Project #2:
Report on the Statistics of
Inequality and Poverty
Ryan Williams, Rebecca Davis, Winsie Lee
Group #2
Table of Contents
I. Introduction
II. Data Findings–Tables and
Graphs
III. Concluding Statements
IV. Limitations and directions for
future data collection and analysis
V. Appendices
1
I.
Introduction
Today’s world faces growing concerns over income inequality, as well as the lingering issue
of gender inequality, especially in the developing world. Using information compiled from a
1990 study done by the U.N.E.S.C.O. Demographic Year Book1 and The Annual Register2, a
dataset of 97 countries and 8 variables can be used to assess differences in growth, birth, and
death rates along with life expectancy and Gross National Product per capita. These variables
are strong indicators in social and economic contexts, and allow for an efficient analysis of
where a country stands compared to others around the world.
II.
Data Findings
Variable
Label
N
Mean
StdDev
Minimum
Maximum
Birthrate
Live Birth Rate (per 1,000 of Population)
97
29.2298969
13.5466952
9.7
52.2
DeathRate
Death Rate (per 1,000 of Population)
97
10.8360825
4.6474945
2.2
25
InfantDeath
97
54.9010309
45.9925843
4.5
181.6
MaleExpectancy
Infant Deaths per 1,000 of population (under 1
years old)
Life Expectancy for males (Years)
97
61.485567
9.6159697
38.1
75.9
FemaleExpectancy
Life Expectancy for Females (Years)
97
66.151134
11.0053907
41.2
81.8
GNPpercap
Gross National Product per capita ($)
91
5741.25
8093.68
80
34064
Growthrate
Growth Rate (in Population per Thousand)
97
18.3938144
11.995501
-1.8
37.8
Using the statistical data package SAS, we found interesting results using several techniques. This
table shows the descriptive statistics for each of our variables, namely birth rate, death rate, infant
death rate, male life expectancy, female life expectancy, and GNP per capita and growth rate. The mean
1
U.N.E.S.C.O. 1990 Demographic Year Book (1990), New York: United Nations.
Day, A. (ed.) (1992), The Annual Register 1992, 234, London: Longmans.
2
2
birth rate is greater than the mean death rate for all countries, implying that the world is growing overall
and doing so at an average rate of around 18 people per 1,000 of the population, which is confirmed by
the mean of the variable ‘Growth rate’ : 18.4. We compiled this variable with the intention of finding
trends and comparing rates of population growth between country groups. It was created by using the
equation:
Birth rate – Death rate = Growth rate
This variable measures the percent points of growth per thousand of the population (e.g. a value of 20.0
represents a 2% growth in the population in 1992).
Another interesting measure is the difference between the male and female life
expectancies, which are 61.5 and 66.15 years respectively. It would be interesting to examine
why women in developed countries live much longer than men. The minimum and maximum
values show tremendously great gaps in all the variables – birth rate, death rate, infant death
rate, male life expectancy, female life expectancy, GNP per capita, and growth rate. The gap in
GNP per capita is incredibly huge, with the minimum being 80 and maximum being 34064. The
gap in infant death rate is great too, with the minimum being 4.5 and maximum being 181.6.
These gigantic gaps show strong evidence that global inequality is a serious issue today and
needs to be addressed.
3
This and the next bar graph focus on statistics on life and death. This is a graphical
representation of the difference in life expectancies of males and females, compared across the
6 major country groups. The more developed country groups, eastern Europe, western Europe,
North America, Australia, New Zealand, South America and Mexico have a greater difference in
life expectancies of the sexes while developing country groups such as Africa, Asia, and the
Middle East have a smaller difference between female and male life expectancy. This puts the
common notion of greater degree of gender equality in developed countries into question.
However, since women are the ones with higher life expectancy, greater difference in life
expectancies of men and women might actually suggest the rising status and conditions of
women in the more developed regions.
4
This graph shows infant death rate per region. The highest rate of infant death is in the
least developed region, Africa, followed by Asia, South America and Mexico, and the Middle
East. The statistics of South America and Mexico here are vastly different from the statistics of
eastern Europe and western Europe, North America, Australia, New Zealand, unlike in the
previous graph. Despite the fact that South American and Mexico and eastern Europe have
similar GNP per capita (refer to Figures 2 and 3), South America and Mexico have a pretty high
infant death rate. Asia and the middle east also have higher GNP per capita (refer to Figures 4
and 5) than eastern Europe but also higher infant death rate. It is a rather surprising finding
that wealth does not necessarily have negative correlation to infant death rate.
5
Pearson Correlation Coefficients, N = 97
Prob> |r| under H0: Rho=0
Birthrate
DeathRate MaleExpectancy FemaleExpectancy
Birthrate
Live Birth Rate (per
1,000 of Population)
DeathRate
Death Rate (per
1,000 of Population)
MaleExpectancy
Life Expectancy for
males (Years)
FemaleExpectancy
Life Expectancy for
Females (Years)
1.00000
0.48620
InfantDeath
0.48620
-0.86652
-0.89441
0.85835
<.0001
<.0001
<.0001
<.0001
-0.73347
-0.69303
0.65462
<.0001
<.0001
<.0001
0.98256
-0.93684
<.0001
<.0001
1.00000
<.0001
-0.86652
-0.73347
<.0001
<.0001
-0.89441
-0.69303
0.98256
<.0001
<.0001
<.0001
0.85835
0.65462
-0.93684
-0.95535
<.0001
<.0001
<.0001
<.0001
1.00000
1.00000
-0.95535
<.0001
InfantDeath
Infant Deaths per
1,000 of population
(under 1 years old)
1.00000
By doing a correlation analysis, we are able to get a comprehensive picture of the
relationships between a multitude of variables. By computing correlation coefficients, we are
able to find the strength and direction of relationships between variables. As figure 2 shows,
nearly every relationship is significant (with p-values <.0001), and most have a strong
relationship (coefficient value greater than 0.50). These correlation coefficients predict how
change in one variable affects another variable. There is a significant positive correlation
between birth rate and death rate(r=0.48620) and an even higher positive correlation between
birth rate and infant death(r=0.85835). This may seem contradictory at first because the two
variables are opposites. However, there is a possible explanation for the correlation between
the two variables. It is possible that the high rate of overall deaths and infant deaths lead to a
higher need for giving birth, bringing up the birth rate. There is a high negative correlation
between birth rate and male life expectancy(r=-0.86652) and female expectancy(r=-.89441),
6
which again does not seem to quite make sense. It is possible that high birth rate has negative
effects on a mother’s health and life expectancy. It may be the same for males because of the
stress of providing for their children. There are high negative correlations between death rate
and both male life expectancy(r=-0.73347) and female expectancy(r=-0.69303), with the one for
males higher which indicate that males have higher life expectancy overall. This puts the
common knowledge that females have longer life expectancy into question. Death rate has a
rather high positive correlation with infant deaths(r=0.65462). There is a positive correlation
between female and male life expectancies(r=0.98256), which indicates that the long life of one
sex also predicts the long life of the other sex, as opposed to one sex living longer at the stake
of the life expectancy of the other sex. There is a strong negative correlation between infant
death and male life expectancy (r=-0.93684) and between female life expectancy(r=-0.95535).
This indicates that infant death has great negative effects on the length of life of both sexes.
This contradicts previous findings that birth rates are negatively correlated to both female and
male life expectancies.
Although this table gives a comprehensive picture of the relationships between
variables, they are not divided by country groups and may not apply to all the countries.
Further analysis of the above variables in these countries could be made with boxplots that
illustrate the variation and distribution in statistics. Social and economic differences among the
country groups are evident, especially in figures 7, 8, and 9.
7
III.
Concluding Statements
These data have shown some interesting findings. Generally, it could be concluded that the
world has a growth rate of 18.4% overall. There are great gaps in all the variables of the
countries examined – birth rate, death rate, infant death rate, male life expectancy, female life
expectancy, GNP per capita, and growth, with the gap in GNP per capita the greatest, ranging
from 80 to 34064, followed by the gap in infant death rate, ranging from 4.5 to 181.6. These
tremendously huge gaps show the great extent of global inequality and that more needs to be
done to reduce the gap.
There have been interesting findings for difference in life expectancies of the sexes and
infant death rate. The developed countries have greater gender gap in life expectancies than
developed countries, questioning the common notion that developed countries have less
gender gap than developing countries. However, since the ones with higher life expectancies
are women, the great gender gap in life expectancies in developed countries might suggest the
rising conditions of women while lower gender gap in life expectancies in developing countries
might suggest that the conditions of women are not improving and healthcare for women are
not satisfactory and should be further examined. When looking at the statistics for infant death
rate, eastern Europe, western Europe, North America, and Oceania have lower rates than the
other regions. When comparing infant death rates to GNP per capita, it was found that they
weren’t really correlated.
Finally, the correlation between 5 of the 7 variables were examined and yielded a lot of
unexpected results. High negative correlations have been found between birthrate and death
8
rate as well as infant death rate. This suggests that the high rate of deaths and infant deaths
lead to a higher need for giving birth, thus higher birth rate. There is a high negative correlation
between birth rate and male and female expectancies. This suggests that high birth rate has
negative effects on the health of both men and women. There are also strong negative
correlations between infant death and male and female life expectancies. This contradicts the
previous findings that birth rates are negatively correlated to both female and male life
expectancies. There are high negative correlations between death rate and both male and
female life expectancies. Death rate has a rather high positive correlation with infant deaths.
There is a positive correlation between female and male life expectancies, which suggests that
the sexes are prospering together rather than competing.
IV.
Limitations and directions for future data collection and analysis
The correlation analysis has found the positive correlation between birthrate and death
rate, especially infant death rate and suggested that higher infant death rate has led to a higher
need for giving birth, thus higher birthrate. This seems to apply only to the developing countries
and the findings might have been skewed by a high number of developing countries being
examined. There may not have been a fair distribution of developed and developing countries
so it is a good idea to examine the data of each country group and not just overall. There were
also findings that both birthrates and infant death rates have the negative correlations to male
and female life expectancies even though birth rates and infant death rates are opposite
variables. These results are confusing and need further investigation.
9
V.
Appendices
Descriptive Statistics by Country
Figure 1
Western Europe, North America, Australia, New Zealand
Figure 2
Eastern Europe
Figure 3
South America and Mexico
10
Figure 4
Asia
Figure 5
Middle East
Figure 6
Africa
11
Descriptive Statistics by variables
Figure 7
Figure 8
12
Figure 9
13