Discuss the usefulness of at least two population measures as indicators of development
Discuss the usefulness of at least two population measures as indicators of development.
The infant mortality rate (I.F.M) measurement is somewhat useful as an indicator of
development. I.F.M is measured as the number of children that die before the age of 1 per
1,000 live births per year. This age group is particularly vulnerable so high rates can indicate
limited resources for ante-natal and post-natal care, vaccinations and medical care of a
specialist nature. This could highlight that a country is poorer where there are high levels of
I.M.R. Moreover, access to clean water is limited in areas with high rates so disease spreads
and attacks the very young. Countries that are poor and prone to natural disasters often
have a high I.M.R, as do countries where HIV/AIDS is prevalent. However, this gives a
distorted view of levels of development as infant mortality rate may be very high as a result
of the spread of disease and may not, therefore, reflect the development as accurately.
The fertility rate is to an extent a useful indicator of development. Fertility rate is the
number of live births per 1,000 women aged 15-49 in 1 year. It is also defined as the number
a children each woman in a population will bear. If the number is 2.1 or higher, a population
will replace itself. We tend to see higher rates of fertility in LEDCs. This could be due to the
higher I.M.R rates in these countries which indicates where there is more risk of losing a
child, there is an incentive to have more children. It is also indicative of lower levels of
education for women, where childbearing is regarded as a woman’s main role. Also in
MEDCs, we see a rise in materialism and individualism which leads to lower fertility rates.
However, this may again be distorted as we have seen that more wealth has sometimes
encouraged higher fertility rates such as the ‘baby boom’ in the UK after WWII.
It may be that population measures are more useful as indicators of development when
they are compared alongside one another in a correlative manner. Development is arguably
more complex than splitting ‘richer’ countries from ‘poorer’ ones, but statistics can have
usefulness where patterns emerge.