Does The Rise Of Big Data Make Marx’s Centrally Planned Utopia Feasible?
May 5th, 2018 marked the 200th anniversary of Karl Marx’s birth. To celebrate the occasion, a handful of writers made the argument that, despite the bad press Marxism has received over the past century, many if not most of Marx’s core critiques of capitalism were “essentially right.”
One of the most widely circulated of these pieces made the increasingly popular argument that, while Marxist regimes have been an unmitigated failure over the past century, his theoretical arguments for the efficacy of central planning and predictions about the inevitable demise of capitalism were in effect spot on. The reason Marx-influenced regimes failed historically, according to this line of thinking, is not because of any inherent problems with central planning. Instead, it was because planners lacked the data necessary to effectively implement the sort of command and control economy that Marx envisioned. In other words, Marxism didn’t fail because it was wrong. It failed, as one prominent leftist economist argued in summarizing Thomas Piketty’s best-selling Capital in the 21st Century, because it was “just early”. By this logic, defenders of Marxism argue, the invention of the computer and rise of “big data” should inject new life to socialist ideas that were once considered dead.
The Socialist Calculation Debate
So does the rise of big data portend the sunset of capitalism and the dawn of a now viable socialist alternative? Will the future look like an egalitarian blend of HBO’s Westworld and Peter Joseph’s Zeitgeist Movement? I recently had this discussion with a student of mine who grew up in Silicon Valley. Although he is by no means a Marxist, he played the Devil’s advocate by pointing out, as many modern Marxists do, that:
Today, we have public access to more knowledge that previously imaginable…Not one person who lived during the Communist revolution could have imagined the scope and magnitude of the available data and information that companies like Facebook, Google, and Equinix gather in an hour. With this in mind, do you think it could be possible to access the knowledge required to efficiently allocate resources?. . . With all of the data that Amazon collects, do you think a centralized government could more effectively allocate resources than a free market under some circumstances?
This logic is tempting. However, its appeal is based on a superficial understanding of the key insights from arguably the most important economic debates of the 20th century, the socialist calculation debate.
Contrary to popular belief, Ludwig von Mises and F.A. Hayek’s argument against their socialist opponents in the 1920s and 1930s wasn’t simply that socialist planners suffer from a lack of information. Their argument was that planners lack access to the specific type of knowledge that makes it possible to efficiently allocated an economy’s means of production among an endless array of possible uses. This lies at the essence of the central question of economics, what economists call “the economic problem”: what is the best (i.e. most efficient) way to allocate an economy’s scarce resources to produce the final output that consumers ultimately desire? In other words, what should we produce, and how should we produce it?
The key ingredient for rational economic calculation, according to Mises and Hayek, is market-determined prices. Prices allow producers to calculate the opportunity cost of various methods of production. Producers can then use the market prices to determine what the least costly means were of producing whatever final goods consumers ultimately desired.
A critical aspect of the calculation debate that is particularly relevant for those interested in sound money is the emergence of a commonly accepted medium of exchange, i.e. money. Money plays an essential role because it provides a common denominator for comparing the value of all of an economy’s goods and services. As monetary equilibrium scholars have long argued, money represents one-half of every non-barter transaction. Because money is so pervasive, it is arguably the single most important good in an economy. This is why sound money and economic prosperity are so closely intertwined.
The key ingredient for rational economic calculation is market-determined prices.
An example helps illustrate this point. Suppose you are a central planner tasked with building a railroad. Even if we assume you’ve been given the most efficient route and ample resources to complete the project, how would you decide what inputs you should use to build the railroad in the most cost-effective manner? Without money price signals, you’d be unable to determine whether it’d be less costly to construct a railroad out of gold or steel. Gold is, after all, a higher quality metal than steel. Absent money prices, it might seem obvious to the average engineer that they should build the railroad out of gold. But for a variety of reasons, many of which might be completely unknown and unknowable for planners, gold is likely to be a significantly costlier input than steel. Absent money prices and the ability to engage in “profit and loss accounting,” the most “technologically efficient” production method would always win out over the most “economically efficient” (i.e. lowest opportunity cost), at a high cost to society.
As Hayek emphasized throughout his writings on this topic, the knowledge that is required to engage in rational economic calculation cannot be assumed to be given outside of the context whereby it is created. That is, economic knowledge can only be generated in an institutional context that protects private property, particularly for the economy’s capital goods. In other words, it can only be created in a market economy. Absent private property in the means of production, there can be no market for them, and hence no money prices to inform entrepreneurs—Mises’s “driving force” of the market economy—what the least costly method is for producing any given good or service.
Data Is Key, but Information Isn’t Enough
To use another example, imagine you are the manager of the New York Yankees (Red Sox fans, bear with me). Suppose you were blindfolded, placed in a sound-proof dugout, and stripped of all prior knowledge about the players on your roster including their statistics and positions. How would you put together a lineup that would give you the best chance to win? The answer is fairly straightforward: you wouldn’t. Even if you had a lineup loaded with talent, you’d be unable to come anywhere close to determining the most efficient (i.e. economical) way to structure your lineup. You’d be just as likely to have Aaron Judge pitching and C.C. Sabathia batting cleanup as the reverse.
Add to that the fact that big data from the past can’t necessarily be used to predict future consumer preferences or spark the sort of innovation and “creative destruction” that economists like Joseph Schumpeter placed at the forefront of why the capitalist system was so dynamic, and you’ve got the recipe for understanding why there are countless examples from recent history of how capitalist systems thrived while socialist systems failed. One need only look at the natural experiments that played out in East and West Germany, or a snapshot of the Korean peninsula at night, to grasp this gist of this.
Big data from the past can’t necessarily be used to predict future consumer preferences.
Boettke (1998) summarizes Mises and Hayek’s core arguments in three related steps. Each point is so critical that I make my students memorize them in every one of my classes.
- Without private property in the means of production, there will be no market for the economy’s means of production
- Without a market for the means of production, there will be no money prices for them
- Without money prices reflecting the relative scarcity of capital goods, economic decision-makers will be unable to rationally calculate alternative uses of capital goods.
So how well have these arguments aged in the era of big data? As it turns out, quite well. Mises and Hayek’s essential argument is that the fundamental problem with socialism isn’t informational—it’s institutional. The earliest centrally-planned economies didn’t just suffer from deficient information. If the problem was, in fact, insufficient or inadequate information, it would stand to reason that the exponential rise in computing power might indeed cure the ills of a command and control economy. But as Mises and Hayek eloquently argued, the knowledge that is necessary to engage in rational economic calculation is only created and only exists in the context of a market economy. Information and knowledge are two different things. And without money prices and profit and loss signals generating the latter, there is no reason to believe that more information will lead to more efficient outcomes.
If the theory behind the socialist calculation debate is too intense for your intended audience, bear in mind that there’s an equally powerful empirical argument for why central planning doesn’t work even in the digital age. We have central planning today in nations like North Korea and Venezuela. So why doesn’t it work, given that they have access to virtually all the supercomputers and big data that modern socialists have? Again, to reiterate our main point, the answer lies in the fact that the fundamental problem with socialism isn’t informational, it’s institutional.
The theoretical case against central planning is valid in the face of the big data revolution. Big data may help give firms and governments the sort of information they need to make better decisions. But it poses no threat to capitalism. And it certainly doesn’t provide a magic elixir to the theoretical and practical ills of socialism. The big data revolution might be alive and well. But Marx’s ideas are still dead wrong.
Scott Burns is an assistant professor of economics at Ursinus College in Philadelphia, Pennsylvania. He graduated with his PhD in economics from George Mason University in 2017. He is a fellow for the AIER Sound Money Project.
This article was originally published on FEE.org. Read the original article.