2.  One common way of deciding if a political entity is a country is membership in the UN.  There were 193 UN members as of 2015

3.  The level of development in a country can be measured in a variety of ways.  SOCIAL DEVELOPMENT includes measures such as literacy rates and the infant mortality rate.  ECONOMIC DEVELOPMENT includes measures of wealth such as Per capita GDP or the ratio of cars to people.

5.  We often group countries according to these measures of social and economic development.  But how should they be grouped?  Too few groups and each member of a group will be very different from the other.  Too many groups and comparisons are complex and difficult.

6. Developed & Underdeveloped Developed & DevelopingNorth & South Confusion in how to define economic and non-economic development Allows (implies) for the movement of countries from the developing into the developed categories. Many exceptions. A better theory is temperate and tropical classifications.

8. First, Second, and ThirdDeveloped, Newly Industrialized, and Developing First World– capitalist countries Second World – formerly or currently communist countries Third World – developing or south countries in the previous classification This model adds a transitional stage for countries moving from agricultural economies to industrial economies.

9. Advanced economies In transitionLess developed Least developed Highest levels or social and economic development Are or were communist. High social development but poorer economic development Well advanced in the transition to advanced economies Early stages of transition towards development. Little evidence of starting the transition towards development.

11. First WorldSecond WorldThird WorldFourth WorldFifth World Highest levels or social and economic development Are or were communist. High social development but poorer economic development Well advanced in the transition to development. Level of social and economic development between First and Fourth Worlds Early stages of transition towards begin developed Little evidence of starting the transition towards development.

12.  Classifying Countries : A graphing Exercise

13. CountryLife Expectancy (years)Doctors per 100 000 people Australia80240 Bangladesh5723 Brazil60206 Canada79209 Chile76109 China70164 Congo447 Germany77362 Iran67105 Japan80201 Kenya5513 Mexico75171 Nigeria5627 Papua N-G585 Singapore77140 Thailand6830 United States77549 Zimbabwe416 An Exercise in Classifying Countries (Making Connections 2 nd edition) Why make graphs? Graphing allows us to see the trends in a set of data which can then be used to read, track, and predict other details. Graphs turn raw data into easy to understand pictures for quick examination.

14. Developed Newly- industrialized Developing It seems logical that the more doctors there are per 100 000 people, the more easily people will be able to access a doctor which will result in better healthcare (diagnosing health issues earlier, more effective treatment) and generate a longer life expectancy. Generally speaking this is the pattern seen in the graph, however the relationship is not perfect since there are several unusual points (outliers); The USA has more than 100 doctors more per 100 000 than Germany but does not have the highest life expectancy. Brazil has a number of doctors that would be expected to generate a life expectancy much higher than 60 years. Thailand has few doctors/100 000 but a very respectable life expectancy of 68 years. This means that doctors/100 000 is important but is not the only factor which determines life expectancy. Education, nutrition, access to shelter and income, as well as conflict and disease are other factors which may impact life expectancy Zimbabwe and D.R. Congo have far lower life expectancies than expected even for such a low number of doctors.

15. Country Natural Increase per 100 population(a) Infant Mortality per 1000 live births(a) Literacy (%) (b) GNI PPP (US$ per capita)(a) GPD from agriculture (%)(c) Motor Vehicles per 1000 people (d) Per capita Electricity use (kWh) (e) Mobile phones per 1000 people (f) Algeria1.926.070129909.023.0850.2826.9 Bangladesh1.52.243281017.340.0148.1228.5 Brazil0.91.889147505.421.42116.7636.7 Canada0.45.499539601.89.416055.6575.9 Czech Rep0.02.599255301.89.06020.51278.3 France0.33.699375801.97.87328.3789.5 Germany-0.23.399445400.815.56641.91283.6 Haiti1.93.453171025.780.031.4252.7 Liberia2.654.05879076.980.0101.9176.2 Mexico1.413.086161103.751.31858.3627.9 Niger3.954.02991039.663.045.769.8 Panama1.514.093192903.826.01518.0737.8 Romania-0.38.598180607.922.22625.71026.9 Russia0.010.399232004.412.76968.61202.5 Singapore0.51.293768500.026.08053.11234.1 S. Korea0.31.298334402.716.57515.6886.9 Ukraine-0.47.0100896010.424.13905.91193.1 USA0.44.899425901.215.112747.5846.8 Vietnam1.02.194503021.511.3602.3389.4 Zimbabwe2.437.091156020.380.0884.699.6 An Exercise in Classifying Countries Use the data in the table above to create 2 scattergraphs which illustrate the relationship between any two pairs of indicators. One of your scattergraphs must make use of a logarithmic scale on at least one of its axes. Write a brief analysis of the pattern revealed by the pairs of data you have chosen. 2 Orders of magnitude 2232 243

19. Item Criteria Score Title, source, author Title reflects indicators, source and author identified /3 Outliers 2 outliers are identified and labelled /2 Labeling axes units and titles are labelled /2 Groupings Country groupings are clearly identified and accurate (1 st to 5 th world) /2 Accuracy Features graphed accurately and completely /6 Total /15 Classifying Countries Graph and Graph Analysis Analysis (answer on back of graph) Which indicators have you chosen and what pattern have they created? (/2) How do you think these indicators are related? (/3) Identify two points which do not fit the pattern (outliers) (/2 ) and, Explain what they might reveal about the relationship between the indicators? (/3)

20. If you prefer to create your graphs in Excel an electronic version of the data can be found at; X:/Documents/Frey/Canadian and World Issues/Introduction/Classifying countries data ‘16.wlsx