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Chapter 2

Population

A natural starting point for a book on human geography is to examine where humans live and how these patterns impact places and regions. As we move through busy cities, gridlocked with traffic, a brown haze of smog in the air, concrete in all directions, we may get the feeling that the world has reached a breaking point and cannot support any more people. But then we can visit vast open spaces, from forests and deserts to savannas and steppes, where humans have left a much more minimal imprint on the landscape. Clearly, the distribution of human population is uneven. As we will see, human populations are clustered and thus impact some locations more than others. In addition, rates of population growth vary substantially from place to place, so that the spatial distribution of clusters can shift. Population centers move over time, as people are born in particular places and move to new locations.

Population patterns and growth rates have a profound impact on places, as seen in their spatial relationships with economic and social conditions. As you can imagine, some countries face fast-growing populations, where women have many children and there is a sensation of overpopulation. In parts of the world, governments struggle to grow their economies and provide enough jobs, housing, and food for rapidly expanding populations. Shortages in these areas can sometimes lead to political instability, as peoples’ anger over unmet needs erupts into street protests, coups and revolutions, and crime.

Less obviously, some places are seeing very slow population growth or even negative growth. This can create a completely different set of problems, as governments face conditions where there may not be enough people to work and grow (or even maintain) an economy. Low population growth can lead to a situation where places have too many elderly residents relative to young, active workers.

There is a strong spatial relationship between economic and urban development, gender roles, culture, and differing rates of population growth. As people move away from farms and into cities, economic and social change has a profound impact on population dynamics. As the reader of this book, you are most likely a resident of an affluent country and live in a city or town. Given your life experience, how many children do you plan to have? Why did you choose that number? If you lived in a poor rural setting, your life experience would be very different and would have a significant impact on the number of children you would want. Where you live has a strong relationship with how much you will contribute to population growth.

Population issues also lead to important discussions on spatial interaction. When population growth is high in some places and low in others, forces of migration can come into play. Migration is covered in more detail in chapter 3, but let it suffice that population pressures on economies and environments can push people to leave some places and move to new countries or cities.

Spatial distribution of population

World population numbers

For much of human history, populations grew slowly, but once humans moved from hunting and gathering to agriculture, improved food supplies allowed population growth to speed up. Later, as more consistent food supplies were complemented by improvements in human health through medical innovation and sanitary conditions, population growth rates increased even more as in Tokyo, Japan (figure 2.1). The global human population did not reach one billion until around 1800 (figure 2.2). After that, however, the population growth rate increased quickly. In just 123 years, by 1927, the population reached two billion. Then, in just thirty-three years, by 1960, world population reached three billion. Four billion was reached by 1974, and five billion by 1987. In 1999, there were six billion, and by 2011, there were seven billion people. The human population as of 2017 was over 7.3 billion, and growing at over eighty million people per year.

These growth patterns appear to be following the S-shaped curve (figure 2.2), whereby a population grows slowly during an initial lag period and then sees a rapid increase of exponential growth.

Figure 2.1.Tokyo, Japan. Understanding human population patterns is a first step in understanding human geography. Photo by aon168, Stock photo ID: 519265849. Shutterstock.

But before getting too worried about the fact that over eighty million people are added to our planet each year, it is important to see how the growth rate is changing over time (figure 2.3). Globally, peak growth was reached between 1965 and 1970. However, since that time, the growth trend has typically sloped downward. So, while the earth’s population is still growing, it is growing less quickly than in the past. Population geographers and others believe that this downward trend will continue, with population reaching 11.2 billion by the year 2100. After that time, projections become more uncertain. The global population could continue to gradually increase, or it could ultimately reach zero growth, where the human population returns to a relatively stable equilibrium. This pattern would represent the later stages of the S-shaped curve, where population growth slows and possibly reaches a more stable plateau.

While the world’s population is over 7.3 billion people, these people are not evenly distributed over the planet. As pointed out earlier, humans are clustered, resulting in very distinct levels of population density.

Figure 2.2.World population over time. Human population size remained relatively flat until the past one-hundred years or so. Data sources: United Nations, 1999; United Nations, 2015.

Figure 2.3.Population growth rate. The human population continues to grow but at a slower rate than in the past. Date source: United Nations, 2015.

Population density

Population density is used to describe the concentration of people in different parts of the world and can be calculated in several ways (figure 2.5). Arithmetic density, the most commonly used measure of population density, is the number of people per unit area, such as people per square mile. Arithmetic density (in countries of one million or more people) ranges from a high of over 7,500 people per square kilometer in Singapore to a low of less than two people per square kilometer in Mongolia (figure 2.4). As a comparison, the United States has about thirty-four people per square kilometer.

Another measure of population density is physiological density, which measures the number of people per unit of arable land and is intended to compare the number of people in an area with the amount of land available to feed them. Arable land is defined in a couple of different ways and thus can be confusing when mapping and analyzing physiological density. One definition holds that arable land is land that is suitable for cultivation. It includes land with the proper soils, elevations, slopes, and climates for growing crops. A narrower definition, which is used by the World Bank and the United Nations, holds that it is land used for annual crops, such as corn, wheat, rice, and vegetables, in contrast to permanent crops planted once, such as coffee, fruit, and nuts.

Figure 2.4.Singapore, which is essentially urban, has one of the highest population densities in the world. Mongolia, a sparsely populated country, has one of the lowest population densities in the world. Singapore photo by Martin Ho Smart. Stock photo ID: 559395556. Shutterstock. Mongolia photo by Jan Peeters. Stock photo ID: 225794263. Shutterstock.

Nevertheless, the idea behind physiological density is that a high value reflects a large population with a limited amount of agricultural land. Singapore, with virtually no agriculture, has an astonishing physiological density of over 945,000 people per square kilometer of arable land. At the other extreme lies Australia, with a physiological density of only about fifty people per square kilometer of arable land. Obviously, the strategies for feeding the people of Singapore are different than those for feeding the people of Australia: either they grow food domestically, they import it, or they do some combination of the two.

A third measurement is agricultural density, the number of rural residents per unit of arable land, which indicates how many people are involved with agricultural production. This measure helps illuminate which countries are efficient at growing food and which are not. For instance, Egypt’s agricultural density is over 1,800, whereas it is 39 for the United States. This means that there are forty-six times more people in rural areas of Egypt relative to arable land compared to the United States. Presumably, these people are involved directly or indirectly in the agricultural economy. A lower proportion of Americans in rural areas relative to arable land implies that they are much more efficient than the Egyptians in agricultural production, possibly because of the availability of agricultural technology such as farm machinery, agricultural chemicals, and precision agricultural mapping and monitoring with geographic information systems.

Figure 2.5.Measures of population density. Common measures of population density include agricultural density, physiological density, and arithmetic density. Explore population densities in ArcGIS Online at https://arcg.is/1LaKue. Data source: World Bank.

When physiological and agricultural densities are high, pressure to expand arable land into new areas may increase. This expansion can cause negative environmental impacts as natural landscapes are converted to agricultural uses, displacing wild plant and animal life. One way to reduce this pressure is to import food. For example, Egypt was the world’s largest importer of wheat in 2016. If a country such as Egypt, with high physiological and agricultural densities, has a weak economy and is unable to earn foreign currency through exports, then it will be difficult to import food, and hunger can ensue. With Egypt’s economy weakened from political instability and violence in recent years, foreign currency shortages and a declining exchange rate have strained the country’s ability to import food (figure 2.6). Prices have increased for consumers, forcing the state to spend more on subsidizing bread. In contrast, countries with high physiological and agricultural densities and relatively strong economies, such as South Korea and Japan, can import food more easily and face virtually no risk of hunger.

Population clusters

As can be seen, the spatial distribution of people around the world varies greatly. Some places have high densities, known as population clusters, whereas others have very low densities. Population clusters are found in several key locations (figure 2.7). East Asia, South Asia, and Europe form the largest population clusters, with other localized clusters found in parts of the Americas, Africa, and Southeast Asia. In East Asia, China alone has over 1.3 billion people, making it the largest country in the world in terms of population size. The region also includes large populations in South and North Korea as well as Japan. South Asia is dominated by India, with a population of just under 1.3 billion, the second largest in the world. Bangladesh, Pakistan, and Sri Lanka also have substantial populations in South Asia. Dense populations can be found in Europe as well, stretching from the Iberian Peninsula (Portugal and Spain) into the western portions of Russia and northwest Turkey. In the United States, dense populations can be found along the northeast seaboard, stretching from Boston to Washington, DC. Smaller population clusters can be found throughout the world in the form of large urban agglomerations, the result of ongoing urbanization of human society.

Figure 2.6.Waiting to buy bread in Aswan, Egypt. Egyptians rely heavily on imported wheat for their bread. With a weakened economy, prices rise, and many people struggle to purchase this staple food. Photo by Olga Vasilyeva. Stock photo ID: 418291645. Shutterstock.

Figure 2.7.Major world population clusters in Europe, South Asia, and East Asia. Sign in to your ArcGIS Online account and explore this map at http://arcg.is/2lDz8WW. Data sources: World Population Estimated Density 2015, Esri.

The distribution of population can be partially explained by the natural environment. As humans migrated out of Africa millions of years ago and spread to the far reaches of the earth’s surface, some environments proved more suitable than others for supporting large populations. Temperate climates (those that are not too hot or too cold, too wet or too dry) tend to form soils well suited for agriculture, a prerequisite for large populations. Environments with more extreme climates, such as deserts and subarctic regions, are suited only for small populations, which tend to be nomadic. Larger human populations also tend to be located at lower elevations. Exceptions to this rule are the mountain valleys of tropical regions, such as Central America, which have milder climates and better soils than the lowland tropics. Other environmental characteristics, such as natural resources or waterways for trade, can influence population distributions. For instance, arid locations with large mineral deposits or tropical rainforests with timber resources can attract people to small local clusters. Likewise, populations can cluster along coastal and river locations that facilitate trade with other areas.

While the natural environment has been an important force shaping where human populations clustered for much of human history, it is no longer the most important determinant. The differing rates of fertility, mortality, and migration that determine world population distributions now depend on a wider range of cultural, environmental, political, technological, and economic forces. For instance, since food can be easily imported from far away, the importance of agricultural potential in the growth of population clusters is diminished. Also, population growth in trade centers can be just as fast along human-built routes such as railroads and highways as along natural rivers and harbors. Airports can now replace natural seaports. With fewer restrictions tied to the natural environment in terms of food production and trade, population clusters can form in many more locations than in the past.

There are several good examples of places where populations are growing despite the natural environment, not because of it. Phoenix, Arizona, with its arid landscape and over 100 days per year when temperatures reach 100 degrees or higher, is now a thriving metropolis. The same goes for Las Vegas, Nevada (figure 2.8). In both cases, economic development and technology have allowed for populations to grow in places with difficult natural environments. Air conditioning and complex systems to deliver food and water from far away allow these places to grow in ways that were unthinkable in the past (figure 2.9).

On the opposite end of the temperature spectrum is the growing population of Astana, Kazakhstan. It lies in the flat, open steppe of Central Asia, where temperatures never rise above freezing for over 100 days per year. Its barren and remote location made it the ideal site for Soviet gulag prison camps when it was part of the Soviet Union. With Kazakhstan’s post-Soviet independence, its capital city was relocated to Astana for political reasons, given that city’s central location in the country. Again, modern transportation and food-delivery systems mean that populations can thrive in large numbers despite the inhospitable environment.

Figure 2.8.Las Vegas, Nevada. Technology such as air conditioning and water delivery infrastructure have allowed cities such as this to grow in relatively inhospitable climates. Photo by littleny. Stock photo ID: 105916268. Shutterstock.

Figure 2.9.Astana, Kazakhstan. Like Las Vegas, this city has a growing population in an inhospitable climate. Coincidentally, both Las Vegas and Astana have fanciful urban landscapes that reject not only their natural environments but also historical and cultural traditions. Photo by ppl. Stock photo ID: 216420808. Shutterstock.

As population clusters shift within countries, such as in movement to a new capital city or into territories previously viewed as undesirable, the population centroid changes location. This point represents the center of the country weighted by the location of the population. Imagine a flat map of the US, with weights representing people stacked up for every city, town, and rural area. The population centroid would be the point where the map would balance perfectly. Figure 2.10 shows how the US population has shifted westward and slightly southward since 1790. This shift is in response to westward agricultural settlement, development of the industrial economy of the Great Lakes region, migration to the West Coast with the railways, and later growth of the Sun Belt states.

Go to ArcGIS Online to complete exercise 2.1: “Spatial distribution of population.”

Figure 2.10.Population centroid of the United States. The centroid has shifted over time in response to population growth west and south from the original colonies. Data source: US Census.

The components of population

As seen so far in this chapter, the spatial distribution of population varies substantially from place to place. Environmental and economic forces play an important role in these patterns, as people cluster in areas with land suitable for agriculture or along important trade routes. But these forces tell only part of the story. As you can imagine, birth and death rates greatly vary from place to place as well. In some countries, women have many children, while in others, women have very few. Likewise, death rates can be high in some places and low in others. Naturally, the relationship between birth and death rates plays a role in population distributions. When more people are born than die, populations increase, while populations decrease when death rates are greater than birth rates. The reasons for the variation in birth and death rates include additional economic and political forces as well as cultural attitudes and beliefs.

Population growth or decline in a specific place can be calculated with a simple demographic equation:

Population change = Births − Deaths + Immigration − Emigration

This intuitive equation simply states that the population of a place changes as babies are born, people die, immigrants move in, and emigrants move out.

This chapter deals with the birth and death portion of the equation, and chapter 3 covers immigration and emigration.

Births

The crude birth rate (CBR) is the number of births per 1,000 people in a given year. The lowest CBR in 2015 was in Monaco with 6.65 births per 1,000, and the highest was in Niger with 45.45 (table 2.1). The CBR for the United States falls within the lower third of national rankings, at 12.49 births per 1,000. This measure is useful for calculating how quickly countries’ populations are growing, but its weakness is that it can be affected by the proportion of women in the population (those who may give birth) and by the age structure of the population (the proportion of young people to old people). These factors are discussed in more detail later in the chapter.

Table 2.1.Measurements of fertility vary greatly from place to place. Some countries face rapidly growing populations while in others births are not sufficient to replace previous generations. Data source: World Bank.

A somewhat more intuitive way of understanding population change, and one that accounts for differences in age structures and the proportion of sexes, is the total fertility rate (TFR) (table 2.1, figure 2.11). The TFR represents the average total number of children a woman will have during her lifetime. TFRs vary significantly. In Singapore, women in 2015 were having an average of just 0.81 children—less than one child per woman! The United States’ 2015 TFR fell in a moderate range of 1.87 children per woman. At the high end, Niger’s TFR was 6.76. As seen in the map, clusters of low TFRs can be found in Europe, and clusters of high TFRs are seen in sub-Saharan Africa.

Replacement fertility is the TFR necessary for a population to replace itself from one generation to the next without growing or shrinking. Replacement fertility of 2.0 would be the theoretical value, since 2.0 children would replace their two parents, resulting in no net gain or loss in population. However, because some women will die prior to reaching their reproductive years, replacement fertility is slightly over 2.0. In general, a TFR of 2.1 is used for replacement fertility. However, in developing countries with high mortality rates for infants and young adults, the rate can be a few decimal points higher. If a country has a TFR above replacement fertility, its long-term trend is toward an expanding population. Conversely, if a country’s TFR is below replacement fertility, its long-term trend is toward a shrinking population.

Look at figures 2.11 and 2.12. The countries represented in white have TFRs close to 2.1 and are in the ballpark range of replacement fertility. Those in the highest category have very high TFRs and should have rapidly growing populations. Most interesting, though, is the lowest category, those with TFRs substantially below 2.1. All those countries are facing the prospect of shrinking populations, since women are having fewer children than required for replacement. It is very rare for a species to voluntarily reproduce below the replacement level, but humans appear to be doing exactly that in many parts of the world.

Figure 2.11.Total fertility rate, 2015. Explore this map at http://arcg.is/2lDAgd5. Data source: World Bank.

Figure 2.12.Total fertility rates in Europe. Europe has some of the lowest fertility rates in the world, with most falling well below replacement levels. This is especially acute in Southern and Eastern Europe. Explore this map at http://arcg.is/2lDHFJs. Data source: World Bank.

The rate of births in a country results from a complex mixture of variables. Obviously, access to contraception is an important variable, but other factors include the spatial relationship of where a woman lives, economic conditions, political and social stability, gender equality, education, and levels of urbanization. These are discussed in more detail later in the chapter.

Go to ArcGIS Online to complete exercise 2.2: “Fertility rates.”

Deaths

Just as populations grow when babies are born, they shrink when people die. For this reason, the second essential component of population to understand is death and the different measurements used for quantifying it.

The crude death rate (CDR) is the number of deaths per 1,000 people in a given year (figure 2.13). As with the CBR, there is great spatial variation in the CDR. At the low end, Qatar had a 2015 rate of 1.53 deaths per 1,000, while Lesotho had a rate of 14.89. The United States falls close to the top third, with 8.15 deaths per 1,000.

When looking at figure 2.13, you can see that the CDR can lead to some surprising results. As you may expect, sub-Saharan Africa includes a large cluster of countries with high CDRs. Poverty, hunger, disease, and lack of medical care are prevalent in many parts of this region, so most people will not find it surprising that there are many deaths. But from there, the map illustrates death rates that many would not expect. Mexico and other Latin American countries have lower CDRs than the United States and Canada, for example. It is also apparent that Germany and Italy have CDRs higher than Iraq’s (figure 2.14). In fact, nearly all developed countries, such as those of North America and Europe, have higher CDRs than less developed countries in Latin America, Asia, and the Middle East.

Figure 2.13.Crude death rate 2015. Explore this map at http://arcg.is/2lDNfM0. Data source: World Bank.

At first glance, it seems strange that rich, peaceful countries can have higher CDRs than poor, war-stricken countries. The reason is that although the CDR can be influenced by obvious factors such as war and violence, hunger and malnutrition, and disease and lack of adequate health care, another factor has an even greater impact on the CDR: the age structure of the population. When a baby boom is followed years later by low birth rates, the proportion of elderly in a society increases. For example, many people were born in Europe after World War II ended in 1945. By 2015, that large group was reaching seventy years of age. But younger generations had low fertility rates, which declined through the 1960s, 1970s, and 1980s, meaning there were fewer children. With a large number of elderly and few children in a country, the proportion of elderly is higher. Since the elderly die at a higher rate than the young, the CDR is high (figure 2.15). In contrast, countries with high birth rates, such as Iraq, have a growing number of young people in relation to the number of elderly people. Since the young die at lower rates than the old, these countries can have a low CDR (figure 2.16).

Another measure of mortality is the infant mortality rate, which is a measure of the death rate of infants from birth through their first year per 1,000 live births. This rate has come down significantly over time but still varies considerably (figure 2.17). The highest rate in 2015 was in Afghanistan, at 115. That tells us that 11.5 percent of all children born in Afghanistan die before their first birthday, an astonishingly high number. At the low end is Monaco, with a rate of 1.82 per 1,000, or 0.182 percent. The United States has a rate of 5.87 (0.587 percent), which is higher than many other developed countries.

Figure 2.14.The crude death rate in Europe, North Africa, and the Middle East, 2015. Unexpectedly to many, much of affluent Europe has higher CDRs than poorer and politically unstable countries in the Middle East and North Africa. This disparity relates to the differing age structure of the populations in each country. Explore this map at http://arcg.is/2kUSGcj. Data source: World Bank.

Figure 2.15.Dusseldorf, Germany. Countries such as Germany with a large number of elderly and a smaller number of young people have a higher crude death rate. Photo by Isarescheewin. Stock photo ID: 477151594. Shutterstock.

Figure 2.16.Kirkuk, Iraq. Countries such as Iraq with a large number of children and a smaller number of elderly have a lower crude death rate. Photo by Serkan Senturk. Stock photo ID: 509928916. Shutterstock.

Declines in infant mortality are among the most important achievements in global health in recent decades. In 1960, there were roughly 122 deaths per 1,000 live births, meaning that over 12 percent of all babies died within their first year of life. Continuous declines led to a rate of under 32 by 2015, a decrease of about 73 percent. Economic development has given more people access to clean water, formal health-care systems, sanitation, and adequate nutrition. Thus, mothers and infants are less likely to get sick, but when they do, they are more likely to have access to medical care. For example, oral rehydration therapy, a liquid mixture of glucose and electrolytes, is a simple cure for dehydration, one of the leading killers of infants.

Education, especially for women, has also helped reduce infant mortality rates (figure 2.18). Educated women are more likely to know how to access and navigate public health systems to obtain health care for themselves and their babies. They are also more likely to have a better understanding of nutrition and sanitation to properly feed their children and avoid waterborne and foodborne contaminants. Additionally, educated women are more likely to work and therefore have fewer children, leaving more resources for a smaller number of children.

Figure 2.17.Infant mortality rate, 2015. Explore this map at http://arcg.is/2mg6FKf. Data source: World Bank.

Figure 2.18.Schoolgirls in Skardu, Pakistan. Educating women is an important strategy for reducing infant mortality rates. Photo by Khlong Wang Chao. Stock photo ID: 426040138. Shutterstock.

Afghanistan, which consistently has among the highest infant mortality rates in the world, illustrates how low levels of economic development and low levels of education for women contributes to a high rate of infant deaths. Most babies are born with midwives at home rather than in medical clinics that can more quickly and safely deal with complications. Poverty and high levels of malnutrition mean that many pregnant women give birth to low-weight babies, increasing their babies’ susceptibility to disease. When babies do get sick, medical care is often lacking, so common infections, respiratory illnesses, and diarrhea frequently lead to death. Lack of education, with its corresponding lack of knowledge about health and nutrition, further contributes to a high infant mortality rate. Only about 50 percent of babies under six months of age are exclusively breast-fed. Instead, mothers often feed their babies tea and biscuits, leading to an increased risk of consuming contaminated food or water in a country with limited potable water and inadequate food storage systems.

The United States also offers a good case study on infant mortality. Although the infant mortality rate in the US is low by global standards, it is significantly higher than in other affluent countries (figure 2.19). For instance, a baby born in the US is 2.8 times more likely to die than a baby in Japan. The explanation for this difference is not clearly understood, but what is known is that more babies are born prematurely in the US than in the countries with lower infant mortality rates. Premature birth, which is associated with cigarette smoking, drinking alcohol during pregnancy, diabetes, and high blood pressure, puts infants at greater risk of mortality. This accounts for some, but not all, of the mortality difference. It is possible that the fragmented health care that some young women receive, especially those of lower incomes, prevents clinicians from detecting and monitoring risk factors.

Figure 2.19.Infant mortality rates lower than the United States’ rate. Data source: World Bank.

Life expectancy is the average number of years a person is expected to live. As with all demographic variables we have seen so far in this chapter, life expectancy varies widely around the world (figure 2.20). At the high end in 2015 was Monaco, at 89.52 years, and at the low end was Chad at 49.81 years. The United States has a life expectancy of 79.68 years.

Life expectancy figures can sometimes paint a misleading picture. For instance, life expectancy in the Ancient Roman Empire was 22 years. More recently, Chad had a 2015 life expectancy of 49.81 years. One may get the impression that Ancient Rome had few people beyond their mid-20s, while Chad has few over 50. This is not true, however, since life expectancy is calculated as an average of the age of death of all people in a year. If a country has a high infant mortality rate, those infant deaths pull down the average. Just imagine a place where five people died in the same year at the ages of 1, 19, 56, 79, and 95. The life expectancy for this group would be (1 + 19 + 56 + 79 + 95)/5 = 50. In this case, three people out of five lived beyond the average of 50 years, with one well beyond. What pulls down the average very quickly is when there are many 1s in the equation from infant deaths. Typically, there is a strong spatial relationship between high rates of infant mortality and low life expectancies. Note the similarities between the two in figures 2.17 and 2.20.

Life expectancy has a strong spatial relationship with socioeconomic and lifestyle variables. As with infant mortality, places with stronger economies are more likely to have the infrastructure necessary for longer lives. In more affluent places, clean water and contaminate-free food, vaccinations, medical care, health and safety standards, and proper housing are more common. Thus, people are less likely to get sick or injured, but when they do, they are more likely to get proper medical attention. Less developed places lack many of these features, resulting in higher mortality and lower life expectancies. But lifestyle also plays an important role in life expectancy. Populations with higher rates of smoking, alcohol consumption, drug use, sedentary lifestyles, and poor diet have lower life expectancies. For instance, the US states with the lowest life expectancies also rank below average in terms of exercise and other healthy lifestyle factors (figure 2.21).

Figure 2.20.Life expectancy, 2015. Explore this map at http://arcg.is/2kUSEkK. Data source: World Bank.

Globally, life expectancy increased by five years between 2000 and 2015. This was largely due to improvements in Africa. During this time, public health improvements have lowered infant mortality rates, but death from diseases such as malaria and AIDS has also been reduced (figure 2.22). Gains in Eastern Europe and Russia have also contributed to an increasing global life expectancy. After the fall of the Soviet Union in 1991, a collapse of public health systems, combined with stress-related increases in alcohol consumption, suicide, and other factors, led to a sharp decline in life expectancy. As the region has adjusted to new economic and political systems, health has recovered.

Figure 2.21.Lack of regular exercise by US county. Lifestyle factors, such as exercise or lack of it, can have a significant impact on life expectancy. Explore this map at http://arcg.is/2eBVJjl. Data sources: 2016 USA Adults That Exercise Regularly. Esri and GfK US, LLC, the GfK MRI division.

Figure 2.22.Kampala, Uganda. Improved public health in Africa has helped increase life expectancy in the region. Photo by Robin Nieuwenkamp. Stock photo ID: 189950711. Shutterstock.

Natural increase

Returning to the idea of the demographic equation from earlier in this chapter, we know that births and deaths are key components of population change. The difference between these two gives the rate of natural increase. Simply stated, Natural Increase is calculated by adding in the number of people born each year and subtracting the number who die.

Natural increase = Crude birth rate − Crude death rate

As an example, the 2015 CBR for the United States was 12.49 per 1,000 people, the CDR was 8.15 per 1,000 people, and thus the rate of natural increase was 4.34 per 1,000 people (12.49 − 8.15 = 4.34). To see the result in percentage terms, divide by 10, for a natural increase rate of 0.434 percent. The highest current estimated rate of natural increase is Malawi, at 3.31 percent (a CBR of 41.56 and a CDR of 8.41). At the low end is Bulgaria, at −0.552 (a CBR of 8.92 and a CDR of 14.44) (table 2.2).

Table 2.2.Highest and lowest natural increase rates, 2015. Data source: World Bank.

It may be difficult to visualize what these natural increase percentages mean for countries. Nevertheless, we can begin to get a feel if we look at the percentages relative to the US. Malawi’s natural increase rate of 3.31 percent, divided by the United States’ natural increase of 0.43 percent, shows that Malawi’s population is growing at about 7.6 times the rate of that of the US! For a low-income African country, that rate represents significant challenges in terms of growing jobs, housing, and food supply at the same rate or more. At the low end of the natural increase rankings are negative numbers. These result when death rates are higher than birth rates and mean that populations are shrinking unless offset by immigration. Just as a fast-growing population can present challenges, a shrinking population presents a whole different set of challenges, which are discussed in the next section of this chapter.

Another way natural increase can be put into context is by calculating doubling time, the number of years it will take for a population to double in size. The rule of 70 is an easy tool for estimating doubling time by dividing 70 by the natural increase rate. Using the preceding data, the doubling time for the US population is 70/0.434 = 161 years. Likewise, the doubling time for Malawi is 70/3.32 = 21 years. It must be remembered that when using natural increase to calculate doubling time, we include only births and deaths; migration is not included in the calculation. Nevertheless, these doubling time calculations illustrate that Malawi is facing much more rapid population growth from high rates of births and low rates of deaths than is the United States. This implies that Malawi will need to produce more schools, housing, and jobs in a relatively short time, while the United States should have the luxury of a much longer timeframe.

When natural increase is zero, meaning crude birth rates and crude death rates are the same, a place is said to be in zero population growth. In 2015, a handful of European countries, including Denmark, Slovakia, and Austria, were very close to zero population growth. Again, it must be recalled that migration is not included in these calculations. Some people consider zero population growth to be a desirable goal, since both growing and shrinking populations can lead to problems, as discussed in the next section.

Go to ArcGIS Online to complete exercise 2.3: “Death rates and natural increase.”

Population structure

As patterns of births and deaths change over time in a society, they create different population structures. Population structure refers to the age and sex distribution of people in a society in terms of the proportion of men and women, young and old in a place. This structure can have profound impacts on a society, determining whether limited economic resources go to the young or the old and influencing opportunities for economic development.

Population pyramids

Population structure can be illustrated as a population pyramid, which shows men and women by five-year age-sex cohorts (figure 2.23). Traditionally, population structures have a pyramid shape, with many young people at the bottom and fewer old people at the top. This form is traditional in that, for most of human history, many babies would be born, creating a wide base to the pyramid, while people would die as they aged, creating a progressive narrowing toward the top. However, as discussed earlier in this chapter, birth rates have declined in many countries, causing population structures to narrow at the bottom. As age-sex cohorts from previous higher-fertility generations age, a bulge of people moves up the pyramid. Eventually the population structure comes to resemble more of an inverted pyramid, with fewer children and more elderly people.

Figure 2.23.Population pyramid of Mexico, 1980. Data source: US Census.

In figure 2.24, Japan’s population pyramid shows this change. Japan, by 1990, was already moving toward low fertility rates. Two bulges are apparent in 1990. The first is of people ages 40 to 44 who were born as part of the “baby boom” just after World War II. Another smaller baby boom can be seen among 15- to 19-year-olds, who would have been born between 1971 and 1975. By 2017, these two bulges had aged and can be seen in the 65- to 69-year and 40- to 44-year age cohorts. Japan’s ongoing decline in fertility meant that no new bulges in young cohorts formed, resulting in a narrowing base. Projections to 2050 show that the population will continue to age, as previous cohorts get older and low fertility rates result in smaller cohorts of young.

Figure 2.24.Population pyramid for Japan, 1990, 2016, 2050. Low fertility rates are resulting in an increasingly narrow base of young people and wider top of elderly people. Data source: US Census.

The dependency ratio

Changing population structures present distinct challenges to societies, which can be illustrated by the dependency ratio. The dependency ratio measures the proportion of people ages 15−64, compared to those under 15 and over 64. The idea behind this ratio is to calculate the proportion that are of working age in relation to those that are typically considered too young or too old to work.

When birth rates are high (resulting in a wide-based pyramid), there is a large proportion of people under the age of 15 (figure 2.25). With a large, dependent population of young people, a country must make large investments in things such as child care and education. High birth rates and a young population also typically mean that a population is growing quickly and will continue to do so as young people enter reproductive ages and have their own children. This means that food supplies, housing, and jobs must increase at least as fast as population to maintain standards of living.

The Middle East and North Africa had the highest youth unemployment of all regions in 2014, in part from relatively high birth rates combined with weak economies and low levels of job creation. The unemployed, combined with many more who are underemployed in the informal economic sector with unstable work selling goods on the street and working for cash under the table, often become disenchanted with their governments. Some researchers argue that this large group of young people with limited opportunities to better their future is more susceptible to radical ideologies, including terrorism. Demographic trends and economic conditions can thus create volatile situations.

Figure 2.25.High youth dependency ratio. Countries with a high dependency ratio due to many children face costs associated with education. Once these children are a little older, the economy will have to provide sufficient jobs as well. Photo: Schoolchildren in Mombasa, Kenya. Data source: US Census. Photo by Juliya Shangarey. Stock photo ID: 619096334. Shutterstock.

Conversely, when birth rates are low, the eventual result will be a higher proportion of dependent elderly people (a smaller base of the pyramid and larger top), which requires greater investment in health-care and retirement systems (figure 2.26). But with fewer young people entering the workforce, paying for these systems becomes a challenge that can lead to cuts in health-care and retirement benefits, higher taxes on the shrinking working population, or increases in the age of retirement. Obviously, all of these potential solutions face resistance from some segments of the population.

This type of population structure represents a declining population, with few children being born and a larger elderly population dying. Maintaining economic strength with a declining population can be difficult. Continuous increases in worker productivity or immigration are necessary to maintain strong economies.

Many European countries are facing the consequences of rapidly aging populations. In some towns, schools are closing for lack of pupils, and nursing homes are opening at an increasing rate as the elderly population grows. You may have heard concerns about the long-term viability of Social Security in the United States as the population ages, but pension programs are facing even greater demographic pressures in Europe. In six European countries, over one-fifth of the population is already over 65 years of age. Furthermore, many people retire well before 65. The effective age for men to retire in France is 59.4, while in Italy and Greece, it is just over 61. Only in Switzerland and Portugal is the effective age of retirement over 65. Lower retirement age puts immense pressure on pension plans. Again, demographic trends can have profound impacts on societies.

Figure 2.26.High elderly dependency ratio. Photo: Unions protest proposed pension reforms in London. Data source: US Census. Photo by Matt Gibson. Stock photo ID: 80226988. Shutterstock.

The demographic dividend

While countries with high dependency ratios face a series of challenges, what about those with low dependency ratios? When the bulge in the population pyramid is largest among the working-age cohorts, countries have the possibility of taking advantage of a demographic dividend. The demographic dividend occurs when a country with previously high birth and death rates transitions to one with low birth and death rates. As explained previously, when birth rates fall, the proportion of young people (those at the bottom of the pyramid) declines. The demographic dividend comes about twenty years later, as those born in the last high birth-rate cohort mature to the point where they enter the workforce. When this occurs, the population structure has a low dependency ratio: there are fewer young people, not yet too many elderly, and lots of working-age people. Thus, costs associated with supporting the young and old are minimal. At the same time, a large number of workers can help increase economic output. Essentially, the demographic dividend is a several-decade window of opportunity that countries can use to grow their economies. Taxes can be kept low, allowing for more private investment, or tax revenue can be used for roads, ports, and power plants rather than schools and pension plans.

China is an example of a country that took great advantage of its demographic dividend. In 2000, when China entered the World Trade Organization and became fully integrated into the world economy, its population structure was nearly perfectly placed (figure 2.27). A large cohort of people born in the mid-1960s through 1971 were roughly in their 30s by that time. In turn, this group had given birth to a second cohort that was entering adolescence. By opening up to the world economy, China was able to move large numbers of these people from low-productivity farm work to higher-productivity factory jobs, fueling its rapid economic ascent.

Of course, a demographic dividend cannot last forever. Eventually, the large working-age cohorts reach the age of retirement and leave the workforce. Due to ongoing low birth rates, no new large cohort is able to replace them, resulting in a higher dependency ratio with a large number of elderly, as with Italy (figure 2.26). By 2017, China’s population was reaching this point, which will have major impacts on the economic model that has been driving its growth. The days of placing large numbers of low-skilled workers into factories is coming to an end.

Figure 2.27.The demographic dividend: China. Large working-age cohorts helped drive growth of its global industrial export sector. China took advantage of its demographic dividend by moving large numbers of workers from inefficient farm work to more productive factory work. Photos: Women in Jiujiang, China, working in tea fields. Women in a clothing factory in Huaibei, China. Data source: US Census. Farm photo by Humphery. Stock photo ID: 611411201. Shutterstock. Factory photo by Frame China. Stock photo ID: 390471148. Shutterstock.

A big question remains as to how well other developing countries will take advantage of their demographic dividend. As birth rates fall in Africa and the Middle East, they will face great opportunities and challenges. If the bulge of working-age people can find employment, these countries can make a big push toward development. If they cannot find jobs, unemployment and discontent can rise, and the demographic dividend will be squandered.

The sex ratio

Population structures can also vary in terms of the proportion of men and women. Generally, the proportions are similar, although in older cohorts, women tend to outnumber men. The sex ratio is the ratio of males to females in a population. Due to higher rates of health problems in male infants and the propensity of males to die younger, nature attempts to even out the sex ratio at birth by making a slight preference for males. Under normal circumstances, the sex ratio at birth is 105 males for every 100 females. However, some societies have a strong preference for male babies, resulting in an even greater difference between male and female births. For instance, in India, the sex ratio at birth in 2015 was 111 males for every 100 females, while in China it was 116 to 100.

The “excess” number of males is the result of sex-selective abortion and female infanticide. Pregnant women sometimes choose to abort after ultrasound examination reveals that the fetus is a female, or less commonly, they kill or abandon newborn girl babies prior to registering their births. The ratio can become more skewed toward males even after birth. A preference for male children means that girls suffer malnutrition more than boys and are given less medical care. This results in higher child death rates for girls than for boys. This imbalance can create social stresses due to a surplus of men unable to marry and form families. In some cases, this leads to human trafficking of women, as male “demand” outstrips female “supply” of spouses. There is also evidence that rates of crime, including rape, are disproportionately committed by unmarried men between the ages of 15 and mid-30s.

Figure 2.28 illustrates how sex selection has become more prevalent in recent decades in three highly skewed countries, China, India, and Azerbaijan. Prior to 1980, the sex ratio at birth in each country was just slightly above the natural rate of 105. However, around the 1980s, the rates rose substantially. This was due to better access to ultrasound technology that allowed the sex of fetuses to be determined. Furthermore, as incomes rose, more families could afford to pay for ultrasound scans. Increased access and affordability of ultrasound scans allowed for greater sex-selective abortion.

Figure 2.28.Sex ratio at birth. Data for China, India, and Azerbaijan show a rapid rise in the sex ratio after the diffusion of ultrasound technology. Data source: United Nations.

In countries with highly skewed sex ratios, cultural norms and laws favor males over females, resulting in a preference for sons. For instance, in India, the families of daughters must pay a dowry when she gets married. In addition, once she is married, she often moves in with her in-laws, caring for them rather than her own parents. Women generally cannot inherit property and are not equal guardians of their children. Furthermore, girls often cannot choose who to marry, and families sometimes feel obliged to commit “honor killings” of their daughters who disobey family wishes. Because of these cultural and legal traditions, parents prefer to have sons.

In some cases, skewed sex ratios come not from a preference for males but from immigration. The most highly skewed sex ratios in 2016 were found in the Middle Eastern countries of Qatar and the United Arab Emirates (figures 2.29 and 2.30). In both cases, the population pyramid shows a dramatic increase in the male population ages approximately 20 to 49, which reflects a large number of male foreign workers in each country.

Figure 2.29.Sex ratio and immigration. Data source: US Census.

Figure 2.30.Foreign construction workers from South Asia in the United Arab Emirates. Photo by Rob Crandall. Stock photo ID: 584348938. Shutterstock.

Go to ArcGIS Online to complete exercise 2.4: “Population structure.”

Population theories

Demographic transition

After observing patterns of fertility and mortality, natural increase, population structure, and more, we want to know why these patterns vary over time and place. The demographic transition model offers a useful explanation by describing how places move through four demographic stages (figure 2.31). In each stage, there are changes in fertility and mortality, which result in differing population growth rates and age structures.

Figure 2.31.The demographic transition model. Image by author.

Stage 1: Preindustrial society

Preindustrial societies are in stage 1 of the demographic transition. From hunter and gatherer societies through agricultural societies, birth rates and death rates tend to be high. Birth rates are high because children are economic assets, helping with hunting, gathering, and farming from an early age. Likewise, children serve as insurance for their parents. They are their “pension plan,” caring for them in their later years, and can also mitigate risk by working for others if output on the family farm is limited. A large number of children are also desirable due to high infant mortality rates. Families must have enough children who survive into adulthood and can, in turn, have their own children and ensure survival of the group. For these reasons, numerous cultures developed that bestow status and prestige on parents with many children and shame those with few children. Gender roles also have a significant impact on birth rates. Stage 1 societies tend to be culturally conservative, where women’s roles are restricted to that of mother and helping with local village tasks. As part of this role, women marry and become mothers at a young age. Given the restricted role that women play in these societies, there is often a preference for sons. Having at least two sons is often the goal of parents to ensure that at least one survives to care for and support the family. Thus, women need, on average, 4.1 children to get two boys. With a reality of high infant mortality rates, an even larger number of births is often required.

Death rates also tend to be high in stage 1 societies due to disease and unstable food supplies. A lack of proper nutrition, along with limited calorie intake in hard times, results in many deaths, especially among infants and young children. At the same time, communicable diseases kill many children due to a lack of medical knowledge. Diseases that are spread by other humans, animals and insects, and unsanitary water—such as influenza, cholera, malaria, and plague—ensure that death rates remain high.

In stage 1, population growth (natural increase) remains flat, or close to zero, given that both birth rates and death rates are high. This was the situation for most of human history.

Stage 2: Early urban-industrial societies

Societies enter stage 2 as they begin to industrialize and urbanize. In this stage, birth rates remain high, but death rates begin to fall substantially for several reasons. With industrial and urban development, agricultural technology leads to more stable food supplies and thus better nutrition and calorie intake. Furthermore, knowledge about germs improves, and the development of public health programs expands. Clean water and sewer systems are gradually built, which reduces waterborne disease. Meanwhile, rodent and insect controls are put in place, and the use of soaps and disinfectants is promoted. In addition, medical technology improves and accessibility diffuses to a larger proportion of the population.

For instance, the smallpox vaccine was widely used by the early 1800s, resulting in many fewer deaths from that deadly disease. Also in the 1800s, English physician John Snow linked a water well to an outbreak of cholera, using spatial analysis to show that the disease was waterborne and not airborne (figure 2.32). His discovery contributed to the eventual development of urban water and sewer infrastructure.

However, during stage 2, birth rates remain high as people maintain the cultural tradition of having large families both in rural areas and in expanding urban areas. Cultural traditions that limit the role of women and esteem large families and deride small ones still influence family size. Furthermore, a preference for sons pushes parents to keep having children until at least two boys are born.

With a large difference between births and deaths, natural increase rises and population growth increases significantly in this stage. Countries in stage 2 see a boom in population growth.

Figure 2.32.By studying the spatial relationship of cholera deaths and water-well pumps, John Snow showed that the disease was waterborne. Map by John Snow is in the public domain.

Stage 3: Ongoing urban-industrial development

With further industrialization and urbanization, societies enter stage 3. In this stage, births fall significantly (figure 2.33). This occurs for several important reasons. First, lower infant mortality rates mean that fewer children are needed to ensure that some survive to adulthood. Second, industrialization and modernization make contraceptives accessible to a larger share of the population. But most important, women want to use contraceptives by stage 3. In contrast with agricultural societies, children in industrial societies are more of an economic liability. They cannot contribute to the household until they are much older, as urban industrial jobs require more physical strength or education. Also, housing is more crowded and expensive in the city than on the farm. Lastly, women have more options and opportunities as societies industrialize and modernize. Whereas a woman on the farm in stage 1 or 2 is largely limited to having children and caring for the house, she has many more options in the city. Traditional gender roles typically loosen as time passes in urban areas and preferences for sons weaken. Women attend school for more years, and their role in the paid workforce grows. As women study and work more, they delay marriage and childbirth until they are older, thus reducing the total number of children they have.

Figure 2.33.Economic and social change leads to smaller family size as societies transition from rural-agricultural to urban-industrial. A large rural family in Nannilam, India. A small urban family in Delhi, India. Photo of large rural family by Ashok India. Stock photo ID: 490150441. Shutterstock. Photo of small urban family by Paul Prescott. Stock photo ID: 52607926.

Death rates in stage 3 continue to decline, although at slower rates than in stage 2, as medical care and nutrition improve. Better treatments for communicable diseases are developed—for example, antibiotics became widely used around the 1940s—while new treatments for degenerative diseases such as heart disease also become more widely available.

As the difference between births and deaths narrows, natural increase slows and population growth becomes moderate.

Stage 4: Modern urban society

Stage 4 consists of modern urban societies. In this stage, both birth rates and death rates are low. Births are low for the same reasons as in stage 3 but by stage 4 have diffused to a larger proportion of the population. Low infant mortality rates guarantee that nearly all children will survive to adulthood, and contraceptives are widely available. Child-rearing is focused more on quality than quantity, as parents have few children but invest great amounts of time and money in their upbringing. Opportunities for women other than motherhood abound. Education levels for women are high, with most finishing high school and many continuing with higher education. This translates into generally high levels of female workforce participation. As women study and work more, they further delay marriage and childbirth to later ages and limit the number of children they have. This can be seen in the median age at first marriage in the United States. While women married at age 20.3 and men married at 22.8 in 1960, by 2010, the age had risen to 26.5 for women and 28.7 for men. Japan and other stage 4 countries similarly have average ages of first marriage around 30 years old.

Likewise, death rates are low due to the availability of modern medicine and ample food supplies. Deaths from communicable disease become less common, while the focus for new medical treatments shifts to degenerative diseases such as heart disease and cancer that are associated with old age.

In stage 4, as in stage 1, births and deaths are about equal, and natural increase approaches zero and population growth is small. However, in this case, both rates are low rather than high.

Stage 5: A new stage of population decline?

Some geographers now discuss a fifth stage to the demographic transition as a handful of countries move toward birth rates that are lower than death rates, with ensuing negative natural increase. Interestingly, while birth rates are universally low in modern, urban stage 4 countries, societies moving into a stage 5, with extremely low birth rates, may have some unique characteristics. In very-low-birth-rate countries, it is more common for women to have little help in raising children. Southern European countries such as Italy, as well as wealthy Asian countries such as Japan, tend to have limited state-run programs, including low-cost day-care and after-school programs that allow women to have children and pursue their careers. At the same time, men in these societies tend to help less with child-rearing, leaving the burden on mothers. It seems that when women get little help from the state and little help from fathers, they tend to have well under an average of two children. In stage 4 countries with family-friendly government programs and more engaged fathers, women tend to have slightly more children, since they can more easily be mothers and pursue their careers. France and the countries of Scandinavia fit this description.

Go to ArcGIS Online to complete exercise 2.5: “The demographic transition in Latin America.”

Malthusian Theory

Thomas Malthus presented a theory in the eighteenth century linking resources and population growth. Malthusian theory holds that populations will grow at a faster rate than food supply. Once population outstrips food, famine and disease will cause populations to collapse (figure 2.34). This theory contrasts significantly with the demographic transition model, which states that population growth declines as urbanization and modernization cause people to choose to have smaller families.

Figure 2.34.Malthusian theory. Image by author.

Whereas Malthus’s theory was developed in the eighteenth century, his predictions have yet to be fulfilled by the twenty-first century. According to the World Health Organization, calories consumed per person have increased, not decreased, over time (figure 2.35). Malthus did not account for increases in agricultural productivity that allow production of more food per acre. Nor did he predict the expansion of agricultural land into arid regions and regions with poor soils through the use of irrigation and fertilizers (figure 2.36).

However, this does not mean that global hunger has been conquered. Whereas food supply has grown at a more rapid rate than population, hunger exists due to natural disasters and drought, disruption of food production and distribution from war, poverty, limited distribution due to poor agricultural infrastructure, and overexploitation of the environment. More on food and hunger is discussed in chapter 6.

Figure 2.35.Despite what many may believe, there are more calories per person now than in the past. This is in spite of ongoing population growth. In essence, our ability to produce food has grown faster than human populations. Data source: Food and Agriculture Organization of the United Nations.

Figure 2.36.Center pivot irrigation in Saudi Arabia. Malthus did not foresee such advances in agricultural technology. Explore this image at http://arcg.is/2ioJJ63. Data sources: World Imagery—Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community.

Neo-Malthusians concede that the world has not yet run out of food but say that it is still a possibility. Human populations continue to grow, especially in developing regions such as Africa, the Middle East, and South Asia. This growth will continue to put strains on food supplies, which will have to grow at least at the same rate. At the same time, more people will be using more resources, such as energy that fuels the agricultural sector. Not only that, but as societies grow richer, people’s diets change as they demand more meat. Producing meat uses more resources in terms of land and energy than does farming plant-based food. For these reasons, some people say that Malthus ultimately will be correct.

Government population policy

The demographic transition model and Malthusian theory do not explicitly discuss the role of governments in population growth. Nevertheless, in some cases, governments try to manipulate population change within their borders, typically through interventions in fertility.

Fertility control

Governments use a variety of means to help women control their fertility. This is often seen as an interest of the state, since uncontrolled births and rapid population growth can pose a threat to economic prospects and put a drain on government services.

Developed countries such as the United States and those of western Europe often have government programs that provide access to birth control. When women can control their fertility more easily, they can devote time to education and careers prior to starting their families. Developed countries benefit from the effect of controlled fertility because an educated workforce is more productive and innovative than an uneducated workforce. Lower-income countries may also want to give women the option of postponing birth, but limited resources make effective birth control less widely available.

Governments in some lower-income countries have taken drastic steps to limit fertility and in some cases have been accused of violating human rights in the process. In the case of India, sterilization programs, beginning in the 1970s but still widespread today, have been an important component of government-sponsored family planning. Critics of this program accuse officials of coercion by using cash incentives and pressure from government family-planning workers to get poor men and women to be sterilized. The process involves health risks as well. In 2014, eighty women in a rural village were sterilized by one doctor and his assistants during a three-hour period. Thirteen of the women died and dozens more were sickened due to insufficient cleaning of surgical instruments. In many cases, women do want to limit family size, but a lack of alternatives makes sterilization the only option. The Indian Ministry of Health and Family Welfare reported in 2016 that contraceptive use among married women ages 15 to 49 years was 56.3 percent, which leaves a substantial portion of women without contraceptives.

Interestingly, male vasectomies are a much quicker and safer means of sterilization but are less commonly promoted in many less-developed countries. Many men see family planning as a woman’s issue and are less likely to voluntarily be sterilized. There are efforts to promote vasectomies, however, including DKT International, the nonprofit organization that organizes World Vasectomy Day.

In China, a state-mandated one-child policy was implemented in 1980. This program also led to coercive policies in some cases. Couples were offered financial incentives and preferences in employment if they had only one child, and those who violated the policy were fined. In some cases, women were forced to have abortions or to be sterilized. Some parents who chose not to pay the fine for a second child were denied documentation of the birth, thus making their child “invisible” from a legal standpoint and unable to get identification, enroll in school, or find employment.

Despite being a rather draconian policy, according to a 2010 report, the one-child policy likely applied to less than 40 percent of China’s population. Rural residents and ethnic minorities could typically have more than one child, while some urban residents ignored the ban, choosing to pay penalties and fines.

As pointed out previously in this chapter, with fewer children, China’s population structure has been aging. China’s ability to be the factory floor of the world economy depends on a large workforce—a workforce that will shrink in coming decades. And remember, an aging population also puts pressure on health-care systems and pension funds. China may well become an “old” country before it becomes rich enough to cover age-related expenses. For these reasons, in 2016, China began to allow families to have two children.

It should also be mentioned that in some countries, there is tension between state family-planning programs and conservative cultural forces. For instance, in the Philippines, a country with a very conservative and influential religious establishment, a family-planning law passed in 2012 was held up in court for two years due to lawsuits despite that the country is poor with a high CBR and high maternal mortality rates. With 72 percent of the population in support of the family-planning law, it was eventually upheld by the Philippine Supreme Court, allowing for free contraceptives at government health clinics, sex education in schools, and medical care for women who have had illegal abortions.

Pronatal policies

As seen in the TFR map (figure 2.11), many countries now have rates below the replacement level of 2.1. In some cases, the number is well below replacement level. As stated previously, with low birth rates, population structures skew toward the elderly, causing a shortage of working-age people and financial strains on pension and health-care systems. Consequently, some countries now promote childbirth through pronatalist policies.

Pronatal policies come in many forms but most often are designed to reduce the costs of child-rearing and promote family-friendly labor laws. In Singapore, cash “baby bonuses” help offset the cost of giving birth; in the United States, child tax-credits help offset the costs of raising children; in France, families are paid a cash child benefit for every child after the first.

While the cost of raising children keeps some couples from having more, negative impacts on parents’ careers also contribute to lower fertility rates. This is especially important for women, who tend to bear the greatest burden of caring for children. Paid maternity leave and, in some cases, paternity leave allow parents to care for newborns without having to sacrifice income (figure 2.37). Both rich and poor countries frequently offer paid leave for mothers. For instance, Kenya and Afghanistan offer thirteen weeks at 100 percent pay, while Germany offers fourteen weeks and Greece offers seventeen. Paternity leave is less common or typically of shorter duration. Kenya offers two weeks at 100 percent pay, while Afghanistan, Germany, and Greece offer none. It must be noted when viewing these benefits that not all mothers and/or fathers qualify. In lower-income countries, few people have formal employment contracts with legal benefits. Therefore, although Kenya and Afghanistan offer generous maternity leave, few women take advantage of it. In more affluent countries, such as those of Europe, most people do have formal employment contracts, so parental leave can be used by most workers (figure 2.38).

Perhaps more important than one-off baby bonuses and parental leave, benefits such as free or subsidized child care, government incentives for companies to provide on-site child care, and low-cost after-school programs can reduce the ongoing burden of balancing careers with raising children.

Pronatalist policies can help increase the TFR in some cases, but regardless of the effort of governments, culture plays a role as well. TFRs in France, Norway, and Sweden are higher than those in Italy, Greece, and Japan. This is partially because fathers help more with child-rearing in the first set of countries than in the second set. Even with government benefits, when fathers help less with child-rearing, women can feel they are left to choose between being stay-at-home moms and pursuing their career. In many cases, they choose the latter, which means having one or possibly no children.

Overpopulation and carrying capacity

Discussions of population growth and population distributions often turn to the idea of overpopulation. Overpopulation is a commonly used term, but the concept can quickly become contentious on closer inspection. For most people, it means there are too many people in a given place. Sometimes the concept is used in terms of a city or country, while other times it is seen in global terms, focusing on the question of how many people planet Earth can support. The concept can become contentious because defining “too many” people is very subjective. When stuck in traffic on the freeway, people may say their city is overpopulated. If housing is too expensive or jobs are scarce, people may say the same thing. But in these cases, it can also be argued that the problem is not too many people but too little public transit, too little housing, or not enough economic growth (figure 2.39).

Figure 2.37.Weeks of maternity leave. Many countries offer paid maternity leave to help women balance having children with remaining in the paid workforce. Explore this map at http://arcg.is/2laPTHp. Laura Addati, Naomi Cassirer and Katherine Gilchrist. 2014.

Figure 2.38.Percentage of women with maternity leave coverage. Especially in poorer countries, paid maternity leave covers only a small fraction of women, since few have formal employment contracts with benefits. Explore this map at http://arcg.is/2laTAwH. Laura Addati, Naomi Cassirer, and Katherine Gilchrist, 2014.

The definition of overpopulation can be limited to the concept of carrying capacity: the number of people that can be supported by the land they live on. This can be seen in terms of land for food, clean air for breathing, and clean water for drinking. But this definition also leads to problems. First, as discussed earlier in this chapter, human populations can be very dense without being overpopulated and, via trade, can exceed their local carrying capacity. Food and water can be brought in from far away to support millions of people. For instance, Los Angeles, California, gets the majority of its water via canals from the northern parts of the state and the Colorado River, and most food is imported from other parts of California, the United States, and the world. Thus, the concepts of overpopulation and carrying capacity depend a great deal on infrastructure, technology, and economic development. Places with sufficient infrastructure, technology, and economic development can use resources from around the world and support massive, dense populations. Places without these advantages can more easily be considered overpopulated because their populations face shortages of food, water, and other resources. There is no fixed population density that can be considered an overpopulation threshold.

Figure 2.39.Is the world overpopulated? Defining the concept can be difficult to agree on. Crowds in Times Square, Manhattan. A lone farmhouse in Tuscany, Italy. Times Square photo by Andrew F. Kazmierski. Stock photo ID: 36538936 Shutterstock. Tuscany photo by Shaiith. Stock photo ID: 141591835. Shutterstock.

Although overpopulation is difficult to define at a local scale, many argue that at a global-scale population will eventually outstrip the earth’s carrying capacity. A 2012 report by the United Nations Environmental Program states that most estimates of the earth’s carrying capacity fall within a fairly wide range of eight to sixteen billion people. As of 2015, the United Nations median estimate for total world population by the year 2100 was 11.2 billion people, a point where population growth may level out.

Earth’s carrying capacity will depend on several factors. One is how much technology can help humans use resources more efficiently. If goods and services can be produced with less energy and material inputs, then the world will be able to support a larger number of people. One reason Malthus has not yet been proven correct is that technological innovations allow more food to be produced with less land, labor, and capital resources. Another factor is the global standard of living. If all 11.2 billion people in the year 2100 live at the same standard of living that Americans do today, then there will be a much greater strain on resources. Consumption of energy and other resources per capita is much higher in the United States and other rich countries than in less developed countries. Technological innovation will have to improve efficiency dramatically to support increased levels of consumption as people get richer.

References

Addati, Laura, Naomi Cassirer, and Katherine Gilchrist. 2014. Maternity and Paternity at Work: Law and Practice Across the World. Geneva, Switzerland: International Labour Organization.

Carberry, S. 2014. “An Afghan Success Story: Fewer Child Deaths.” NPR. http://www.npr.org/2014/02/04/269551459/an-afghan-success-story-fewer-child-deaths. February 4.

Centre des Liaisons Européennes et Internationales de Sécurité Sociale. 2018. “The French Social Security System IV—Family Benefits.” Centre des Liaisons Européennes et Internationales de Sécurité Sociale. http://www.cleiss.fr/docs/regimes/regime_france/an_4.html.

“The Child in Time.” 2010. The Economist, August 19, 2010. http://www.economist.com/node/16846390?story_id=16846390&CFID=145777375&CFTOKEN=91195822.

Cohn, D’Vera, Jeffrey S. Passel, Wendy Wang, and Gretchen Livingston. 2011. “Barely Half of US Adults Are Married—A Record Low.” Pew Research Center’s Social & Demographic Trends Project. http://www.pewsocialtrends.org/2011/12/14/barely-half-of-u-s-adults-are-married-a-record-low.

DeSilver, D. 2015. “Refugee Surge Brings Youth to an Aging Europe.” Factank: News in the Numbers, October 8, 2015. Pew Research Center. http://www.pewresearch.org/fact-tank/2015/10/08/refugee-surge-brings-youth-to-an-aging-europe.

De Soto, H. 2018. “The Capitalist Cure for Terrorism.” Wall Street Journal, October 5, 2018. https://www.wsj.com/articles/the-capitalist-cure-for-terrorism-1412973796.

Food and Agriculture Organization of the United Nations (FAO). 2017. “Country Brief on Egypt.” GIEWS—Global Information and Early Warning System. http://www.fao.org/giews/countrybrief/country.jsp?code=EGY.

International Labour Organization. 2015. Global Employment Trends for Youth 2015: Scaling Up Investments in Decent Jobs for Youth. https://www.ilo.org/wcmsp5/groups/public/—dgreports/—dcomm/—publ/documents/publication/wcms_412015.pdf.

Kimpton, D. 2014. “75 Million Catholics Just Got Free Birth Control.” VICE News, April 9, 2014. https://news.vice.com/article/75-million-catholics-just-got-free-birth-control.

McFalls, J. A. 2007. Population: A Lively Introduction, 5th ed. Washington, DC: Population Reference Bureau.

Omran, A. R. 2005. “The Epidemiologic Transition: A Theory of the Epidemiology of Population Change.” The Milbank Quarterly 83, no. 4: 731–57. doi: http://doi.org/10.1111/j.1468-0009.2005.00398.x.

Organisation for Economic Co-operation and Development (OECD). 2014. Ageing and Employment Policies—Statistics on Average Effective Age of Retirement. Paris: OECD Publishing.

Pletcher, K. 2016. “One-Child Policy.” Encyclopædia Britannica. https://www.britannica.com/topic/one-child-policy.

Population Reference Bureau. 2015. “2015 World Population Data Sheet.” Population Reference Bureau Data Sheet. https://assets.prb.org/pdf15/2015-world-population-data-sheet_eng.pdf.

Samman, E., E. Presler-Marshall, and N. Jones. 2016. Women’s Work: Mothers, Children and the Global Childcare Crisis. London: Overseas Development Institute. https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/10333.pdf.

Singh, K. 2013. Laws and Son Preference in India—A Reality Check. New Delhi : United Nations Fund for Population Activities. https://www.unfpa.org/sites/default/files/jahia-news/documents/publications/2013/LawsandSonPreferenceinIndia.pdf.

Timmons, A. T. H. 2013. “India’s Man Problem.” New York Times, January 16, 2013. https://india.blogs.nytimes.com/2013/01/16/indias-man-problem/?_r=2.

United Nations. 1999. The World at Six Billion. New York: United Nations. https://www.un.org/esa/population/publications/sixbillion/sixbillion.htm.

———. 2015. The World Population Prospects: 2015 Revision. New York: United Nations. https://www.un.org/en/development/desa/publications/world-population-prospects-2015-revision.html.

———. 2017. “Sex Ratio at Birth.” UN Data. http://data.un.org/Data.aspx?q=Sex+Ratio+at+Birth&d=PopDiv&f=variableID%3a52.

United Nations Children’s Fund (UNICEF). n.d. “Health: Ending Preventable Maternal, Newborn, and Child Deaths.” UNICEF Afghanistan. https://www.unicef.org/afghanistan/health_nutrition.html.

United Nations Environment Programme. 2012. “One Planet, How Many People? A Review of Earth’s Carrying Capacity.” UNEP Global Environmental Alert Service (GEAS). https://na.unep.net/geas/archive/pdfs/geas_jun_12_carrying_capacity.pdf.

Walker, J. 2014. “Instruments ‘Rusty’ at Indian Sterilization Camp Where 13 Women Died.” The Huffington Post, November 12, 2014. http://www.huffingtonpost.com/2014/11/12/india-sterilization-deaths_n_6143894.html.

Weeks, J. R. 2016. Population: An Introduction to Concepts and Issues. Boston, MA: Cengage Learning.

Williams, S. C. P. 2013. “Gone Too Soon: What’s Behind the High U.S. Infant Mortality Rate.” Stanford Medicine 30, no. 3: 12–15. http://sm.stanford.edu/archive/stanmed/2013fall/article2.html.

Wolchover, N. 2011. “Why Are More Boys Born than Girls?” Live Science, September 9, 2011. http://www.livescience.com/33491-male-female-sex-ratio.html.

World Food Programme (WFP). n.d. “Zero Hunger.” http://www1.wfp.org/zero-hunger.

World Health Organization (WHO). n.d. “Global and Regional Food Consumption Patterns and Trends.” http://www.who.int/nutrition/topics/3_foodconsumption/en.

———. 2016. “Life Expectancy Increases by 5 Years, But Inequalities Persist.” Press release. http://www.who.int/mediacentre/news/releases/2016/health-inequalities-persist/en.

Worldometers. n.d. Current World Population. Accessed April 25, 2017. http://www.worldometers.info/world-population.

Yüksel-Kaptanoğlu, I., M. A. Eryurt, I. Koç, and A. Çavlin. 2014. Mechanisms Behind the Skewed Sex Ratio at Birth in Azerbaijan: Qualitative and Quantitative Analyses. Baku, Azerbaijan: United Nations Population Fund.