Economics Links
Noah Smith on Krugman and economics; Deming, Ong, and Summers on the changing job structure; John Cochrane on costs of regulation; Krugman's substack on crypto
if, as a low-ranked professor or grad student, you loudly proclaim that Acemoglu or Chetty is wrong, the general response will essentially be “Who are you to talk?”. The pervasive, ever-present power of this internal hierarchy is something you really have to have existed inside the econ profession in order to understand.
This is spot on. Academic economics is a suck-up culture. Yes, there are economists who rebel against it, who behave like resentful outsiders in spite of having been awarded Nobel Prizes. Paul Romer, Joseph Stiglitz, and Krugman all have issued nasty screeds against other prestigious economists. But I do not find that admirable.
What I admire is the rare economist who can rise above the snobbery. Hal Varian, the long-time chief economist at Google, is willing to praise good economic reasoning wherever he finds it, even from a non-economist. He called attention to John Perry Barlow’s article in Wired (“The Economy of Ideas”) on information goods.
Smith highlights Krugman’s attempts to explain his economic ideas in simple terms. I will give Krugman that, but I thought that this occupied less than 1 percent of his New York Times columns. Those instead consisted of asymmetric insights, claiming to know the true motives of conservatives. I was hoping that on substack Krugman would go back to being an explainer of economics, but so far he seems to have brought over his NYT persona.
David Deming, Christopher Ong, and Lawrence H. Summers write,
From 1880 to 1960, workers moved out of agriculture jobs. In 1880, 41 percent of all workers in the US economy were employed as farmers or farm laborers. This share fell consistently by 4 percentage points per decade, and by 1960 only 6 percent of US employment was in agriculture.
Pointer from Timothy Taylor.
A similar thing happened to manufacturing production workers from 1950 to 2000. And this was not due primarily to China. For manufactured goods, productivity was growing faster than demand. For services, such as health care and education, demand was growing faster than productivity.
Classify jobs as working with things, working with people, or working with symbols.1 From 1880 to 1950, “working with things” changed from farm labor to factory labor. In agriculture, productivity grew faster than demand, releasing workers to move into manufacturing.
Since 1950, the growth has been in jobs working with people or working with symbols. This facilitated a big increase in female employment and a relative decline in male employment.
The authors speculate about the effect of artificial intelligence. I think that is difficult to do, because we do not know what AI is going to be good at. It could be good at working with people—the robot home health aid and the AI tutor. Or it could be good at working with symbols—helping in science and in creative arts.
John Cochrane points to an estimate that in Argentina cutting regulations has reduced prices in those goods by 30 percent.
A price decline of 30% tells us that the economic benefit of deregulation is at least 30% of current income. Real GDP is price times quantity, so even if the quantity of the deregulated god does not change, a 30% decline in prices gives people that much more real income to spend on other things. And it’s a lower bound. If rents, textiles, and logistics decline in price by 30%, rent-paying businesses, clothes makers, and everyone who sends something anywhere by truck can expand their businesses.
Even 30% is a lot. That’s a decade of 3% extra growth. That’s the difference between the US and most of Europe. That’s orders of magnitude more than most conventional economists will allow as the cost of regulation.
Most people can go a whole year without ever seeing a $100 bill — yet there are roughly 60 such bills in circulation for every man, woman and child in America. Who’s holding them? Well, indirect evidence suggests that they’re mostly being held outside the country. And while getting a precise number is almost by definition impossible, there’s no real doubt that many and probably most of the Benjamins out there are being held for illicit purposes.
He argues that the main use case for crypto is like the main use case for $100 bills: to facilitate illegal activity.
I like to say that banking is adjacent to government and crypto is adjacent to criminals.
Government needs to be perceived as sturdy, and banks can help with that. Banks need to be perceived as sound, and government can help that. Banking policies help maintain what I call the “two-drunks model” (think of two inebriated guys walking down the street, holding one another up): banks are incented to lend to government and government-favored sectors; and government gives banks competitive advantages, such as deposit insurance and bailouts.
Criminals need a way to process transactions, and crypto can help with that. Crypto needs a use case, and criminals can help with that.
Unlike the Biden Administration, I don’t see going after crypto as a great tool for going after crime. I think that you should punish crime, not the financing that is adjacent to it. And I instinctively dislike the surveillance state that is set up to address “money laundering.” (why is “money laundering” even a crime, separate from any crime that it facilitates?) In the WSJ, Alyssia Finley writes,
The Bank Secrecy Act requires banks to build profiles on customers, monitor their activity, and file Suspicious Activity Reports, or SARs, with the Treasury Department’s Financial Crimes Enforcement Network if they suspect illicit activity. Such “know your customer” rules are intended to prevent money laundering.
Banks have a strong incentive to file reports if there’s any unusual transaction, given that inadvertent lapses can result in sanctions, including heavy fines. Last year banks filed 4.6 million SARs. Compiling these reports is a nuisance and rarely exposes illicit activity since criminals tend to circumvent the banking system, where they know their activity will be monitored.
The overbreadth in bank reporting is a plus for the government, since it gives the Federal Bureau of Investigation a trove of reports to scour without a warrant. The more info it has on more bank customers, the better, even if most haven’t committed a crime. Regulators prohibit banks from notifying customers if they have filed a SAR.
This sounds like a great system for hassling innocent people while doing very little to deter or detect real crime.
substacks referenced above: @
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This is not original with me. I trace this classification at least as far back as Robert Reich in The Work of Nations.
Javdani and Chang (2019) (https://substack.com/redirect/687940e2-679b-4f9f-96b8-034c09838bde?j=eyJ1IjoiMjg2Zmt3In0.tEnleokpnpl5rK9THKJ8ZgpFU8vKEDmAxqVN7Jl4p_I)
did an experiment on economists themselves, and found that they gave much more credence to ideas that they thought came from prominent mainstream figures in the field:
Using an online randomized controlled experiment involving 2425 economists in 19 countries, we examine the effect of ideological bias on views among economists. Participants were asked to evaluate statements from prominent economists on different topics, while source attribution for each statement was randomized without participants’ knowledge. For each statement, participants either received a mainstream source, an ideologically different less-/non-mainstream source, or no source.
We find that changing source attributions from mainstream to less-/non-mainstream, or removing them, significantly reduces economists’ reported agreement with statements. This contradicts the image economists have of themselves, with 82% of participants reporting that in evaluating a statement one should only pay attention to its content.
On Argentina: I’ve heard a complaint that DOGE doesn’t go after entitlements spending, and therefore isn’t serious because entitlements spending is the bulk of government spending. But Argentina’s example is that a lot of the impact of government isn’t government’s spending, but rather it’s “costing” – and “costing” in terms of regulation is, I would bet, an order of magnitude more of a drag on the economy than taxation.
At least taxation is visible and quantifiable. But when you see something like the new AI Diffusion export control rule, how many people, how many hours, will be diverted across private industry from otherwise economically productive activities and into compliance, simply because of this one rule? I’m not arguing here whether that rule is good or bad, per se, but the cost is incredible.