Category: Data Source

Callum Williams on cybersecurity prices

by  Tyler Cowen in

Share prices of cyber firms have jumped around a lot in recent weeks, leading one side or the other to claim victory. But the crucial point is that, relative to the historical norm, the market is not really pricing ANYTHING big to change. There was a much bigger move in cyber stocks in both 2020-22 (up) and 2022-23 (down) but no one read “AI x-risk” into this.

Here is the full post with graph.

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Hardly the final word, and I am myself more pessimistic than those numbers indicate.  But at least with this we are getting somewhere concrete and scientific rather than just scare stories.  As for meta-commentary on the discourse itself, you really should be asking who are the people insisting on data here, and who are the people trying to talk you away from focusing on the data so much.

Did the ACA reduce mortality?

by  Tyler Cowen in

Many of us brought up related points at the time, but basically we were booed off the reservation:

While recent research has provided evidence that the Medicaid expansions of the Affordable Care Act (ACA) reduced mortality, there is no evidence on the effect of the Affordable Care Act (ACA) net of the Medicaid expansions on mortality. This is an important gap in knowledge because the ACA significantly increased health insurance coverage in non-expansion states. In this article, we exploit the large increase in health insurance coverage brought forth by the ACA to examine the effect of the ACA and Medicaid expansions on mortality. Unlike prior studies that relied solely on geographic variation in Medicaid expansions to estimate the net effect of the expansion, we use a novel empirical approach that allows us to investigate the effect of the ACA net of Medicaid expansion on mortality, the incremental effect of the Medicaid expansion, and the overall effect of the ACA including Medicaid expansion. We use longitudinal data from the NHIS Linked Mortality Files (LMF) and a nationally representative sample of 40 to 58-year-olds combined with a difference-in-differences and a difference-in-differences-in-differences research design to obtain estimates of the effect of the ACA on mortality. We find no evidence that the Medicaid expansions had a beneficial effect on mortality but do find that the ACA net of Medicaid expansion reduced mortality.

That is from a new NBER working paper by Anuj Gangopadhyaya, Cuiping Schiman & Robert Kaestner.  Via Glenn Mercer.

Does AI assistance enhance or erode expertise?

by  Tyler Cowen in

From a new NBER working paper:

Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment afterward without it. All work was scored by blinded expert patent attorneys. Paralleling findings from other white-collar domains, AI access raised the quality of work delivered on benchmark patent drafting tasks at 10 days (0.34 SD, p = 0.03) and 90 days (0.38 SD, p = 0.01), with larger gains among junior lawyers. After three months, all subjects redlined an existing patent application without AI, a core task of patent practice requiring expert judgment. Treated lawyers outperformed controls by 0.32 SD (p = 0.04), but this advantage was concentrated entirely among senior lawyers (0.45 SD, p = 0.02). Junior lawyers showed no average gain; their scores instead bifurcated, with sharply fewer mediocre scores offset by more poor and more good ones. The largest gains from AI thus accrued to the lawyers who retained the least. Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice.

That is by David Autor, et.al.  Do note that over time the allocation of humans to tasks will evolve so that more of the humans become more productive, not less.  RCTs somehow have the odd disadvantage of requiring too many things to be held constant, and so they can miss the benefits of longer-term adjustments.

The Prediction Archive

by  Alex Tabarrok in

The Prediction Archive is a public database of tens of thousands of world predictions. Using AI it tracks predictions over many decades and marks to market. I was surprised to discover that I am currently the highest ranked individual predictor in the world! Huh, I would not have predicted that.

Ranks are based on the Wilson score so you get credit not just for accurate predictions but for making enough predictions so that uncertainty about accuracy is reduced.  Bryan Caplan was more accurate than I was but makes fewer predictions. Tyler made more many predictions than I did and was only somewhat less accurate. What the AI marks as predictions seem sometimes to be more about contemporary events, so take the numbers with a grain of salt. I expect to fall in ranking as the archive expands. Other people the archive covers include Peter Zeihan, Scott Alexander and Warren Buffett.

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.