Short version: A few ultra-rich people in the US own a lot of real estate, bringing up the average, whereas in china its more equally spread out.
When the mean is very different from the median, it means the standard deviation is very high (IE, the values are really spread out, rather that clumped close). A simple example would be 2 economies each with 3 people.
Economy A: 1 person owns everything (Lets say $100 bucks), the 2 others own nothing. Average: $33, Median: $0..
Economy B: Each person owns $33. Average: $33, Median: $33.
The gini coefficient refers to the same thing, but measuring inequality within an economy specifically: 0 gini means everyone has the same net worth, 1 gini means 1 person has 100% of the value. In capitalism, the end-result is just like in a game of monopoly: the end result will be 1. In socialism, the end result, acheived via planning an equitable distribution, should be to get as close to zero as possible.
Short version: A few ultra-rich people in the US own a lot of real estate, bringing up the average, whereas in china its more equally spread out.
When the mean is very different from the median, it means the standard deviation is very high (IE, the values are really spread out, rather that clumped close). A simple example would be 2 economies each with 3 people.
Economy A: 1 person owns everything (Lets say $100 bucks), the 2 others own nothing. Average: $33, Median: $0..
Economy B: Each person owns $33. Average: $33, Median: $33.
The gini coefficient refers to the same thing, but measuring inequality within an economy specifically: 0 gini means everyone has the same net worth, 1 gini means 1 person has 100% of the value. In capitalism, the end-result is just like in a game of monopoly: the end result will be 1. In socialism, the end result, acheived via planning an equitable distribution, should be to get as close to zero as possible.
The standard deviation can be very high with the mean and median being the same.
What the difference between the mean and median indicates is a highly skewed (not normal) distribution.
Excellent explanation!
Thank you for your explanation comrade! This helps a lot!