Jan 24, 2021
Visualizing Competitive Non-Efficiency
In the traditional capitalist model, the most efficient and capable company succeeds.
The above sentiment is one we’ve heard countless times before, to the point that it is taken as common sense. We are told that the free market is the most efficient way of producing and allocating resources. However, you’ll find that there is no math justifying this conclusion. As it turns out, “efficiency” is an overloaded term in economics with many meanings; some are mathematically justified, some are not. The conflation of all these meanings has undoubtedly led to misunderstanding of even basic things about the economy. For this post, I want to show why the above quote isn’t true. I hope to show this by visual analogy.
Related to this is the assertion that free markets will reduce inequality. This follows from the belief that (in economic equilibrium) a person’s income is equal to their contribution to society. Whatever was the initial cause of inequality, once you let the free market mechanism run its course, society will configure itself in the most fair and optimal way. Firms that pay their workers too little will lose them to firms that are willing to pay more. Firms that overpay their employees will be less profitable, and will be outcompeted by firms that can operate at lower cost. Thus people’s wages should then stabilize at their respective contributions. In this view, any resulting unequal wealth is due to the natural unequal contribution/ability of each individual. This remains the dominant view in both economic and political discourse today.
I’ll use the game Starcraft 2 as a tool for visualization. SC2 is a war game and not an economics simulator, but the underlying mechanism that I’m trying to show is simple enough that it appears in countless systems. SC2 is just the simplest way to visualize that mechanism. I hope to make clear the connection to economics by the end of the post.
Above is a basic example of the SC2 simulation we’ll be using. What you see is two teams, the blue team on the left and red team on the right. Each team will send soldiers from their respective side, and the soldiers will run toward the middle. Above each unit is a health bar. It starts full (green) and gets reduced as the unit is hit by enemy fire. If it is depleted, the unit dies. Though it may seem complicated from the outset, the soldiers are programmed to behave simplistically. Once an enemy unit is in range, a soldier will fire their weapon until either they die or the enemy dies. If a soldier finishes firing and doesn’t have an enemy in range, it’ll return to the center of the screen.
In this first example above, both teams are given the exact same properties. So the battle shows them trading blows equally. (Note the simulation is slightly imperfect so the soldiers don’t shoot and die at exactly the same time, but you get the point. I may write a follow-up post using just simple math to avoid the simulation imperfections.)
Below I’ll start changing the simulation.
The video above shows what happens when one of the parameters of our simulation is changed. In this case, the rate of production of blue soldiers has increased by 11.6% (blue creates a soldier every 6 seconds, red creates a soldier every 7 seconds). We can think of this as representing how much more efficient the blue team is compared to the red team.
As you’d expect, since the blue team produces soldiers more quickly, by the end of the video the number of blue soldiers on screen is larger than the number of red soldiers.
Below I’ll change a different parameter of the simulation. Before viewing the video can you guess what happens when both teams produce soldiers equally quickly, but one team starts with more soldiers? I will let the video run long enough so the long term trend becomes apparent.
In the above video both teams create soldiers equally fast. However, the red team begins with an additional 3 units. Here too we see that there is an accumulation of soldiers. Despite creating soldiers at exactly the same speed as the blue team, by the end of the video, the red team has increased its soldier surplus from 3 to 5. This dynamic is not immediately intuitive. Prior to seeing the video, you might think that since both teams create soldiers equally quickly, in the long run the teams will even out. This is partially true, the total number of soldiers produced by both teams will even out (i.e. the ratio of red to blue soldiers starts at 4:1 and continues as (n+3):n. As n gets bigger, the ratio gets closer to 1:1).
However in terms of soldiers remaining on screen, the opposite happens and the red team will accumulate more and more soldiers as the simulation is run longer.
Finally, the last video will pit the two simulation parameters against each other.
For this last video, the blue team produces soldiers 60% faster (blue creates a soldier every 2.5 seconds, red creates a soldier every 4 seconds). Again, red begins with an additional 3 soldiers. We can see that even though the blue team produces soldiers more efficiently, they are never able to overcome the red units.
This gives us two seemingly contradictory results. If we were to run the simulation forever, the blue team would have produced infinitely more soldiers than the red team. At the same time, the red team will have accumulated infinitely more living soldiers.
Of course, changing the relative parameters of the simulation would change the outcome. There is a level of soldier production that would allow the blue team to “beat” the red team. But we can imagine a greater context here too. Say the blue team beats the red team, it then begins to accumulate more and more. After some time, a green team comes along. How much more efficient would this team need to be to win out?
Hopefully it’s clear by now that “efficiency” is not the sole factor that determines the outcome in the long run. The current number of soldiers matters as well. So the initial number of soldiers can allow an inefficient side to win out, even in the long run.
This kind of behavior will happen in any system that bears some similarity to our SC2 model. That is, there are two factors which affect future outcomes: power and efficiency. In the military (SC2) context, this means military power and efficiency of military unit production. In the economic context, this would be economic power (wealth) and productive efficiency.
Imagine the soldiers are not shooting each other, rather, there is an ambient force which causes the soldiers’ health to decrease. You can think of this “force” as starvation or depreciation. Imagine also that the soldiers are rushing towards the center not to shoot at the other team, but rather to collect a resource which can alleviate this starvation. So the more soldiers one team has, the more of that resource that team can collect, the less it succumbs to starvation. This also means that the more resource one team is able to collect, the less there is for the opposing team, and thus that opposing team will starve at a faster rate. Lastly, imagine that the soldiers represent money/wealth. Imagine the two teams as two firms bringing their wealth and engaging in economic competition. The underlying math will be the exact same as the SC2 videos shown above, the rates of change and long term trends will be the same too. So we still have our two factors: economic power (number of “soldiers”/wealth) and productive efficiency. (If it’s still unclear, the next section on WeWork will provide a concrete example.)
Put simply: if money matters, if the amount of wealth that a firm controls has any bearing on economic competitivity, then there’s no guarantee that the most efficient firm will win out, even in the long run. This is what happens when you use competition to allocate resources, but those resources are what fuels competitivity.
For the remainder of this post I’ll be giving a few basic examples of how money/wealth does matter.
The New Yorker recently posted an article titled “How Venture Capitalists Are Deforming Capitalism”. In it, the author covers the rise and fall of the WeWork company.
The company was able to amass a ridiculous amount of venture capitalist (VC) funding despite being horribly mismanaged. There was nothing that made it particularly special, nothing that made it more efficient than its competition. However, the article notes how the VC money allowed WeWork to set unsustainable prices which effectively steamrolled all of its competition. WeWork could survive operating at a loss due to its wealth, whereas its competition could not. The company only really started to collapse after it was set to become public and its finances were revealed. The author of the article recognizes this fiasco as problematic, yet misses the root cause. He states:
In the traditional capitalist model, the most efficient and capable company succeeds; in the new (venture capitalist) model, the company with the most funding wins.
This is the falsehood that I put at the top of this page, and the reason I’ve written this post. The author recognizes the problem with VCs but ends up categorizing it separately from “traditional capitalism”.
Hopefully it’s clear by now that “venture capitalism” and “traditional capitalism” are the same; the problem is that money/wealth matters. A core aspect of every economic agent is that it needs resources in order to survive; this is true of firms and it is true of people. There is no such thing as economic competition where its just “efficiency” which matters, the survival aspect is inextricable. As seen in the case of WeWork, the price of products on the market are not solely determined by the cost of production; it is also a reflection of the wealth and precarity of all involved.
Economists and politicians are aware of these problems, however they are always framed as anomalous “market failures”. In high school economics you’re taught about the dangers of monopolies and how they’re able to engage in “predatory” behavior. A firm with monopoly power can undercut its competition in the same manner as described above. Yet you’re only taught that this behavior is exclusive to monopolies, rather than an element that pervades the entire economy, to varying degrees. By pigeon-holing this predatory behavior to the special case of monopolies, economists end up saying that capitalism is efficient except in the case of monopolies (or some other edge case). This kind of thinking blinds you to the many inefficiencies that exist throughout the economy.
Another example of how money matters is the interest rate. The interest rate basically tells you how much it costs to get money. Notably, the rate that one is able to obtain is broadly correlated to how much wealth they have. There is the top level rate that is available to the private banks from the central bank (known as the bank rate). Obviously quite a bit of wealth is required to become an accredited bank and to get access to this central bank rate. Through transactions with other financial entities it’s possible that a bank can deal at rates lower than those of the central bank but it never has to borrow at a higher rate; the central bank rate acts as an upper bound for the private banks. This privileged status gives banks the opportunity to make money. If a citizen (who doesn’t have access to the central bank rate) is in need of money, they’ll need to borrow from a private bank. That bank simply needs lend at a rate higher than what it would cost to borrow from the central bank and then a profit can be made.
Loans from banks to citizens also vary in interest rates as well. The lowest rate loan that most people are familiar with are mortgage rates. These rates are relatively low precisely because a house is used as collateral. To take out a mortgage, the debtor needs to prove that they would make enough money to pay for the house over the period of the loan. Once again, wealth matters.
As you progress down the societal totem pole and take a look at the loans available to the less wealthy, you’ll notice that the interest rates start to sky rocket. At the very bottom you have loans that prey on the poor. One case of these are called “payday” loans: high interest loans which will generally eat a large chunk out of the debtor’s next pay check (if not it’s entirety). A term generally associated with these loans is “predatory”. As with the discussion on VCs, “predatory” doesn’t signify some special economic phenomenon. Rather, it is a product of how money matters in society. It is only a matter of subjectivity where the line gets drawn and some loans get labelled “predatory”.
Politicians have sought to pass laws regulating the power of predatory lenders, however this misses the root of the problem. The reason individuals need to take out these absurdly high interest loans in the first place is because money is required to survive. Rents need to be paid, food needs to be bought.
You can kill predatory lending by simply ensuring that each citizen does not have to depend on money and the market in order to survive. This requires redistributing a lot of the economic power in society and building a strong welfare state.
Hopefully it’s clear by now that the market doesn’t trend towards productive efficiency. It can’t, because money matters. Of course an economist may say that productive efficiency is achieved in the case of perfect competition. But that’s pretty absurd, obviously we don’t live in conditions of perfect competition.
Lastly, I’ll note that my little Starcraft 2 examples are themselves just a model. The real world economy doesn’t trend towards absolute inequality as it does in some of the videos I’ve shown. But despite the simplicity of the Starcraft 2 model I hope it’s helped illuminate some aspects of the economy that get ignored.
