Economics of loans to the poor and how technology will help

"One lends only to the rich."

The usual reason given for high interest rates on loans to the poor is the high default rate. If you need to make 5% to cover your costs, but only half your customers are going to pay you back, then you need to charge 210%, so that the ones who do repay can make up for the ones who don't.

This logic assumes that high default rates are a given, not a variable that can be influenced, or even selected. The default rate has a few key drivers and understanding them can lead to ways to influence the economics of a loan.

1. COLLATERAL
One of the basic protections that lenders seek is collateral - something of value that the borrower puts at risk if he doesn't repay. Wealthier borrowers can pledge larger collateral. Often the value of the collateral is proportional to the size of the loan. This explains why poorer borrowers can borrow less, but it doesn't explain why their interest rates should be higher.

2. INFORMATION
If the borrower has no money after using up the loan, then the lender will not be able recover his loan. So the lender needs to know how likely the borrower is to repay the loan, the nature of his income and his financial situation. The lender may even want to monitor the borrower and periodically recollect some of this information. These efforts have a cost that has to be covered by the interest rate. And this cost doesn't scale down with the size of the loan.

To make it worse, this is a bad spiral. When the interest rate goes up, the borrower has a larger incentive not to repay, which means the lender needs to be more thorough in collecting information and monitoring the borrower - further pushing up the cost and further increasing the incentive not to repay.

3. CONTRACT ENFORCEABILITY
The flip side of information is the enforceability of the contract. If the lender had a way to enforce the contract, then they wouldn't incur the costs of having to collect information. While this seems like a theoretical possibility, examples occur all through the real world. The mafia is reported to be the largest bank in Italy (and most profitable). In India, the legal systems takes years to review litigation. This keeps banks out of lending to the poor and those with more innovative ways to collect have stepped in.
 

IMPACT OF TECHNOLOGY
Now look at how technology can affect these drivers. Everyone has a cell phone and increasingly people have smart phones. These are essentially mini sensing and computing devices that everyone has become tied to. The widespread adoption of cell phones and the movement of information to the cloud make the ability to collect and monitor information much much easier.

Further, people are taking their social relations online (1 billion of them on facebook!) and are creating digital assets that have value (think of online timesheets, points, miles, twitter accounts, BBM contact lists, blogs etc) that are not directly correlated to their wealth. It's not inconceivable that social networks can be a platform to enforce contracts or that digital assets can be put up as collateral.

Eventually technology will drive down the cost of loans to the poor. For an example of this has already started, take a look at activehours.

 

A version of this post was originally published at blog.activehours.com.

Prosperity creates poverty

“Capital is that part of wealth which is devoted to obtaining further wealth"- Alfred Marshall

Continuing from my previous post, where we saw that the same wealth distribution patterns across time and across cultures, you'd wonder what causes this. Is it a result of how we as people are, how we interact and how are economic systems function? Can we create a mathematical model that is based on human behavior as it relates to money and would it give us the same distribution?

People engaging with each other and exchanging something of value is very similar to particles bumping into each other and exchanging energy - a basic thermodynamic system that has been studied in a good amount of detail by physicists. You could run a simulation with a large number of people who all start of with the same amount of money. Then they engage with each other at random, exchanging a random amount of their money at each interaction. At the end of a number of iterations, the wealth distribution will be similar to distribution above - with the poorest 10% having 2% of the wealth and the richest 10% having 24% of the wealth. This is roughly what you'd see if you spread everyone evenly between the poorest and the richest person, that is without a huge amount of inequality.

To make the system behave more like normal people do, let's add another condition - that the richer person will never offer up for exchange more than what he could get from the poorer person. After you take this system through a number of iterations, the results are completely different - there are many poor people, with the wealth concentrated among the few rich.


Remarkably, a basic wealth maximization condition makes this system quite similar to the real world. Now that we have a sandbox that works quite like the real world, we can ask some questions, introduce certain changes and see what the results are.

Is the world really a zero sum game?
Can the poor actually carry out their wealth maximization objective?
Would forcibly removing the wealth maximization objective lead to prosperity?

Is economic inequality inevitable?

"The Utopian scheme of leveling [wealth distribution] and a community of goods, are as visionary and impractical as those which vest all property in a crown. These ideas are arbitrary, despotic, and in our government, unconstitutional" - Samuel Adams

Study after study has shown that wealth and income distributions across countries and and cultures follow similar power law functions for the wealthy and log-normal distributions for the rest. In simple English: There are usually a few wealthy people and many poor.

In the US the wealthiest 5% have over 50% of the wealth (the wealthiest 1% have one third of the wealth while the poorest 40% hold just 4% of the wealth). In the UK, the wealthiest 5% have over 40% of the wealth and in India they have 38% of the wealth. This pattern holds even if you look within certain age groups or demographics. And it's been repeating itself through history.

Excavations in the ancient Egyptian city Akhetaten, which was populated for a short period during the 14th century BC, yielded a distribution of the house areas. Assuming that the house area is a measure of the wealth of its inhabitants, we find the same type of wealth distributions existed in the 14th century BC.

A study of the wealth and income distribution at the height of the Roman Empire in the 2nd century had the same type of distribution (and used wheat equivalents as the hypothetical currency).

Another study of the distribution of wealth in the medieval Hungarian aristocratic society around the year 1550 again found the same type of power-law based wealth distribution. The study assumed the wealth of a noble family was based on the amount of land and serf families that they owned.

In 1906, Italian economist, Vilfredo Pareto observed that 80% of the land in Italy was owned by the 20% of the population. He then carried out surveys on a variety of other countries and found to his surprise that a similar distribution applied. This is now known as the Pareto Principle or 80/20 rule. (Pareto supposedly also observed that 20% of the pea-pods in his garden contained 80% of the peas.)

This type of wealth distribution appears repeatedly throughout our history and continuous to occur even today - like an natural law that is invisible, but acting around each one of us right now.  While the Egyptian civilizations didn't survive, the Roman Empire fell and the medieval times ended, each left behind evidence that this natural law was active during their time. What makes this natural law work? What are the structural elements and interactions, the physics, behind it?

Can it be influenced or is it inevitable?