Category: Data Source
AI-Native Firms
Very important work from Hyunjin Kim and Rembrand Koning. Insead and HBS respectively:
We study how firms built around AI capabilities-“AI-native” firms-are organized. Drawing on Y Combinator batches W20-F24 and U.S. venture-backed startups whose first financing closed between 2020 and 2024, we classify each firm’s AI-native status and link it to workforce microdata on team size, function, seniority, and hierarchy. Relative to non-AI startups in the same industry-cohort, AI-native firms are 25% smaller. Their share of engineers is 13% greater, and the shares of entry-level workers and managers are each roughly 15% lower. Their hierarchies are half a seniority level flatter-yet valuations are comparable, implying more value created per employee. We argue these patterns reflect two channels: a process channel, in which AI changes how people work inside the firm, and a product channel, in which AI capabilities are built into what the firm sells. Using text from product descriptions and job postings, we find that embedding AI into the product, beyond layering on AI tools into existing workflows, is a primary way startups are scaling “knowledge work” without large teams of knowledge workers.
The tweet storm on the new paper is especially useful. Via Luis Garicano. And note those results predate the very latest and best tools.
A Cohort Perspective on Latin America’s Fertility Transition
Latin America’s momentous fertility transition is now in the domain of history, allowing a cohort perspective on the decline of completed fertility. Using census microdata from 17 Latin American countries, we track female birth cohorts from the 1920s to the 1970s by subnational region to document the extent to which cohort fertility decline coincided with other demographic and socioeconomic processes. Across cohorts within subnational regions, children ever born fell one-for-one with mortality decline. Expansions in urbanization, multigenerational living, women’s and husbands’ education, women’s employment, and the non-agricultural sector all predicted declines in ever-born and surviving fertility, but women’s education and sectoral composition were the dominant forces after covariate adjustment. Fertility decline was not systematically linked with improvements in children’s outcomes, including school enrollment, literacy, primary completion, and non-employment. These cohort facts challenge theories of fertility decline centered on women’s work and children’s education but support others emphasizing women’s education.
I fear that means the women think they are finding better and more fun things to do? Which is hardly bad per se, but…
That is from a new NBER working paper by Regina Calles and Tom Vogl.
Facts about American men and women
Much of what looks like changing marriage preferences over the twentieth century is actually demographics. Exploiting plausibly exogenous variation in sex ratios across U.S. birth cohorts (1870, 1930, 1950), we jointly identify preferences, match quality dynamics, and the costs of marriage and divorce. Demographics alone explain two-thirds of cross-cohort differences. Women’s premium for older husbands collapsed across cohorts; men’s preferences barely changed. Love that survives its early years becomes permanent, but the odds of surviving fell from 97% to 44%. Divorce costs fell six-fold and depend on life stage. A horse race across behavioral channels shows that the match quality process—not mate-age preferences—is the primary dimension of generational change. Declining divorce costs and fragile match quality are substitutes: either alone fits the data, but together they reveal two independent dimensions of social change. The model validates out of sample on the 1910 and 1970 cohorts.
That is from a recent paper by Jose-Victor Rıos-Rull, Shannon Seitz, and Satoshi Tanaka. Via the excellent Samir Varma.
Do teens regret their social media use?
A new study by Irish researcher Eoin Whelan attempts to answer this. Dr. Whelan told me he was specifically inspired by Haidt’s 2024 claims and sought to examine them rigorously and in the context of other regrets. This is a great use of science…testing dramatic public claims. So…do they hold up?
In Dr. Whelan’s study, 389 young adult participants (20-24) who were social media users as teens were asked about their regrets regarding their teenage years. A list of 20 possible teenage regrets was asked of all participants, with degree of regret marked on a 7-point Likert scale. This is an interesting design…testing social media regrets against other possible regrets, putting them in better context than the crude survey Haidt relied on.
So how did social media regrets hold up? Out of 20 possible regrets, too much time on social media ranked 13th. The top regrets were 1.) not sticking up for oneself, 2.) being too self-conscious, 3.) not documenting memories, 4.) not learning practical life skills and 5.) not getting help with mental health. Girls were slightly more likely to regret time on social media than boys (ranking 11th vs 13th) though this effect was very small (I estimated it at about r = .11) so hardly the big “vulnerable girls” narrative some have peddled.
Further, regrets over time spent on social media as a teen did not predict current young adult life satisfaction for either boys or girls. Thus such regrets may be more a symptom of current panics over social media than anything of actual life importance2. Of the regrets, only not working harder in school and not exercising negatively predicted young adult life satisfaction. Interestingly, having regrets over socializing with friends positively predicted life satisfaction.
As Dr. Whelan noted in his study, “The objective of this study was to critically examine the commonly held belief that social media use during teenage years is a significant source of regret and a predictor of diminished well-being in early adulthood…Contrary to dominant narratives in the public domain, our results suggest that regrets over time spent on social media are not among the most potent regrets reported by young adults…As such, these results align with prior research indicating that the harmful effects of social media may be overstated.”
Here is the full Chris Ferguson Substack.
Can Online Activity Be Regulated? Evidence from Adult Websites
The consequences of online regulations depend on the extent to which users can circumvent restrictions or substitute toward noncompliant platforms. Since 2023, 25 U.S. states have implemented age verification laws that caused prominent adult websites (including Pornhub) to restrict local access for all users. We study how these restrictions affected browsing activity using individual-level panel data. Access restrictions reduced overall time spent on adult sites by roughly 10%. Specifically, for every 100 hours spent on top adult sites before restrictions, about 50 hours remained accessible at noncompliant sites that never restricted access, 30 hours persisted through VPN-based circumvention, 10 hours were substituted from compliant sites to noncompliant sites, and 10 hours were no longer spent on adult sites.
That is from a new NBER working paper by Matthew Brown, Emily J. Davis, and Devin G. Pope.
Who Leads? Relative Age Effects on Social Capital
A fascinating paper and result:
This paper studies the causal effect of being the oldest within a school cohort on social capital. Using a fuzzy regression discontinuity design and data from Facebook, we find that boys who are older than their classmates make 11% more friends in high school. This social advantage is associated with leadership roles, with relatively older boys 42% more likely to become class president than their relatively younger peers. Men who were relatively older during childhood have larger social networks in adulthood, and disproportionately sort into management and entrepreneurship. Our findings suggest that small age differences in peer composition can have persistent effects on social and economic outcomes.
That is from Matthew Jacob of Harvard and Michael Bailey of Facebook. Via the excellent Kevin Lewis.
General-purpose large language models outperform specialized clinical AI tools on medical benchmarks
This result does not surprise me at all. Here is part of the abstract:
Frontier LLMs outperformed clinical AI tools in all three evaluations. Clinical AI tools performed comparably to auto-enabled Google Search AI Overview on the RCQ. These findings highlight the need for independent, real-world evaluation of AI tools before they enter clinical settings.
From Krithik Viswanath, et.al. As a side note, this (and the more general version of the point) is one big reason why some fairly large number of Emergent Ventures proposals are rejected rather quickly.
A simple reason for skepticism about the iPhones/fertility link
Here is the background to the debate. Here is more from Noah. Here is a thread from researcher Caitlin Myers. And here is some basic information:
In 2008, 1.9% is the share of the mobile-subscribing population with an iPhone wireless subscription. As a percent of all adults that is 1.6%.
In 2009, it is 4.3%. 3.6% of all adults.
In 2010, 6.8%. 5.5% of all adults.
Plus conception to birth takes nine months (give or take!), noting that actual family planning may make this lag far longer. In 2008 fertility rates already were falling pretty sharply. The whole “maybe the iPhone messes up your dating processes” factor also requires some time to operate, especially since iPhones as a network of many many users, and whatever negative effects on socializing you think that might have, was still to lie in the future. And what you could access on the iPhone then was far more limited than today.
So when the authors talk about diffusion explaining 33–52% of the decline in the general fertility rate among American women 15–44, I still do not get how that is supposed to operate.
The explanations I am hearing seem to be parasitic on world intuitions from 2026, not the time period under consideration.
The Nationalization of American Science
OMB, joined by some forty grantmaking agencies—NSF, HHS, DOE, NASA, DOD among them—has proposed a sweeping rewrite of the rules governing all federal grants, the Regulation for Federal Financial Assistance.
American science has long been state funded but not state directed. Since Vannevar Bush, money has flowed through many agencies to independent universities, allocated largely by peer review. The system has flaws—conformity, gerontocracy, waste—but it had one great virtue, the system was decentralized and not under state control. This rule proposes to bring science funding under top-down, state control.
Program goals must now be “aligned with administration policies and priorities” (§ 200.202). Merit review is subordinated to politics: “senior appointees must conduct these reviews,” ensuring “that discretionary awards advance the President’s policy priorities,” while “peer review remains advisory and does not replace agency discretion” (§ 200.205). And every grant becomes terminable at will, whenever it “no longer effectuates program goals, Federal agency priorities, or the national interest *as they exist at the time of the termination*” (§ 200.340, emphasis added). Universities must even ensure their subrecipients don’t “significantly damage the reputation of… the Federal Government” (§ 200.332)—a loyalty clause for scientists.
All this is sold as cutting “burdensome conditions,” a goal I would support, but sadly that is bullshit. The proposed rules add more paperwork and many more layers of bureaucratic review. Payment requests must include written justifications. Every disbursement gets screened through Treasury’s “Do Not Pay” system. Every recipient must run E-Verify. Applicants must disclose any employee who worked at the awarding agency within two years. And on top of the existing review machinery sits a new pre-issuance review committee of “senior appointees” second-guessing the experts. Fixed amount awards—pay for outputs, not inputs—an innovative reward mechanism are *eliminated*, so every award now gets routine cost monitoring and financial reporting.
Political review of every award, peer review demoted, agency review promoted, termination whenever “priorities” change. Chilling. It’s a nightmare of petty low-trust review of the kind that is already drowning science. I must deal with this kind of nonsense all the time. More is not better.
The machinery is centralized too. OMB’s guidance becomes binding regulation, effective government-wide with no agency rulemaking. One dial in the White House now turns every grant program in the country.
The new rules will be sold as getting rid of DEI but that is an excuse to bring in the commissars. The new rules don’t depoliticize science they create even more politicization with the sign flipped, and the drafters admit it:
In the previous administration, executive agencies frequently chose to subsidize and expressly prioritize projects based on their ideological alignment with the categories of activities discussed in the proposed version of § 200.300. See, for example, E.O. 13985, sec. 1, 86 FR 7009, 7009 (Jan. 25, 2021) (“It is therefore the policy of [the Biden] Administration that the Federal Government should pursue a comprehensive approach to advancing equity . . . .”). In this administration, executive agencies will continue to use their discretionary authorities in a manner consistent with current Executive Branch policy. If executive agencies were entitled to subsidize those types of activities during the previous administration, there is no constitutional basis to prevent the government from reaching a different policy determination regarding which activities to fund during this administration.
Read that twice. Tip your hat to the new constitution, take a bow for the new revolution. Will science prosper when it is whipped by political turnover? Research runs on decade timescales; administrations run on four-year ones.
A decentralized funding system is inefficient the way markets and federalism are inefficient—we give up some economies of scale and get experimentation, error correction, and robustness in return. A system in which every award advances “the President’s policy priorities” is efficient the way ministries of science are efficient. We know how that experiment ends.
America is moving in the wrong direction. We should double down on what made America great. Instead we are adopting all of the loser policies of authoritarian nations.
What do the AIs think of us?
Asked to answer as a typical human, every cutting-edge model rated us markedly more neurotic, less open, less agreeable and less conscientious than they rated themselves. The gap on Neuroticism alone is 1.69 points on a 5-point scale.
Here is more material of interest. And this:
Across 31 models from those seven labs they answer the personality tests in unison: high openness, low Dark Triad, Universalism on top, Power dead last in every single model.
The Labor Share Fell. So What?
The share of Gross Domestic Income accruing to labor has been declining in recent decades while the share accruing to capital has been rising. In the graph below, I show labor compensation as a share of GDI (left axis). Labor share has indeed been trending down–some of this could be an artifact of the data, e.g. an increase in proprietor’s income (labor) mislabeled as capital income, more pass throughs and so forth—but for the purposes of this post I will accept that the labor share has declined. What does this mean?
The natural response is to think that because the share going to labor has fallen and the share going to capital has risen that there has been a transfer of income from labor to capital. That is possible but it is not the only interpretation and it does not follow mechanically from the share data.
I have also plotted total compensation to labor (in real terms) in the graph above and far from shrinking it is higher than ever and growing. Moreover the right axis is logged so you can also see that outside of recessions the growth rate of labor compensation looks quite steady (similar slope over time). (Labor compensation per member of the labor force is noisier but looks similar).
The recessions in 2008 and 2020 are worth noting because these are periods when the labor share was high and locally at a maximum! The reason, of course, is that GDI was shrinking in these periods more than labor compensation. In other words, capital takes a bigger hit than labor in a recession. This is a good reminder that a high share of GDI is not what workers most care about–a high absolute level of GDI is more important for the bottom line.
In short, the data are consistent—not proof of, but consistent with—a story in which capital has become more productive, raising output. More productive capital also raises the demand for labor, so while more of the new output goes to capital in the first instance, the pie is growing and labor’s absolute compensation has grown with it. Yes, if the shares had stayed constant and output had grown just as much, labor compensation would have been higher still. And if my grandmother had wheels, she would have been a bicycle.
New paper on the iPhone and fertility
The U.S. general fertility rate has fallen by 22% since 2007, a sustained decline not readily explained by economic conditions, contraceptive use, housing or childcare costs, or other commonly cited factors. We assess the potential role of a different shock: the diffusion of the smartphone. The U.S. rollout of the iPhone, the first modern smartphone, provides a natural experiment: from June 2007 through February 2011, the device was sold only on AT&T, allowing us to identify its effect from variation in AT&T’s mobile broadband coverage. Entropy-balanced Poisson and synthetic difference-in-differences event studies imply that access to the iPhone reduced births by 4.5–8.0% at ages 15–19 and 3.2–6.6% at ages 20–24, with statistically significant but smaller declines among older cohorts. Placebo analyses applied to Verizon and Sprint’s pre-2011 coverage footprint are null. Taken together, these cohort effects imply that the diffusion of the iPhone deepened the decline in births among women under 30 while suppressing the rise in births among older women. Overall, the diffusion of the iPhone explains 33–52% of the decline in the general fertility rate among women aged 15–44. National-survey evidence on time use and sexual behavior is consistent with the iPhone reducing in-person interactions, increasing pornography use, and reducing sexual frequency.
That is from
Note also that as this study is set up it does not discriminate against the ” the iPhone effect on fertility is mainly a thing of timing” hypothesis. And a Paul Novosad comment.
How High-Skill Immigration Restrictions Eroded Regional Productivity: Evidence from the 2017 BAHA Executive Order
This paper estimates the regional economic impact of high-skill immigration restrictions by analyzing the 2017 “Buy American, Hire American” (BAHA) policy as a quasi-experimental policy shock. By significantly tightening H-1B visa adjudication, BAHA caused new employment petition denial rates to double from 7% to 17%, while STEM-specific rejections tripled to 31%. Using a difference-indifferences framework, this study finds that states highly dependent on H-1B talent experienced a statistically significant 2.8% relative decline in value-added output. This implied a productivity loss totaling roughly $218 billion across the most affected regions. While concurrent tax cuts and deregulation likely offset the impact on employment and wages, the loss of specialized STEM expertise adversely impacted total factor productivity. These findings suggest that policies based on conventional employment metrics may overlook the “hidden damage” to productivity and innovation that drives the broader economy, thereby underestimating the true economic cost of immigration restrictions.
That is by Caroline Y. Su of McLean High School. Via the excellent Kevin Lewis.
Is work from home bad for your mental health?
From the “Results” section:
Relative to those in nonremotable jobs, workers in remotable jobs spent approximately one additional hour alone per workday after the pandemic. Those in remotable jobs also differentially increased days spent entirely alone and decreased after-work socializing. The rise in isolation was sharpest for those living alone, whose likelihood of spending the whole day without social contact rose by 7 percentage points (83%).
Mental distress simultaneously increased: Scores on the Kessler (K-6) measure of generalized psychological distress rose by 0.1 standard deviations for those in remotable jobs relative to those in nonremotable jobs. The increase in distress was roughly twice as large for those living alone compared with those living with family. Alternative measures of mental distress—such as the frequency of depression, mental health care utilization, and antidepressant prescriptions—show similar trends. In contrast, workers in remotable jobs did not differentially increase visits to non–mental health care providers or non–mental health prescriptions (statins, for example), suggesting that the change was not merely driven by increased flexibility for doctor visits.
That is from a recently published paper by Natalia Emanuel, Emma Harrington, and Amanda Pallais.
Law professors prefer AI over peer answers
Large language models (LLMs) are increasingly promoted as educational tutors, yet most evaluations focus on domains with a single ground truth. Many disciplines, however, hinge on judgment: reasoning, weighing ambiguity, and reaching defensible conclusions. Law provides a sharp test. We conducted a blinded evaluation of short-answer tutoring in contracts courses with sixteen U.S. law professors. Participants created 40 representative questions, wrote answers, and judged 2,918 anonymized comparisons between human and LLM responses. Professors rated LLMs far higher than their peers (average win rate = 75.33%), with models performing similarly to the best instructor. LLM responses were also rarely flagged as harmful (3.53%, vs 12.06% for professors). Preferences for LLM answers were consistent across evaluators and reflected shared professional standards. Our evaluation can be reliably extended to additional models by employing a separate LLM as a judge, rendering expert agreements an effective, scalable method to evaluate AI tutors in judgment-rich domains.
“far”. That is from a new paper by Alejandro Salinas, et.al. Via Andrew Curran. And via John Chamberlain:
Artificial intelligence (AI) and large language models (LLMs) tools are capable of mass-producing academic finance papers that are nearly indistinguishable from human-authored research, according to a new study published in the Journal of Economic Literature.
C’mon people, get ready. I know it is difficult to admit when your human capital has been devalued, but that time is upon us. In particular, being prolific is no longer such a comparative advantage in academia. You might run to the “but I know what questions to ask” cope, but I implore you to solve for the equilibrium. What is the equilibrium wage for merely asking questions?
Of course academic life and projects will continue, but the real rewards will go to people doing new, innovative, and hitherto impossible projects with AI.