Category: Population Growth

China are said to be developed nation with highest number of population. In 1970 there was drastic decrease in China’s fertility rate, after a period of fluctuation of fertility rate around replacement level in 1920’s, there was drop of fertility rate below replacement level in the year 1990’s. Through the census analysis of 2000 there was decrease of fertility rate to 1.4%-1.6% (Quabao Jang, Shuzhuo Li and Marcus W. Feldman, 2013, as cited in Morgan et al. 2009), and the analysis of 2010 census reveals that the fertility rate of China is below the replacement level (Quabao Jang, Shuzhuo Li and Marcus W. Feldman, 2013, as cited in PCO, 2012). The dramatic decline of the fertility rate of China is due to policy emplaced by the government and the socio economic developments (Quabao Jang, ShuzhuoLli and Marcus W. Feldma, 2013).

During 1958 there was decrease in population rate due to plan rapidly modernize China’s economy, a catastrophic famine was ensued which resulted in the death of tens million of Chinese (conett, 2019). According to the recent demographic analysis there were 680 million births and 225 million deaths which give approximately of 2 percent average growth rate in China (Jowett, july 1984). However there was varied in growth rate due to economic disaster (1962-65),where the death rate exceed the birth rate leading to decline of population by several million, an increase of death rate and decrease in birth rate lead to decrease in food production by 25 percent (Jowett, july 1984).

In the early 1970’s there was decrease in population due to the family planning program and one child policy which was implemented by the government in China and by the mid 1970’s the China’s government focuses on family planning program where they provided incentives and reward(better employment opportunities, higher wages and government assistance) to the families who adhere to the one child policy and those who ignored the policy were subject to fines , access to government assistance and employment opportunities would be difficult for them. In 1979 the one child policy was introduce by the leader Deng Xiaoping in order to curb the rapid growth of population (conett, 2019). The strict population policy have brought up decline in fertility rate in the 20 years as well as the unforeseen side effects like high male biased sex ratio at birth and rapid aging (Quabao Jang, Shuzhuo Li and Marcus W. Feldma, 2013). China one child policy lead to the abortion of female fetuses, abortion is legal in China but sex selective is not legal in China (conett, 2019).

With the advancement of medical sectors it leads to the growth of population in China. China is a gender imbalance country due to their cultural preference for male offspring’s in order to take their linage throughout the generation (conett, 2019). The decrease in fertility rate is not solely due to population policy, it is also due to economic development (Quabao Jang, Shuzhuo Li and Marcus W. Feldman, 2013, as cited in Tien, 1984). Population policy and socio-economic development exerted equal influences in decline of fertility rate by the year 1970 -1980, but the proper family planning influence the decrease in fertility rate / population rate in the 1900’s and 2000’s (Quabao Jang, Shuzhuo Li and Marcus W. Feldman, 2013, as cited in Greenhalgh and Winkler, 2005).

From the analysis of 1900-2000 census reported that the population growth was 4.7% (1.06 per % annually) and during 2000-2010 census, the population growth was 5.8% (0.58 per% annually) respectively. Many investigations were done in different area of China based on the decreasing of population rate. From investigation it had shown that socio-economic have high emphasis in decreasing fertility rate (Quabao Jang, Shuzhuo Li and Marcus W. Feldman, 2013, as cited in Cai, 2010). Due to development and modernization there is increasing of education cost and high cost of child raising lead to decrease in fertility rate below replacement level, would remain below even government were to give up on the current one child policy (Quabao Jang, Shuzhuo Li and Marcus W. Feldman, 2013, as cited in Merli and Morgene, 2010).

China growing population led to the growing concern to the nation as they had the highest population in the world with 667.1 million in 1960. China government in order to control the birthrate they came up with the one child policy which had been very effective measure taken by the Chinese government in the year 1970. Chinese government they had given incentives and more employment opportunities to the people who had followed the policy and people who dined the policy were made to pay fines. Though these policies reduce the population but it brought negative impacts such as aging population, gender discrimination and lead to abortion of fetus. By the year 1980 with the development there was increase in education cost, raising child was also expensive so, people they tend to prefer two children since they find them as liability (Quabao Jang, Shuzhuo Li and Marcus W. Feldma, 2013). Chinese government initiated family planning programs which also help the government to reduce the fertility rate.

References

1. Bank, W. (1960). China population. United states Census Bureau.
2. Conett, W. (2019, july 7). investopedia. understanding the China’s former one child policy .
3. Jowett, A. (july 1984). The growth of China’s population 1949-1982(with special references to the demographic disasters of 1960-1961). The geographical journal , 155-170.
4. Quabao Jang, Shuzhuo Li and Marcus W. Feldma. (2013). China’s population policy at the crossroad: social impacts and prospects. HHS public acess , 193-218.

Growth of population has been a rising issue in both developed and developing nation and its fluctuation have now become a major concern as it has a huge impact in the places where there is increase in population. Population growth refers to the increase in the number of people that live in a country, state or city (Business Dictionary n.d.) It has caused disorientation in terms of economy, environment and social aspects. Rapid change in population statistics can be dangerous to the upcoming generations. Population exceeding the carrying capacity of the earth are caused due to better medical facilities which eventually lead to decline in death rate, changes in literacy rate and fertility rate.

Bhutan, even though being in its developing phase is facing the major concern of rising population. According Population and Housing Census of Bhutan (PHCB) 2005, the total population was 634,892 which were expected to increase by 887000 i.e. 40% within next 25 years but the most recent PHCB which was conducted in 2017 reveals the increase in total population to 735,553 including non-Bhutanese which means the total population size has increased by 100,571 persons (16%). The Population Growth rate from 2005 to 2017 is 1.3% per annum (National Statistical Bureau of Bhutan, 2018). As stated in worldometer, the population of Bhutan as of Tuesday, March 10, 2020, based on worldometer elaboration of the latest United Nations data is 768,962. Bhutan’s population is about 0.01% of the world population. 48.8% of the Bhutanese population is located in urban area and rest in rural areas. The median age in Bhutan is 28.1 year (2020).

With advancement in health sector, there has been decrease in mortality or the death rate of the people of all age (infants, adolescence and old age). As per the National Statistical Bureau of Bhutan, (2017) Crude Death Rate of Bhutan was 6.7 in 2017. The CDR in urban area is quite less than in a rural area that is 5.5 and 7.5 respectively. For instance, in Thimphu the CDR was 5.2 and in Trashigang it was 8.6. In the same year the CDR for male population was 7.1 and for female it was 6.3. In addition, the Maternal Mortality Ratio (MMR) was 89 in 2017. Similarly, the infant or Child Mortality Rate in 2005 was 40.1 which has decreased to 15.1 in 2017. The reasons for these changes are improvement in health infrastructures, better service coverage in different areas, more skilled workers and advocacy programmes.

Bhutan has worked hard to provide equal distribution of health facilities nationwide. At a community level health facilities and services are provided through Basic Health Units (BHUs). According to the Druk Journal, most of the district hospital we have can at the maximum accommodate 20 people with basic diagnostic tests including X-ray. At present there are two regional referral hospitals one located at Mongar in the east and one in Gelephu in the central region. The Bhutan Living standard survey of 2012 shows wide disparities in access to health care though health care and services are provided free may be due to the cost of expenditure goes in order to render the health services, lack of awareness or some population preferring local healer than the modern health facilities. Allopathic and traditional medicines are integrated under one roof to further provide better health facilities. As of now there are 54 traditional medicine units attached to district hospitals and BHUs in the country (Ministry of Health, 2016).

As cited in The Kingdom of Bhutan health system review, “government revenue is the source of health financing. In 2014, the total health expenditure made by the government was 36% of the Gross Domestic Product. Though the country is covered with difficult geographical terrain and dispersed population it has achieved its objectives in improving the health services. Bhutan is recognized as one of the top global performers in gaining the life expectancy in the last 40 years and by 2010 Bhutan has maintained an immunization level of 95%” (WHO, n.d.). Due to the enhancement made by the government in the health sector, the life expectancy has increased from 66.3 in 2005 to 70.2 in 2017 which has caused increase in the dependent population (age between 15-25 and 60), the set of population that are economically inactive. Even though different diseases are emerging the improvement in medical equipments and development of cure for different diseases has caused the growth in population.

The term literacy is characterized as the capacity to read and write Dzongkha, English and any other languages. In our consequences, 6 years or more is the official passage age for pre-essential education. Out of 656,522 people aged 6 years and above, 1407 people had not reacted to the proficiency questions. The education rate is accordingly, in view of an absolute population of 655,115 people. The 2017 PHCB revealed that 467,647 people in the nation are literate, speaking to an education pace of 71.4%. The grown-up (15 years and more) literacy rate is 66.6%. There is checked contrast in the literacy level between the male and the female population with 78.1 of the male population proficient as contrasted with 63.9% of the female population. The design is comparative in both urban and rural territories in spite of the fact that the dissimilarity is moderately bigger in provincial zones at 16.8 rate focuses when contrasted with 10.1 rate focuses in urban territories (NSB, 2018).

The proficiency rate has expanded from 59.5% in 2005 to 71.4% in 2017, an expansion of 11.9 percent. The proficiency rate for both male and female population have expanded by 9.0 rate and 15.2 rate separately, while the sexual orientation dissimilarity in 2017 has tumbled to 14.2% from 20.4% in 2005. Also, there is an improvement in the divergence between urban and country zones, which has declined from 23.8% in 2005 to 20.5% in 2017. At first, the literacy rate increased with age. At 98.4%, it is the most elevated among people in the age group 10-14 years and at that point diminishes with increment in age. It drops to 22.9% for the population 65 years and more. The literacy rate for male and female population for 65 years or more are 36.0% and 9.4% individually. There is no noteworthy sex disparity in literacy rates among youngsters. In any case, the male population is more educated than the female population as age increases. The most elevated sex divergence is seen among the age group 50-54 years at 36.4% and afterwards drops progressively to 26.6% for population 65 years or more (NSB,2018).

The education paces of the six dzongkhags of Sarpang, Bumthang, Paro, Chhukha, Trongsa and Thimphu are higher than the national literacy rate of 71.4% while the literacy rate is lower than the national average in the 14 dzongkhags. The most elevated proficiency rate is in Thimphu dzongkhag at 83.9%, trailed by Trongsa and Chhukha dzongkhags at 77.2% and 75.1% individually, while the most minimal is watched in Gasa at 59.8%. Trongsa dzongkhags encountered the highest improvement in education level between 2005 and 2017 trailed by Wangduephodrang and Sarpang while the education level of Pemagatshel dzongkhags was the least improved (NSB, 2018). Changes in literacy rate have caused changes in growth rate. Since women are provided with an opportunity to enrol themselves in education the opportunity to give birth is delayed leading to decrease in fertility rate. Though the exact figure is increasing the growth has declines over the years.

On contrary to better health facilities and increase literacy rate which led to growth in population of Bhutan, there has been considerable decrease in Total Fertility Rate. First, compared to the rural, urban areas have slightly lower fertility rate i.e. 1.8 and 1.7 respectively. Second, District wise Lhuentse, Trashiyangtse, and Wangdue Phodrang has most noteworthy TFR of 2.3, while Samtse Dzongkhag, at 1.4 has the most reduced. Trashiyangtse Dzongkhag revealed the most noteworthy TFR in 2005 (3.5). Finally, among Thromdes, the most noteworthy TFR is in Samdrup Jongkhar Thromde at 1.9 and the most minimal in Gelephu Thromde at 1.3. In comparison to data collected in 2005, there is a decrease in the TFR in 2017 for all dzongkhag. The TFR for Trashiyangtse Dzongkhag which was the most noteworthy for both 2005 and 2017 has decreased from 3.5 to 2.3 respectively. Samtse Dzongkhag supplanted Thimphu as the dzongkhag with the lowest TFR (1.4) in 2017; Thimphu dzongkhags TFR in 2005 was 2.0. The TFR for Bhutan has diminished from 2.5 in 2005 to 1.7 in 2017, which is the replacement level of 2.1. In the event that the nations TFR stays underneath swap level for a quite a while, it would prompt expanding maturing population, in this manner expanding the absolute dependency proportion and decrease in overall work power. Moreover, expanding maturing population would involve higher weight to government in providing medical services and social administration (NSB, 2018). Some of the reason for decrease in fertility rate could be due to increase literacy over the years, advocacies on family planning, and etc.

Over the years, fluctuation in the growth rate of Bhutan can be seen. The growth of population has due to factors like better medical facilities, increased literacy rate and total fertility rate. Though there is increase in the exact figure of the population in the country, the growth has declined as compared to 2005. Growth rate of a population should be taken into consideration and reviewed in the following years to come.

Introduction

Can technology solve all problems caused by population growth? There is no doubt that development of technology cannot solve the population problem. However, the issues which are caused by population growth will no longer be a problem in a highly technologically developed society. More population means more food demand, more land supply housing demand, more waste emissions and more resource requirements. When technology can meet all of the above needs, technology can solve most problems caused by population growth. This paper will discuss the reasons for the impact of the current increase in population on the earth’s resources and the environment, and the relative impact and technical support of these vertical impacts from an engineering perspective.

Influence of the food supply

Before exploring the relationship between population size and food supply, it is important to understand a person named Thomas Robert Malthus. The increase in urban area and population has led to the need for more arable land to provide adequate food crops. A sharp increase in the population will inevitably lead to food shortages, which will keep the population under control. This idea is consistent with the ‘Principles of Population’ written by Malthus in 1798. In this book, he believes that the increase in the supply of food is relatively slow and linear. But population growth is exponential. As a result, sometimes the amount of people far exceeds what the food can provide. As a result, humans will die on a large scale. There are many causes of death, such as hunger caused by food shortages, large-scale epidemics, wars caused by uneven distribution of resources, and crime. ( Malthus, 1992). He believes that the above reasons lead to a substantial reduction in the number of humans, which is also reasonable and consistent with the laws of nature. Therefore, when the above disasters occur, they should be allowed to occur, because any remedy is futile, and humans must not fight the laws of nature. In addition, he also thinks that the poor are the most damn, because the poor actually add to the burden on society. He called the poor a surplus. Therefore, he believes that the subsidies to the poor should not be too much, nor should they give the poor a good living environment. ( MacRae, 2019). Karl Marx considered Malthus’s views to be extremely immoral. But historically, some people support Malthus’s theory, such as some British ministers. As a result, the worst famine in the history of Britain and Ireland was called ‘ failure of the potato crop.’

Traditionally, the food grown in Europe is mainly wheat, with lower yields. After Columbus discovered the New World, it shipped high-yielding crop potatoes to Europe. The high yield of potatoes has resulted in ample food, which has led to a significant increase in the population of Ireland. According to Malthus, there should be an overpopulation after Ireland’s population has grown significantly. If disaster strikes, the poor can die. Coincidentally, the potatoes in Ireland at the time had a fungal-related infectious disease that led to massive potato deaths. When potatoes fell, Ireland’s food supply plummeted. Ireland was part of Britain at the time, and the British Minister believed that the situation in Ireland was in line with Malthus’ theory and that some Irish people should be accepted to die. As a result, Britain’s failure to provide substantial relief to Ireland resulted in the massive death and flight of Irish farmers. ( The Irish Potato Famine, 2017)

There are two main arguments for Malthus. First, the slow growth of the world’s population can prove that Malthus’s point is reasonable. In AD, the total human population was 2.5 billion. By 1500, the population had grown to 500 million. (Current World Population, n.d.).It took humans more than 1,000 years to double the population. The reason for the slow population growth is that once the population grows, there will be various problems leading to human death. As mentioned above, for example, the Black Death killed the population of near 1/3 in Europe. (The Black Death, n.d.).Malthus thought it was accord with natural law. Another thing is that the wages of workers can also prove Malthus’s argument. From the 16th century to the 18th century, the wages of workers were little changed. Proponents of Malthus provide an explanation for this phenomenon. They believe that once workers’ wages are high, the growth rate will increase. That is to say, the number of workers’ children is that the salary level of workers can only be maintained above and below the food and clothing line, and there will be no conductive change.

But in modern times, the human population has been rising steadily but has not fallen sharply. That is, the fact that the human population has continued to increase since modern times runs counter to Malthus’s theory. This is mainly due to advances in engineering technology. For example, industrial production has been very developed since Britain entered the industrial revolution. As a result, the productivity of various industries is high. This has caused a dramatic increase in its population. Later, improved crops like hybrid rice were developed around the world, which were characterized by high yields. This has resulted in a significant increase in human food supply. For example, India has had a food crisis in recent times and almost entering the Malthus trap. The invention of hybrid rice saved the Indian people in time. The population avoided the large-scale death period, but continued to increase. From this point of view, the advancement of industry and the development of technology have made some developed and developing countries out of the Malthus trap and allowed the population to continue to grow.

The influence of access to clear water

Before analyzing the impact of population growth on water resources, it is important to understand why human activities pollute water resources. The places of world ’s population growing the most are not that developed regions but the developing and underdeveloped countries. Moreover, the underdeveloped regions have the fastest population growth. The less developed countries are characterized by low economic capacity and unadvanced technology. Therefore, in order to satisfy the citizens’ quality of life as much as possible, the government and small enterprises are more inclined to develop high-income industries that do not need high technology. However, the disadvantage is high pollution.

Africa and South America is one of the regions with the largest population growth in the world, and its rivers are numerous. Due to the lack of knowledge, many highly polluting items such as various chemical organics, nitrogen and phosphorus, and toxic and hazardous substances have caused pollution of the entire ecology. As a result, water resources are also polluted. Of course, there is not only one reason of water pollution. For example, some of the leakage of fertilization in farmland will also cause the discharge of nitrogen and phosphorus; in terms of domestic sewage, most of the rural areas are discharged directly without treatment, and the urban sewage retention rate does not reach 100%. Even after treatment, there is still a small amount Discharge of some pollutants; desertification and weather changes have led to reduced rainfall, increased temperatures, and reduced total water bodies in rivers and lakes, coupled with increased human and industrial water consumption, resulting in insufficient total water consumption. Regarding how to alleviate water pollution, the United Nations and various countries are making corresponding efforts. For example, some countries have invested a lot of infrastructure construction funds in underdeveloped areas to help improve the local living environment. At the same time, they have invested in building factories in these areas and used scientific environmental improvement measures to improve pollution discharge conditions to help less developed countries reduce the harm to the global environment. However, this is just one of the ideas for treating sewage.

Various countries should also strengthen the supervision of industrial wastewater. The level of pollution of industrial wastewater by a drop of water is more than 10 times that of domestic sewage. However, in many small workshops, sewage is basically not treated. There are also hidden emissions from medium-sized and large enterprises. It is also necessary to promote the construction of reclaimed water reuse. After meeting the standard of domestic sewage, it can be used as a source of industrial water supply, thereby alleviating the problem of insufficient water consumption. In the non-industrial environment, the government should pay attention to strengthening the domestic sewage interception rate, ensure that the existing urban domestic sewage is effectively treated, strengthen the rural domestic sewage decentralized on-site treatment, and strengthen farmland pollution control. Finally, the government should strengthen the construction of vegetation coverage, improve climatic conditions, and avoid soil erosion. If people want to make the above ideas a reality, they must rely on engineering knowledge and technical means. For example, research into more complete industrial systems to treat industrial wastewater, and develop new environmentally friendly energy sources, such as solar energy and wind energy.

Conclusion

The growing population does have a major impact on the environment and energy. In this regard, human beings have two solutions. First, each country strictly controls its own population and implements family planning. Second, the continuous development of new technologies has minimized the impact of population growth issues. Either way, they are working for the sustainable development of humanity in the future. However, the implementation of the first option is likely to bring adverse consequences such as slow development or economic recession. Therefore, the idea that each country is focusing on developing technology to maintain the environment is very correct and necessary. After all, the earth will not expand infinitely like the universe, and there will be a day of exhaustion of the resources on the earth. The rational use of these limited resources will eventually be one of the required courses for human beings.

God created humanity multiply and occupy the entire earth. He equally added that human beings should utilize the available resources for life sustainability. Initially, human population was small and the need to multiply quickly was necessary to escape the danger of extinction, which literally threatened humankind through various calamities such as floods, volcanic eruptions, wars, and threats from wild animals. As time rolled by, human population actually rose gradually to reach the current population explosion experienced in various nations across the world especially in many developing and some developed countries (Lutz, 2017). Overpopulation in various parts of the world led to overexploitation of limited natural resources causing massive destruction to the previously beautiful environment.

Actually, fauna and flora has in the past experienced and still experiences destruction that causes excessive emission of huge amounts of greenhouse gases into the environment. These gases eventually cause severe depletion to the ozone layer and this subsequently results into gradual climate change. It is true that the current widespread challenge of global warming traces its origin mainly to reckless depletion of ozone layer because of the destructive activities of human beings on the environment (Gleick, 1989). Consequently, there is urgent need for concerted worldwide efforts to reduce human population growth to help conserve the ecosystem from degradation and depletion.

Greenhouse Gases and their Contribution to Global warming

Greenhouse gases are those gases whose presence in the atmosphere tends to absorb as well as emit some radiant energy regarded mostly to be within the range of the thermal infrared. These gases usually lead to the famously known and dreaded greenhouse effect in the atmosphere. Major greenhouse gases on the earth’s surface include ozone, hydro fluorocarbons, water vapor, nitrous oxide, methane, and carbon dioxide (Dietz, et al., 2015). Large quantities of these gases especially carbon dioxide, reach the atmosphere because of human activity during deforestation, mining, and exhaust fumes from industries among others. Industrialization in developing nations like Kenya, located in East Africa alongside massive rape on forestlands such as Mau Forest significantly increases the amount of carbon dioxide in the atmosphere.

These greenhouse gases once emitted into the atmosphere accumulate and take reasonably longer periods to fade away. This exposure exposes them to the relatively delicate stratospheric ozone layer. This layer has an important role of regulating dangerous ultra violet rays from reaching the earth’s surface through absorption of the same. They act on this important layer and eventually cause its depletion. After depletion, these harmful rays from the sun eventually reach the surface of the earth to cause changes in temperatures hence global warming and subsequent change in climate (Hurt, 2017). Consequently, there is need to control the growth of population and human activity to help protect the ecosystem from the dangers of global warming resulting from depletion of stratospheric ozone layer.

Economic, Security, Political, and other Related Challenges Emissions Pose

Sincerely, economic as well as demographic factors are assumed as the key driving factors of environmental impact in a society including developing nations (Dietz, 2015). The exposure of these harmful greenhouse gases into the environments bears certain adverse consequences on the environment, which eventually pose considerable impact on economy, security, as well as political issues in the community. Industrialized counties like the US have made significant steps towards reduction of greenhouse emission. Developing nations have a challenge of following cue because of low economic growth and inability to embrace green economy. The US has a policy aimed at reducing emission of these gases in other nations through introduction of logical greenhouse tariffs and caps.

This may help reduce emission of these gases by influencing other trade partners to embrace green economy. Despite being a noble idea, it may affect the economic situation in struggling nations like Kenya. Developing nations such as Kenya whose economies, rely heavily on activities that emit voluminous greenhouse gases, the economic growth would meet serious challenges with introduction of greenhouse caps alongside greenhouse tariffs imposed on imports and exports across the world (Lutz, 2017). Politically, developing nations like Kenya should aspire to change its politics by advocating for policies and practices that does not entrench environmental stress as happens among some developed countries like America.

Additionally, challenges like cross border conflicts commonly witnessed among developing countries trace their origins to economic and political issues, which find meaningful solution in environmental preservation and control of population growth. It is true that environmental stress is among the major causes of tensions and conflicts around the world (Gleick, 1989). Therefore, reduction of greenhouse gas emission would significantly reduce environmental stressors like poverty and conflicts that gravely affect humankind among others. This eventually makes the society a better place to inhabit today and into the future.

Control of Population Growth

To help reduce the menace of global warming, one of the sure ways is controlling population growth rate in all places of the world. This would enable enhancement of sustainability through the available scarce resources. One method that may help in population growth control is the provision safe and consistent use of contraceptives to people of childbearing age (De Irala et al., 2011). Accessibility to contraceptives would help reduce conception rates among fertile females. On top of that, education of the population is equally significant in reducing the rate at which population grows. Formal education helps especially females in reducing the number of off springs. It is true that females who pursue formal education to advanced levels bear fewer children compared to those without formal education. Similarly, sensitization of all people concerning sexuality and sex education concerning the importance of family planning is equally beneficial.

Moreover, integrating lessons concerning population growth and the impact of the same on development and on environment in learning institutions appears meaningful (De Irala, 2011). This makes people change behavior concerning family size and environmental matter. On top of that, governments should discourage policies, which reward parents because of the number of children sired. Instead they should increase taxes alongside other perceived benefits especially finances to discourage new births. Provision of birth related incentives encourages new births while its withdrawal would significantly play a role in reduction of new births.

In conclusion, global warming and subsequent climate change is currently a social issue of great international concern. Consequently, it requires concerted efforts of all stakeholders around the world to contain. It is a problem that gradually but surely invades the universe through depletion of the ozone layer. Depletion of this important layer that regulates and absorbs dangerous rays from the sun happens because of accumulation of greenhouse gases in the atmosphere. Large amount of these harmful gases come about because of human activity that degrade or rape the ecosystem such as forest encroachment and use of fossil fuels among others. In brief, reducing population growth alongside advocacy for the use environmentally friendly energy sources helps preserve ozone layer.

The research question for this extended essay is “To what extent are differential equations an accurate representation of human population modelling?” Differential equations can effectively be used to predict things in our everyday lives. They are used in many disciplines including biology and physics.

In this extended essay, I will collect data on the Canadian population from the 1900s to the 2000s and compare it to predicted populations given by two models: Malthusian (exponential) and Logistic. I will also analyze the effectiveness of these two models for accuracy and their limitations. In this analysis, I will use statistical tests to evaluate data significance between the actual population data for Canada versus the predicted populations. I will also do analysis on what carrying capacity1 will accurately represent the Canadian population and what this value means. My goal for this exploration is to determine the effectiveness of differential equations in modelling human population growth as well as any ways to improve them. Furthermore, I hope to enhance my skills in mathematics, not only differential equations but its application in our real world.

1 Carrying capacity is the maximum population an organism can sustain denoted by K in the logistic differential equation.

Introduction

In our globalized world, the population is on a rapid increase and it is now important for countries to do the right predictions for the future of their native people as well as their immigrants. Acquiring knowledge about population is necessary for future planning, concerning education, health, job, housing, safety requirements, etc. However, in our current society, a problem which many arise is overpopulation. Overpopulation is where the human population exceeds the carrying capacity of Earth. Overpopulation is caused by a number of factors. Reduced mortality rate, better medical facilities, depletion of precious resources are few of the causes which result in overpopulation. I have also learned about the effect of an increasing population on the environment in many of my classes and the sustainability of the earth. Therefore, I decided to undergo this exploration to see if population growth can be effectively modelled with differential equations to sustain our environment in the future.

Differential equations are simply equations that relate functions with their derivatives. Most continuous models of population dynamics are based on differential equations, which can be solved using a variety of techniques, which will be omitted from this exploration. However, only the simplest of models are algebraically so I will be using the two types of first-order differential equations to predict the population evolution of Canada. Each of the two will be compared for accuracy by coefficient of determination and any ways to improve the model. My dependent variable is the population size of Canada and my independent variable is time. The controlled variable is the country

Table 1 below shows the Canadian Population2 from 1900 to 2000 recorded every decade. The predicted data will be created using the initial population in 1900 and then compared to the actual data in table 1.

Table 1: Actual Canadian Populations from 1900 to 2000

Year Population

1900 5,310,000

1910 6,988,000

1920 8,435,000

1930 10,208,000

1940 11,382,000

1950 13,382,000

1960 17,870,000

1970 21,297,000

1980 24,517,000

1990 27,512,000

2000 30,689,000

2 Censuses of Canada 1665 to 1871: Estimated Population of Canada, 1605 to Present, Statistics Canada, www150.statcan.gc.ca/n1/pub/98-187-x/4151287-eng.htm.

Malthusian Growth Model

A Malthusian growth model or more commonly called a simple exponential model is the population model where growth occurs exponentially, so it increases occurring to the birth rate of the populous. This means the growth rate stays the same regardless of population size, making the population grow faster and faster as it gets larger. This model was introduced by Thomas Malthus in “An Essay on the Principle of Population” in 1798. In this essay, he mentioned that it is only a matter of time before the world population will become too large to the point where it can feed itself.

The Malthusian Model in the form of a first-order differential equation:

dP/dt = kP

Where dP/dt is the population growth rate, which is a measure of the number of individuals added per unit of time, k is the per capita rate of increase or per capita growth rate. If k is positive, the populous is experiencing exponential growth and if it is negative the populous is experiencing exponential decay. The variable P is the population size, or simply the number of individuals in the population at a given t. In this model, we make some assumptions to validate the relationship. Firstly, we assume that the population is living in ideal living conditions including unlimited resources, no factors affecting mortality rate, no land claims etc. We also assume that for at any time t, the growth rate remains constant regardless of the population growth.

We can solve this differential equation for P(t) to give the population size at any time

dP/dt = kP (Multiply both sides by dt)

dP = kP • dt (Divide both sides by P)

1/P dP = k • dt (integrate both sides)

 1/P dP =  k • dt (apply rules of integration)

ln | P | = kt + C

eln | P | = ekt + c (apply exponent rules)

P(t) = Cekt (simplify as ec a constant)

P(0) = Cek(0) (Further simplification)

P0 = C

Therefore,

P(t) = P0ekt

where P0 is the initial population

Constructing The Prediction Model:

Given the base year being 1900 we can use the actual data for population where

P(0) = 5,310,000 and P(10) = 6,988,000 to solve for k.

Substituting respective values gives:

6,988,000 = 5,310,000ek(10)

k = 1/10 ln (6,988,000/5,310,000)

k = 0.0274602557

Therefore the model we will use to predict the Canadian population from 1900 to 2000 is written as:

P(t) = 5,310,000e0.0274602557t

Using the formula we derived above, we can construct the following table:

Table 2: Predicted Population of Canada Using the Malthusian Growth Model

Year Predicted Population

1900 N/A

1910 6,988,000

1920 9,196,260

1930 12,102,348

1940 15,926,781

1950 20,959,764

1960 27,583,207

1970 36,299,709

1980 47,770,690

1990 62,866,589

2000 82,732,904

The actual data and the predicted data can be graphically presented as seen below:

Graph 1

Evaluation:

As seen in the exponential model over predicts the growth rate of the populous. The rate of change gradually increases exponentially however, the actual population increases at a much lower rate. This is due the non-accountability for things that could limit population growth. There are also many assumptions being taken into account for this model such as continuous reproduction (no seasonality), all organisms are the same (no age structure), and the resources are unlimited. This is why the exponential model is unrealistic and not ideal to utilize for long term predictions.

Coefficient of Determination:

The coefficient of determination or R-squared is the percentage of the dependent variable variation that a linear model explains.3 It provides a measure of how correlated a group of actual data is to the predicted data. In this case, we will use it to see how accurate the predicted models are in terms of each other.

R-squared is calculated by applying the following formula:

R^2 = (Variance Explained by the Model)/(Total Variance)

To find the relationship, we will plot the Actual Data as the independent variable, and the predicted data as the dependent variable. This will display the proportion of the variances and we can solve for the according R^2 value. R^2 values are between 0 and 1 and a predicted model which accurately fits the actual data will have a value as close as possible to 1.

The graph for the Actual Data vs the Predicted data by the Malthusian equation can be seen in Graph 2:

3 Frost, Jim, et al. “How To Interpret R-Squared in Regression Analysis.” Statistics By Jim, 30 May 2019, statisticsbyjim.com/regression/interpret-r-squared-regression

Graph 2

Our Actual vs Predicted graph returned a value of about 0.947. This value means it is relatively well predicted but generally our model should return R^2≥0.95. We will not compare this R^2 to the logistic model to see which better predicts the population dynamic of Canada.

Logistic Model

The Malthusian growth model is appropriate for population growth under ideal conditions, but it is important to recognize that a more realistic model must reflect that there are limited resources. Although exponential growth can occur in environments with a very low population and many resources oftentimes, the population will increase or resources run low. The validity of the Malthusian model was investigated in the 1800’s until the Belgian Pierre-Francois Verhulst proposed a revised model that would take into account the occurrence of exponential growth. This model recognizes that when the population increases, there is a tendency for the populous to interfere and alter the number of resources, for example, fighting for food, land, wars. When resources are limited populations are said to be exhibiting logistic growth. In logistic growth, the populous starts by increasing in an exponential manner, but the population levels off as it approaches its carrying capacity, K (the maximum population size that can be supported by an environment). At carrying capacity, it is expected that the

population will neither shrink nor grow, meaning that the population versus time graph will level out. To modify the Malthusian model to take into account for the carrying capacity, Verhulst shows the per capita growth rate, k is proportional to the population size P and its difference from the carrying capacity, K to give the differential equation:

dP/dt = kP ( 1 – P/K )

Where K is carrying capacity, P is the population, k is the per capita growth rate, and dP/dt is the population growth rate.

In the Malthusian model, population growth was mainly dependent on P, as each human added to the populous was contributing to growth equally since the value of k was fixed. However, in the logistic model, the resources available are taken into account so as the population increases, fewer resources are available which causes the birth rate to gradually decrease. We can solve this differential equation for P(t) to create an expression for population size at any given t.

dP/dt = kP ( 1 – P/K ) can be written as:

dP/dt = (kP(K-P))/K (multiply both sides by P(K-P))

dP/(P(K-P)) = k/Kdt (multiply both sides by K and integrate)

 K/(P(K-P)) dP = k dt (Apply partial fraction and integration rules respectively)

ln | P | – ln | K – P | = kt + C

ln | P/(K-P) | = kt + C

P/(K-P) = Cekt

To solve for P:

P = KCekt – PCekt

P + PCekt = KCekt

P(t) = □((KCe^kt)/(1+Ce^kt ))

Now solving for C:

To find the constant C, we represent the initial population as P(0) = P0

P0 = KC/(K+C)

P0 + C P0 = KC

P0 = KC – C P0

C = (P0 )/(K- P0 )

Subbing C back into P(t) gives us the solution for our logistic model:

P(t) = 〖KP〗_0/(P_0+(K- P_(0)) e^(-kt) )

Constructing The Prediction Model:

Using over solution of the logistic differential equation we can now create a model to predict the Canadian Population given base year 1900. The carrying capacity of humans is difficult to determine as we have developed new ways of agriculture and other technologies. These revolutions have increased our carrying capacity however, it the earths predicted carrying capacity still is expected to be about 10billion4 so for this prediction model we will start with 300 Million as the carrying capacity of Canada and later alter the value to see what accurately displays the Canadian population growth pattern. Using K as 300,000,000, the derived expression for P(t), and two known points from the given data we can create our model for prediction.

4 “How Many People Can Earth Support?” LiveScience, Purch, www.livescience.com/16493-people-planet-earth-support.html.

Using: P(0) = 5,310,000 and P(10) = 6,988,000, K = 300,000,000 , and P0 = 5,310,000 we form the equation:

6,988,000 = ((300,000,000)(5,310,000))/((5,310,000)+(300,000,000-5,310,000)e^(-10k) )

6,988,000 = (1.593  10^15)/((5,310,000)+(29469000)e^(-10k) )

k = – ln⁡〖(0.7555472564)〗/10

k  0.02803129494

Finally, the equation to solve for population at any given time t with a carrying capacity of 300,000,000 is depicted as:

P(t) = ((300,000,000)(5,310,000))/((5,310,000)+(300,000,000- 5,310,000)e^(-0.02803129494t) )

P(t) = (1.593  10^15)/((5,310,000)+(294,690,000)e^(-0.02803129494t) )

The constructed Logistic model’s predicted populations are displayed in the table 3:

Table 3: Predicted Population of Canada Using the Logistic Growth Model

Year Predicted Population

1900 N/A

1910 6,988,000

1920 9,179,743

1930 12,030,687

1940 15,719,189

1950 20,458,212

1960 26,492,812

1970 34,090,373

1980 43,520,058

1990 54,018,393

2000 65,740,462

The actual data and the predicted data from this model can be graphically presented as seen below:

Graph 3

The logistic model, similar to the Malthusian model over predicts the model but by much less. This more improved model takes into account the carrying capacity of the populous and therefore is approaching a levelled off population however, it is too high. We will once again the coefficient of determination to see how accurate the predicted model is.

The logistic model has an R^2 above 0.95 which is defined to be a correlated prediction model.

Evaluation of Both Models:

After evaluating both models individually we can clearly see that the logistic model gave us a more accurate prediction for the population of Canada as it has a higher R^2 value. The three datasets we now have can be illustrated on one plot for further analysis.

As seen in the graph, both models predict the population relatively close until about 1940 where both the lines overpredict the population growth rate. The model cannot effectively predict sudden jumps in the population such as seen from 1950 to 1960The Malthusian model’s plot increases exponentially, and population always grows larger and larger without any finite limit thus, this model is not appropriate to use for long periods of time.

The overall trend of the actual population is increasing gradually with no sudden changes and therefore our model can be improved to better represent this data.

The Malthusian model also only has one parameter, time, and therefore cannot be modified to more accurately represent the trend. However, the Logistic model has K, carrying capacity has another parameter which we can alter to correctly illustrate the population growth. In our original model, we used a carrying capacity of 300 million which may be unrealistic in the 1900 to 2000 time period. Therefore, we will choose a more realistic carrying capacity of Canada by referring to the actual data.

The population of Canada in 2000 was 30,689,000 and therefore we will set our carrying capacity, K to 31 million.

We will once again use our formula for P(t):

P(t) = 〖KP〗_0/(P_0+(K- P_(0)) e^(-kt) )

Using: P(0) = 5,310,000 and P(10) = 6,988,000, K = 300,000,000 , and P0 = 5,310,000 we form the equation:

6,988,000 = ((3,000,000)(5,310,000))/((5,310,000)+(300,000,000-5,310,000)e^(-10k) )

6,988,000 = (1.6461  10^14)/((5,310,000)+(25690000)e^(-10k) )

k = – ln⁡〖(0.7102411898)〗/10

k  0.03421506627

Which forms our prediction model:

P(t) = (1.6461  10^14)/((5,310,000)+(25690000)e^(-0.03421506627t) )

The constructed Logistic model’s predicted populations are displayed in the table 4:

Table 4: Predicted Population of Canada Using the Improved Logistic Growth Model

Year Predicted Population

1900 N/A

1910 6,988,000

1920 9,009,734

1930 11,340,690

1940 13,893,653

1950 16,537,821

1960 19,122,614

1970 21,510,440

1980 23,603,796

1990 25,356,414

2000 26,768,069

The actual data and the predicted data from this model can be graphically presented as seen below:

Evaluation:

From the predicted curve we can tell it is much more accurate compared to the Malthusian and original logistic. The population is underpredicted after 1970 which could be due to the fact that carrying capacity can be increased as a populous develops new technologies or ways to sustain resources. We will once again the coefficient of determination to see how accurate the predicted model is.

The new logistic model has an R^2 above 0.95 which is defined to be a correlated prediction model and it is higher than the previous one with a carrying capacity of 300 million.

Assumptions/ Problems

In this exploration, we were able to create an accurate model with a high R-squared value to show correlation however, there are possible errors in this model. To improve our model, we used the actual data’s final value to approximate the carrying capacity. This would not be an ideal model for population prediction as we do not have future value’s if we were not doing past years predictions. If given an accurate carrying capacity of humans for the future the logistic model could be applied to predict future populations however, we have limited access to data and limited knowledge for the carrying capacity. The logistic model also cannot take into account any advancements or changes to carrying capacity as a populous advance and this would furthermore make it inaccurate.

Conclusion

In this extended essay, differential equations were used and discovered to be accurate in predicting population dynamics of humans. The Malthusian model to predict the population was not very accurate as it began overpredicting more and more. This is due to the fact that it simply predicts the population growth is infinite. This model could be better applied in the population growth of bacteria or any microorganisms as they do not have as much limitations as humans. On the other hand, the Logistic Model effectively predicted the population once we gave it an accurate carrying capacity, K. The carrying capacity takes into account many of the factors that could slow down population growth which the Malthusian model did not for example limitation of resources and/ or pollution. Therefore, if we are able to determine an accurate carrying capacity of a country or populous, we can use the logistic equation to accurately predict the growth.

Introduction:

This report is portraying a detailed comparison of the change in population in two contrasting countries; Germany and Malawi. Germany which is a high-income and developed country is bound to differ from Malawi which is a low-income and less developed country in terms of various population characteristics and trends.

Map of Germany Map of Malawi

(‘The World Factbook – Central Intelligence Agency”)

(‘The World Factbook – Central Intelligence Agency”)

Malawi has been ranked among the least developed countries in the world. With the country being at the second stage of demographic transition, many things have changed for the better but some key determinants still hinder and delay the country’s progress. An example is the country’s economic instability.(“Africa :: Malawi — The World Factbook – Central Intelligence Agency”) Germany on the other hand is among the most developed countries in the world. The country has economic stability and proper social and public facilities such as schools and hospitals. In this report, I will collect data and explore various demographic indicators and historical, political, social and economic events and factors that have shaped the population throughout the years. (“Europe :: Germany — The World Factbook – Central Intelligence Agency”)

Important factors that have shaped Germany’s population

• In the mid 1950s, Germany encouraged workers to migrate from their country in order to “spur economic growth” which was intended to be for a temporary period but over time, the workers had their families join them which grew the number of foreigners to more than 7 million by 2001. (John Michael Wallace-Hadrill and Berentsen)
• Wealth is distributed favourably which encourages development and growth. The government assists those who do not meet certain requirements in terms of taxes and other fees such as hospital bills and tuition which closes the gap between wealthy people and the less advantaged. This improves the quality of life and has an overall impact on life expectancy and migration
• The country has a favourable health care system which is accessible to all. It is efficient and progressive, with the government being able to fund the right equipment and staff. A system like that has had a lasting impact on life expectancy due to the good health care provided.
• In 1945, after the second World War, Germany gained over 12 million refugees which increased their population significantly. Once the refugees gained residence and started families, there was a rise in population growth around 1950.
• Germany has always been reliant on migration to grow their population which means that people do not have big families. After the sudden increase, migration reduced and the population began to decrease. Now the country is facing very low birth rates and the government continues to encourage larger families.

Important factors that have shaped Malawi’s population

• Malawi relies on other countries and organisations such as the World Bank and IMF to support some major findings such as improved health care and more efficient education. This may slow down industrialisation and impact population growth due to the hesitation and caution that these donors take because of Malawi’s history of political and economic corruption. In the long run, this leads to limited necessities such as hospitals furthermore decreasing life expectancy.
• An inconsistency in policies may also lead to an unnecessary growth in population. This refers to shifting of ideas and lack of proper implementation of practices like family planning which Malawi needs in order to have a stable population.
• Rural areas tend to be less modernised which results in ignorance when it comes to things like healthcare and child vaccination which increases child mortality rates.
• Malawians used to migrate to areas such as Zimbabwe and South Africa in search of work. A few years after their independence in 1964, international migration became less practiced due to the country’s initiative to improve its economy. This made other Malwaians return to their country and internal migration came to a rise. This led to an increase in population which shaped the country’s demographic patterns today.
• Malawi practices agriculture among the people on a small scale and with a growing population, there is a lot of pressure in terms of long term jobs and agricultural land which impacts the economy.

Glossary

Child Mortality

Probability of a child dying before reaching a certain age (“World Population Prospects – Population Division – United Nations”)

Crude Birth Rate

Number of births over a given period divided by the person-years lived by the population over that period. It is expressed as the number of births per 1,000 population. (“World Population Prospects – Population Division – United Nations”)

Crude Death Rate

Number of deaths over a given period divided by the person-years lived by the population over that period. It is expressed as the number of deaths per 1,000 population. (“World Population Prospects – Population Division – United Nations”)

Natural Increase

The surplus (or deficit) of births over deaths in a population in a given time period. (Glossary of Demographic Terms – Population Reference Bureau)

Demographic Transition Model (DTM)

A model used to show the shift of birth and death rates from high to low levels in a population. The mortality decline is usually prior to the fertility decline, resulting in rapid population growth during the transition period. (Glossary of Demographic Terms – Population Reference Bureau)

Literacy Rate

Percentage of people in a certain sample of population or country that have the ability to read and write. (“World Population Prospects – Population Division – United Nations”)

Population pyramid

A graphical representation of the age and sex of a specific population that changes shape according to the structure of the population. It may take the form of a pyramid, have a columnar shape (box) , or have an irregular profile (cup). (Glossary of Demographic Terms – Population Reference Bureau)

Life expectancy

The average number of years of life expected by individuals who would be subject during all their lives to the mortality rates of a given period. It is expressed as years. (Glossary of Demographic Terms – Population Reference Bureau)

Processing past and future population data

Population trends and predictions for Germany

Germany’s population has three clear demographic trends. A low birth rate, high life expectancy and an ageing society. This proposes that the country is currently at the fourth stage of demographic transition where birth rate and death rate are both at a low level and they are nearly balanced. The birth rate is roughly equivalent to the death rate and there is little growth in population. It is fixed at a low level. This is evident in the graph above which shows a fluctuating natural increase between 1970 and 2020 way below the zero mark. The death rates remain stable with no drastic change between 1950 and 2000. The birth rates plummeted between 1960 and 1970 and proceeded to decrease with slight and hardly noticeable growth between 1975 and 2015. With the declining birth rates, you should have expected a shift to larger families but this is not the case. After the second world war, Germany received millions of refugees from former German territories and after the partitioning of Germany in 1949, more migration followed making the country more populace. The immigrants were young and skilled which contributed to their economic boom. Up until recently, Germany’s population growth has relied on migration gains. With an ageing population, the need for young skilled people is indisputable. The number of ageing people also puts a strain on the economy’s ability to support the retired citizens’ pension requirements.

Population trends and predictions for Malawi

Malawi’s population has three main determinants of its demographic pattern. A high birth rate, low life expectancy. This suggests that Malawi is currently in the second stage of demographic transition where an increasing growth in population is brought about due to a stable decrease in birth rate and death rate. Death rates remain stable and low due to successful and impactful economic and social changes that have led to the improvement of living standards among the people. There are multiple explanations as to why the birth rate decreases. Mostly, maternal and child health has been significantly improved which strengthens the quality and length of life. The country’s population continues to rise at a steady and more manageable pace as the death rates proceed to decline while the rate of birth decreases but stays higher than the birth rate; this information can be seen on the graph above. There has been a significant decrease in crude birth rate since 1990. The number of births are dropping due to the positive effects of growth in the economy, more modernised social views and the growing and encouragement of family planning facilities and practices. Though in the rural areas and some urban areas, the idea of contraceptives and the importance of planning has not yet sunk in and the people have become so accustomed to unplanned birth that transitioning proves to be difficult. This proves to be the main factor that leads to rapid population growth in the country.

Analyzing Population Trends

The impact of life expectancy and income on population trends in Germany and Malawi

Using the data shown on the above scatter plot, I will be exploring my hypothesis that women who achieve a lower level of education, are likely to have more children. There seems to be an evident correlation between babies per woman and the mean years in school for women during the reproductive age (15-44). Malawi with an increasing population and high birth rates is expected to have a higher number of children born than Germany and this is supported in the scatter plot above. As the number of babies per woman rises, the years of school attendance reduces by a significant number. Germany on the other hand, has low birth rates and the natural increase rate is very low therefore the country is expected to have few births and smaller families. The country is also more modernised than Malawi in terms of social practices and policies. Germany has family planning policies and efficient education systems which makes the women focus on other educational opportunities. In Malawi, their social practices are still outdated and things like early marriage are very common. This leads to school dropouts and early pregnancies, explaining why the years of education are so low and the number of babies had are very high. Babies per woman is an important indicator of population trend because knowing the statistics of the total fertility rate, indicates which stage your population is at. It is also the main source of population growth. Mean years in school for women aged 15-44 is equally important because this indicates that this age group is the most capable of reproduction.

Conclusion

Malawi and Germany are evidently in different stages of demographic development. Malawi is a developing country with a substantial increase in population due to the high birth rates; which are encouraged by the community’s social practices and the limited years that the women in the country attend school. Germany is the opposite with a low natural increase which is threatening their population growth; this is due to modernised social practices and the pursuit of a career and efficient education policies which encourage everyone to attend school. Furthermore, Malawi’s and Germany’s pyramid shapes differ. Malawi has an expanding, youthful population which is displayed by a Christmas tree shaped pyramid. Germany has a contracting, ageing population which is supported by the pyramid’s columnular structure with a bulge in the middle.

In conclusion, Malawi has made visible progress in controlling population growth and advancing skills, knowledge and healthcare. This shows potential development in the future.

Works Cited

1. “Africa :: Malawi — The World Factbook – Central Intelligence Agency.” Cia.Gov, 2019, www.cia.gov/library/publications/the-world-factbook/geos/mi.html.
2. “Europe :: Germany — The World Factbook – Central Intelligence Agency.” Cia.Gov, 2019, www.cia.gov/library/publications/the-world-factbook/geos/gm.html.
3. “Flag of Germany Image and Meaning German Flag.” Country Flags, www.countryflags.com/en/flag-of-germany.html. Accessed 14 Apr. 2020.
4. “Flag of Malawi Image and Meaning Malawia Flag.” Country Flags, www.countryflags.com/en/flag-of-malawi.html. Accessed 14 Apr. 2020.
5. “Gapminder Tools.” Gapminder.Org, 2020, www.gapminder.org/tools/#.
6. “Germany – Population Structure.” Encyclopedia Britannica, www.britannica.com/place/Germany/Population-structure. Accessed 17 Apr. 2020.
7. Glossary of Demographic Terms – Population Reference Bureau. www.prb.org/glossary/.
8. John Michael Wallace-Hadrill, and William H Berentsen. “Germany | Facts, Geography, Maps, & History.” Encyclopædia Britannica, 20 Mar. 2019, www.britannica.com/place/Germany.
9. klaus kästle – nationsonline.org. “Germany – Political Map – Nations Online Project.” Nationsonline.Org, 2015, www.nationsonline.org/oneworld/map/germany_map.htm. Accessed 5 Aug. 2019.
10. News, A. B. C. “Germany’s Population in Crisis.” ABC News, abcnews.go.com/International/story?id=5667781&page=1. Accessed 17 Apr. 2020.
11. “Political Map of Malawi – Nations Online Project.” Www.Nationsonline.Org, www.nationsonline.org/oneworld/map/malawi_map.htm. Accessed 14 Apr. 2020.
12. “Population Pyramid | Sociology | Britannica.” Encyclopædia Britannica, 2020, www.britannica.com/topic/population-pyramid. Accessed 10 Jan. 2020.
13. “World Population Prospects – Population Division – United Nations.” Un.Org, 2019, population.un.org/wpp/GlossaryOfDemographicTerms/.

A report discussing the potential risk towards the nation’s economic well-being, through the influence of population growth. Whilst considering the effects upon the following factors on the upcoming federal election, immigration, education, The Australian budget, living standards and wages.

Bibliography/Reference

1. https://www.abc.net.au/news/2018-12-14/population-growth-and-economic-growth-intertwined/10615352 “Population growth should be part of the economic conversation”
2. https://www.abc.net.au/news/2017-04-24/verrender-immigration-and-the-economic-illusion/846542
3. “Immigrants advance Australian economy, but what happens if we ‘close the door?” https://www.aph.gov.au/About_Parliament/Parliamentary_Departments/Parliamentary_Library/FlagPost/2019/April/Population_Policy_and_the_Budget
4. http://eprints.lse.ac.uk/81820/1/Is%20population%20growth%20good%20or%20bad%20for%20economic%20development_%20-%20IGC%20Blog.pdf
5. https://journals.sagepub.com/doi/full/10.1177/2158244017736094
6. http://www.economicsdiscussion.net/economic-development/population-growth-and-economic-development-2/26308

The discussion surrounding population growth and whether it should be part of the economic conversation has been faced by many countries especially powerhouse nations. Although population growth would seem to benefit economic development, this is not the case. The increase in population will influence a nation’s economy, both positively and negatively which are measured through the public and private sectors in addition to dignified living standards.

1.1. Impact on the Australian Economy

With an increase of over 388,000 people, Australia’s population has grown more than twice the rate of both USA and the United Kingdom. Cities such as Melbourne and Sydney will progressively suffer more density due to high levels of immigration intake, which for some is considered a positive. This supports the notion that a higher population leads to economic growth. However, Senators such as Pauline Hansen believe that its necessary Australia should impose an immigration ban. In favour, the Morrison Government has decided to “freeze” immigration allowing infrastructure to catch up. Citizens also claim that roads, buses and trains are starting to become congested for Australian cities to handle, decreasing their non-material living standards. In addition to this, houses are starting to become overpriced, due to the relatively high demand, which will ultimately influence the limited opportunities Australians have in the housing market.

Source 1

The graph provided in Source 1 displays the trend in population growth between ‘natural increase’ and ‘net overseas migration’, which calculates the immigration levels in comparison to births subtracted by deaths within Australia. We are given the idea that through the time period following the global financial crisis to 2018, ‘net overseas migration’ increased and ‘natural increase’ had progressively decreased. This means that more deaths are frequently happening in contrast to births due to Australia’s well-known aging population, and their growing immigration intake. Whilst to some, immigration may mean the drainage of resources and the rising competition between workers relative to the decreasing job supply. Although the Migration Council released a report, stating that Australia’s population will reach 38 million by 2050, where migration will contribute to $1.6 trillion (AUD) to Australia’s GDP, if the current trend continues. In addition, this’ll mean that migration will have added 15.7 per cent to our workforce participation rate and 5.9 per cent in GDP per capita in growth.

1.3. Impact on the Global Economy

The relationship between population growth and economic growth within the global economy has been a controversial issue. On one hand, some analysts believe that economic growth in high-income countries is likely to progressively slowdown in the upcoming years, as population growth in these countries is predicted to slow considerably. Others argue that population growth has been and will continue to be a problem for society, as a larger population means more use of the finite resources available on earth, therefore reducing long-term potential growth.

For example, in a country such as India, population growth will act negatively as limited resources and a larger population puts pressure on the resources that do exist. Whilst some other countries such as Russia are willing to accept more immigrants and a higher population, due to their large land mass in comparison to the population.

1.4

Stakeholders such as the Liberal Government in Canada have been facing the issue on how to increase their population. The quote on quote “bold” advice recommended that permanent immigration should gradually rise to 450,000 people per year by 2021, which would be a 50% increase to current immigration levels. Moreover, if the Canada decided to cut immigration, Government spending would increase by more than 40% towards the national GDP and their aging population would additionally increase, meaning more pensions for the elderly would be imposed.

1.5. Impact on salary/wages

Source 2

The diagram shown in Source 2 displays the ‘real wage growth’ in comparison to the ‘average real wage growth’. Real wage growth measures the goods and services than can be bought by consumers through their income, whilst average real wage growth refers to the rate of real wage growth and determines whether there has been an increase or decrease in those sectors taking inflation into account. In this case, we see the trend gradually decrease overtime. The main factor of this issue is immigration, as Australia receives high levels of overseas migration, which would mean more workers and an increasingly competitive labour market. This will benefit big businesses, increasing the amount of consumers and applicants they receive which positively affects their profits. However, workers will more likely suffer the after effects of lower wage growth with factors including stagnant and sticky wages as there are more people looking for jobs. Although, some politicians argue that the issue is that Australia has a ‘skills shortage’, where businesses struggle to find skilled workers for a role for the pay and conditions that they are willing to offer.

1.6. Impact on the Budget

Countries commonly rely on high population growth to assist their budget into surplus. This supports the notion that having a higher population means more economic growth, explaining why mass migration in underpopulated nations such as Australia is highly anticipated. Although, the Morrison Government plan to “freeze” migration levels of permanent visas, this will not overcome the 17 per cent increase in net overseas migration towards the 2019-20 Budget. This ultimately means that others factors such as overseas workers and international students are influencing higher rates of migration within the short-term perspective. However, inconsistent rates of migration could be due to the notion that around half of permanent visas that are granted are provided to individuals already living in Australia. Therefore, slowing down the rate of which migration falls.

1.7. Evaluation

The relationship between population growth and the economic growth has had recent discussions in shambles, where speculations have been thrown around amongst economists. In theory, population growth gradually decreases the finite amount of resources to more scarce amounts, where higher quantities of individual suffer the effects of disease, starvation and war. Efforts to reduce population without wasting any resources have been exploited such as China’s one child policy, where couples only had one child in order to stabilize long-term economic prosperity and control population. Whilst other nations are making efforts to increase population such as Germany, where immigration is becoming a substitute for the low birth rates occurring amongst couples. This has become both an economic and social problem for long-terms projectiles and has the potential to affect the GDP, as the youth get older less workers will be available economic growth. In addition, if a high populated country with low-income levels will experience slow developmental rates. For example, countries like Nigeria accommodate around 191 million people but receive a real wage of 42,500 NGN, which is equivalent to 117.40 USD meaning the income being earned by individuals cannot suffice the nations GDP in comparison to the growing population. This supports the idea that the effect of population growth on a countries per capita output level, relies upon the population pattern.

1.8. Conclusion

Majority of this report supports the idea that population growth is an important factor towards economic development and to an extent contributes to increased growth in per capita output in some situations. In low income nations, population growth would act negatively within short term perspectives, however in the long run will create a demographic dividend where the next generation will experience more efficiency than the last. In high-income countries, population is most likely low and creates a larger proportion of elderly citizens. The burden on the elderly can be decreased if population growth tends to happen towards the younger generation to prevent Government spending amongst pensions and allocate those funds somewhere else. There are still many who make exclusions to verdicts such as these, debating that the world is currently overpopulated putting “unsustainable” pressure on resources and the environment but because population growth plays an important role in overall economic growth, the evolution of world population will continue to be a major global concern.

1.9. Glossary

• Real wage/income – refers to the income of an individual or group after taking into consideration the effects of inflation on purchasing power.
• Net overseas migration – is the net gain or loss of population through immigration (migrant arrivals) to Australia and emigration (migrant departures)
• Natural increase – The difference between the number of live births and the number of deaths during the year. The natural increase (or natural decrease) is negative when the number of deaths exceeds the number of births.
• Inflation – is a situation of a sustained increase in the general price level in an economy. Inflation means an increase in the cost of living as the price of goods and services rise.
• Surplus – The extent to which generation of goods, services, and resources (such as capital) exceeds their consumption.
• Stagnant wages – Stagnation is a situation that occurs within an economy when total output is either declining, flat, or growing slowly. Stagnation results in flat job growth, no wage increases, and an absence of stock market booms or highs.
• Sticky Wages/Salary – refers to a wage that is slow to adjust to its equilibrium level, creating sustained periods of shortage or surplus in the labour market.
• GDP – is the sum of the market values, or prices, of all final goods and services produced in and economy during a period of time.
• GDP per capita – GDP per capita is a measure of a country’s economic output that accounts for its number of people. It divides the country’s gross domestic product by its total population.
• Non-material living standards – non-material living standards are made up of intangible things like environment, freedom of speech, free elections, crime rates, and time off work.
• Material living standards – Material living standards refer to goods and services including cars, houses, health services, etc. The total number of goods and services available measures material living standards.
• Demographic – is the study of a population based on factors such as age, race, and sex. Demographic data refers to socio-economic information expressed statistically, also including employment, education, income, marriage rates, birth and death rates and more factors.
• Dividend – is the distribution of reward from a portion of the company’s earnings and is paid to a class of its shareholders.