oppression, poverty and life expectancy

by the auroran sunset

a briefing document

• ends and means and the individual
• authoritarianism and liberty
• denialism
• the nature of cult recruitment - jihadi bombers
• categories, analogy and reification

• socialist religions
• psycho-babble 1 of 9: lie detection
• drugs, smoking and addiction

related item: the just war

Index

introduction
poverty: the world’s biggest killer
oppression causes poverty
socialism kills big
endnotes

Poverty is the world’s number one killer. Oppression is the world’s number one cause of poverty. Meanwhile, whenever socialism gain sufficient power, they not only impoverish the victims, but also murder wholesale, invade their neighbours and spread their misery as far as they allowed.

poverty: the world’s biggest killer

Poverty is well known to be far and away the greatest factor in Loss of Life Expectancy (LLE) [1]. In other words, if you want to make people live longer, the single best thing you can do is make them richer:

“The data on poverty are truly impressive. In one study, the Chicago area was divided into sections based on socioeconomic class. The difference in life expectancy between the highest- and lowest-class sections was 9 years for white males, 7 years for white females, and nearly 10 years for nonwhites of both sexes. [...]

“There is also an abundance of data from foreign sources. In one study, Montreal was divided into sections by socioeconomic class, and the difference in life expectancy between the highest-and lowest-class sections was 10.8 years for men and 7.3 years for women. A Canadian Ministry of Health Study in 21 cities divided the population into five equal parts (quintiles) by income, and found a difference in life expectancy between the highest and lowest quintiles to be 6 years for men and 3 years for women. (Note that the 20% with lowest income would hardly be classed as poverty cases.) A study in Finland divided the nation into four "social groups" and found a 7 year difference in life expectancy between the highest and lowest. A study in France found that professional and managerial men live 4 years longer than white collar workers, 9 years longer than skilled laborers, and 13 years longer than unskilled laborers.

“Within technologically advanced nations there are effective programs to provide poor people with reasonable medical care and an adequate diet, but such programs are much fewer and less effective on an international basis. This leads to much larger variations of life expectancy with socioeconomic level, as evidenced by correlations between life expectancy and per capita gross national product for various nations. In well-to-do countries like the United States, Western Europe, Australia, and Japan, life expectancy is about 75 years, whereas life expectancy in a sample of other countries is 72 years in Poland, Czechoslovakia, and Rumania; 67 years in Mexico and Central America; 64 years in Turkey, Brazil, and Thailand; 59 years in India, Iran, and Egypt; about 45 years in most central African countries; and 38 years in Afghanistan and Gambia. Lest these differences be ascribed to racial factors, it should be noted that Japanese have 10 years more life expectancy than other East Asians, and blacks in the United States have more than 20 years longer life expectancy than African blacks. The history of white versus black life expectancy in the United States is illuminating here: in 1900, there was a 18-year difference (50 years versus 32 years), whereas it is now reduced to 6 years, reflecting the improving socioeconomic status of blacks.”

Here is a plot of "Life Expectancy at Birth in Years" against "GDP per Capita in PPP adjusted US Dollars" for 49 representative nations:

[Data taken from the CIA World Factbook, Feb. 2007. Life expectancy list, 2015. GDP per capita list, 2015.]

Be aware that you can never trust government statistics. You should be especially wary of statistics from dictatorships, and even more especially wary of statistics from socialist dictatorships, for whom lying for the cause is mainstream dogma.

However - despite the likely gaming of life expectancy statistics in Cuba, North Korea and others - there is a clear high correlation between increasing GDP per Capita and increasing life expectancy. The R² [or r²] [2] figure of 0.6071 means that 60.71% of the differences in life expectancy can be predicted just by knowing the difference in GDP per capita!

Note that the logarithmic relationship [3] - “Life Expectancy = 7.059 Ln(GDP per Capita) + 6.395” - means that a little bit more money makes a much bigger difference to the life expectancy of the poor than to that of the rich.

Notice also that by a GDP per capita of $13,000, the life expectancy has already reached over 73 years old. This reflects research suggesting that “happiness” doesn’t increase much as income rises above $13,000.

oppression causes poverty

There is a similar causal relationship between freedom and wealth, or put differently, between oppression and poverty. Here is a plot of “GDP per Capita in PPP adjusted US Dollars” against “Combined Average Freedom House Rating” for 73 representative countries:

[GDP per capita data taken from the CIA World Factbook, Feb. 2007. GDP per capita table. Freedom data taken the Freedom House report for 2006. Combined average ratings table.]

Note that in the Freedom House rating, 1 represents “most free” and 7 represents “most repressive”. There is a clear high correlation between the amount of repression and decrease in GDP per capita. The R² of 0.6916 means that 69.16% of the (negative) changes in GDP per capita can be predicted just by knowing the repressive index for country’s regime!

Note that the logarithmic relationship [3] - “GDP per Capita = -14964 Ln(Amount of Oppression) + 29379” - means that a little loss of freedom in a relatively free country has a far greater negative effect on wealth than a little loss of freedom in a relatively oppressive country. Alternatively, you need to introduce far more freedom into a repressive country to make a difference, than you do in an already relatively free country.

This explains why it has taken so long for the countries of the Soviet Bloc and East Asia to recover from socialism, while relatively minor reductions of government power under Reagan and Thatcher had such major wealth generating effects. It also explains how socialist governments manage to so quickly impoverish rich countries, and how the USA has managed to stay so consistently richer than Europe. Freedom is not just the sum of its parts.

socialism kills big

Meanwhile, socialist dictatorships kill far more than any other. They also have a much greater tendancy to invade their neighbours and to export their disfunctional dogmas.

Here is a small selection of socialist mass murderers and their main crimes:

This list is far from exhaustive, nor has socialism stopped killing around the world.

endnotes

1. Cohen give a nice simple explanation of LLE:-

“There are many ways of expressing quantified risk, but here we will use just one, the loss of life expectancy (LLE); i.e., the average amount by which one’s life is shortened by the risk under consideration. The LLE is the product of the probability for a risk to cause death and the consequences in terms of lost life expectancy if it does cause death. As an example, statistics indicate that an average 40-year-old person will live another 37.3 years, so if that person takes a risk that has a 1% chance of being immediately fatal, it causes an LLE of 0.373 years (0.01 x 37.3).

“It should be clear that this does not mean that he will die 0.373 years sooner as a result of taking this risk. But if 1,000 people his age took this risk, 10 might die immediately, having their lives shortened by 37.3 years, while the other 990 would not have their lives shortened at all. Hence, the average lost lifetime for the 1,000 people would be 0.373 years. This is the LLE from that risk.”

2. r is known as the “correlation coefficient”. r² is known as the “coefficient of determination”.

3. A logarithmic relationship is the reverse of an exponential relationship. In an exponential relationship, things get bigger faster and faster. In a logarithmic relationship, things get bigger slower and slower.

For example, the following equation describes the simplest exponential relationship:-

y = 10^x (1)

As x gets bigger, y gets bigger and y gets bigger faster and faster. Any positive feedback system, like a nuclear explosion, can be described by an exponential relationship.

x = log₁₀ y (2)

Equation (2) means exactly the same as equation (1), except this time we care more about what happens to x. As y gets bigger, x gets bigger, but x gets bigger slower and slower. Equation (2) describes a logarithmic relationship. Any negative feedback system, like the way your body regulates your temperature, can be described by a logarithmic relationship.

Every exponential relationship can be looked as a logarithmic relationship, and vice versa.

Mathematicians often use a special number called ‘e’ (~2.72), instead of 10:

y = e^x (3)
x = log_e y = ln x (4)

e is used because it has some special properties that make the sums in calculus - mathematics for calculating the slopes of curves and the areas under curves - easier. The logarithm base e is sometimes called the “natural logarithm” and written as “ln”. When the base of the logarithm is not made explicit, for example “y = log x”, it is assumed to be a base 10 logarithm, as in equation (2).

4. Figures for the number of people killed by various socialist leaders are by necessity only estimates. This site is a fascinating, and very thorough, resource for those interested in where the various estimates come from.

5. Genocide refers to mass murder amongst a particular group/race of people. Democide refers to indiscriminate mass murder across acountry’s whole population.An example of a genocide was the mass killing of Tutsis by Hutus in Rwanda. An example of a democide was Mao’s Cultural Revolution.

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© the auroran sunset, 2007, 3 february

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