Quantitative Project: World Income and Health Inequality

            3.  investigate the relationship between income and health status.

            B. Quantitative Skills: Students will be able to

            1.  sort
a single variable and examine its
distribution

            2.  calculate
within
-group adjusted-means weighted
by population

s

            3.  produce
a scatter plot to investigate the relationship between two

variables

Data and Variables

The data are from “2008 World Population Data Sheet” published by the Population Reference Bureau (

http://prb.org/Publications/Datasheets.aspx

). 

            Three variables are used for this project:

Gross National Income (GNI) PPP per
capita

                        Life expectancy

                        Population (in millions)

These three
variables for more than
100 countries are already compiled in an Excel file.

Validity of the Measurement

                        Income level

Q_1: Why can
’t Gross National Income be directly used as a  

          measure
of income level?  What does the PPP adjustment  

          take
into account?  Why has it to be per capita? 

                        Health Status

Q_2: How is life expectancy defined?  Why not to use Crude

         Death Rate (CDR)?  What is the advantage of using life  

         expectancy?

Data Analysis

Corresponding to the three objectives
stated above, the analysis section is composed of the following three parts:

1.  Investigation of income inequality between rich and poor countries       

Q_3: Find out the top five
countries with the highest GNI PPP per capita

         and the bottom five countries with the lowest

values
.  List these  

         countries’

names and their income.

Q_4: How much is the difference
between the highest and lowest country

?

Q_5: If we want to find out the overall difference between these two  

         groups

, can we simply

take an average of the five values of GNI PPP

         per capita within each group and compare

the two means?  Why or

         why

not? 

A better
way is to compare the population-weighted means.  We first need to calculate the total income for each country by multiplying GNI PPP per capita by its population.  Then, add all five total income within each group.  Finally, divide the sum
within each group by the corresponding sum of population.

Q_6:  What is the average income for either group?  How much is the 

          difference and how to interpret it?

            2.  Investigation of inequality in life expectancy 

Q_7: Find out the top five countries with the highest life expectancy

         and the bottom five countries with the lowest values.  List these 

         countries’ names and their life expectancy.

                                    Use the same method for Q_3 to answer this question.

Q_8: How much is the difference between the highest and lowest country?

Q_9: If we want to find out the overall difference between these two 

         groups, can we simply take an average the five values of life 

         expectancy within each group and compare the two means?  Why or 

         why not?  

Similarly, a better way is to compare the population-weighted means.  We first need to calculate the total expected life-years for each country by multiplying life expectancy by its population.  Then, add all five total expected life-years within each group.  Finally, divide the sum within each group by the corresponding sum of population.

Q_10:  What is the average life expectancy for either group?  How much 

            is the difference and how to interpret it?

            3.  Investigation of the relationship between GNI PPP per capita and life

                 expectancy

                        One intuitive way to
assess such a relationship is to put these two 

variables in a two-dimension chart, where
GNI PPP per capita takes the horizontal axis and life expectancy the vertical
axis.  Each country is represent
ed
by a single dot, whose position on this chart is determined by the values of these two variables.

                        This can be done in Excel:

                                    Highlight all the numbers of the two columns of “GNI PPP per

                                                Capita” and “life expectancy;”

Click “Insert” on the command bar, and select “Chart”;

                                                Select the “XY (scatter)” chart type;

                                    See the example handout for more details.                 

Q_11: Produce a scatter plot chart for these two variables by using Excel. 

What kind of general trend does it show
?  Some dots seem to be distant from the bulk of the dots.  They are called
outliers

.  Find three of them
.  Which countries do these dots represent?  Why are they outliers?  What are the possible explanations for them?

Conclusion and Discussion

Q_12: Based on the findings above, what conclusions can be drawn about
income and health inequality between countries in the world?  What is the general relationship between income and health status?  Why do you think
there is such a relationship? What does the existence of the outliers tell
us regarding the impact of income on health?  Does higher national income always lead to better health of the citizens?  Overall, what can be learned from this study regarding how to maintain or improve people’s
health? 

 — I attached all the requirements 

Type your answers to all the questions above on separate paper
.  Attach the chart.

Dimension

Unacceptable

Acceptable

Excellent

Calculation: Ability to perform mathematical operations (Able to sort data and to calculate group means in Excel)

Having no idea of how to sort data or to calculate the group means

Able to sort the data and generally able to calculate the group means but with minor errors in calculation in Excel

Able to sort and correctly calculate all the group means without errors in Excel

Representation: Ability to construct a scatter plot with all required elements of the chart in place in Excel

Unable to construct a scatter plot in Excel

Generally able to create a scatter plot with some minor errors in Excel

Able to create a scatter plot with all required elements of the chart in place in Excel

Interpretation: Ability to explain the calculated means and the scatter plot

Unable to correctly explain and compare the group means, and the meaning of the scatter plot

Generally able to explain and compare the group means, and the meaning of the scatter plot with minor errors

Able to correctly explain and compare the group means, and the meaning of the scatter plot
without errors

Analysis: Ability to address the research questions based on the empirical results from above and draw correct conclusions

Unable to link the empirical results with the research questions together and to draw correct conclusions

Generally able to link the empirical results with the research questions with minor errors in the logic of analysis; Conclusions were generally correct

Able to clearly link the empirical results and to draw correct conclusions

2

>Sheet1

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0

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1120 54 10.3

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330

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400 57 5

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71

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0

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72

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72 0.1

69 1.3

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72

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71

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71

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1040 64 27

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80

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79

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73 0.6

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82

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81

67 0.1

4370 68

61 0.1

80

57 6.5

3570 73 0.2

62 0.5

71 0.1

67 0.2

Sheet2

Sheet3

An Illustrative Example for the Project

Variables

% Undernourished → Infant Mortality Rate

Q3.

Copy and Past the data to a new worksheet.

Keep the original data unchanged.

Highlight all columns without the title of each column, then click Data on the

command menu, select “Column B” in the window under “Sort by”, then “Ok.”

The lowest is Libya with 2.5%; the highest is Eritrea with 75%.

Q4.

The difference is 75% – 2.5% = 72.5%

Q6.

1) To calculate the number of people undernourished in Libya:

In a new column and in the same row for Libya, enter a formula,

“=B1*D1”. It will give you “15.75”.

2) Then use the “Copy” and “Paste” function to calculate the number of people undernourished in all the other 9 countries.

3) To calculate the total number of people undernourished in the five lowest countries:

In a separate cell, enter “=sum(f1..f5)”

(Two dots between f1 and f5)

It will give you “1006.75”.

4) To calculate the total population of the five lowest countries:

In a separate cell, enter “=sum(d1..d5)”

It will give you “402.7”.

5) To calculate the average undernourished rate of the five lowest countries,

divide the total number of people undernourished from 3) by the total

population of these five countries 4):

1006.75 / 402.7 = 2.5

6) Similarly, repeat 3), 4), 5), calculate the average undernourished rate of the

five highest countries:

6334.2 / 88.4 = 71.7

7) The difference between these two groups: 71.7% – 2.5% = 69.2%

Interpretation: While only 2.5% of the population are undernourished in the world’s five least undernourished countries, 71.7% of the population are undernourished in the world’s five most undernourished countries. The percentage difference is 69.2%.

Q7 – Q10.

Copy and Past the data to a new worksheet.

Refer to Q3 – Q6.

Q11.

Highlight all the numbers of the two columns of “% Undernourished” and “Infant Mortality Rate”

Click “Insert” on the command bar, and select “Chart”;

Select the “XY (scatter)” chart type. Then click “Next”.

Click “Next”,

Give a title.

“Figure 1. The Relationship between % Undernourished and

Infant Mortality Rate”.

Label the X and Y axis.

Click the “Legend” button, uncheck the “Show legend” box.

Click “Next”, check the “As new sheet” box.

Click “Finish”.

2

>Sheet1

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13 5 0.2

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2.5 3.1 4.5

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2.5 3.1 4.8

2.5 2.5 9.2

2.5 4.9

2.5 3.7

2.5 3.7

2.5 3.6

2.5 3.9

2.5 4.4 0.5

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4 6 9.7

8 9.2 7.6

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2.5 5.9 10

11 12

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9 8 3.8

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2.5 4.2

5 13 2

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3 3.1 2

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5 17

4 6.8 0.3

7 52 0.1

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2.5 5

4 20 0.2

21 48 0.5

11 27 0.2

Sheet2

Sheet3

Lowest Life Expectancy Countries GNIPPP Life Expectancy Population

43

Highest Life Expectancy Countries GNIPPP Life Expectancy Population

Singapore

48520

81

4.8

Japan

34600

82

127.7

San Marino

37080

82

0.03

Switzerland

43080

82

7.6

China, Hong Kong SAR

44050

82

7

Lowest GNIPPP Countries GNIPPP Life Expectancy Population

Liberia

290

46

3.9

Congo, Dem. Rep.

290

53

66.5

Burundi

330

49

8.9

Eritrea

400

57

5

Guinea-Bissau

470

45

1.7

Highest GNIPPP Countries GNIPPP Life Expectancy Population

Singapore

48520

81

4.8

Brunei

49900

75

0.4

Kuwait

49970

78

2.7

Norway

53690

80

4.8

Luxembourg

64400

80

0.5