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