One of the most useful things we can do to understand development and how it changes over space from place to place is map it! There are many different things that we could map to show how development varies from place to place, and we could map them at different scales. The most common way to map data or information is to use a choropleth map, which is basically a coloured in map where the colours represent certain values. We could map the number of people per doctor, the literacy level of a place, the number of calories consumed, the number of mobile phones in a population, the average wage, the number of AIDS sufferers etc. to see how these features of a population vary over space.
For a long time Geographers have been trying to CLASSIFY or put into groups countries around the globe with similar characteristics. This has been particularly relevant to development, and these characteristics have changed over time.
First, second, and third worlds
One of the earliest classifications was a 3 fold division used by the United Nations for the first time in 1945.
1. The First World included mainly capitalist free-market countries found in Western Europe and their old colonies such as the USA and Australia.
2. The second world comprised centrally planned, socialist or communist countries. These countries had different structure to those of the first world and had much more government control of business and public services. The second and first worlds were at odds for decades during the cold war.
3. The third world comprised the least developed countries and developing countries.
This division had a bias towards the democratic first world and hid huge differences between countries in the third world.
The North-South Divide
The North-South Divide is a division that exists between the wealthy developed countries, known collectively as "the North", and the poorer developing countries (least developed countries), or "the South." The divide was part of a report by Brandt on the state of world development in 1971 and classified countries broadly as economically wealthy manufacturing countries (the North) or agricultural (the South).
1. Is home to four of the five permanent members of the United Nations Security Council
2. Has all members of the G8, the group of the 8 most powerful nations/economies on Planet Earth
3. Has enough food and water for 95% of its population
4. Has 95% of people with access to a functioning education system.
5. Controls four fifths of the world income.
6. Owns 90% of the manufacturing industries
This distinction has fallen out of favour because;
• It is too simple – large variations in wealth are hidden in both the rich North and poor south
• It is geographically incorrect – Australia and New Zealand are geographically south but included in the North, whilst more poor countries that make up the South are above the Equator than below it!
• Development changes over time –the BRIC economies of Brazil, India and China (but not Russia as it was already north of the line) have grown massively since the map was made
• Economies have become more varied that manufacturing and agriculture.
The five - fold division based on wealth
1. Rich industrialising countries e.g. UK, USA, Japan, Australia, etc.
2. Oil Exporting countries e.g. UAE.
3. New Industrializing countries e.g. India, China.
4. Former centrally planned economies (previous communist systems) e.g. Russia.
5. Heavily indebted poor countries e.g. Chad, Congo.
This is a more recent method designed to try and classify countries and their level of development. It makes a better distinction between countries of lower levels of development and accounts for different reasons for wealth. It also takes into account the variable success of the formally centrally planned economies such as Russia. The UN also has a program for LDCs, the Least Developed Countries.
Generally, we would expect many of the indicators to be correlated together. The expression “correlated” means that they should link to one another and affect one another.
For example, in a country with high Gross National Income we would expect a high number of Internet users as the country has the money available for a high quality cable network. Similarly, we might expect countries with Low Gross National Income to have high numbers of people per doctor as it is expensive to train and pay doctors, and to pay for the facilities they would require.
There are 2 examples below dealing with the following measures of development
• Gross Domestic Product is the total market value of goods and services that a country produces in a year per person. This is measured in US$.
• Life Expectancy is the length of time the average person in a country can expect to live for.
• Infant mortality is the number of babies who do not survive past the age of 1 year old for every 1000 live births in a population.
The graph reveals a POSITIVE correlation that is reasonably STRONG. This is because the points are close to the line and as one variable goes up so does the other. This means that as GDP goes up so does the average age a person can expect to live to. This makes logical sense, in countries with high GDP such as the USA there are very good food distribution systems, clean water, education facilities and excellent medical care. All of these features maximize people’s chances of living a long and healthy life. Sadly, in poorer countries such as Burkina Faso their lack of wealth and high levels of debt mean that they cannot afford the same things as the USA, resulting in a lower life expectancy.
The scatter graph above shows a NEGATIVE correlation between the 2 variables. Here, we can see that as the GDP per capita (person) goes up, the infant mortality falls rapidly. In Burkina Faso, a huge infant mortality of 81 infants under 1 dying before the age of 1 in every 1,000 live births is a tragedy. It is a reflection of their status as a Highly Indebted LDC, which cannot afford decent maternal care, vaccines and medical care for newborn infants. This will not be the case in richer countries such as the UK and Japan.
Not all variables will be linked together in this way, but the majority will be, and there will always be countries that disrupt the trend. However, when correlating data this way some distressing patterns emerge for the world’s poorest countries which have;
• The highest infant mortalities
• Shorter life expectancies
• Lower calorie intake per person
• Higher maternal death rates
• Higher incidents of HIV, AIDS and Malaria
• Lowest access to safe drinking water
• The highest % of people undernourished
• The lowest per capita incomes
• The poorest literacy rates and shortest educations
This is where Aid and fairer trading can really make a difference to the poorest people in the world, who have been dubbed the “bottom billion”.