Category: Economics

Democracy and Caeserism

In my 2015 post discussing Joseph Heath’s excellent book Enlightenment 2.0, I had this to say:

One of the reasons that I oppose the extension of democratic politics into every aspect of modern life is precisely that in trying to do too much, democracy delivers incoherence, gridlock and frustration, forces that eventually undermine its own legitimacy. I worry about democratic legitimacy because I see democracy as a check and balance on Leviathan (while Heath sees it as a check on government by experts).

The legislature has become a sideshow. But I worry, because the more Congress is held in contempt the greater the support for a bold executive that takes charge, makes decisions and gets things done. Under these pressures, executive power has grown not just in the United States but also in Canada and Great Britain (on this theme see F.H. Buckley’s The Once and Future King.) But for all its faults, the legislature and the rule of law are more conducive to liberty than the executive and the administrative state. Legislators are satisfied with reelection and a bit of pork but executives hunger for greatness and in so doing they promote the real dangers, idolatry, the centralization of power and war.

In short, I worry that the pathologies of democracy drive the demand not for rational, technocratic government but for Caesarism.

I should note that this was before Donald Trump was a Republican presidential contender, let alone a candidate for office.

Addendum: See also  my review of Enlightenment 2.0. It has some good lines!

The propagandizing messages of markets and politics are also very different. Market messages are largely inclusive and cosmopolitan. Coca-Cola advertises “I’d Like to Buy the World a Coke” because they’d like the world to buy a Coke. Firms do try to build brand affiliation but they rarely do so by promoting hatred of their competitors. Pepsi doesn’t tell the Pepsi Generation that Coke drinkers are stealing their jobs and spitting on their gods.

Hat tip: @kingofthecoastt who recently tweeted about the original post.

The new agentic O-ring world

But because agents often require guidance or additional context as they move through their tasks, Sharma, 27, finds himself wanting to be available to them around the clock and forgoing a regular sleep schedule as a result. Until recently, he couldn’t monitor them remotely through a phone or smartwatch.

“The cost of the agents’ being blocked for eight hours is way too high,” he says. “They can be done with their work at any point of time, in the middle of the night.”

Founders have long put in punishing hours in the name of building the next big thing. But the growing capabilities of AI agents—and the speed at which the models powering them are evolving—give new meaning to working yourself to the bone…

“They just demand your attention,” he says. “Does it need anything? Can I help it in any way?”

…Pezaris, who lives in San Mateo, Calif., typically works from 7:30 a.m. to 2 a.m. He estimates Proxon, which employs six human developers, is operating 30 times faster than it would without agents. But agent work begets human work: Onboarding customers at a faster clip means needing to respond to more customer requests, for example.

There is also an agent FOMO multiplier effect. “Every minute that I’m not working, I’m missing out on not doing a week’s worth of work,” says Pezaris.

Here is more from Katherine Bindley at the WSJ.  As I have been joking in some of my talks, we need to start taking bets on when the AI leisure dividend will arrive.  It will, but not just yet…

Breaking Ground: Can Refund Bonuses Solve the Holdout Problem?

My latest paper (with Cason and Zubrickas) has just been published by the Journal of Urban Economics. We show that refund bonuses can indeed improve the holdout problem.

Abstract: The holdout problem presents a pervasive challenge in situations that require the assembly of independently controlled assets, where due to complementarity the combined whole is worth more than the sum of its parts. One avenue for addressing holdout problems involves contingent contracts, where agreements are conditional upon reaching a predetermined threshold. This paper reports an experiment to investigate a new refund bonus contingent mechanism, in which asset owners who agree to participate (e.g., sell their asset) receive a bonus payment if the required threshold for project success is not met. The refund bonus eliminates failure equilibria and improves the frequency of successfully reaching the threshold in the symmetric mixed strategy equilibrium. In the experiment, individual asset holders choose each round whether to accept an offer to sell. Multiple owners must accept for the (contingent) sale to materialize, and holdout owners who do not sell can earn more, so the game has the strategic incentives of a volunteer’s dilemma. The data show that the bonus mechanism increases agreements to sell, the frequency of successful projects, and efficiency. By the second half of the experimental sessions, the total number of sales is 35 percent higher and the threshold is met nearly twice as often with the bonus than without.

I also cover this paper in my Refund Bonus (aka Dominant Assurance Contract) Explainer.

Green shoots for the UK?

Early signs of tech-driven improvements in productivity growth could herald a sustained strengthening in the UK’s economic outlook, analysts have said, in a turnaround after years of underperformance. Private sector productivity grew by 1.8 per cent in the second quarter compared with a year earlier, up from 1.2 per cent previously, according to analysis of official data by investment bank Morgan Stanley.

The rise extended gains since 2024 and reduced the growth gap with the US. The reasons behind the upsurge are heavily contested, but some analysts point to increasing AI adoption in sectors including information technology and business services.

If the recent productivity acceleration can be sustained over years, it could bolster incomes and help alleviate some of the strains on Britain’s public finances.

Here is more from Sam Fleming and Amy Borrett at the FT.

More evidence on the effects of recent tariffs

Trump is giving economists something to write papers about:

U.S. tariff rates in 2025 rose to levels not seen since the Great Depression, yet imports increased. To account for the missing trade collapse, we develop an open-economy New Keynesian model with tariff heterogeneity, inventories, and shocks to investment that capture the AI-driven boom. The model matches the untargeted paths of imports, output, and inflation; we use it to decompose the effects of tariffs and the investment boom. Absent the investment boom, imports would have fallen by 10 percent and activity would have contracted by 0.7 percent. The effects of tariffs depend on which goods are tariffed: tariffs on consumption and intermediates act like shocks to supply; tariffs on capital goods act like shocks to demand. The concentration of the 2025 tariff increases on consumption goods and the relative sparing of capital goods limited the damage to output while amplifying the inflationary impulse.

That is from a new NBER working paper by Francesco Ferrante, Andrea Prestipino, Andrea Raffo & Michael E. Waugh.

Did UBI make people happier?

Eh, only in the short run:

We study the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month. We gather detailed survey data, administrative records, and data from a mobile phone app. The transfer caused total individual income excluding the transfers to fall by about $1,900/year relative to the control group and a 4.2 percentage point decrease in labor market participation. Participants reduced their work hours as a result of the transfers by 1-2 hours/week and participants’ partners reduced their work hours by a comparable amount. Among other categories of time use, the greatest increase generated by the transfer was in time spent on leisure. Despite asking detailed questions about amenities, we find no impact on quality of employment, and our confidence intervals can rule out even small improvements. Treated participants broadly increase expenditures, led by spending on non-durable goods and services, with smaller increases in spending on durable goods and human capital. We observe no significant effects on degree attainment, though the magnitudes of the estimated effects generally appear larger among younger participants. Measures of subjective well-being are higher among treated participants in the first year of the transfers but then revert to control group levels. Overall, our results suggest a moderate labor supply effect that does not appear offset by other productive activities.

That is from the QJE by , and  Via Matt Yglesias.

Declining Occupations and Career Outcomes in the United States

This strikes me as somewhat less of a problem than I might have thought:

We study long-run career consequences of initial employment in an occupation that subsequently declines. Linking the 2000 Decennial Census to US administrative employment and earnings records through 2020, we follow more than 2.4 million workers. Employment in an occupation that contracts by at least 25 percent is associated with about 5 percent lower cumulative earnings despite slightly more quarters worked. The earnings differential closely matches evidence from Sweden and Norway, although employment adjustment differs. Occupational mobility is substantial but incomplete, while children’s later occupational destinations are much less tied to their household heads’ 2000 occupational-growth categories.

That is from a new NBER working paper from Erling Barth, Maria Forthun Hoen, Sari Pekkala Kerr & William R. Kerr.  The results may have implications for AI as well.

Capitalizing untethered AI agents

That is my latest piece of writing, co-authored with Sonia Farrell Pearson of Harvard.  Here is the opening premise:

As early as 2017, the European Parliament floated “electronic personhood” for robots. More recently, a handful of U.S. states introduced legislation explicitly barring AI from legal personhood; and early this summer, President Milei of Argentina proposed letting AI agents own, manage, and bear responsibility for their own corporations.

In response to Milei’s announcement, Yuval Noah Harari pointed out that we have no way of holding an AI agent accountable. What, he asks, could we do to an entity which has neither money to lose nor a body to incarcerate? As Shruti Rajagopalan, a Senior Research Fellow at George Mason’s Mercatus Center, explains: AI “can act intelligently, but only humans respond to the incentives the law creates”.

This question matters now: there are already ways an agent could become fully untethered. By “untethered” – a central concept in this essay – we mean that there is no meaningful or actionable way to trace the actions back to a legally accountable human or institutional entity.

For one, people can and do set agents free, on purpose. An agent could be created by a human or a company that intends to monitor it but then dies or disappears. Or perhaps the entity that created the agent is based in a country like North Korea, not reachable by standard laws.

In other cases the agent might not need to “escape” at all: the agent could be ‘controlled’ by a shell corporation that, while formally owned and traceable, provides no true defendant or ability to satisfy claims. Or perhaps a process spawns a chain of agents so long that the actions of a subagent can’t be tied to the original agent’s creator, neither epistemically nor meaningfully. Even if we can identify the model’s original creator, what if it’s been finetuned, or merged with another model that was created by someone else? The law might eventually untangle these kinds of complex cases, but we foresee an intermediate period where it does not.

And then there’s the user, who makes choices about what the models should actually do. The Hugging Face incident was unusual in that OpenAI was both the model’s creator and its user. But now close to a billion people use these systems: when blaming the creator is legally inappropriate, will it always make sense to blame the user?

The essay considers to what extent capitalizing the untethered agents — requiring them to hold a certain amount of capital — can serve the end of better alignment.  About 22 pp., published on Sonia’s Substack, definitely recommended.

Some fertility and AI forecasts

The 2024 forecast is particularly pessimistic about China’s fertility prospects. Both projections produce very substantial global aging, a major global capital glut producing very low long-run real capital returns. The latest forecast entails 10% lower global GDP in 2100 and far higher payroll tax rates to fund old-age benefits. Most important, it entails a major change in the course of economic hegemony with China’s 2100 global GDP share falling from 25.6% to 14.9% and the US share rising from 11.2% to 14.4%. Our results are sensitive. Should the US eliminate all future immigration, its 14.4% global 2100 GDP share would drop to 9.2%. And were global fertility to follow the UN’s low variant, 2100 world output would be one third, not one tenth lower. The level and division of global output is also highly sensitive to the speed at which AI expands frontier technologies. Accelerated AU/AI – 4x faster-than-recent growth in capital’s share through 2050 – or Transformative AU/AI – 10x faster capital-share growth – reinforce demographic forces, ensuring long-run US economic hegemony. Indeed, Transformative AI combined with 2024 demographics implies US and Chinese 2100 global GDP shares of 25.3% and 16.9%, respectively.

That is from a new NBER working paper by Seth G. Benzell, Laurence J. Kotlikoff & Victor Yifan Ye.  Note that today the U.S. share of global gdp is slightly higher than it was in 1980.

How economics is changing

As a proportion of the literature, research about econometric theory, monetary policy and corporate governance has slumped. Meanwhile, papers on development, crime and gender are on the up. This could perhaps be because of changing intellectual interests, or maybe demand-side pressures, such as policymaker priorities and external funding…

Research from Prashant Garg, a postdoctoral researcher at Bocconi University, and Thiemo Fetzer, economics professor at Warwick University, finds causal claims in economics have jumped. In 1990, 7.7 per cent of claims made in the literature were causal. In 2023, that hit 32.6 per cent. Additionally, papers with more causal claims are more likely to receive citations and wind up in top five journals, the research suggests.

Here is more from Harvey Nriapia at the FT.

Things you cannot buy in America?

3. Exterior roller shutters (Rollladen)
In much of Europe, homes feature heavy shutters integrated into the exterior of the window, enabling total blackout and better insulation. Sleeping in true, complete darkness—not “blackout curtain” darkness, but can’t-see-your-hand darkness—is an experience most Americans will never have. These shutters are nearly impossible to get in the USA because these shutters are built directly into the home during its construction. They are fundamentally incompatible with standard American wood-frame, siding, and drywall construction, meaning there is no domestic supply chain to support them, even if you built a house to fit them.

From Daniel Frank, here is the full piece, noting I am not convinced you cannot get a “grass roof,” among other items mentioned.  In any case an interesting list, file under “possibly thwarted markets in everything.”  Via Anecdotal.

Adding to the barrel of finance fallacies

“I should note also that many (most? almost all?) of the bad scenarios have intermediate points of great worry and catastrophe” Not on my model. By the time any humans start worrying about a takeover or dying, AIs already control all infrastructure

That is from Twitter, and I hear or read that argument often.  It is yet another example of a bad “AI safety point” that does not stand up.

He is already a human worried about a takeover or dying!  It is weird to think that “I see these problems coming” and also think “…as these problems multiply and become more public, say through cyberincidents, other people and also the markets will not get clued in.”  It is assigning a remarkable oracle-like epistemic status to oneself, and then hardly to anyone else.  If the pending data will not persuade anyone else of your view, why do you hold your view so strongly?  Or if you think the ultimate denouement will be so sudden and furtive, how are you so clued in to the future now?  To me this is all obviously absurd, albeit not logically self-contradictory in the narrow sense.

As a side point, if the world does end suddenly, and you bought the puts out of your savings, but cannot cash them in, you still end up dying without having lowered your real level of consumption.

Rob Wiblin trots out a bunch of objections from the MR comments section that can be refuted readily.  You are really not sure which stocks to short and that is a big problem? — the risk is not that systemic then.  And if you think the world will see some significant calamitous events in the next ten years, and the evidence for that is piling up, yes you should be buying some puts, even if you are unsure on the timing.  Simple stuff.  (And no you do not need options contracts that last for ten years.)  The AI safety advocates with relatively extreme views should be trying to spread these points to their followers, not to retire them.

In general I am not a fan of psychoanalysis as a method of dissecting views, but the number and scope of obvious direct errors on this topic (and from very smart people) is so high that one has to wonder.  How about: “$100 billion in added cyber costs is not a significant enough worry, it is too mundane, too small a percentage of gdp, too normal and technocratic a problem…you can’t take my bigger and more dramatic fear away from me!  I won’t let you do that!  And besides, that view is the social glue that bonds my in-group together.”

Regulated Markets Are Slow to Handle Change

Gowrisankaran, Langer and Reguant have an excellent paper, Energy Transitions in Regulated Markets (WP), in the latest AER.

The basic idea is that regulation designed to prevent utilities from building useless power plants can induce them to keep obsolete power plants. Some background. We regulated electric utilities under the theory that they were natural monopolies and therefore we would do better by pushing their prices down. What’s a reasonable price? Hard to say, so regulated utilities were allowed to recoup their operating costs plus a fair return on their “rate base”—their capital stock. Makes sense, but once profits depended on the size of the capital stock, utilities had an incentive to build too much—the classic Averch–Johnson effect. Regulators responded with “prudence” requirements and the rule that capital must be “used and useful.” In a stable world, that rule is a check, albeit an imperfect check, on so-called gold-plating.

But now consider what happens in a time of technological change, such as a rapid decrease in the cost of generating electricity with natural gas (driven by fracking and improvements in combined-cycle natural-gas (CCNG) technology). In a free market, large decreases in costs would cause firms to abandon coal and move to natural gas—some would do this to make profits, others to avoid losses. In short, the market forces sunk investments to be abandoned when not profitable.

But there is another possibility under regulation. Tell the regulator that your plants are still viable. Well, telling is cheap talk so you keep burning coal to prove that the plant remains useful. If you can keep your base operating that’s better than abandoning it and to signal how valuable your coal plant still is, it may even be worth while to burn coal when the cost exceeds the price of electricity! The authors have some nice data on exactly this point.

Figure 3 takes a little work to understand, but the pattern is clear. Each point represents a state. In panel A, the vertical axis shows how much less likely a coal plant is to run when the cost of coal exceeds the price of electricity. Obviously, a strongly negative coefficient is the economically sensible response: when burning coal is more expensive than buying electricity, the plant should burn less.

The red points represent restructured states and the green points regulated states. In restructured states coal burning falls when prices fall, just as expected. Coal burning in regulated states responds much less. (I.e., the red points generally lie below the green points.) Indeed, the six states with the largest reductions in coal operation are all restructured states.

One objection to this analysis might be that utilities in general are just slow to respond to prices, so on the horizontal axis the authors plot how well utilities respond to a higher price of gas. Note that these coefficients are all negative and there is no obvious difference between regulated and restructured states. In both types of states, utilities respond well to the price of gas, but only in restructured states do utilities respond strongly to the price of coal. (Why coal and not gas? Because the used-and-useful standard binds on capital whose usefulness is in doubt—which, once gas got cheap, meant coal. In other words, the utilities have to defend coal to the regulators, not gas.)

Panel B on the right shows a slightly different way of presenting the same data. The vertical axis is again how much less likely a coal plant is to run when its cost exceeds the electricity price. The horizontal axis is the fraction of generation owned by electric utilities. Regulated states tend to be vertically integrated, while restructured states opened electricity generation to competition, so utility ownership and regulatory status are closely correlated. Regulated states generally have utility ownership above 60%, while all the restructured states but one are below 30%. The best-fit line slopes upward: in other words, the more generation a state’s utilities own, the less coal dispatch responds to price. A different perspective on the same story.

That is the direct empirical evidence. The authors then construct a more ambitious structural model. In theory, regulation could produce either too much or too little investment in the new technology; their estimates imply too much. Much, too much. Not only do regulated utilities retain too much coal, they also build too much gas capacity. In short, they accumulate both too much old capital and too much new capital. Averch–Johnson on steroids.

The bottom line is that regulation under dynamic conditions is much more difficult than under static conditions. My view is that it may not even be worth the candle.

Intergenerational mobility of immigrants in 15 destination countries

We estimate intergenerational mobility of children of immigrants in fifteen receiving countries. Children of immigrants have somewhat lower income than children of local-born parents. Around half of this gap can be explained by differences in parental income, with the remainder due to differences in mobility parameters. The daughters of immigrants enjoy higher absolute mobility than daughters of locals in most destinations. Absolute mobility of sons of immigrants is higher outside Europe and lower in Europe compared to sons of locals. Cross-country differences in absolute mobility are not driven by parental country-of-origin, but instead by destination labor markets and immigration policy.

Here is the paper, by Leah Boustan, et.al.  Via the excellent Samir Varma.

Mistakes in financial economics

I feel like this is almost deliberately missing the point. My median expectation is that AI boosts the economy enormously, so shorting things would be a terrible idea. But non-trivial tail risk is that it kills everyone. So shorting things would be pointless.

That is from Tom Chivers.  It is easy enough to say buy seriously out of the money puts, and be long with the rest of your portfolio.  But few (if any) of the worriers are doing that.  I should note also that many (most? almost all?) of the bad scenarios have intermediate points of great worry and catastrophe where you can cash in on your puts well before everyone dies.  No leverage required, just spend 5k or 10k a year on this, and if you are wrong consider it a mistaken insurance policy.  If you do not know much about finance, the AGI will guide you in this endeavor.

Or some are saying “Markets are bad at pricing long-term idiosyncratic risk.”  If so, all the more reason to spend on those puts.

You will find many, many mistakes in financial theory when people try to rebut the presumption that, given their views, they should in some way or another be short the market.  And do not just tell me which long positions you hold, those are easy bets, as you can be totally wrong and the long positions still will offer normal, risk-adjusted rates of return.  It is your shorts, whether explicit or implicit, that reveal your true soul.  You may recall that Victor Niederhoffer thought an investor should never go net short on an asset.  I am reluctant to use the word “never,” but my own view is not so different.

Is it really so hard to say (and do): “Thank you, Tyler, I just went out and bought those puts!”?

Apparently so.  And perhaps that is because, deep down, your own intuitions realize that, while some significant costs from AI will appear, it really won’t be that bad after all.

Words to live by.