# Quantitative Project: World Income and Health Inequality

Quantitative Project:  World Income and

Health Inequality

Based on what
we have discussed so far, it seems that the

re
is a lot of variation around the world in terms of income

, wealth, education, health

status
, and many other characteristics.  And these characteristics seem to be related with
one another.  For example, people from

wealthier countries tend to live longer. In this project, you
international data to empiricall

y investigate

the relationship
between income and health status.  The following sections
provide a general description of this project and raise questions that you need to answer
.

Objectives:

A. Substantive: Students will be able to

1.  investigate world inequality in income.

2.  investigate world inequality in health status.

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
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

.  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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4370 68

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3570 73 0.2

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71 0.1

67 0.2

 GNI PPP Life Expectancy Population per Capita, (\$) (years) (millions) Algeria 5 4 7 34.7 Egypt 5 400 72 74 Libya 11 50 73 6.3 Morocco 3990 70 31.2 Sudan 18 80 58 39.4 Tunisia 71 1 0.3 Benin 13 10 56 9.3 Burkina Faso 1120 51 1 5.2 Cape Verde 2940 0.5 Cote D’lvoire 1 59 52 2 0.7 Gambia 1140 1.6 Ghana 1 33 2 3.9 Guinea Guinea-Bissau 47 45 1.7 Liberia 290 46 Mali 1040 1 2.7 Mauritania 2010 60 3.2 Niger 63 57 14.7 Nigeria 1 77 1 48 Senegal 1 64 62 Sierra Leone 66 5.5 Togo 800 6.8 Burundi 49 8.9 Comoros Djibouti 2260 0.8 Eritrea Ethiopia 78 7 9.1 Kenya 1540 53 38 Madagascar 920 18.9 Malawi 75 13.6 Mauritius 11390 1.3 Mozambique 69 43 2 0.4 Rwanda 860 9.6 Seychelles 8 67 0.1 Tanznia 1200 4 0.2 Uganda 2 9.2 Zambia 1220 12.2 Angola 4400 16.8 Cameroon 2120 18.5 Central Africa Repulic 740 4.4 Chad 1280 10.1 Congo 27 3.8 Congo, Dem. Rep. 6 6.5 Equatorial Guinea 21230 0.6 Gabon 13080 1.4 Sao Tome and Principe 1630 Botswana 12420 1.8 Lesotho 1890 36 Namibia 5120 2.1 South Africa 9560 48.3 Swaziland 4930 1.1 Canada 35310 3 3.3 United States 45850 30 4.5 Belize 5100 Costa Rica 8340 EI Salvador 4840 7.2 Guatemala 4120 13.7 Honduras 3160 7.3 Mexico 12580 107.7 Nicaragua 2080 5.7 Panama 3.4 Antigua and Barbuda 12 61 Barbados 10880 76 Dominica 5 65 Doominican Republic 5050 9.9 Grenada 6010 68 Haiti 1050 Jamaica St. Kitts-Nevis 10430 0.05 Saint Lucia 7090 St. Vincent & the Grenadines 5720 Trinidad and Tobago 14580 Argentina 12990 3 9.7 Bolivia 4140 Brazil 9370 195.1 Chile 12590 Colombia 6640 44.4 Ecuador 7040 13.8 Guyana 2600 Paraguay 4380 6.2 Peru 7240 27.9 Suriname 6000 Uruguay 11040 Venezuela 11920 Armenia 5900 3.1 Azerbaijan 6370 8.7 Bahrain 34310 Cyprus 26370 Georgia 4770 4.6 Israel 25930 7.5 Jordan 5160 5.8 Kuwait 49970 Lebanon 10050 Oman 19740 Saudi Arabia 22910 28.1 Syria 4370 19.9 Turkey 12090 7 4.8 Yemen 2200 22.2 Bangladesh 1340 147.3 Bhutan 4980 India 2740 1149.3 Iran 10800 72.2 Kazakhstan 9700 15.7 Kyrgyzatan 1950 Maldives 5040 Nepal Pakistan 2570 172.8 Sri Lanka 4210 20.3 Tajikistan 1710 Turkmenistan Uzbekistan 1680 27.2 Brunei 49900 Cambodia 1690 Indonesia 3580 239.9 Laos 1940 5.9 Malaysia 1 3570 27.7 Philippines 3730 90.5 Singapore 48520 81 Thailand 7880 66.1 Timor-Leste 3190 Vietnam 2550 86.2 China 5370 1324.7 China, Hong kong SAR 44050 82 Japan 34600 127.7 Korea, South 24750 79 48.6 Mongolia Denmark 36740 Estonia 19680 Finland 35270 5.3 Iceland 34060 Ireland 37040 Latvia 16890 2.3 Lithuania 17180 Norway 53690 Sweden 35840 United Kingdom 34370 61.3 Austria 39090 8.4 Belgium 35110 10.7 France 33470 Germany 33820 82.2 Luxembourg 64400 Netherlands 39500 16.4 Switzerland 43080 7.6 Belarus 10740 Bulgaria 11180 Czech Republic 21820 10.4 Hungary 17430 Moldova 2930 4.1 Poland 15590 38.1 Romania 10980 21.5 Russia 14400 141.9 Slovakia 19330 5.4 Ukraine 6810 46.2 Albania 6580 Bosnia-Herzegovina 7280 Croatia 15050 Greece 32520 11.2 Italy 29900 59.9 Macedonia 8510 Malta 20990 Montenegro 10290 Portugal 20640 10.6 San Marino 37080 0.03 Serbia 10220 7.4 Slovenia 26640 Spain 30110 46.5 Australia 33340 21.3 Federated States of Micronesia 3710 Fiji 0.9 Kiribati 2190 New Zealand 26340 4.3 Papua New Guinea 1500 Samoa Solomon Islands 1400 Tonga 3430 Vanuatu 2890

## 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

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2.5 4.9

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Netherlands 2.5 4.4

2.5 4 7.6

4 6 9.7

8 9.2 7.6

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11 12

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5 17

4 6.8 0.3

7 52 0.1

10 7 0.2

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21 48 0.5

11 27 0.2

 % Undernourished Infant Mortality Rate Population Population per 1,000 millions Algeria 4 2 7 3 Egypt 33 74 9 Libya 2. 5 21 6 Morocco 43 31 Sudan 26 8 39 Tunisia 2.5 19 10 Benin 12 98 9.3 Burkina Faso 15 89 1 5.2 Cote D’lvoire 13 100 20 Gambia 29 93 1.6 Ghana 11 71 2 3.9 Guinea 24 113 0.3 Guinea-Bissau 1 17 1.7 Liberia 50 133 Mali 96 1 2.7 Mauritania 77 3.2 Niger 32 14 Nigeria 1 48 Senegal 61 Sierra Leone 51 1 58 5.5 Togo 91 6.8 Burundi 66 107 8.9 Comoros 60 69 0.7 Djibouti 67 0.8 Eritrea 75 59 Ethiopia 46 7 9.1 Kenya 38 Madagascar 18 Malawi 35 80 1 3.6 Mauritius 1 5.4 1.3 Mozambique 44 108 2 0.4 Rwanda 86 9.6 Seychelles 0.1 Tanznia 4 0.2 Uganda 76 2 9.2 Zambia 12.2 Zimbabwe 47 1 3.5 Angola 132 16 Cameroon 18.5 Central Africa Repulic 102 4.4 Chad 106 10.1 Congo 3.8 Congo, Dem. Rep. 92 66.5 Gabon 1.4 Sao Tome and Principe Botswana 1.8 Lesotho Namibia 2.1 South Africa 45 48.3 Swaziland 22 85 1.1 Canada 3 3.3 United States 6.6 30 4.5 Belize Costa Rica 9.7 EI Salvador 7.2 Guatemala 34 1 3.7 Honduras 23 7.3 Mexico 107.7 Nicaragua 27 5.7 Panama 3.4 Bahamas Barbados Cuba 5.3 11.2 Dominica Doominican Republic 9.9 Grenada Haiti 57 Jamaica Netherlands St. Kitts-Nevis 0.05 Saint Lucia 19.4 St. Vincent & the Grenadines 1 7.6 Trinidad and Tobago Argentina 13.3 39.7 Bolivia Brazil 195.1 Chile 8.8 16.8 Colombia 44.4 Ecuador 25 13.8 Guyana Paraguay 36 6.2 Peru 27.9 Suriname 0.5 Uruguay 10.5 Venezuela 16.5 Armenia 3.1 Azerbaijan 8.7 Cyprus Georgia 4.6 Israel 7.5 Jordan 5.8 Kuwait Lebanon Palestinan Territory 4.2 Saudi Arabia 28.1 Syria 19.9 Turkey 7 4.8 United Arab Emirates Yemen 22.2 Bangladesh 52 147.3 India 1149.3 Iran 72.2 Kazakhstan 15.7 Kyrgyzatan Maldives Nepal Pakistan 17 2.8 Sri Lanka 20.3 Tajikistan 56 65 Turkmenistan Uzbekistan 27.2 Brunei Cambodia 1 4.7 Indonesia 239.9 Laos 70 5.9 Malaysia 27.7 Myanmar 49.2 Philippines 90.5 Thailand 6 6.1 Timor-Leste 88 Vietnam 86.2 China 1324.7 Japan 127.7 Korea, North 23.5 Korea, South 48.6 Mongolia 41 Denmark Estonia 4.9 Finland Iceland Ireland Latvia 2.3 Lithuania Norway Sweden United Kingdom 61.3 Austria 8.4 Belgium 10.7 France 62 Germany 82.2 Luxembourg 16.4 Switzerland Belarus Bulgaria Czech Republic 10.4 Hungary Moldova 4.1 Poland 38.1 Romania 21.5 Russia 141.9 Slovakia Ukraine 46.2 Albania Bosnia-Herzegovina Croatia Greece Italy 59.9 Macedonia Malta Portugal 10.6 Serbia 7.4 Slovenia Spain 46.5 Australia 21.3 Fiji 0.9 French Polynesia Kiribati New Caledonia New Zealand 4.3 Samoa Solomon Islands Vanuatu

## Sheet3

Lowest Life Expectancy Countries GNIPPP Life Expectancy Population

43

 Swaziland 4930 33 1.1 Lesotho 1890 36 1.8 Zambia 1220 38 12.2 Mozambique 690 43 20.4 Central Africa Republic 740 4.4

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