Isegoria

Patrick Fitzsimmons has been mulling a paradox:

U.S. GDP keeps going up, yet it seems like we make less stuff and that most of the smart people I know work fake jobs. Growing up in the nineties, most of my toys and clothes had tags saying “Made in Hong Kong” or “Made in Vietnam.” But the high-skill, high-tech goods—the washing machine, the car, my computer—were often made in America. Now? From my e-bike to my laptop, from my refrigerator to my mattress, very few goods I own, high-tech or low-tech, were made in the USA.

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Sometimes there are discrepancies between your real-world observations and the data. But this goes far beyond just being a discrepancy: the data is saying the complete opposite of what we see with our own eyes, hear from our acquaintances in the job market, and deduce logically from our knowledge of demographics, technology, industry, and trade. How is this possible? The answer is actually very simple: the data is completely wrong. But you can only figure this out if you go line-by-line into the hundreds of pages of government GDP calculation methodology documentation. Which is exactly what I did.

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I challenge anyone who believes in these statistics to tell me what in the real world happened so that raw tonnage of steel was down, real gross output of steel was flat, usage of inputs was up, but “real value-added” was also up, and up hugely. Nobody can explain these numbers. The BEA cannot—I have asked them! If the raw data still exists, nobody has access to it because it was confidential.

The basic problem is that real value-added calculations only work if there are no quality adjustments and there hasn’t been any substitutions in the inputs. If those assumptions do not hold, you can get wild and nonsensical results. Since those assumptions do not actually hold in the real world, those nonsensical results are mixed into the overall calculation in ways that are impossible to account for, thus making the entire number bogus.

My guess is that what happened with steel production is that factories have moved from using raw iron ore to scrap metal as an input. The scrap metal is actually closer to a final good and requires much less energy to turn into steel. But GDP calculations do not know that scrap metal is closer to a final good. What the GDP calculations see is that materials have become more expensive and that energy inputs are less, so it seems like the steel factories are maintaining output with much less input, and thus value-added is greater. The reality, though, is that the United States is not producing any more steel out of factories, the United States is not producing a greater percentage of the steel value chain than in 1997, and the 125% increase in real value-added is a spurious result that represents neither making more stuff nor making better stuff.

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This is not just my critique: a former deputy chief at the BEA, Professor Doug Meade, has sharply criticized real value-added as a metric. In a 2010 conference paper, he wrote, “more than 60 years after it was first introduced, there is still no fundamental agreement on the meaning of real value added, or its price. Most who use it for the study of productivity loosely describe it as a measure of ‘real output’ although strictly speaking it is not that.” He continues to argue that comparing real value-added between years only works under the conditions of no quality adjustments, no input substitutions due to price changes, and no changes to the terms of trade. If those conditions do not hold, then, he says diplomatically, “it would be unclear what [real value-added] is measuring” Or as economist Thomas Rymes, observing the same issues, put it more directly: “a fictitious measure of output with no meaning.”

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Since nominal value-added is not adjusted by price indexes, it avoids all the problems we discussed with real value-added.

But, once again, the problem with the nominal value-added comparison is that it is not a comparison of actual things—it is a comparison of sales receipts. Thus a given quantity of products that is produced by a bloated cost structure will count as more “GDP” than the same number of products produced by an efficient factory. This is not just a theoretical problem—we know for a fact that the Chinese company BYD produces an equivalent to the Tesla Model 3 for half the price. Thus, $30,000 of manufacturing value-added in the U.S. might represent one car being produced, while for China it might represent two cars, and thus is actually double the output. In general, the China-U.S. dollar exchange rate is not a market rate and thus the conversion does not reflect in any meaningful sense the value of products.

Worse, many U.S. products are more expensive not because they are higher-end and better quality, but because they are protected from competition by tariffs, patents, regulation or national security requirements. For instance, Purism makes an all-in-the-USA phone for $2,000—the phone is no better than a $500 Chinese or South Korean phone, but sells at a premium for the U.S. security market. Others in procurement tell stories of getting quotes for printed circuit boards that cost $5,000 from China but $50,000 in America, thus only government and regulated industries buy American circuit boards. American-made municipal buses can cost three times the price as those made in China, but cities often face rules requiring them to buy American. For a particularly egregious example, thanks to the protections of the Jones Act, American ships cost an astounding ten times as much to build as their foreign counterparts.

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Which is more “output”—one million drones sold for a total of $2 billion dollars, versus one B2 stealth bomber for the same price? A $2,000 custom-made dress for the Met Gala, or one hundred pairs of denim work pants? Nominal value-added comparisons treat them as equivalent.

Nominal value-added cannot tell the difference between a country like 1790s Spain, a manufacturer of luxury goods with inflated nominal prices thanks to New World gold, and 1790s England, a ruthless manufacturer of inexpensive goods that is on its way to world domination. A comparison between countries that simply looks at sales revenues—not at the actual amount of ships, phones, and things produced for that revenue—is simply not a useful comparison.

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When we read a headline saying GDP data shows “car output has increased,” we think the U.S. has made more cars. We then apply our own views as to whether the quality of the car has changed. When we sneak quality into a measure but still call it “output,” we are double-counting and embedding the subjective in the objective, and we lose track of the hard numbers. We are not making more quantity of cars per person like the data says, we are making fewer cars, but with Bluetooth and crumple zones.

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While the BLS provides general information about the quality adjustment process, the specific methodology and the actual decisions are not documented. At the heart of GDP we find this subjective, bureaucratic black box. When we see that “output” of cars has increased since 1997, it is impossible for any commentator to know how that increase in “output” breaks down between actual number of cars, horsepower boosts, safety features, durability improvements, convenience features, blue tooth, power locks, and on and on.

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The point is not that any of these methods is right or wrong. The point is that if you have a half-dozen plausible ways of adjusting for quality, none of which from first principles is more objective than another, and you rule out one method for giving ludicrously low results, and one method for ludicrously high results, and just choose a middle route that feels reasonable, then the result of this adjustment is not an objective measure of output. All you have done is launder vibes into something that has the appearance of an objective number.

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The point is not that any of these methods is right or wrong. The point is that if you have a half-dozen plausible ways of adjusting for quality, none of which from first principles is more objective than another, and you rule out one method for giving ludicrously low results, and one method for ludicrously high results, and just choose a middle route that feels reasonable, then the result of this adjustment is not an objective measure of output. All you have done is launder vibes into something that has the appearance of an objective number.

Brian Albrecht explains why you can’t just compare tax rates between, say, income taxes and tariffs:

Double a tax rate, and you quadruple the deadweight loss. This is a standard result in public finance, and it suggests we should spread our tax burden across many bases rather than concentrate it in one place.

Here’s the intuition. When you impose a small tax, you only kill off marginal transactions—deals that barely made sense in the first place. The buyer was almost indifferent about purchasing, or the worker was almost indifferent about working that extra hour. These marginal transactions don’t create much surplus, so losing them doesn’t cost much.

But as you increase the tax rate, you start killing off transactions with larger and larger surplus. Beyond eliminating the deals that barely made sense, you’re now eliminating deals where both parties really wanted to trade, where there were substantial gains from the exchange. The surplus lost from these inframarginal transactions is much larger.

This is why deadweight loss grows with the square of the tax rate. Double the tax, and you lose transactions that had twice the surplus. The effect multiplies. A 10% tax might eliminate deals that create $1 of surplus each, but a 20% tax eliminates deals worth $1 and deals worth $2. The total loss is 4x, not 2x.

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If you want to compare across markets, you need another basic idea from taxation: deadweight loss depends on elasticities.

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Some supplies are essentially fixed—you can’t create more of them no matter how high the price goes. Other goods can be produced in unlimited quantities at constant cost. Some demands are highly elastic (people readily substitute to alternatives), while others are inelastic (people need the good regardless of price). These elasticities determine how much distortion a given tax rate creates. The tax rate alone tells you nothing.

More elastic demand or supply curves generate larger deadweight losses. The flipside is the classic Ramsey result: tax less elastic goods more heavily.

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Consider taxing a good with a perfectly inelastic supply—say, land in a specific location. The supply curve is vertical. No matter what price landowners receive, they supply the same amount of land because they can’t create more of it. By definition, there is no deadweight loss. The tax doesn’t change behavior.

What happens when we increase the tax rate on land? The tax raises revenue, but it generates no deadweight loss. Landowners absorb the entire tax through lower prices, but the quantity of land traded doesn’t change. There’s no distortion in the allocation of resources. You could tax land at 100%, and the deadweight loss would still be zero.

This demolishes the idea that you can look at tax rates in isolation. There is no nice connection between tax rate and deadweight loss that transcends the specific good being taxed.

Now compare this to a tariff on imported goods, where supply and demand are both elastic. The tariff creates a wedge between what consumers pay and what producers receive. This wedge distorts both consumption decisions (people buy less than they would otherwise) and production decisions (domestic producers make more than they would in an undistorted market). We get the classic deadweight loss triangle.

And it’s not just that imports aren’t perfectly inelastic. They’re very elastic! Estimates vary but one recent paper puts the long-run elasticity at 14, implying a huge deadweight loss.

The formula that deadweight loss increases with the square of the tax rate applies to both taxes. It tells us doubling tariffs with quadruple the deadweight loss. But it tells us nothing about which tax we should increase and the deadweight loss across the two markets. The land tax, even at a 100% rate, might generate zero distortion. The tariff, even at a 2.5% rate, creates real costs because of the huge elasticities. Elasticities matter. You can’t compare tax rates across different bases without accounting for how responsive behavior is to each tax.

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But tariffs are worse than general consumption taxes because they tax only some goods—and imports are a small share of total consumption.

In the US, imports are roughly 10% of consumption. This means tariffs apply to a base that’s one-tenth of a general consumption tax would. When Lott compares a 2.5% tariff to a 40% income tax, he’s ignoring that these rates apply to completely different denominators.

Think of it this way: if you want to raise $100 from a tax that applies to everyone’s $1,000 of consumption, you need a 10% rate. But if you want to raise that same $100 from a tax that only applies to $100 of imports (10% of consumption), you need a 100% rate. The narrow base means you need a much higher rate to raise equivalent revenue.

This logic applies to any narrow excise tax.