Women, men and money

From a dataset on restaurant tip included in the Python seaborn library, we investigate using Python with Jupyter Notebook how much money women and men relatively make, and why.

Most people don’t tip more or less because of server is a man or woman, but we do tip according to tip amount (for equal services).

I will tell you base on data analysis the real reason women make less is mostly because:
1. women work mostly lunch shifts
2. lunch bills are smaller than dinner ones.

Indeed, in every industry, including food services like in the restaurants, women have shouldered more share of the most difficult job in the world: parenting, and dedicated their time and energy to their families.

As a result, they have earned less money than men on average. Let’s give our appreciation to women for their roles in taking care of families!

There is a profound reason why we say “Mother nature”!

Should women work more dinner shifts and leave the important job of taking care of children to …?

That is a difficult question… Take a look at this Jupyter Notebook for the detailed data analysis.

What is collateral | 什么是抵押 (dǐ yā)

I was writing a document on credit risk while the children were playing.  Suddenly one sneaked up behind my back and asked “what is collateral?”  什么是抵押 (dǐ yā)?

Hmm. It is something you use to secure money borrowed. That answer is not clear to children.

So, how about this:

If your aunt just bought a house with a mortgage (i.e. with money borrowed from a bank or some other places), if your aunt does not pay back the money borrowed, what will the lender do? The lender (for example, a bank) will take over the house and sell it to get the money back. The house is the collateral for the money borrowed.

Recall in Shakespeare’s play “The Merchant of Venice”, Antonio borrowed money from moneylender Shylock. If Antonio were unable to repay it at the specified date, Shylock would have taken a pound of Antonio’s flesh according to their agreement. Wow. That’s unthinkable in today’s world. But in 16th century, a pound of human flesh could be conceived as “collateral”.

Herstory of money-1 | 钱的历史

This is the first installment of a series of post about money, cryptocurrency and credit scoring, accompanied by Python Jupyter Notebook in our GitHub repo on credit scoring.

In this post we talk about paper money 纸币.  The reason why we keep it in the practical math category is because the herstory of money is also the herstory of math.  In God we trust and in math we trust.  God made the universe with beautiful math.

Did you know that paper money 纸币 was first used in ancient China around the 11th century 北宋朝?

Paper money was used broadly during those days due to shortage of copper and the convenience of paper money. However, the convenience combined with the unlimited power of the government to print money lead to inflation, subsequently the loss of credibility of the government, and its eventual downfall. So, even though the Northern Song dynasty had an advance monetary system, its credit failed due to long and costly wars.

Did you know that the Chinese Southern Song 南宋 dynasty government printed money in no less than six ink colors to prevent counterfeiting?

They printed notes with intricate designs and sometimes even with mixture of unique fiber in the paper to avoid counterfeiting. That was in 1107!

Backed by gold or silver too?

Isn’t it amazing that their nationwide standard currency of paper money was backed by gold or silver?! That was in between 1265 and 1274.

In the 13th century, Chinese paper money of Mongol Yuan 元 became known in Europe through the accounts of travelers, such as Marco Polo

“All these pieces of paper are, issued with as much solemnity and authority as if they were of pure gold or silver… with these pieces of paper, made as I have described, Kublai Khan causes all payments on his own account to be made; and he makes them to pass current universally over all his kingdoms and provinces and territories, and whithersoever his power and sovereignty extends… and indeed everybody takes them readily, for wheresoever a person may go throughout the Great Khan’s dominions he shall find these pieces of paper current, and shall be able to transact all sales and purchases of goods by means of them just as well as if they were coins of pure gold”
— Marco Polo, The Travels of Marco Polo

Math for our national debt 国债数学

$20 trillion debt divided by American population is over $60k per person (that includes babies and the elderly).

  • We want to be responsible and we teach our children to be responsible.

Estimated $100 trillion unfunded liabilities of our nation equals about $300k per person.  Think about what we are leaving for our children?!

  • To be responsible, let’s pay off our $60k per person existing national debt and deal with how we will fund the $100 trillion unfunded liabilities before (or while) we want to solve all the problems in the world.   If we don’t fix our problems, how can we help others, right?

National debt at end of 2016

The math part:

What is $20 trillion?  It is 20,000,000,000,000.   Have you ever seen a number this big in real life?  This is our national or federal debt.  Where is $20,000,000,000,000 in this picture?  It is rounded from “$19,947,304,555,212”.

Source: TreasuryDirect https://www.treasurydirect.gov/NP/debt/current

$60K is $60,000.  That’s more than what an average American make for a whole year before tax.

Calculation: money owe by each person=  divide debt by number of people = $20,000,000,000,000/323,425,550 number of people = over $60,000 debt per person, even for a kid.

  • Next time when someone tells you he/she is saving the world,  you can ask “would you like to pay off your share of national debt? And how many more shares if you can?”

The economics part:

What is debt?  It is the money we owe.  It is money we did not have  we borrowed from somewhere, including to foreigners, and we spent already.

What is unfunded liability?  It is the money we have to pay but do not have the money for, including money for the elderly, the disabled, those on welfare, and other promises the government made.