Human Geography – Population Dynamics

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Human Geography – Population

1. Population Dynamics

  • Fertility

  • Contemporary fertility differentials at global and national levels

  • Proximate variables affecting fertility

  • Biological (years in marriage, IVF, sterilisation, contraception, abortions)

  • Socio-economic (economic/social value of children, status of women in work)

  • Institutional (government policies and religion)

  • Contemporary mortality differentials at global and national levels

  • Factors affecting mortality

  • Demographic (age and gender)

  • Medical technology (advances in medical care)

  • Public health measures (sanitation, vaccination)

  • Socio-economic changes (standard of living)

  • Political (genocide, wars)

  • Epidemics (Spanish flu) and pandemics (AIDS)

  • Migration

  • Concepts

  • Time (short and long term movements)

  • Distance (national and transnational, origin and destination)

  • Transnational movement (skilled and unskilled labour)

  • Issues in migration

  • Labour migration and impact

  • Identity and nationhood

2. Implications of Population Change

  • Population Composition and Distribution

  • Overview of world population growth and future projections in LDCs and DCs

  • Population distribution across space and inequalities in distribution

  • Need for and problems of population forecasting at national scale

  • Problems of population prediction

  • Future problems and opportunities resulting from population change and policies in different parts of the world

  • Interpretation of population pyramids

  • Interpretation of population pyramids and relationship to growth rates

  • Dependency and its implications

  • Coping with population growth (housing, work and services)

  • Coping with population loss (maintaining viable communities)

  • Value and limitations of population pyramids in prediction

  • Socio-economic composition

  • Ethnic diversity

  • Inequality between different populations and different sectors of the same population

  • Indices used to measure inequalities in population

  • Population Change and Planning

  • Population growth in relation to Demographic Transition Model

  • DTM and its usefulness

  • Consideration of the 5th stage

  • Anti-natal and pro-natal policies

  • Role of governments in planning developments in light of forecasts of population change (in numbers and composition of national populations)

  • Population forecasting and government policies affecting population growth and their implications

  • Population-Resource Relationships

  • Carrying capacity, optimum population, overpopulation and underpopulation

  • Population-resource theories, origins and value

  • Malthus

  • Meadows

  • I=PAT (Impact = Population x Affluence x Technology, Ehrlich and Holdren)

  • Development and resource use

  • Global variations in resource use

  • Changes in society (technological, economic and political) result in changing appraisals of resources and environments, their use and management)

  • Hedonist vs. conservationist views of population-resource relationship and their consequences

  • Relationship of population growth to environmental conditions and the changing resource base

  • Contrasting assessment of population-resource relationships

  • Sustainable development

1. Population Dynamics


  • Discuss the fertility differentials between LDCs and DCs.

  • Discuss how the different proximate variables affect fertility in LDCs and DCs.

  • Evaluate the impact of governments’ influence on the proximate variables in attempting to achieve a desired fertility rate.

Key Terms Regarding Fertility

Fertility: The natural capability of giving life, dependent on nutrition, culture, economics, way of life, sexual behaviour.

Fecundity: The ability to reproduce – differs from fertility in that fecundity represents the potential to reproduce biologically, while fertility means actually reproducing.

Crude Birth Rate (CBR): Measures total number of births over a unit of time per 1000 in any given population. CBR varies between countries, and is usually between 10-55 per 1000. ‘Crude’ is termed as such because factors such as age, sex and migration are not accounted for.

Total Fertility Rate (TFR): Average number of children born to a woman throughout her reproductive years of 15-49. The sum of averages of number of babies born by age n/number of women age n gives a grand average. TFR in the US is 2.1, in 2008 Zambia was 5.8 and Singapore was 1.28.

Age-Specific Fertility Rate (ASFR): The average number of children born to a woman in a specific year or age group concerned.

Replacement Level: Measure of the extent to which a given population is able to produce enough offspring to replace itself. The ratio is approximately 2 per family, but to account for possible premature deaths, the replacement rate is a 2.1 TFR.

Proximate Variables Affecting Fertility

Biological Variables

Biological variables are split into three sections: intercourse, conception and gestation.

Intercourse Variables

1a) Age of Entry into Stable Sexual Unions – the socially acceptable age of entry into formal marriages. This, of course, differs over space and cultures. The earlier the age, the higher the chance of a higher fertility rate. For many LDCs, marriage is almost universal – there are many cases of children as young as 13 marrying due to specific cultures. For example, in India prearranged marriages are common. However, in DCs, the pattern is that marriages are late in life or non-existent. As education and opportunities for employment increase for people, they become more financially and economically independent. There is more choice and freedom as to whether they want to get married or not, unlike in LDCs where marriage is almost bound by social and cultural norms.

1b) Interruption of Stable Sexual Unions – various factors which inhibit marriage, such as the death of a spouse, or divorce. For LDCs, death seems to be more common due to, generally, poorer health care systems. The key problem for DCs today is the increasing rate of divorce. One of the reasons is the increased status and choice of women in DCs due to higher education, thus they are more able to be financially independent than their counterparts in LDCs. The way which society views the family unit may have also changed, contributing to this phenomenon.

2a) Exposure within Unions: Postpartum Abstinence – where mothers do not get pregnant again immediately after giving birth due to various reasons. One reason is postpartum amenorrhoea, where women are generally less fertile while breastfeeding. This serves as a natural birth spacer between children, along with a social/cultural or natural inhibition of coitus and pregnancy during lactation. The spacing of births enhances the survival of the offspring. However, in recent years with infant formula on the rise in LDCs (the situation in DCs vary, depending largely on the choice of the women), this method is becoming less effective.

2b) Periodic Abstinence – for example seasonal abstinence when men/women migrate in search for employment. It is estimated that without seasonal migration, fertility could increase by 25%. This is especially so for LDCs, where many migrate to other countries and DCs to find jobs, leaving their families back in home countries. For example, in the Philippines, many women leave the country as domestic workers. For many other LDCs, it is largely the male population. Regardless, this leaves behind a skewed gender ratio as indicated by their population pyramids. On the other hand, this migration might be the reason that BRs in these LDCs have not been rising out of control, in comparison to other LDCs.

Conception Variables

1) Voluntary Conception Variables – these include the use of contraception (mainly condoms and pill usage), affected by the accessibility, availability and affordability of such methods. For example, in most DCs contraception is widely used due to it being more accessible and more available due to more affluent conditions, in contrast to many LDCs. As such, contraception seems to have greater impacts in DCs. Some LDCs hold sexual education for females, and give out free condoms in an attempt to alleviate the problem of imperfect information. Contraception is also affected by cultural factors – some religions, such as Catholicism, prohibit contraception. Some Gulf States also require contraceptive measures to be prescribed.

Another factor would be technologically-assisted pregnancies, such as IVF and artificial insemination, to boost fertility. Naturally, due to high costs and specialized technology and expertise required, this is more applicable to DCs than LDCs.

2) Involuntary Conception Variables – one of the key factors here would be the prevalence of diseases and health problems. Infectious diseases such as smallpox, as well as problems of malnutrition leads to lower health levels overall. This has the effect of lowering the fertility rate due to subfecundity, where the ability to reproduce is reduced. This naturally has greater impacts on LDCs due to poorer healthcare standards.

Gestation Variables

Gestation refers to the period between conception and birth, approximately 9 months for humans.

1) Abortion Rates – abortion during pregnancy is a main problem – especially in some DCs where it might be seen as less of a cultural taboo, and there is safe technology for the operations. In some LDCs, such as India and China, the prevalence of ultrasound scans and a cultural preference for boys has resulted in gender specific births and a skewed gender ratio. This was exacerbated in China by the one-child policy.

2) Foetal Mortality – the chance of miscarriages. This risk depends on conception age, malnutrition, healthcare levels, but mostly differs between individuals. Generally, in DCs conception age tends to be higher due to career focuses, increasing miscarriage rate. Malnutrition and healthcare during pregnancy also seems to be lower in LDCs. Otherwise, there is no defining pattern in miscarriage rates between DCs and LDCs.

Other Proximate Variables

  • Value of Children – in LDCs, children are traditionally seen as a form of security in old age or a source of labour (such as helping out as farmhands). This is known as survival theory, where more offspring is key to survival. The cost of raising children in DCs is, however, rather high, due to education fees etc.

  • Status of Women – with regards to both work and education. Due to greater and more equal opportunities nowadays, the cultural and social restraint on women is far less than it used to be. More women are employed, make independent decisions, and as such the age of marriage increases. Women who are highly educated with stable careers tend to have lower fertility rates, and with later marriages, fecundity is also likely to fall. In LDCs, the status of women is likely to be lower in comparison with their DC counterparts.

  • Socio-economic/Political – factors needed to be taken into consideration include cost of living, degree of urbanisation, and the role of government demographic policies (pro-natal or anti-natal). For example, the one-child policy and China, and currently the benefits given to families with more children in Singapore.

Governmental Influence on Fertility Variables

In attempting to achieve a desirable fertility rate (which is usually towards replacement level of 2.1), governments have implemented various policies to either boost or reduce fertility levels. The extent to which governments have been successful largely varies.

For a majority of LDCs, and in the history of many DCs, it is likely that governments are implementing or have implemented anti-natal policies in order to reduce the birth rate, worrying about future population booms and whether such booms will be sustainable by the country on economic, social and environmental fronts. Currently, when many DCs are experiencing falling fertility rates, governmental efforts have been targeted at boosting these flagging rates.

The most infamous example of anti-natal policies is China’s One-Child Policy, restricting the number of children urban couples can have to only one. Flouting the policy incurred heavy fines of up to 30% of annual income. The main objective of the policy was to reduce the rapidly growing population with a TFR of 3 in 1980 so as to alleviate its impact on future social services such as healthcare and education, as well as cutting off possible social impacts such as that of increased unemployment, slum development and poverty. It was extremely, and perhaps all too successful, with the TFR dropping to 1.8 in 2008. Problems caused by overpopulation have been solved to a great extent. Of course, many side effects were also caused, including the increased prevalence of infanticide of female babies. The imbalanced population is commonly called the 4-2-1 problem, forecasting a population with a high dependency ratio in the future. However, it is certain that the reduction in fertility rates was a success, due to forceful implementation and strict regulation – which may have been far too strict, as cases of alleged abuse of families who flouted the policy have surfaced.

Also, India has attempted to target conception variables, by sponsoring the provision of contraceptives and family planning education. As a result, contraceptive use has more than tripled from 13% of married women in 1970 to 49% in 2009, and the TFR has halved from 5.7 in 1966 to 2.7 in 2009.

However, the extent to which it is the government’s efforts that have played the main role in reducing fertility is questionable. Many of these successes in fertility reduction have been accompanied by economic growth, increasing education levels and an overall rise in affluence. This in turn has caused the cost of living to rise, as well as the social status of women to increase in standing, requiring smaller family units. Perhaps the governments’ anti-natal policies have only assisted in speeding up this trend which has been already underway.

This can be seen due to two factors. For one, the impact of government policies on different areas of the country has varied in accordance with literacy levels and affluence. In India, while contraceptive policies and family planning have reduced TFR to 1.8 in Tamil Nadu (HDI of 0.736, one of the richest, most literate states of India (rate 80.3%), also the most urbanised), in Uttar Pradesh the TFR has been only reduced to 3.8 (literacy rate of 69.7%). This is, of course, assuming that government expenditure and implementation between states is roughly equivalent, and all other factors aside from socio-economic levels are similar.

Secondly, when dealing with pro-natal policies, governments in DCs have seen significantly less success than when implementing anti-natal policies. Singapore’s efforts at increasing the fertility rate with “Have Three or More, If You Can Afford It” (maximum tax rebate of $20,000 per couple for third child, priority for housing choice for bigger families, priority for school enrolment for third child), along with Baby Bonuses, parental leave (8 weeks of paid maternity leave up to $20,000) and cash benefits have been largely ineffective, with the 2011 TFR estimated at 1.11.

Japan in 1994 implemented the Angel Plan, emphasizing work-childcare compatibility, providing public support for childcare services. Cash benefits, abolishing of daycare waiting lists and allowing paid childcare leave of 50% of wages were implemented, but this has appeared to be ineffective, with TFR dropping further to 1.23 in 2008. For DCs, many have attributed this failure in pro-natal policies being due to the shift in mindset since urbanisation and increased affluence along with costs of living in these countries, leading the shift of focus onto the quality of life and smaller family units.

This is of course not to suggest that government efforts to raise fertility levels have been largely useless in DCs. Such efforts can be successful if standards of living reach a high enough level, where, according to Maslow, social fulfillment becomes more important than economic growth. In France, extensive subsidies, tax breaks and concessions, along with high female participation in the economy and protection of their jobs, it now has the highest TFR in Europe (2.02), and is on an increasing trend.


  • Discuss the variations of mortality between LDCs and DCs.

  • Discuss how different factors affect mortality in LDCs and DCs.

  • Discuss why the infant mortality rate is regarded as one of the best measures of a country’s socio-economic progress.

Key Terms Regarding Mortality

Mortality: The incidence of death in a population, normally measured by crude death rate.

Morbidity: The state of being diseased – can in turn be a factor for mortality.

Crude Death Rate (CDR): Total number of deaths over a unit of time, normally a year, per 1000 persons in a population. In 2008, Singapore’s CDR was 4.4, Zambia’s was 17. Interestingly, Mexico’s CDR is lower than the US’ because of higher birth rates, leading to a smaller number of deaths per 1000. Potential for lapses in comparison.

Infant Mortality Rate (IMR): Number of infants who die within the first year of age per 1000 live births, excluding aborted babies and still births. In 2008, Singapore’s IMR was 2.1/1000, and Zambia’s was at a very high 92/1000. A good indicator of the healthcare system of the country, as well nutritional intake and income.

Life Expectancy: The average number of years a person is expected to live in his lifetime. It is calculated by sum of all ages of deaths divided by the number of deaths. In 2008, Singapore’s was 80.9, Zambia’s was 45, Japan’s was 83.

Epidemic: Outbreak of a contagious disease that spreads rapidly, affecting large parts of the community.

Pandemic: Epidemic occurring over a very wide area, across international boundaries and affecting large numbers of people.

Mortality Differentials

Overall Mortality Changes and Differences

When looking at differences in mortality and the variations in factors leading to it, comparisons can be drawn between DCs and LDCs, as well as between rural and urban areas of the same country. Statistics used for comparison include life expectancy, CDR and IMR.

Mortality rates largely depend on the availability, accessibility and affordability of medicine, healthcare, sanitation and hygiene. Medicine refers to technology and procedures such as chemotherapy, radiotherapy etc., pharmaceuticals such as pills, mixtures, genetic research and development, and surgical methods. Healthcare refers to the social institutions generally, such as public education, campaigns and health programs, nutrition, provision of geriatric facilities, old-age homes, palliative care, polyclinics, private and public hospitals and the provision of the quantity and quality of medical workers. Sanitation and hygiene refers to knowledge of sterilization, heating, proper sewage and rubbish disposal systems, purifying and filtering systems.

Generally, the trend worldwide is that generally, mortality is declining in both DCs and LDCs, the only difference being in rates of decrease. LDCs have a faster rate of decrease today, which only makes sense seeing as they come from a base of higher mortality rates to begin with. At a certain point in time, an LDC generally will have lower accessibility and availability of medicine and healthcare compared to a DC. Due to lower incomes, advanced technology and latest healthcare methods are also unlikely to be affordable for LDCs, also in part due to the likelihood of more corrupt governments. This is why mortality rates are at any point in time usually higher in LDCs.

As such, the likely reasons for reductions in mortality rates for LDCs are that overall, due to increases in the accessibility, availability and affordability of medicine as a result of development. Such developments drastically decrease the IMR, because they directly affect the knowledge of childcare requirements, nutrition requirements and overall healthcare. Improvements in healthcare and medicine, along with improvements in social institutions and overall economic growth have a large impact on IMR, which is also why it is one of the best indicators of socio-economic development.

With the level of healthcare in DCs already decently high, falls in mortality rates in DCs are more modest. Reductions are normally due to further increasing income levels, economic development and improving equity, requiring targeting the poorest in society, allowing more levels of society to afford healthcare by improving distribution. Focus on specific healthcare sectors such as palliative care, old-age pensions and health insurance also bring up the healthcare level in the country as a whole.

Differences in Mortality between Certain Groups

Socio-status Differentials

Within a population there tends to be different strata of people, normally divided by income and/or social class. Mortality rates are higher among people on lower incomes and in manual occupations because they have less access to good healthcare and live in poorer environmental conditions than higher income groups. High rates of heart disease are concentrated in the old heavy industrial areas of the UK and in inner city areas. Lung cancer is also more prevalent in industrial and densely populated areas with higher than average levels of pollution.

Race and Ethnicity Differentials

Due to different ethnic groups in society and the tendency for bias to occur, minorities are normally economically disadvantaged when it comes to healthcare and have higher mortality rates. Studies in the USA have shown that while whites live up to 76.5 years in life expectancy, the life expectancy of blacks is 6 years lower at 70.8. Blacks are economically disadvantaged, as 31% of all black families live below the poverty line while only 9% of white families do. This stems from disadvantaged health, environmental and economic situations.

Gender Differentials

In general, women have a longer life expectancy than men do. Factors that influence gender differences in mortality include biological factors such as hormonal influences on physiology and behaviour, and environmental factors, such as cultural influences on gender differences in health behaviours. The importance of specific factors may reflect the environmental context. How developed a country is can affect or shape the most important influences on gender differences in mortality.

In developed countries, men's more risky unhealthy behaviours are a major reason they die younger. Their higher rates of cigarette smoking, heavy drinking, gun use, employment in hazardous occupations, and risk taking in recreation and driving are responsible for males' higher death rate due to lung cancer, accidents, suicide, and homicide.

Men's risky behaviours also contribute to their having higher mortality rates in developing countries, but in developing countries the gender gap in mortality has been smaller than in developed countries. Environmental factors such as unsafe water and inadequate nutrition increase the death rate due to infectious diseases for both sexes. Women, however, face additional risks associated with childbirth. Maternal mortality is high in sub-Saharan Africa, and there are higher suicide rates for women than men in China.

Another reason the gender gap in mortality is smaller in developing countries is because in many of these countries, women have much lower social status than men. As women's status catches up with men's in these countries, the gender gap is expected to increase in the developing nations. But in developed countries, the gender gap is expected to decrease as women adopt unhealthy behaviours similar to men's—drinking and smoking more, experiencing more job-related stress.

Differences in what is expected of men and women and how they are taught to behave also contribute to variation in health-related behaviours. For example, many cultures encourage or condone men's heavy drinking, but discourage it in women. Also, in many cultures, women are not expected to work outside the home in the cash economy while men are expected to be part of the labour force. Because women are less likely to be part of the work force than men, they suffer less from the ravages of work. As a result, their health deteriorates less quickly.

Changes over time can affect the gap in life expectancies. In most developed countries, men's widespread adoption of cigarette smoking during the first half of the 20th century was a major factor behind males' widening mortality disadvantage. Later, in the United States, the mortality gap narrowed as women began to smoke more and men smoked less than before. The difference in male and female life expectancy has narrowed in recent years, from at least 7.7 years from 1972-1979 to 5.2 years in 2004, according to the U.S. National Centre for Health Statistics. Changes in smoking patterns tend to affect men more than women, because more men have smoked and because smoking has elevated death rates more for men than for women. As smoking becomes even less common, mortality rates will probably decline further.

Age Differentials

The reasons and causes of mortality can also be divided into age strata. For children, infant deaths are susceptible if there is poor medical care and poor health of the mothers. This is largely to do with the individual family’s socioeconomics, but naturally IMR is higher in LDCs. The main causes of mortality in young adults are due to accidents, homicides and suicides. The reasons for deaths for people of older age are normally major illnesses such as heart disease and cancer.

Urban-Rural Differentials

Up to a few decades ago, mortality rates were higher in cities due to overcrowding, poor sanitation and infectious diseases. This was especially true of most cities in LDCs and in DCs before healthcare was widespread and accessible. Due to the high population density of cities, along with poor sewage and garbage disposal systems, living conditions were squalid and similar to slums. Today, mortality rates are much lower in cities, with improved urban conditions and greater affluence.

Epidemics and Pandemics

Epidemics and pandemics are cases where contagious diseases are spread rapidly amongst a large population, with pandemics being on a larger and more global scale than epidemics. These diseases induce morbidity, which in turn has a high probability of leading to mortality.

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