An introductory class session to introduce the dimensions of development
- Peter Smith
- University of Southampton
- Published August 2010
This case study is adapted from materials shared on the TRUE wiki for Development Economics. Dr Smith's supporting materials are available under a Creative Commons Attribution-Noncommercial licence via the link below.
On a typical Development economics module at a UK university, the audience will be a mixed group made up of some students from developing countries and others who have never visited a less developed country. Some may have clear views of what is meant by “development”; others will have a much more tenuous grasp of the idea.
This session is designed to be held very early in the module; it aims to get students thinking about what is meant by “development”, and to emphasise the diversity of what are known as developing countries. Students who participate in the exercise should go away with a firmer grasp on what is meant by development, and of the importance of being aware of diversity. In particular, any attempt to devise a policy for development for a country must be tailored to the particular configuration of problems, resources and characteristics of that country.
The session requires some prior preparation, and can fit into a 45-minute teaching slot (although a bit longer is always helpful). The structure of the discussion is also good at the start of the module, as it gets the students to talk to and work with each other.
You need to choose a range of countries at different GDP per capita levels, and collect data on key variables. When I conducted this session in 2010, I used 16 countries: Chile, Ethiopia, Madagascar, Swaziland, Morocco, Cameroon, Botswana, Viet Nam. Saudi Arabia, Kenya, Senegal, Pakistan, Nepal, South Africa, the UK and South Korea (arranged in that order). This group includes 6 low-income countries, 7 middle-income, 2 high-income plus one Newly-industrialised country. You need to compile a spreadsheet that contains a range of indicators for these countries, which takes a little while. I use the World Development Report (WDR) and the Human Development Report (HDR), although the most recent HDR has redesigned tables of indicators that are less useful for this purpose than earlier editions. Some indicators need to be sourced from elsewhere – such as the World Development Indicators. Copyright prevents me from providing my spreadsheet online – sorry about that.
I try to have a reasonably wide range of indicators available, although students tend to pick from a relatively narrow set. (This will make more sense when you have read about how the session works. I ran 4 groups in 2010, taking with me the following indicators. Those in bold are those that were actually requested by students during the sessions:
[GDP per capita – for reasons that will become clear, this is needed but not used.]
Preparation for students
Because this session is intended to take place very near the start of the module, there is little preparation for students to undertake beforehand. However, I suggest that they read the introductory chapter of their textbooks that deals with the meaning and measurement of development.
Running the session
At the beginning of the session, I tell the students that we will conduct a simple experiment to discover whether GDP per capita is a sufficient indicator of the level of development of a country. In other words, do we need to consider other indicators? I point out that if GDP per capita is highly correlated with “development”, then it should be possible to categorise countries by the World Bank income categories by just looking at other indicators. I then divide the students into groups of about 4 (depending on how many students are in the group), and give them a few minutes to think about the indicators that would help them to identify the level of development of a country. They are told how many countries are in each category, and that their task is to discover which countries belong to each category.
On screen is an empty grid that I will populate with data that they request. After they have discussed this in their groups, each of the first 3 groups is invited to suggest an indicator. I then provide the data for each country. After they have 3 pieces of information about each country, they are asked to make a preliminary categorisation. They are then offered further indicators until the grid is full, or we run out of time. The last part of the session is used to reveal the answers.
How it works
How well they do depends heavily on the order in which they request data, and which indicators they choose. In the group of countries that I used in 2010, the UK immediately stands out as a high-income country. I use this to emphasise how wide the gap in living standards is between a developed country like the UK and much of the developing world. By including Saudi Arabia, South Korea and Chile in the sample, they can get confused early on, given that Saudi has lower life expectancy and adult literacy than Chile or South Korea. However, the main confusion occurs in trying to distinguish the low- from middle-income countries. What they very rapidly discover is that countries face very different combinations of characteristics, perhaps performing well in some areas but poorly in others. Nonetheless, again there are some countries like Ethiopia that always stand out as being at a very low state of development. On the other hand, no group picked Viet Nam as a low-income country – and of course, I included it because I knew that it would be hard to spot. It would be no fun if they could get it right easily!
I have been using this exercise for several years, and students seem to learn a lot from it, as it gets them to think through how they would recognise development. It also brings home to them that there is a substantial gap in living standards between different parts of the world, and that there is great diversity between countries in terms of the configuration of characteristics that they face. I point out to them that this has implications for policy design, and that we should not imagine that a one-size-fits-all approach can be valid.