Abstract:
See Conclusion
3. The relationship between
country size and life expectancy:
Using the same methodology,
Table Four, below, shows the relationship between these two variables.
Table Four: The Relationship
between Country Size and Life Expectancy
Long life expectancy
|
Short life expectancy
|
Total
|
|
Small Countries
|
8
|
4
|
12
|
Large Countries
|
4
|
7
|
11
|
Total
|
12
|
11
|
23
|
Chi Square: 2.2. Not significant
at .10 level, however, “directional”
support for hypothesis.
Comments: The world’s
average life expectancy is 70.00 years. It ranges from 45.33 years (Senegal) to
89.57 (Monaco).
All seven countries whose life
expectancies are below 50 years are in Africa: Senegal, Botswana, Lesotho,
Swaziland, the Central African Republic, the Congo Democratic Republic and
Mozambique. The seven countries with the longest life expectancies are Monaco
(89.57 years), Andorra (83.52),Hong Kong (83.48), San Marino (83.32), Japan
(83.10), Italy (82.94) and Iceland (82.92).
The United States’ life
expectancy is 78.74 years. It is ranked a dismal 52nd (or 63rd,
according to another source)
Table Four shows that the
predicted relationship between country size and life expectancy exists,
although it is not very strong.
In the worldwide distribution of life expectancy, many large European
countries (Germany, France, Italy, Spain, the United Kingdom) and some large
Asian countries (Japan, South Korea) have among the world’s longest life
expectancies. Thus, this variable is strongly associated with Continent, rather
than country size. Africa, of course, has by far the shortest life expectancy.
4. The relationship between per capita income and
murder rate:
Using the same methodology, Table Five, below, shows
the relationship between these two variables.
Table
Five: The Relationship between per capita income and murder rates.
Rich Countries
|
Poor Countries
|
Total
|
|
low murder rate
|
5
|
6
|
11
|
high murder rate
|
7
|
5
|
12
|
Total
|
12
|
11
|
23
|
Chi Square: .38. Not significant at .10 level.
The parameters of these two variables have already
been given. My prediction that the standard of living and the murder rate would be INVERSELY related was common-sensical, and based on what is generally viewed as a truism: Poverty breeds crime.
It therefore comes as a surprise that the
relationship is in fact the OPPOSITE of the prediction: While the evidence is
not overwhelming, Table Five reveals
that the murder rate tends to be HIGHER in more
affluent countries than in poor countries. Some might say, so much for
economic development as the panacea for all our social problems.
However, this finding deserves a better response
than that. Remember that our unit of analysis is the country. WITHIN countries, there is a strong inverse relationship between
income and murder. It is even stronger when taking inequality into account.
However, the relationship washes out at the level of whole countries.
A similar seeming paradox is the fact that on the
whole, American Democrats are richer
than Republicans. This unexpected fact is also resolved when one breaks down
the total national population of Democrats and Republicans: WITHIN each state,
Republicans are richer than Democrat. However, Blue (Democratic) states are
richer than Red (Republican) states -
the former are coastal states such as California and New York, whereas the
latter are generally in the South, the Midwest and the Rocky Mountains. As Leon
Festinger Explained long ago
with his Social Comparison Theory,
people compare themselves with those who are near them, not with distant others.
5. The relationship between per capita income and
life expectancy:
Using the same methodology, Table Six, below, shows
the relationship between these two variables:
Table Six: The Relationship between per capitaincome and life expectancy.
Rich countries
|
Poor countries
|
Total
|
|
long life expectancy
|
9
|
3
|
12
|
short life expectancy
|
3
|
8
|
11
|
Total
|
12
|
11
|
23
|
Chi Square: 5.2. Significant at
.05 level.
Comments: The correlation
between per capita income and life expectancy is strong, as expected. This
relationship is rather obvious. On the whole, of course, affluent countries can
afford better medical services than poor countries. However, there are
exceptions to this. The major exception is the United States. That country
enjoys the 19th highest per capita GDP in the world, yet its life
expectancy is the 52nd or 63rd , depending on the source
used.
6.
The relationship between the murder rate
and life expectancy:
Using
the same methodology, Table Seven, below, shows the relationship between these
two variables:
Tabe Seven: The
Relationship between the Murder Rate and
Life Expectancy
High
murder rare
|
Low
murder rate
|
Total
|
|
long life expectancy
|
5
|
7
|
12
|
short life expectancy
|
7
|
4
|
11
|
Total
|
12
|
11
|
23
|
Chi
Square: 1.1. Not significant at .10 level, however, “directional” support for hypothesis.
Comments: Table Seven shows that the predicted relationship between
the murder rate and life expectancy is real, although it is not exceptionally
strong. Some might see this as a no-brainer, almost as a tautology: After all, ceteris paribus, the more people are murdered, the shorter the
average life expectancy becomes. However, murder is always an extremely small
contributor to overall life expectancy. The vast majority of mortality is
caused by illness.
Take
mortality in the US for example. In Table Eight, I tabulate the major
categories of cause of death.
Table
Eight: Causes of Death in the United States.
Cause
|
number
|
%
|
Illness
|
2,220,000
|
85.37
|
Injuries,
drugs, alcohol, war, fires, etc
|
193,000
|
7.45
|
accidents
|
131,000
|
5.02
|
Suicide
|
41,000
|
1.58
|
Murder
|
15,000
|
.58
|
Total
|
2,600,000
|
100
|
Source:
Leading causes of Death
Conclusion
This
article attempts to show that country
SIZE (population) , as an independent variable, can predict quality of life.
That is, smaller countries enjoy a better quality of life than larger
countries. The dependent variable - quality of life - is operationalized through the use of three indicators: per
capita GDP, the murder rate and life expectancy. It is shown that smaller countries
indeed enjoy higher per capita income, lower murder rates and longer life expectancy. Correlations
between the three dependent variables are also examined: As expected, the
relationship between per capita GDP and life expectancy is positive, and the
relationship between the murder rate and life expectancy is negative. However,
the relationship between per capita GDP and the murder rate turned out to be
POSITIVE, which came as a surprise.
This
study is largely descriptive, not explanatory. While I offer a few
explanations, my aim is not to provide a
detailed causal analysis. The relationships I
examine are quite possibly spurious. They are certainly part of a much
more complex set of variables, including political, cultural and
geographical factors. However, these
data offer a global view of how four
major variables interact.
