Contrasts in Development

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Contrasts in Development

There are many different ways which are used to try to determine how developed a country is. These are called indicators of development and they can be easily compared using scatter graphs or the Spearman's Rank Correlation to show how they affect development. Examples of many of the most common indicators are outlined in this section.

The North-South divide

The most common indicator of development is to look at the wealth of a country, and compare it to others. This is done by calculating the Gross National Product (GNP) of a country. The GNP is the total value of goods and services produced in a country as well as its gain from overseas investment. It can also be calculated on a per capita basis by taking all sources of income and dividing it by the total population. It is always calculated in US dollars so that you can easily compare countries.

Using GNP an alternative map of the world can be created, showing the developed and developing countries. There are many different ways of describing these countries. Developing countries used to be known as the "Third World" and commonly are called LEDC's (Less Economically Developed Countries). Developed countries used to be called the "First World" or MEDC's (More Economically Developed Countries).

However another way of describing them is to divide them up as North (the developed countries) and South (the developing countries).

Using GNP per capital, a distinct North-South divide can be seen, and this is shown on the map below:

As you can see the "North" does not mean the Northern Hemisphere (this is a common mistake that people make). Although most of the countries in the "North" are in the Northern Hemisphere, countries like Australia and New Zealand are most definitely not.

Just using GNP however does not always give an accurate picture of how developed a country is. Other indicators of development can be used to identify social,economic and environmental differences between countries that will affect their standard of living.

Many different indicators can be used to assess the development of a country. Some of the most common are listed here:

- Infant mortality rate (per 1000): The number of children who die before they are 1 year old, measured per 1000 born. You would expect a less developed country to have a high rate due to poorer diet and health care. Example countries: UK = 6; Mozambique = 123)

- Life expectancy (years): The average age that someone living in that country will live to. You would expect it to be highest in the more developed countries, where there is better access to health care and a better diet. Example countries: UK: Male = 74, Female = 79; Mozambique: Male = 44, Female = 46)

- Daily calorie intake: The amount of food eaten by a single person on average. There is a recommended daily calorie intake for an adult which is not reached by many developing countries, especially in rural areas.

- Population per doctor: The total population divided by the number of doctors in the country. Example countries: UK = 300; Mozambique = 33,333)

- Adult literacy (%): The percentage of the population who are literate (in other words they can read and write). Example countries: UK = 99%; Mozambique = 37%)

- Percentage of GNP spent on education (%): The amount of money spent each year on education, as a percentage of the total wealth of the country. This can be sometimes a rather mis-leading figure though, as you can see from the example. The amount of money spent on education is this country is far more than that spent in Mozambique, however it is a smaller percentage ofthe overall wealth of the country. Example countries: UK = 5.3%; Mozambique = 6.3%)

- Percentage working in agriculture (%): A less developed country would be expected to have a far higher percentage of people still working in agriculture, mainly as subsistence farmers, growing only enough for them and their family. A more developed country would have far more technology in farming, and therefore less workers, as well as having far more people working in the manufacturing and service industries. Example countries: UK = 2%; Mozambique = 85%)

- Percentage living in urban areas (%): As countries develop, there tends to be a mass in-migration into the cities, causing rapid urban growth. Therefore you would expect a more developed country to have a higher percentage of people living in the urban areas. Example countries: UK = 90%; Mozambique = 32%)

- Access to clean water (%): In Britain, we take clean, safe water for granted, but that is not the case in many of the less developed countries of the world. This can lead to outbreaks of diseases such as cholera, dysentry and typhoid.

The Human Development Index:

The Human Development Index was devised by the United Nations in 1990 and uses a number of indicators of development to give each country in the world a development score. The score ranges from 0 to 1, with 1 being the most developed. No country has reached a score of 1, although some, such as Japan and Canada have attained marks well over 0.9.

The indicators of development used in the index are:

  • Literacy
  • Life Expectancy
  • The GNP per person, adjusted to take into account the cost of living in that country.

The HDI is a more effective measure than just using GDP, as it brings in social considerations also. However it still has problems because it does not show any of the regional differences within a country.

Scatter graphs:

It is easy to compare two different indicators of development, using the figures for a number of different countries to plot a scatter graph. Scatter graphs show whether there is any connection between the two sets of figures. This is called a correlation and it can be either positive or negative.

The graph below shows the comparison between life expectancy and population per doctor. The figures for five countries have been plotted and a "line of best fit" drawn in. This slopes down from left to right, meaning that the figures show a negative correlation.

The graphs below show more clearly what positive and negative correlations are:

  • A positive correlation slopes up from the left axis to the top right hand corner of the graph. This tells us that if one of the indicators increases, for instance the GNP, then the other (literacy rate perhaps) will also increase.
  • A negative correlation slopes from the left axis down towards the right of the graph. This tells us that if one indicator increases, for instance the number of people per doctor, the other indicator will fall (in this case the life expectancy).

Spearman's Rank Correlation Coefficient:

This is a statistical analysis of two indicators of development to find their correlation. It results in a figure between -1 and 1. A score close to 1 shows a strong positive correlation. A score close to -1 shows a strong negative correlation, whilst a score around 0 shows there is little correlation at all between the indicators.

The method for working out a Spearman's Rank Correlation Coefficient is as follows:

  • Draw a table similar to the one below, including the figures for the two indicators of development that you want to compare.
  • Rank both lists of figures, best score being 1, the next being 2 and so on.
  • In the next column work out the difference between the ranks for indicator A and indicator B. This is called d.
  • In the final column square the difference between the ranks that you wrote in the last column. This is called d2.
  • Add up the d2 column, to give you a total figure. This is called Σd2.
  • Now you can work out the correlation coefficient using the correlation coefficient equation. In it n is the number of countries that you have used in your table, and R is the correlation coefficient.

Worked example: Average income and percentage in agriculture:

Country Average Income ($) Rank A % in agriculture Rank B Rank A - Rank B (d) d2
Brazil 3,640 5 25 6 -1 1
Ecuador 1,390 8 33 5 3 9
France 24,990 3 6 8 -5 25
Japan 39,640 1 7 7 -6 36
Kenya 280 9 81 2 7 49
Mozam-bique 80 10 85 1 9 81
Thailand 2,740 7 67 3 4 16
Turkey 2,780 6 47 4 2 4
UK 18,700 4 2 10 -6 36
USA 26,980 2 3 9 -7 49

The totals:

N = 10

Σd2 = 1 + 9 + 25 + 36 + 49 + 81+16 + 4 + 36 + 49

= 306

The Equation and working:

R = 1 - (6 xΣd2/n3 - n)

R = 1 - (6 x 306/1000 - 10)

R = 1 - (1836/990)

R = 1 - (1.855)

R = - 0.855

The final result shows us that there is a strong negative correlation between average income and the percentage of the population working in agriculture.