Category: Data Source

Is there now a better trend in youth mental health?

by  Tyler Cowen in

A key U.S. government report on mental health, substance use and addiction showed continued improvements in several metrics, especially among young people.

There was some uncertainty about the future of the National Survey on Drug Use and Health last year, when the entire 17-member team responsible for it was laid off by the Trump administration. But the Substance Abuse and Mental Health Services Administration released the latest report Monday, with data that lets researchers look at trends from 2021 to 2025.

Fewer adolescents aged 12 to 17 reported using tobacco, alcohol, marijuana and binge drinking in the previous month, the survey found.

And in the past year, fewer in that age group reported:

— illicit drug and marijuana use

— starting drinking alcohol, vaping or marijuana

— substance, alcohol or drug use disorders

Adolescents also saw declining trends for major depressive episodes and fewer reported serious thoughts of suicide, making a suicide plan and attempting suicide.

Here is the link, via Chris Ferguson.

Short Videos, Big Self-Control Problems

by  Tyler Cowen in

I study how short-form design amplifies self-control problems in digital media. Short units repeatedly renew temptation that lasts longer than each unit, turning local temptation into sustained overconsumption. Using microdata from a U.S. short-drama platform, I exploit a nonlinear top-up menu to infer viewing plans and show that paying users watch 82.1% more than intended. Structural estimates imply an average temptation horizon of 11.2 minutes, short relative to the full drama but long relative to one-minute episodes. Counterfactuals show that larger decision units, default limits, and breaks improve long-run welfare. A short-video calibration highlights the broader welfare relevance.

That is from Renjie Bao of Princeton University.  I believe a Princeton job market candidate?  Via Quan Le.

The Kalshi Citizen Debt Forecast (CDF)

by  Alex Tabarrok in

Kalshi Research is doing interesting work on the fundamentals of prediction markets and also on how data from prediction markets can be used to improve other forecasts. Economists at the Fed, for example, recently wrote Kalshi and the Rise of Macro Markets finding:

Prediction markets offer a new market-based approach to measuring macroeconomic expectations in real-time. We evaluate the accuracy of prediction market-implied forecasts from Kalshi, the largest federally regulated prediction market overseen by the CFTC. We compare Kalshi with more traditional survey and market-implied forecasts, examine how expectations respond to macroeconomic and financial news, and how policy signals are interpreted by market participants. Our results suggest that Kalshi markets provide a high-frequency, continuously updated, distributionally rich benchmark that is valuable to both researchers and policymakers.

Kalshi gives one example of how this data might be used, the Citizen Debt Forecast (CDF). The CBO forecasts the future debt path but it updates only twice a year and is limited to a legislative baseline even when most observers expect, for example, taxes to increase or spending to be cut. The Kalshi CDF updates continuously and can build in market expectations about future legislative changes.

The Kalshi forecast, as seen below, is slightly more optimistic than the CBO forecast but I don’t read too much into that. The larger issue is how prediction market data can be integrated into a wide variety of forecasts.

How are the market valuations for the U.S: insurers doing?

by  Tyler Cowen in

U.S. insurance stocks have been doing quite well since the beginning of May 2026, and they have materially outperformed the overall market. I’m using the May 1 close through the September 3 close so that we compare complete trading days; these are price changes, excluding dividends.

The cleanest broad measure is the iShares U.S. Insurance ETF (IAK), which covers U.S. life, property and casualty insurers. It rose from $132.01 on May 1 to $147.87 on September 3: +12.0%. An alternative, more equal-weighted measure, the SPDR S&P Insurance ETF (KIE), rose from $56.79 to $64.80: +14.1%.

For comparison, the S&P 500 ETF (SPY) went from $720.65 to $773.17 over the same period, +7.3%. So insurers have beaten the market by roughly 5–7 percentage points in four months.

That is from GPT Pro.  Here is my earlier post on numbers and market valuations.  Do any market prices reflect a realistic chance of very bad outcomes from advanced AI?

Here is advice on how to short those shares.

Shout it from the rooftops (of the data centers)

by  Tyler Cowen in

Data-center investment has become one of the largest capital-expenditure cycles in financial markets, with U.S. hyperscalers expected to deploy roughly $700 billion in 2026. This investment boom has raised concerns that large computing loads impose external costs on households through higher electricity prices. Using a 50-state panel for 2021-2024, we find no statistically significant evidence that data-center presence, installed capacity, or capacity expansion predicts residential electricity-price inflation across extensive-margin, intensive-margin, fixed-effects, and timing specifications. We propose an energyinternalization mechanism: hyperscalers can partially internalize incremental electricity demand through contracted or dedicated generation, including solar and wind energy. Consequently, gross datacenter electricity consumption need not translate one-for-one into net pressure on residential electricity supply. The findings suggest that the extraordinary AI capital-investment cycle has not, thus far, produced a detectable residential electricity-price externality.

Here is the article by Yosef Bonaparte, via the excellent Kevin Lewis.

The College Wage Premium in the Generative AI Era

by  Tyler Cowen in

After expanding for four decades, the U.S. college wage premium is experiencing a sustained contraction, dropping sharply from 0.626 in 2022 to 0.575 in 2026. Using Current Population Survey Outgoing Rotation Group data through 2026, we show that standard market-clearing supply-and demand accounting implies an unprecedented drop in relative demand for college labor-the first sustained negative relative demand growth in a series spanning back to 1914. Linking individual wage data to task-based generative AI exposure, we document that post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of -0.086. Combined with the college-non-college exposure gap, this mechanism accounts for roughly 28 percent of the total drop in the college wage premium from 2022 to 2026. While noncausal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.

I do not see AI as driving these changes, but an interesting result nonetheless, from José Azar, Mireia Gine, and Javier Sanz-Espín. Via Anecdotal.

Who values democracy?

by  Tyler Cowen in

This paper examines the conventional view that redistribution is central to the democratization process using data from stock markets. Consistent with this view, democratizations have a large, negative impact on asset valuations driven by a rise in redistribution risk. Across 90 countries over 200 years, risk premia are substantially elevated— similar in magnitude to financial crises—prior to and during democratizations. A shift in Catholic church doctrine in support of democracy provides causal evidence that democratizations increase risk premia. Successful democratizations lead to substantial redistribution: the size of the public sector grows, income inequality falls, and the labor share of income rises. An extended version of the canonical redistribution-based model of democratization that includes asset prices can quantitatively explain these effects. Reductions in inequality and increased taxes explain approximately half of the results. The rest comes from greater economic competition and equality in government spending. The model also explains the negligible asset pricing response to autocratizations. Neither an increase in macroeconomic risk nor generic political risk can explain the results.

That is by Max Miller, now published in the JPE, ungated copy here.

More optimistic results on AI and job markets

by  Tyler Cowen in

Here is a good Jon Hartley thread.  Here is the paper, with Jolevski, Melo, and Moore.  From Jon’s thread: “Generative AI adoption is widespread, but substantial aggregate labor-market disruption is not yet visible. Workers nevertheless perceive substantial displacement risk, especially when firsthand use reveals that AI can perform key tasks for their job.”  And again here is Alex’s post from yesterday.

AI and Employment: So Far, So Good

by  Alex Tabarrok in

In September 2023, the Census Bureau added questions about AI to its Business Trends and Outlook Survey. Census asked hundreds of thousands of businesses whether they had used AI in the previous two weeks to produce goods and services. At that time, 3.7% said yes; by late 2025 the figure had reached about 10%. (In November 2025 Census broadened the question to ask about AI use in any business function, producing a jump in measured adoption to about 18%.)

Twice the Bureau has asked a key question:

In the last six months, how did the use of Artificial Intelligence affect this business’s total employment?

In Dec. 2023 to Feb 24, when ~5% of firms were using AI the answers were 2.8% increased, 2.6% decreased and 94.6% reported no change. Two years later, in the Nov 2025–Feb 2026 supplement, the answers were: 2.3% increased, 2.0% decreased, and 95.7% reported no change. The answers were similar by firm size.

Some sectors reported more action. Information is the one sector where fewer than 92% report no change. But overall, almost all firms report no change and of those reporting change it’s about evenly divided between increasing and decreasing employment.

The supplement also asked about tasks. Among firms using AI, 44% say it supplemented or enhanced work an employee already does. Ten percent say it performed a task an employee used to do. Eleven percent say it introduced a task no one had been doing.

Among those using generative AI, 85% of firms cited writing or editing documents and email as the biggest uses, half cite searching for information, 45% summarizing documents, and 13% coding. Sixty-four percent of adopters say they changed nothing about the business in order to use AI, 15% trained existing staff, another 15% built new workflows, and just over one percent hired anyone with AI skills.

Among firms where AI has taken over some employee tasks, the degree of substitution is growing. The share reporting that AI took over “a large number” of tasks rose from 2.4% to 7.1%, while the share reporting “a moderate number” rose from 13% to 22%. But this group is still small: only about a tenth of AI adopters, who themselves make up about a fifth of firms.

I have reported firm-weighted estimates but employment-weighting gives essentially the same result. Thus, we have unusually direct evidence from a very large sample, and it says that the overwhelming majority of firms using AI do not yet report any effect on total employment. Very consistent with what Tyler and I said in our talk to OpenAI.

I used Fable and ChatGPT Sol in producing this post.

Scholar Data

by  Alex Tabarrok in

In our paper, A Skeptical View of the National Science Foundation’s Role in Economic Research, Tyler and I point out that if the NSF is doing what it should, it ought to be doing very different things than other funders:

Public goods theory tells us that the National Science Foundation should support activities that are especially hard to support through traditional university, philanthropic, and private-sector sources. This insight suggests a simple test: to the extent that the NSF allocates funds to genuine public goods as opposed to subsidies on the margin, we ought to see a large difference in the kinds of projects the NSF supports compared to what the “market” sector supports. But what stands out from lists of prominent NSF grants (like the one provided by Moffitt in this symposium) is how similar they look to lists of “good” research produced by today’s status quo. If we take public goods theory seriously, what areas of economics should be supported?

We suggest replication studies and support for producing public datasets. Which brings me to Scholar Data, a neat new project that won NIH’s competition to create a data-sharing index. Scholar data creates an S-Index, like an H-Index for papers, but instead the S-Index measures a dataset’s ease of access, citations and other mentions. The point is to create a metric to reward the creation of a public good:

Researchers invest years collecting and sharing datasets that underpin reproducibility, transparency, and discovery across every field. Without shared data, findings can’t be verified, built upon, or trusted. And yet, the metrics that define academic careers, such as citations, h-index, and impact factor, only count publications. Data sharing goes unrecognized and unrewarded.

This creates a broken incentive: researchers are expected to share data, but get no credit for doing so. The result is that data sharing is treated as a chore rather than a contribution.

Scholar Data and the S-index are aimed at fixing this. By measuring how impactful your datasets are, the S-index gives data sharing the same visibility and recognition as publishing, turning it from an obligation into a career asset.

Bravo! Your dataset may already be catalogued. You can get credit at ScholarData.

A Big Bet on Explosive AI-Driven Growth

by  Alex Tabarrok in

Wow! The excellent Ben Moll has put together a high-stakes bet on US economic growth.

A bet on near-term explosive AI-driven growth: whether U.S. real GDP per capita will grow by at least 15% within a single year (measured relative to prior peak) by the end of 2033. Agreed on X, 15–16 August 2026. This document summarizes the agreed terms.
1. Parties and stakes
Fast-growth side (wins if the growth condition in Section 2 is met):
● William MacAskill https://www.williammacaskill.com/ — $10,000
● Samuel Albanie https://samuelalbanie.com/ — $25,000
● Tom Cohen https://x.com/tomcohen — $25,000
● Total: $60,000
No-fast-growth side (wins otherwise):
● Benjamin Moll http://benjaminmoll.com/ — $40,000
● Andrew Ho https://andrewho.xyz/ — $200,000
● Total: $240,000

Odds: 4:1 — every $1 staked by the fast-growth side is matched by $4 from the no-fast-growth
side. Implied probability of the fast-growth scenario: 20%.

I’m known for quipping that a bet is a tax on bullshit but I don’t think either side is BSing. We have a genuine and important disagreement. I’d side with Moll, for what it is worth.

Polling results on capitalism and socialism

by  Tyler Cowen in

The results suggest that the public is feeling down about capitalism but is still wary of full-blown socialism. And many are deciding whether a few socialist candidates might help shake up the system. We found that:

—Just a third of Americans privately say they have a positive view of capitalism.

—Less than half of Republicans privately express a positive view of capitalism, though two-thirds support it in public.

—Almost half of women privately say they would consider voting for a socialist.

—Nearly a third of Gen Z publicly say that America would be better off as a socialist country, but only 14 percent say so in private.

—The majority of blacks publicly say capitalism is the cause of America’s social ills, but only 37 percent say so privately.

—Of people earning more than $150,000 a year, 58 percent privately say they are open to voting for a socialist, though only 42 percent say so publicly.

That is from Mene Ukueberuwa at The Free Press.