Category: Economics
Some non-obvious reasons why AI will create some transitional problems in employment
I do not find the mass unemployment hypothesis persuasive, and I have covered this extensively in the past. But here are three other problems which may end up being noticeable in the short run, though likely absent longer term:
1. Many of the new jobs to be created may come in highly regulated sectors, and that will slow their creation. Energy and health care — especially biomedical trials — are two examples I have in mind here. Let’s say we opt for more nuclear power to ease constraints of compute — how long will it take for most of those jobs to come on line?
2. At least initially, job search and matching might be less efficient. We have lots of practice judging which workers are best for which jobs in a pre-AI world. But say most jobs involve working with AI in some manner? How well can actual HR departments judge who is good at that? Are the HR departments themselves even decent at that?
So expect slower matches, though at some point AI itself might give us better and faster labor market matches.
3. Government fiscal policy might be less effective at putting people to work in an efficient manner, given that the government is likely, at least for some while, to be a poor judge of who is good at working with AI. That may slow hiring, or lead to quicker dismissals and quits, or simply result is less output from the fiscal policy investments, thus making them less effective.
These features of the problem all could use a bit more consideration, and likely there are others I have not thought of.
Data centers are good
Data centers are the physical infrastructure behind cloud computing, artificial intelligence, and enterprise software. The rapid diffusion of artificial intelligence (AI) is intensifying demand for compute, accelerating investment in data centers, and raising concerns about the local economic and environmental footprint of these facilities. Their expansion creates a local policy tradeoff. A data center can bring capital investment, construction activity, and specialized employment, but it can also increase demand for electricity, land, and grid capacity. This paper studies these effects at the U.S. county level. We assemble a facility-level panel of global data centers with precise coordinates, scale metrics, and annualized revenue. We map facilities to U.S. counties and combine them with County Business Patterns, county-level IRS income, county-level house prices, and electricity prices. To address endogenous siting, we instrument for data center growth using two shift-share instruments, which leverage pre-existing proximity to InterTubes long-haul fiber nodes and the 1980 county share of U.S. urban college population as shares, and both Chinese and rest-of-the-world data center revenue growth as shifts. The IV estimates show positive effects on total employment, data-processing employment, construction employment, establishments, house prices, and electricity prices at different horizons after data center growth. We also find positive effects on tax returns, adjusted gross income, and wages, while annual payroll responds less robustly. The results suggest that data centers create measurable local activity, increase house prices, and affect local electricity markets through higher prices.
That is from a new NBER working paper by
Using agents to build economic datasets
Constructing datasets from primary sources is one of the costliest tasks in empirical economics. We propose Deep Research on a Loop (DRIL), a methodology that uses AI agents to assemble datasets from publicly available sources. DRIL applies a fixed research instrument across a mapped unit space (e.g., countries by years), with a two-stage architecture separating design from implementation. The instrument specifies variables and coding rules, an evidence policy governs sources and citations, and data quality mechanisms track gaps and uncertainty explicitly. We exercise DRIL on a 2025 update of the Global Tax Expenditures Database for eight Latin American and Caribbean countries. The run produces 129 sources and 136 evidence records, covering 22 qualitative fields fully and 6 quantitative estimate types with documented gaps, at the cost of a standard LLM subscription comparable to a few hours of research-assistant work. We argue that even partial automation of dataset construction can shift the production function of empirical economics.
That is from a new NBER working paper by
Why are stock prices still so high?
That is the topic of my latest Free Press column, here is one excerpt, with the general theme that plenty is going well in the global economy:
A second important fact is that American presidents, whether Democrat or Republican, usually have very little influence on the economy. That is a hard truth for people to hear, since partisan sentiments often run strong, especially when it comes to President Trump. Yet the research literature is clear that most business cycles are not caused by presidents.
As for the current cycle, the core reality is that our economy continues to hum along. Yes, gas prices above $4 a gallon cause dismayed news stories and consumer worry. But energy prices have less influence on the overall economic picture than they once did. The chances of a recession have been falling, and a recent jobs report showed strong progress in hiring.
Of course the Trump administration will take credit for such developments, but mostly they are due to underlying structural factors.
And this:
During the current war, many parts of the global economy have shown more resilience and fortitude than might have been expected. Stocks in South Korea at first plummeted 20 percent, due largely to its dependence on Middle Eastern oil. Today, the Korean stock market, pushed along by the chip-making achievement of Samsung and memory maker SK Hynix, is reaching new highs.
…In previous times, sharp oil price hikes often brought catastrophe to the economies of Latin America. These days Latin American government bonds have held up well and are even considered a safe haven.
Recommended.
Another use of AI in research (from my email)
“Another thing we (John [Horton] and I) have thought about is having a swarm of AIs “fight” over a literature. They could take the cumulative datasets available and continuously argue until they understand the question. One line of thinking says they reach a stalemate (as scientists currently do). But we think not. More likely, they push evidentiary understanding to the limit and coalesce around what’s most probable — if not definitive!”
That is from Benjamin Manning.
The interstate trade effects of autonomous trucks
Recent advances in autonomous and semi-autonomous vehicle technologies promise substantial cost savings for goods shipped by truck. In this study, we quantify the impacts of these transport cost reductions on the US interstate trade using a structural gravity model of domestic trade. Based on projected cost savings from the widespread adoption of self-driving technologies, we estimate significant increases in total interstate trade value. State-level impacts vary from 40.3% of GDP in Mississippi to 5.9% in Florida, while the largest impacts in dollar value are observed in Texas and New York. The sectoral analysis highlights motorized vehicles, mixed freight, and electronics as the industries experiencing the largest trade value growth. Additionally, goods with low value-to-weight ratios—where shipping costs represent a large share of the delivered value—are expected to benefit most in relative terms. These findings underscore the transformative potential of autonomous vehicle technologies in reshaping US trade patterns and sectoral dynamics.
That is from a recent paper by Taejun Mo, et.al., via the excellent Kevin Lewis.
Will AI kill the research paper?
Imagine taking a macroeconomics paper and adding a little button at the end “Press this button to update this paper with the latest macro data.”
All of a sudden you have multiple papers rather than one, and no single canonical version. It is the latter versions, not created directly by the authors, that people will look at.
Imagine adding another button, to either micro or macro papers “Please rerun these results using what the AI thinks might be five other different yet still plausible specifications.”
Then you have more papers yet.
Ultimately, why not just build a “meta-paper,” using AI, to answer any possible question about the subject area under consideration. This meta-paper would allow the reader, using AI, to make many sorts of modifications and additions to the basic work. The meta-paper also would allow the reader to add new data, to run additional robustness checks, and to do whatever else you might think of. Once again, the canonical version of the paper evolves away.
A researcher might spend a significant part of his or her career building such a meta-paper. Imagine a meta-paper, or sometimes I call it a “box,” devoted to answering questions about say fiscal policy, minimum wage hikes, or maybe the Industrial Revolution. Fed researchers would spend their entire careers, not writing papers, but improving the Fed’s “box” that answers questions about monetary policy and also prudential supervision.
Who will be good at doing such things? Is it the people today who become the top economists, or not? Will it be a highly decentralized endeavor, or, given the compute and team work requirements, a highly centralized one?
Economics is going to change a lot, as will many of the other sciences.
It is funny, and tragic, how much some of you are still obsessed with writing and publishing “papers.”
Public Choice Outreach!
Just a few spots left! Lots of great speakers including Tyler, myself, Bryan Caplan, Robin Hanson, Jon Klick, Shruti Rajagopalan and more.
Please apply and encourage your students to apply.
A simple point about diversification
In recent times a significant percentage of the S&P 500 run-up has been driven by a small number of tech and AI stocks. Plus the effects of AI can be expected to be further reaching yet for some while.
That makes it harder to diversify against risk, as there is a single dominant variable, namely “AI risk” or something similar. There is AI risk both in your portfolio and on your human capital, though possibly those will offset each other to some degree.
Presumably the equity premium should rise as a result? People will want more portfolio safety as a protective offset, and be gunshy about such a heavy equities bet on one major technology.
If you have a longish time horizon, do you feel brave enough to act on that view?
Or perhaps instead there is some simple way to hedge against AI risk?
One “stupid” equilibrium that no one will want to talk about is the following: buy lots of Nvidia, but if that doesn’t pay off make sure you are doing an MBA and planning a career in non-AI-implementation consulting.
How Poverty Fell
The share of the global population living in extreme poverty fell dramatically from an estimated 36% in 1990 to 9% in 2015. We describe how this decline happened: the extent to which changes within as opposed to between cohorts contributed to poverty declines, and the key changes in the lives of households as they transitioned out of (and into) poverty. We do so using cross-sectional and panel sources that are representative or near-representative of five countries that collectively accounted for 75% of global poverty decline between 1990 and 2015. The data show that overlapping birth cohorts experienced the decline of poverty together over time, such that poverty decline can be viewed as a primarily within-cohort phenomenon. Within cohorts, the data reveal substantial churn, casting the challenge of escaping poverty as a “slippery slope” more than a long-term trap. The data also illustrate a diversity of pathways out of poverty: sectoral transitions, migration, and changing occupational choices and female labor force participation can all account for some part of poverty reduction, but in all but a handful of cases, a majority of households exiting poverty did so without experiencing these changes.
That is from a new NBER working paper by
AGI Could Lower Interest Rates
Standard models predict that expectations of artificial general intelligence (AGI) should elevate long-term interest rates. I show that this prediction need not hold. I develop a heterogeneous-agent asset pricing model in which AGI, or more broadly, transformative AI (TAI) capable of automating most human labor, can lower interest rates even as it dramatically accelerates growth. Under baseline calibrations, the risk-free rate falls to near zero despite growth rising from 2% to 11%, and the equity premium expands from 6% to over 20%. The effect on yields is negative and muted for all maturities, even under aggressive assumptions about the speed of AI adoption. These results advise caution when interpreting long-term bond yields as a signal of market expectations of transformative AI.
That is from a new paper by Caleb Maresca of NYU. Via the excellent Kevin Lewis.
Justin Wolfers update
Wolfers’s moment of clarity ultimately sent him down a road less traveled by academic economists: creating his own media company.
On Wednesday, Wolfers, 53, announced that he had founded Platypus Economics, an independent media start-up that aims to reach a mainstream audience. The name is a nod to his Australian roots, cheekily referring to the odd-looking mammal native to his birthplace. He’s funding the business himself, using the income from his textbook sales.
…To get his content channels off the ground and build an audience, Wolfers is teaming up with Initial Digital, the digital media division of the Initial Group, an entertainment company that’s backed by the private equity firm TPG.
Here is the full NYT story.
ICE has not improved U.S. labor markets
We provide the first causal, national empirical analysis of the labor market impacts of heightened immigration enforcement during the second Trump administration. Enforcement increased everywhere, but, we take advantage of the fact that the increases have been uneven across geographic areas to classify areas as treated or control and then implement an event study and difference-in-differences design. Areas that experienced particularly large increases in the number of arrests also experienced a decrease in work among likely undocumented immigrants who remain in the U.S., compared to areas with smaller increases in arrests. We find no evidence of positive spillover effects to U.S.-born workers and U.S.-born workers who work in immigrant-heavy sectors are harmed.
That is from a new NBER working paper by Elizabeth Cox & Chloe N. East.
Rose Farts and the Invisible Hand
In Modern Principles, Tyler and I show the invisible hand by telling the story of how the increase in oil prices in the 1970s encouraged millions of adjustments in how goods were produced and allocated, everything from an increased use of brick for driveways to a movement of the flower market from the US, which relied on heating greenhouses, to warmer climes like Columbia and Kenya. See the I, Rose video!
The FT has an amusing update:
“When my sheep break wind, it smells of roses,” he said, recounting one of the more bizarre and far-flung consequences of the decision by US President Donald Trump and Israel’s Prime Minister Benjamin Netanyahu to bomb Iran in February.
Since Tehran hit back by firing drones and missiles at US allies in the Gulf — grounding cargo flights and closing off the Strait of Hormuz through which booming east African trade with the region used to flow — Mahihu has been forced to jettison millions of rose stems.
Trade and the End of Antiquity
What was the role of trade, and how did economic activity evolve at the End of Antiquity, when political power shifts away from the Mediterranean towards northern Europe and the Middle East? To answer those questions, we assemble a database of hundreds of thousands of ancient coins from the fourth to the tenth century, estimate a dynamic model of trade and money where coins gradually diffuse along trade routes, and recover granular regional trade and real consumption time series. Our estimates suggest that: Mediterranean trade was disrupted by the newly formed border between Islam and Christianity; economic activity shifts away from the Mediterranean starting in the fifth century; real consumption peaks in the Middle East in the eighth century; and by the end of the ninth century, Atlantic regions from Islamic Spain to Frankish northwestern Europe have become the wealthiest regions of the ancient western world.
That is from a new NBER working paper by .