Constraints & Slackness as a Worldview Generator

Last post speculated that capital was much more scarce (relative to labor) in medieval/​renaissance China than Europe. We introduced the hypothesis that this was a major cause (though not a root cause) of the failure of China to automate and, by extension, the failure of China to kick-start the industrial revolution. More importantly, we introduced some simple first-pass ways to test this hypothesis: go look at market prices of labor and capital in medieval/​renaissance China and Europe.

We did not go into depth actually testing that hypothesis, nor will we. Instead, we’re going to go up a meta-level, and generalize this whole technique. We have a very general hypothesis about the large-scale historical evolution of China compared to Europe. It’s part of a whole worldview, in which the central gear—the central mediating factor—is the scarcity of capital relative to labor. What’s the process which generates this sort of hypothesis? Can we generalize it, to generate other broad worldviews?

Here’s the idea, in 4.5 steps.

Step 1: Pick a General Resource/​Constraint

Pick some very general resource, which serves as an input to production in a wide variety of areas. Our China/​Europe example mainly considers capital as the resource (though we also use labor as a baseline for comparison in this case). Other important examples in economic history include transportation, energy, and fertile land. More modern and abstract examples might include research/​innovation, communication/​coordination, signals of status/​virtue/​intelligence, or material goods as a whole.

Step 2: Qualitative Analysis

Reason qualitatively about what would happen as the constraint becomes more taut/​slack. What technologies (machines, contracts, organizational structures, etc) would be adopted? What would happen to market prices? What other constraints would become more/​less slack as a result?

For instance, as capital constraints become more taut, we expect to see more people performing tasks which could be performed by machines (i.e. not adopting capital-intensive technology), and we expect returns on capital investments to be higher (i.e. higher market price of capital). When capital constraints become more slack, we expect the opposite.

Step 3: Compare to the Real World

Compare real-world observations (market prices, technology actually used) to our predictions to figure out (qualitatively) how taut/​slack the constraint actually is, across many different industries.

In the China/​Europe example, we can do this by looking at adoption of capital-intensive technologies (i.e. textile machinery, water mills, automation in general) or by looking at market prices of capital (i.e. rate of return on investments).

Step 3.5: Sanity Check

If our hypothesized constraint is actually the right gear for this model, then we should see similar tautness/​slackness in different industries—e.g. we shouldn’t see a relevant technology adopted in one industry but not another, or different industries paying radically different market prices to relax the constraint.

In the China/​Europe example, we should check that there actually is a broad pattern of China using labor on tasks where Europe used machines. This doesn’t mean China didn’t use any machines—our comparisons are relative, we’re thinking about more/​less at the margin—but we should at least see the difference across many different industries. Similarly, we should check that capital investments had higher economic returns in China across whatever industries the Chinese invested in (though it may be that investors had a tough time capturing those returns… another potential gear in our model).

This should give us some idea of how generally-relevant our constraint is, in practice.

Step 4: Generalize

What other qualitative predictions can we make, beyond what we’ve already observed?

If we’re thinking about new technologies or new business ideas, to what extent do we expect them to be enabled/​blocked by slackness/​tautness of the constraint? What other constraints do we expect to become more taut/​slack in response to this one? Is the constraint becoming more taut/​slack over time? If so, what changes do we expect that trend to induce?

If you want a quick exercise in this, consider computational capacity as the constraint. You’ve probably already heard plenty about how the tautness of that constraint has changed over time, what new technologies have been adopted as a result, and what other constraints have become taut/​slack as a result. Can you translate that information into the language of constraints/​slackness/​prices? Does the constraints/​slackness/​prices framework suggest other questions to ask, or new predictions to make?


We’ve already talked a bit about two resources to which this framework applies: labor and capital. One can build a whole worldview this way, and much of macroeconomics does exactly that—see MRU’s videos on the Solow model for a friendly starting point to the macro models.

There are many other very generic inputs we could think about. The next two posts discuss two other general constraints:

  • Manufactured goods. Across the board, real prices of most manufactured goods have fallen dramatically over the past two centuries—suggesting those constraints are relatively slack today. Yet that does not mean that we live in a world of total material abundance. What would we expect a world of slack material constraints to look like? What other constraints would become taut?

  • Coordination. Producing and selling goods or services requires coordinating salespeople, engineers, designers, marketers, investors, customers, regulators, suppliers, shippers, etc, etc. Jobs which have the potential for making very large amounts of money—e.g. entrepreneurship, management, investment banking, mergers & acquisitions, etc—are largely characterized by being primarily about coordination. Similarly, if we look at the list of highly successful tech companies over the past 25 years—Google, Facebook, Amazon, Uber/​Lyft… - the primary business of most (though not all) of them is to solve some particular coordination problem. Both of these suggest a very taut constraint.

Some other examples which I might explore in future posts:

  • Transportation. Before the modern era, pack animals’ food & water requirements sharply limited the range of overland transport, especially in arid regions. This constraint shaped the paths of armies and locations of cities, suggesting a very taut constraint. How taut was the transportation constraint historically, and how taut is it today? What would the world look like with a totally slack transportation constraint?

  • Interfaces. If Alice wants to produce something for Bob, then first they must accurately communicate to Alice what Bob wants. Some interesting cases:

    • Alice is a company, Bob is a customer

    • Alice is a computer, Bob is a programmer/​product designer/​user

    • Alice is an employee, Bob is an employer

    • Bob is Don Norman, Alice is a fridge. Yes, a fridge. We’ll get to that.

How taut/​slack are communication/​translation constraints, in general?

  • Theory and data, especially in the sciences. In the past few decades, a number of fields (e.g. biology, economics, and many industries) have built up massive piles of data. Yet the ability to turn that data into useful knowledge and insights—i.e. theory—has lagged behind.

Also, there might be another post going into more depth on capital, especially on the main places where capital is deployed today.