Lognormal Distribution of Labour Force Incomes and Income Disparity

In summary, the annual incomes of a labour force are lognormally distributed and the top 10% earns 37% of the total annual incomes. Using the formula for the log normal distribution, the bottom 10% earns approximately 1.28% of the total annual income. There is no mean or variance provided in the given information.
  • #1
mwendazimu
2
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A labour force's annual incomes are lognormally distributed. If the labour force is arranged in order of decreasing annual incomes and the top 10% earns 37% of the total annual incomes, what proportion of the total annual income does the bottom 10% earn?


Kindly help on this one. It looks simple until you start solving and you realize that there is no mean or variance!
Also take note that all the information is provided. There is nothing missing in this question,
 
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  • #2
Welcome to PF

What is the density of the log normal distribution and how would you rephrase the given information in terms of this density?
 
  • #3
mwendazimu said:
A labour force's annual incomes are lognormally distributed. If the labour force is arranged in order of decreasing annual incomes and the top 10% earns 37% of the total annual incomes, what proportion of the total annual income does the bottom 10% earn?


Kindly help on this one. It looks simple until you start solving and you realize that there is no mean or variance!
Also take note that all the information is provided. There is nothing missing in this question,

proportion bottom 10% = [TEX]1 - \Phi ( \Phi^{-1}(0.9) + \Phi^{-1}(0.37) - \Phi^{-1}(0.1) )[/TEX] = 1.28%
 
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  • #4
I like Serena said:
proportion bottom 10% = [TEX]1 - \Phi ( \Phi^{-1}(0.9) + \Phi^{-1}(0.37) - \Phi^{-1}(0.1) )[/TEX] = 1.28%

I gave the exact solution and was given a big X. Could my professor be wrong?
 
  • #5
mwendazimu said:
I gave the exact solution and was given a big X. Could my professor be wrong?

What I gave is my 2 cents, which I derived using the formulas given on wikipedia.
If you came out to the same answer that should be enough reason to go talk to your professor I guess.
 

FAQ: Lognormal Distribution of Labour Force Incomes and Income Disparity

What is a lognormal distribution?

A lognormal distribution is a probability distribution of a random variable that is normally distributed after being transformed by taking the natural logarithm. This distribution is commonly used to model data that is highly positively skewed, meaning that there are a few very large values and many small values.

How is a lognormal distribution different from a normal distribution?

The main difference between a lognormal distribution and a normal distribution is that the data in a lognormal distribution is positively skewed, while data in a normal distribution is symmetrically distributed around the mean. This means that in a lognormal distribution, most of the data is concentrated on the left side of the graph, with a few extreme values on the right side.

What are some real-life applications of a lognormal distribution?

A lognormal distribution can be used to model data in a variety of fields, such as biology, economics, and finance. Some specific examples include modeling the size of income and wealth in a population, the size of earthquakes, and the size of particles in aerosol pollution.

How is a lognormal distribution related to the central limit theorem?

The central limit theorem states that when a large number of random variables are added together, their sum will tend towards a normal distribution. Since a lognormal distribution is formed by taking the logarithm of a normally distributed variable, it can be thought of as the result of adding many small values together, making it related to the central limit theorem.

How do I calculate probabilities with a lognormal distribution?

To calculate probabilities with a lognormal distribution, you can use the cumulative distribution function (CDF) or the probability density function (PDF). The CDF gives the probability that a random variable is less than or equal to a specific value, while the PDF gives the probability of a specific value occurring. Both of these can be calculated using statistical software or by using mathematical formulas.

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