birth rate in Stage 3 has been less rapid than the model suggests due to religious and/or political opposition to birth control (Brazil), whereas the fall was much more rapid, and came earlier, in China following the government-introduced ‘one child’ policy.
The timescale of the model, especially in several South-east Asian countries such as Hong Kong and Malaysia, is being squashed as they develop at a much faster rate than did the early industrialized countries.
4 Countries that grew as a consequence of emigration from Europe (USA, Canada, Australia) did not pass through the early stages of the model.
Total fertility rate and changes in fertility
Definition: The number of children who would be born per woman (or per 1,000 women) if she/they were to pass through the childbearing years bearing children according to a current schedule of age-specific fertility rates.
Purpose: The TFR is the most widely used fertility measure in program impact evaluations for two main reasons: (1) it is unaffected by differences or changes in age-sex composition, and (2) it provides an easily understandable measure of hypothetical completed fertility.
Although derived from the ASFR (age-specific fertility rate for women in age group a (expressed as a rate per woman), a period fertility rate, the TFR is a measure of the anticipated level of completed fertility per woman (or per 1,000 women) if she/ they were to pass through the reproductive years bearing children according to the current schedule of ASFRs. The TFR is only a hypothetical measure of completed fertility, and thus women of reproductive age at any given point in time could have completed family sizes considerably different from that implied by a current TFR, should age-specific fertility rates rise or fall in the future.
Issue(s): Because the TFR is derived from a schedule of ASFRs, the comments and caveats regarding the ASFR also apply to the TFR (i.e., method of computation from different sources of data, effects of changing exposure to pregnancy, and implications of computation for currently married versus all women of reproductive age). As was also the case for the ASFR, the TFR may be computed for women who were continuously married or in union during the reference period of the measure in order to decrease the potentially confounding effects of differences in exposure to the risk of pregnancy (to the extent that differences are associated with marital status). This measure is known as the Total Marital Fertility Rate (TMFR).
Note also that whereas the standard age range for the TFR is ages 15-49, TFRs for other age ranges (e.g., 15- 34) are sometimes used for analytic purposes, for example, in order to decrease the influences of truncation when examining cohort trends from birth history data.
Follow the link bellow to look into how TFRs for different countries have changes over time.
Infographic
Life expectancy
Life expectancy at birth compares the average number of years to be lived by a group of people born in the same year, if mortality at each age remains constant in the future. Life expectancy at birth is also a measure of overall quality of life in a country and summarizes the mortality at all ages.
Follow the link bellow to look into how life expectancy of different countries have changes over time.
Age/sex pyramids and population structure
HOW TO READ AN AGE-SEX PYRAMID GRAPH
An age-sex pyramid breaks down a country or location's population into male and female genders and age ranges. Usually you'll find the left side of the pyramid graphing the male population and the right side of the pyramid displaying female population.
Along the horizontal axis (x-axis) of a population pyramid, the graph displays population either as a total population of that age or a percentage of the population at that age. The center of the pyramid starts at zero population and extends out to the left for male and right for female in increasing size or proportion of the population.
Along the vertical axis (y-axis), age-sex pyramids display five-year age increments, from birth at the bottom to old age at the top.
Population pyramids: Powerful predictors of the future - Kim Preshoff
Source: https://www.youtube.com/watch?v=RLmKfXwWQtE
Dependency and ageing ratios
The total demographic dependency ratio is the ratio of the combined youth population (0 to 19 years) and senior population (65 or older) to the working-age population (20 to 64 years). It is expressed as the number of "dependents" for every 100 "workers":
youth (ages 0 to 19) + seniors (age 65 or older) per 100 workers (aged 20 to 64).
The youth demographic dependency ratio is the ratio of the youth population to the working-age population; the senior demographic dependency ratio is the ratio of seniors to the working-age population.
The demographic dependency ratio is based on age rather than employment status. It does not account for young people or seniors who are working, nor for working-age people who are unemployed or not in the labour force. It merely reflects population age structure and is not meant to diminish the contributions made by people classified as "dependents."
Importance of indicator
A sizeable share of seniors aged 65 or older and children and youth younger than age 20 are likely to be socially and/or economically dependent on working-age Canadians, and they may put additional demands on health services. The demographic dependency ratio measures the size of the "dependent" population in relation to the "working age" population who theoretically provide social and economic support.
Changes in demographic dependency ratios highlight changes in the age composition of the population.
Source: Nagle, Garrett. Development And Underdevelopment ... 1st ed. Nelson, 1998. Print.
Consequences of megacity growth for individuals and societies
Over the past 60 years Mumbai has urbanized rapidly from its origins as a fishing village. The site of the fishing village soon became a port region as the site favored development. Protected from the Arabian Sea by a peninsular art the southern end of Salsette Island, it had access to sea on two sides and the British colonial administration in India developed the sheltered inlet into a major port. The British viewed the port and surroundings as the”Gateway to India”. This made it the closest port of entry to subcontinent for travelers from Europe, through the Suez Canal. As with many major global ports area around the port became industrialized – processing goods for export and handling imports. The city grew during British rule as variety of services grew up around the port and continued to grow after British left in 1947. Since 1971, the graph shows the inexorable rise in the population of Mumbai, from 8 million in 1971 to 21 million now. The other significant factor to note is that slum dwellers make up an ever increasing proportion of the population, creating numerous problems for people and planners. It should be noted that the original urbanization phase of Mumbai focused upon the southern tip of Salsette Island, and outside of this the city suburbanized in a Northern direction.
The causes of urbanization are multiple, but involve a high level of natural increase within Mumbai itself and in-migration principally from the surrounding district of Maharashtra but also from neighboring states. Mumbai booming economy means that migrants come for job opportunities in the expanding industries, financial institutions and administration.
For consequences of megacity growth for individuals and societies in Mumbai read the articles below:
Causes and consequences of forced migration
Syria
Causes
The Syrian crisis is an on-going armed conflict in Syria between forces loyal to the Ba'ath government and those opposing them.
In 2016, reports estimated that fatalities caused by the civil war in Syria amounted to 470,000.
An estimated 4.5 million refugees have fled the country, many to neighboring countries such as Lebanon and Jordan. The infographic below shows the figures in 2016.
In addition, over six million people are estimated to be internally displaced within Syria trying to escape escalating violence.
Effects
Men, Women and children walking through the desert with all their belongings
A large share of Syrian refugees in Jordan are not in camps and have fled into urban areas, beyond the reach of direct assistance from the UN and other donors.
Roughly 70 per cent of these refugees are estimated to be hosted in local communities, resulting in enormous strain on public resources.
This leads to tensions with the native community as resources are strained.
Read articles below for more input:
Synthesis and evaluation
Use the inputs from this post to plan and answer the following exam style question: “To what extent can impacts of population change and spatial interactions be categorized and represented graphically?” 10 marks
Use mark scheme on page 56 from the new syllabus guide (AO3)
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