Patent Law and Economic Growth

The purpose of the patent system is to "promote the progress of the arts and sciences." In other words, the patent system should promote the development of knowledge. Currently, the U.S. government grants a patent holder a temporary (20 years) monopoly on the patent in exchange for the inventor disclosing to the public how the invention works. The idea is that by granting a temporary monopoly, an inventor has an incentive to invest money in discovering knowledge because the inventor can recoup his investments during the monopoly period. When the monopoly period expires, anyone can make, use, or sell the invention.

Without this monopoly protection, inventors would be reluctant to invest substantial amounts up front. Without it, another party could make and sell the invention after the inventor creates it, but that party would not have to recoup any up front investments. Thus the second party could sell for cheaper, and the inventor would be unable to recoup his investment.

Patents can only be granted on inventions; they cannot be granted on phenomena, principles of nature, and abstract ideas. So mathematical formulas are unpatentable. But the application of a forumla to manufacturing process for making rubber is patentable. The line between an abstract idea and an application of that idea in a particular context can be difficult to draw.

One current issue in patent law is how to treat software patents, and whether software is patentable. Software can often be more like a mathematical formula than an invention. It can be tricky to figure out when software is merely an abstract idea and when it is an application of an idea to solve a problem. Part of this debate over whether software needs patent protection. Progress in the software field moves quickly. By the time a software patent application is processed, the underlying software is often obsolete. Software is more accessible than many other areas of innovation. The investments required to create software are much lower than those to create pharmaceuticals, industrial machines, etc. So patents may not be necessary to encourage innovation because 1) rapid innovation and the first mover advantage obviate the need for a temporary monopoly and 2) inventors do not need to recoup substantial initial investments.

Part of the debate over software patents is over how to best encourage innovation. The patent system applies not just to software, but to chemicals, pharmaceuticals, manufacturing, aerospace engineering, etc. Software inventors do not need to recoup large initial investments. Most other fields, such as the chemical and pharmaceutical industries, are different. Developing chemicals and drugs involves huge up front investments in things like labs, equipment, testing, and regulatory approval. Additionally, the chemical industries are less predictable than software - it is more difficult to invent a new compound for fighting cancer than it is to develop a piece of software that does something new. So the patent system must provide the proper incentive for investment for many industries that are very different.

During the temporary monopoly period, inventors can recoup their investments because others cannot sell the invention. Consequently, the size of the initial investment is dependent upon what the inventor can likely get back during patent term. For software, it seems that protection is often unnecessary to encourage innovation. For pharmaceuticals, however, patent protection is essential to encourage the millions and billions of dollars that companies invest in research and development. Given that much research yields no salable results, companies recoup their investments in many different projects from the few projects that create results.

In a later post, I will discuss how the patent system is relevant to the health care debate. And in another, I will discuss why investment, and not spending, is the driver of economic growth.

Income Distribution

Eventually I will make another post about how we should expect wealth distribution to change given a change in income distribution. In the meantime, Steve Horowitz notes how the income distribution has changed from 1980 to 2006.

"From 1980 to 2006, the percentage of US households earning $100,000 or more (in constant 2006 dollars) grew from 8.6% to 19.1%. The percentage between $75k and $100K grew from 10.3 to 11.3 percent. At the other end, the percentage under $15K fell from 16.6% to 13.4% and the percentage between $15K and $34K fell from 26.2% to 23.3%.

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Let me repeat that: over 30% of US households in 2006 earned above $75K compared to under 20% in 1980. Over the same period, the percentage of US households earning under $35K fell from 42.8% to 36.7%. Fewer households are poor, fewer are middle class, and a hunk more are above $75K."

So the income distribution is becoming a bit flatter. Given what we know about various distributions, we should expect this flattening to result in a greater concentration of wealth. But this does not imply less fairness.

My Key Principles of Health Care Reform

Over at Cafe Hayek, Russ Roberts suggests that we establish the key principles of health care reform. Here is my take.

My Health Care Reform Manifesto:

1. The true cost of a health plan should not be hidden from the individual.

2. Individuals deserve the same tax benefits as corporations when purchasing a health plan - the preferential treatment given to employer sponsored health care reduces employees' choices, causes employee "lock-in", and both reduces the availability and increases the cost of private health plans to individuals.

3. Only the individual can choose the balance of cost and coverage that is appropriate for him or her.

4. Rules should require more transparency regarding a health plan's cost, coverage level, and limitations rather than obfuscating them through labyrinthian regulations.

5. Only the individual can decide whether a treatment is worth its cost.

6. Individuals should have the choice to purchase true health insurance; individuals should not be forced to chose between plans that spread the costs of ordinary health care across all members and no coverage at all.

7. The most effective way to provide assistance to those unable to afford health care is through measurable subsidies or tax credits to those in need rather than through government control over prices or coverage terms.

8. We have an obligation to ensure everyone has access to a minimum, basic level of health care, but not an obligation to provide everyone with the highest quality health care available.

U.S. Wealth Concentration - Part 2

After re-reading my first post, I realize that I did not clearly delineate my goals for this set of posts. I am hoping that my analysis of income distribution will show two things. First, by looking at the wealth concentrations that result from various income distributions, I hope to show a baseline wealth concentration (essentially a Lorenz Curve) that can be compared to the actual wealth concentration. For any income distribution where all incomes are not the same, the top bands of earners will naturally have a greater concentration of wealth. Whatever your conception of a “fair” distribution, I would like to show what a baseline wealth concentration would look like for you.

Second, by examining the wealth concentrations from various distributions, I hope to show which features of the U.S. income distribution are responsible for the U.S. wealth concentration.

I have created a chart that compares the U.S. income distribution to a variety of statistical distributions. The purpose here is to get a mathematical approximation of the actual distribution.


Here, you can see the 2005 U.S. income distribution in red. The data I have from the Census is in $2500 increments below $100K, in $50K increments up to $250K, and then it has no increments. For my chart, I used an increment of $5K up to $250K, and then added a category for $350K and $500K. For the values over $100K, I just uniformly distributed the Census amounts. So if the Census said 10% of people earned between $100K and $150K, I uniformly distributed that 10% across $5K increments. Consequently, the income line appears stepped above $100K. Because I compressed the increments for $250K-$500K, there appears to be a spike on the right side.

For all the distributions, I used truncated distributions. So the distributions stop at 0, and all the other values are increased correspondingly. The normal distributions both have a mean of 50K. I had to write some VBA to make the skewed normal distribution. The uniform distribution is from $5K to $250K, not $500K, because I used a U(5K, 250K) in my first post. A U(5K, 500K) would be a straight line across 0.01, instead of 0.02. Finally, I am not familiar with the Weibull distribution, but Excel had some built-in functions for it. The results seemed so similar to the actual distribution that I have decided to keep it.

I created two sets of Lorenz curves that show wealth concentration for the above distributions. Excel did not want to cooperate, so I used a tool called Ploticus (http://ploticus.sourceforge.net/doc/welcome.html) to generate these graphs. Its functionality is somewhat limited (though it can do at least one thing that Excel cannot), so I had to create two different charts. The first one is a comparison of some of the distributions above. The x-axis is cumulative distribution and the y-axis is percent of all income.


The second one has actual, normal, uniform, and line of perfect equality:


Although the U.S. income distribution is quite different from the “line of perfect equality,” it is not all that different from a uniform or normal distribution. If income were distributed normally, the U.S. distribution is not far off.

You may note that the variance of the normal distribution is equal to the mean. This is because U.S. income distribution is quite varied. Thus far, I have found that as the standard deviation of income increases, the concentration of wealth at the top seems to increase as well. In my next post, I plan to explore how changes in the standard deviation affect wealth concentration.

U.S. Wealth Concentration

Income inequality has been a bit of a hot topic recently. The claim is that income inequality in the U.S. is at an all time high, and inequality has been increasing over the past few decades. One recent statistic is that the top 20% of earners take home 50% of the income. This may be true, but that number means nothing in isolation and its presentation is misleading. I will attempt to show why in a series of posts.

To start, let’s look at the actual distribution of income in the U.S. According to the U.S. Census, the following shows the distribution of income across quintiles in 2007.

The top 20% earn about half of all income, which seems like quite a share. This figure is compared to historical data, e.g., 1920 & 2008, 1975-2005, or 1998-2008. These comparisons show how distribution has changed across income bands, but the numbers really do not mean anything in the abstract. For example, is it unusual that the top 20% earned almost 50% of the income? Does this seeming concentration of wealth mean anything statistically?

Although we can compare to the past, we really do not have a baseline figure to compare with the income distribution figures. The mental baseline is likely the line of perfect equality, a term relating to Lorenz Curves and the Gini Coefficient. The line of perfect equality would be the following distribution.

But for this distribution to occur, all incomes must be equal. By equal I mean exactly the same. So the line of perfect equality would only occur in a completely socialist world. Thus, it may not be a good baseline to use.

This post was spurred by an observation I had during one of my prior consulting jobs. Back when I used to analyze revenue and sales data, I noticed that revenue tended to follow a pattern (I have changed the exact numbers to protect any confidentiality). A client, or more precisely annual revenue from a client, fell into one of three categories: small, medium, and large. Typically, 90% of clients provided a small or medium amount of revenue, and maybe 10% provided a large amount of revenue. Although revenue from small and medium clients comprised the bulk of total revenue, the largest 10-20 clients (maybe 1% of all clients) frequently provided over 25% of revenue.

At first, the revenue distribution seemed odd. On second glance, the distribution seemed less odd. (Since I am changing these numbers, I have made them approximate the income distribution in the U.S.) The average annual revenue for small clients was maybe $40,000 and for medium clients maybe $150,000. The largest clients, however, typically provided over $1,000,000 in revenue. If, for example, the firm had 700 clients at $40K, 200 at $150K, and 10 at $1.5M, those 10 clients will make up over 20% of total revenue. The 10 largest clients seem to make up a disproportionate share of revenue, but when you consider that $1.5M is almost 40 times larger than $40K, it starts to seem less odd.

It seems to me that income is distributed in a similar fashion – with most households clustered around some lower values, and a few households out at the extreme. The extreme values may comprise a substantial portion of total income, but looking at them does not tell us anything about what is happening across the rest of the income distribution. Additionally, the concentration of income at the top, like the concentration of revenue, may not be as large an indication of inequality as it appears.

Let’s look at a uniform distribution. For those not statistically oriented, a uniform distribution means that each income band contains the same number of people (or households). I assume that the maximum income is $250,000. I use income bands of $5K, so incomes between $0 and $5K are in the $5K band, $45K to $50K are grouped in the $50K band, etc. In a uniform distribution, the top percentiles earn a greater share of the total income pie than the lower distributions. It also looks more similar to the income distribution in the U.S. than if income were “perfectly equal.”

Here is the Lorenz Curve for this distribution compared to the “line of perfect equality.”


I am not sure how fair a uniform distribution of income would be in the absolute sense, but I think it is fairer than giving everyone the same income. Does a uniform distribution strike people as unfair? If a uniform distribution is not unfair, does the U.S. income distribution look less concentrated at the top now that you have seen this chart? Rather than using the mythical line of perfect equality, it may make more sense to compare income distribution to the uniform distribution.

I am in the process of analyzing a variety of possible income distributions to see what income inequality would look like. I am working on some normal distributions, truncated normal distributions, skewed normal distributions, and Weibull distributions. This process is taking much longer than I thought it would, so this post contains only some very basic, initial findings. Unfortunately I do not have mathematical solutions to what I am trying to show. The calculus involved is ugly, it has been years since I dealt with this kind of math, and we did not do much math in law school. Hopefully I will have something interesting to post soon.

Relief

Studying for the Virgina Bar Exam was awful, and it sucked up way too much of my summer. It is finally over, at least for now, but hopefully for forever.

I should start posting again soon.

Free Market vs. Strict Regulations

Free markets give people incentives to innovate and improve inefficiencies. They also encourage technological advancement and increase product diversity. Although highly regulated markets still allow some innovation and some advancement, they are more likely to stagnate growth. A good example of this comes purely from the private sector: the personal computer.

In the 1980s, many computer companies were developing personal computers. For this discussion, the two relevant companies are Apple and IBM. These companies employed two different business models. Apple chose control: it manufactured its hardware, software, and peripherals. Apple did not allow many third parties to make Apple-compatible products. IBM chose open source: it published its specification and allowed any company to make and sell compatible hardware, software, and peripherals. Apple chose strict regulation. IBM chose free markets.

Apple had (and still has) lots of smart, innovative people. They are frequently credited with creating the first graphical user interface. Their products are still innovative and sleek. However, they do not let many third parties make compatible products. Even today, you still have to ship your computer back to Apple if a part has a problem. Dedicated users say their machines have fewer problems and more intuitive. Perhaps this is the case. But at what price?

Anyone could and still can develop a product for the PC. Since the 80s, hundreds and maybe thousands of companies have developed PC compatible hardware and peripherals. The diversity of PC compatible products grew rapidly, much more rapidly than for Apple. Many companies, such as HP, Compaq, and IBM, made PCs. My first computer, a 386, was built by a neighbor who purchased the parts and built it for us. Anyone could service a PC. Replacement parts were cheap and easy to come by. Prices for PCs were and still are significantly lower than Apple's computers.

Microsoft developed Windows, which continued using the free market model. Although Microsoft is very protective of its operating system, it lets anyone develop compatible software. Owners of Apple computers will be very familiar with new products that are only available on the PC. Even today, many companies that develop products for the PC do not support Apple. But the model employed on PCs allows anyone and everyone to innovate.

The result is that the innovative people at Apple were no match for the collective power of the people - the power of the market. PCs dominate the personal computer market. They have cheaper, better, more widely available hardware. More software companies make applications for PCs than for Apple computers. Most peripherals such as digital cameras, printers, mp3 players, and scanners can just be plugged into a PC and they work. Not so with an Apple product.

The contrast between these two products is stark. Apple machines are more expensive, have less hardware variety, less software, and fewer compatible products. They are also less popular.

Some people may complain that PCs are prone to crashes. An Apple machine never gets a Blue Screen of Death. But an Apple machine cannot use the same variety of hardware and software that a PC can. Creating a product for an Apple computer requires more work and is more expensive and results in less sales than creating a product for a PC.

This is the difference between free markets and a strictly regulated economy. Sure, the market will have blips, and sometimes things won't run perfectly smoothly. But markets will create more products, spur faster innovation, and reduce prices through competition. Most importantly, markets let anyone participate. If you have an idea or if you think you can build a better hard drive, you can do it without getting approval from some central agency.

Strict regulation leads to stagnation. Apple has struggled over the past two decades. It is starting to reemerge through non-computer products. But it has stagnated over the past 20 years. Consumers chose lower prices and more diversity over stability. PCs are stable enough. Regulation will lead to the same results. Only those few chosen by the central agency will be able to develop products. Stability will be valued over innovation. Certainty over growth.

Whether they knew it or not, by choosing PCs over Apple computers the American people have shown that they prefer the results of the free market over the results of strict regulation.