Top 5 Posts of 2020

Well readers, 2020 is finally done. In most respects, it was a terrible year. But it turns out that being stuck in the house is good for a few things … one of them being writing. I churned out 50 posts this year and typed about 150,000 words. Here are the top 5 posts by views:

  1. What Trait Affects Income the Most?
  2. Stocks Are Up. Wages Are Down. What Does it Mean?
  3. Debunking the ‘Productivity-Pay Gap’
  4. Why Isn’t Modern Monetary Theory Common Knowledge?
  5. Why America Won’t Be ‘Great’ Again

I feel guilty writing a post without any empirical analysis, so here’s some data to chew on. It turns that on this blog, the distribution of views per post roughly follows a lognormal distribution. Figure 1 tells the story.

Figure 1: Distribution of views per post on Economics from the Top Down. This is data since the inception of the blog in 2019. The x-axis shows the number of views per post. The y-axis shows the relative number of posts with the corresponding number of views. Note the log scale on the x-axis.

What’s a ‘lognormal’ distribution? It’s like the familiar normal distribution — aka the bell curve — with one big difference. With a lognormal distribution, you see a bell curve when you plot the distribution of the logarithm of values. Hence the name — lognormal.

The data in Figure 1 looks like a bell curve. But note that the horizontal scale is logarithmic — each tick mark is a factor of 10. So the data (views per post on Economics from the Top Down) is actually lognormally distributed.

I’ve plotted this data not because it is intrinsically interesting, but because the lognormal distribution is important in economics. When we study income, the lognormal distribution appears everywhere. It’s a key signal of inequality.

On this blog, the top posts got hundreds of times more views than the bottom posts. That’s much like individual income. Top earners receive hundreds of times more money than bottom earners. And here’s an eerie fact. The views per page on this blog have a Gini index of 0.6 — about the same as the Gini index of income inequality in the United States.

What creates this inequality? Many things, obviously. But it turns out that we can generate a lognormal distribution using a very simple process. It’s called the ‘rich get richer’. It happens when the probability of receiving more income is proportional to your existing income. The CEO, who already earns millions, will likely get a bigger raise than the janitor, who earns a pittance. On the internet, something similar holds. The probability of getting more views is proportional to the existing views. Popular pages become more popular. Duds remain duds.

Unless checked, this ‘rich get richer’ process will lead to runaway inequality. Exploring this process, and its counterbalances, is one of the things on my agenda for 2021.

Thanks for reading this year. I have lots of interesting analysis in the works. Stay tuned.


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This work is licensed under a Creative Commons Attribution 4.0 License. You can use/share it anyway you want, provided you attribute it to me (Blair Fix) and link to Economics from the Top Down.


One comment

  1. Some COVID questions
    Chiefly, what happens when you combine two processes each following a lognormal distribution?
    I suspect, but have no real evidence, that COVID spreaders follow a lognormal distribution
    Similarly, that locations where transfer is likely to occur follow a lognormal distribution.
    This may be relevant to recombination, which will occur when an individual is infected with two different strains of the virus at the same time. This would be incredibly rare, if spreaders and locations were normally distributed. But Lognormal?
    Mutation is far simpler, the UK went for 1,000 new cases a day in August, to currently 60,000 a day. A more infectious variant is 60-fold more likely to arise.
    There will be strong selective pressure for the virus to mutate or recombine to a form against which a vaccine will be less effective. So that numbers and distributions will be critical.

    Like

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