Category: Data Source
More optimistic results on AI and job markets
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
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
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
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: $240,000Odds: 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
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.
Important results on economic mobility
We use more than 900 million linked employer–employee records covering the entire formal workforce of Brazil to examine when cities provide upward mobility for the initially poor. We find that upward mobility for low-wage workers is strongest in workplaces that combine low-wage individuals with high-wage workers. This association is particularly pronounced in Brazil’s southern cities, where more complex industries include workers with a wider range of skills. The association is weaker in northern cities, where formal employment is dominated by the public sector.
Here is the paper by Radu Barza, Edward Glaeser, César A. Hidalgo, and Martina Viarengo. And here is a useful thread on the paper. This is one of the best and most important economics papers I have seen in the last few years.
Daddy’s Girl
Using Danish registry data, we study how managers’ gender attitudes shape gender inequality in the workplace by exploiting the birth of a daughter – as opposed to a son – as a plausibly exogenous shock to male managers’ gender attitudes. Comparing within-firm changes in women’s labor outcomes depending on the gender of the manager’s newborn child, we find that women’s relative earnings and employment increase by 4.4% and 2.9% respectively following the birth of the manager’s first daughter. These effects are driven by an increase in managers’ propensity to substitute male hires with female hires that have comparable education, hours worked, and earnings. Consistent with this substitution channel, we do not detect any significant effect on firm performance. Because these effects emerge rapidly and persist over time, we can rule out the need for prolonged exposure to gender issues or for personal incentives tied to improving conditions for their own daughters as prerequisites for managers to promote gender equality within their firms.
That is by Maddalena Ronchi and Nina Smith, forthcoming in Review of Economic Studies.
Overreaching causal language in the social sciences
Across the social sciences, many studies use cross-sectional designs that reveal associations but are generally unable to support direct causal claims, yet authors of such articles may make or imply causal claims anyway. Here, to examine the prevalence of such ‘overreaching’ causal language, we analysed 194,631 cross-sectional articles using large language models. Over the period 1980–2024, an average of 46% of articles contained causal language in their titles or abstracts, where the annual rate has risen almost threefold since 2000 from 20% to 60%. To examine the effects of such language, we conducted a human-subjects experiment (N = 1, 105), finding that readers frequently indicate abstracts with this phrasing provide causal evidence but that methodological labels (β = −0.4, 95% confidence interval −0.56 to −0.19) and associational wording (β = −0.3, 95% confidence interval −0.43 to −0.07) reduce this tendency. Experiments with five LLMs revealed that model summaries of these articles (N = 100 each) can amplify causal overstatement, removing hedges and introducing causal claims where articles used strictly associational phrasing; however, prompting caution diminishes this pattern.
That is from a recent paper by Calvin Isch, Timothy Dörr, Neil Fasching, Grace Jennings & Duncan J. Watts. Note that Isch is on the job market this year, working with Tetlock and Watts.
Common sense in charge
…children are increasingly seen as interfering with the freedom of parents; views on whether mothers of young children should work have become markedly more progressive and account for a substantially larger share of the decline among the tertiary-educated; and fewer people believe that women or men need children to lead a fulfilled life. This last attitude changed most over the decade and is the largest contributor to the fall in intended fertility, above all among less-educated women.
That is from a new NBER working paper by
Childhood Exposure to Joint Custody Reforms and Adult Family Formation
Joint custody reforms are among the most consequential family-law changes for children, yet little is known about their long-term effects. Exploiting staggered adoption across US states and 13 million ACS observations, we show that childhood exposure reduces adult fertility by 7 percent, symmetrically for women and men and operates primarily through lower parenthood and couple formation. Effects are sub-additive within couples, while exposed individuals assortatively match. Our findings point to the formation of family preferences during childhood and align with a US fertility decline increasingly driven by rising childlessness and declining couple formation rather than smaller families among parents.
That is from a recent research paper by Daniel Fernández-Kranz and Sébastien Fontenay. Via the excellent Samir Varma.
The End of Friday Nights with Friends
This study examines how Americans’ time with friends has changed over the week, focusing not on how much social interaction has declined but on when it has. Using American Time Use Survey data from 2003 to 2024 (N = 243,095), I map hourly patterns of “friend time” across the days of the week. In the early 2000s, social life followed a clear weekly rhythm: Modest weekday interaction built toward pronounced peaks on Friday and Saturday nights. Since the mid-2010s, that rhythm has collapsed. Friday night, once a central site of social activity, now looks like a typical weeknight. Saturday remains elevated but less so than before. Overall, time with friends has fallen by more than half. The mix of social activities, however, has remained stable. Americans have not replaced one form of interaction with another; they have reduced social interaction across the board. These findings point to a temporal reorganization of social life marked by the disappearance of the “night out.”
That is by Neal Caren, via the excellent Kevin Lewis. So what did you go last evening?
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.
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 Eva Vivalt, Elizabeth Rhodes, Alexander Bartik, David Broockman, Patrick Krause, and Sarah Miller. 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
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