Why are some Countries Better than Others? A Look at Size, Income, Crime and Longevity_Part Four

by Tom Kando

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  52^nd (or 63^rd, 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 19^th highest per capita GDP in the world, yet its life expectancy is the 52^nd or 63^rd , 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

As you can see, murder only  makes up half of one percent of all deaths in America. Even if the murder rate in the US were as high as it is in Honduras - 90.4 per 100,000 instead of the existing 4.6%, or twenty times higher - it would only account for slightly  more than 10% of all deaths. My point is that it is quite possible for a country to have a dismal murder rate and yet a long life expectancy, which depends far more on the state of public health than on the crime rate.

                                                                                        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. leave comment here

© Tom Kando 2015