Category: Data Source
Is it the phones?
Or perhaps we should just credit Sydney Sweeney? That is from Chris Said.
Human growth sentences to ponder
The most striking finding is that males born in the 1960s appear to have had a later or smaller adolescent growth spurt than those born a decade earlier. Combining the NHANES surveys and their precursors, I show that males born in the 1960s were the same height in childhood as those born a decade earlier, but then fell behind and were around half an inch shorter in adolescence. By adulthood, the heights of the two cohorts were nearly identical. These patterns are consistent with the 1960s cohort experiencing a slower growth tempo in adolescence through either a later or smaller adolescent growth spurt, followed by catch-up growth by growing longer into early adulthood (later ”age at final height”). Similar patterns are not evident in the height of females; however, females born in the 1960s experienced menarche (first menstrual period) later than those born a decade earlier.
That is from a new paper by Nicholas Reynolds. Via the excellent Samir Varma.
A new RCT on banning smartphones in the classroom
Widespread smartphone bans are being implemented in classrooms worldwide, yet their causal effects on student outcomes remain unclear. In a randomized controlled trial involving nearly 17,000 students, we find that mandatory in-class phone collection led to higher grades — particularly among lower-performing, first-year, and non-STEM students — with an average increase of 0.086 standard deviations. Importantly, students exposed to the ban were substantially more supportive of phone-use restrictions, perceiving greater benefits from these policies and displaying reduced preferences for unrestricted access. This enhanced student receptivity to restrictive digital policies may create a self-reinforcing cycle, where positive firsthand experiences strengthen support for continued implementation. Despite a mild rise in reported fear of missing out, there were no significant changes in overall student well-being, academic motivation, digital usage, or experiences of online harassment. Random classroom spot checks revealed fewer instances of student chatter and disruptive behaviors, along with reduced phone usage and increased engagement among teachers in phone-ban classrooms, suggesting a classroom environment more conducive to learning. Spot checks also revealed that students appear more distracted, possibly due to withdrawal from habitual phone checking, yet, students did not report being more distracted. These results suggest that in-class phone bans represent a low-cost, effective policy to modestly improve academic outcomes, especially for vulnerable student groups, while enhancing student receptivity to digital policy interventions.
That is from a recent paper by Alp Sungu, Pradeep Kumar Choudhury, and Andreas Bjerre-Nielsen. Note with grades there is “an average increase of 0.086 standard deviations.” I have no problem with these policies, but it mystifies me why anyone would put them in their top five hundred priorities, or is that five thousand? Here is my earlier post on Norwegian smart phone bans, with comparable results.
The politics of depression in young adults
From a recent paper by Catherine Gimbrone, et.al.:
From 2005 to 2018, 19.8% of students identified as liberal and 18.1% identified as conservative, with little change over time. Depressive affect (DA) scores increased for all adolescents after 2010, but increases were most pronounced for female liberal adolescents (b for interaction = 0.17, 95% CI: 0.01, 0.32), and scores were highest overall for female liberal adolescents with low parental education (Mean DA 2010: 2.02, SD 0.81/2018: 2.75, SD 0.92). Findings were consistent across multiple internalizing symptoms outcomes. Trends in adolescent internalizing symptoms diverged by political beliefs, sex, and parental education over time, with female liberal adolescents experiencing the largest increases in depressive symptoms, especially in the context of demographic risk factors including parental education.
Here is the link. This is further evidence for what is by now a well-known proposition.
AI-led job interviews
We study the impact of replacing human recruiters with AI voice agents to conduct job interviews. Partnering with a recruitment firm, we conducted a natural field experiment in which 70,000 applicants were randomly assigned to be interviewed by human recruiters, AI voice agents, or given a choice between the two. In all three conditions, human recruiters evaluated interviews and made hiring decisions based on applicants’ performance in the interview and a standardized test. Contrary to the forecasts of professional recruiters, we find that AI-led interviews increase job offers by 12%, job starts by 18%, and 30-day retention by 17% among all applicants. Applicants accept job offers with a similar likelihood and rate interview, as well as recruiter quality, similarly in a customer experience survey. When offered the choice, 78% of applicants choose the AI recruiter, and we find evidence that applicants with lower test scores are more likely to choose AI. Analyzing interview transcripts reveals that AI-led interviews elicit more hiring-relevant information from applicants compared to human-led interviews. Recruiters score the interview performance of AI-interviewed applicants higher, but place greater weight on standardized tests in their hiring decisions. Overall, we provide evidence that AI can match human recruiters in conducting job interviews while preserving applicants’ satisfaction and firm operations.
That is from a new paper by Brian Jabarian and Luca Henkel.
The evolution of the economics job market
In the halcyon days of 2015-19, openings on the economics job market hovered at around 1900 per year. In 2020, Covid was a major shock, but the market bounced back quickly in 2021 and 2022. Since then, though, the market has clearly been in a funk. 2023, my job market year, saw a sudden dip in postings. 2024 was even worse, with openings falling 16% lower than the 2015-19 average.
At the time, the sudden fall in 2023 seemed mysterious—it was an otherwise healthy year for the broader labor market. In hindsight, it seems like the 2021-22 recovery masked some underlying weakness. The 2020 job market had 500 fewer openings than the 2014-19 average; 2021 and 2022 together produced only around 100 more jobs than the 2014-19 average. In other words, the recovery never made up for the pandemic; by this crude logic, around 400 economist jobs were “destroyed”.
…And of course, all of this decline occurred before the litany of disasters that have recently hit the Econ job market. In May, Jerome Powell announced that the Federal Reserve—perhaps the largest employer of economists in America—would cut its workforce by 10%. The federal government has frozen hiring, as has the World Bank. Hit by the dual threat of fines and looming cuts to federal funding, Harvard, MIT, the University of Washington, Notre Dame, Northwestern University, among others, have announced hiring freezes and budget cuts.
Here is more from Oliver Kim, who also offers a much broader discussion of the meaning of all this.
One look at negative emotional contagion
This paper studies how peers’ genetic predisposition to depression affects own mental health during adolescence and early adulthood using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health). I exploit variation within schools and across grades in same-gender grademates’ average polygenic score—a linear index of genetic variants—for major depressive disorder (the MDD score). An increase in peers’ genetic risk for depression has immediate negative impacts on own mental health. A one standard deviation increase in same-gender grademates’ average MDD score significantly increases the probability of being depressed by 1.9 and 3.8 percentage points for adolescent girls (a 7.2% increase) and boys (a 25% increase), respectively. The effects persist into adulthood for females, but not males. I explore several potential mechanisms underlying the effects and find that an increase in peers’ genetic risk for depression in adolescence worsens friendship, increases substance use, and leads to lower socioeconomic status. These effects are stronger for females than males. Overall, the results suggest that there are important social-genetic effects in the context of mental health.
That is from a recent paper by Yeongmi Jeong, via the excellent Kevin Lewis.
It would take more than one paper to establish these claims
Nonetheless these are interesting results, worthy of further examination:
The measurement of intelligence should identify and measure an individual’s subjective confidence that a response to a test question is correct. Existing measures do not do that, nor do they use extrinsic financial incentive for truthful responses. We rectify both issues, and show that each matters for the measurement of intelligence, particularly for women. Our results on gender and confidence in the face of risk have wider applications in terms of the measurement of “competitiveness” and financial literacy. Contrary to received literature, women are more intelligent than men, compete when they should in risky settings, and are more literate.
That is from the September JPE, by Glenn W. Harrison, Don Ross, and J. Todd Swarthout. Here are ungated versions of the paper. Here is Bryan Caplan on the limitations of any single paper.
What determines business school faculty pay?
We examine the determinants of business school faculty pay, using detailed data on compensation, research, teaching, and administrative service. We estimate that a top-tier journal publication is worth $116,000, with significant variation across disciplines. Second-tier publications are worth one-third as much, and other publications have no impact. Further analysis of salaries and cross-discipline publication records suggests that researchers are compensated based on the journals they publish in rather than the departments they belong to. Conference presentations and teaching evaluations have significant but smaller effects than top-tier publications. Faculty administrators earn a premium, with department chairs receiving 11-35% and deans 58-94%. Post-Covid-19, real faculty pay has fallen more than in comparable fields and the sensitivity of pay to research performance has weakened.
That is from a new paper by Michelle Lowry, Daniel Bradley, April M. Knill, and Jared Williams. Via Arpit Gupta.
The robustness reproducibility of the American Economic Review
We estimate the robustness reproducibility of key results from 17 non-experimental AER papers published in 2013 (8 papers) and 2022/23 (9 papers). We then subject each robustness report to two independent, expert reviews. Including robustness tests rated as equally or more valid than the original analyses by expert reviewers, the fraction of significant robustness tests (p<0.05) varies between 0% and 93% across papers with a mean of 51%. The mean relative t/z-value of our robustness tests varies between 11% and 152% with a mean of 70%. Surveyed economists overestimate robustness but are able to predict which papers are most robust.
That is from a new paper by Douglas Campbell, Abel Brodeur, Anna Dreber, Magnus Johannesson, Joseph Kopecky, Lester Lusher, and Nikita Tsoy. Here is very useful Twitter coverage from Douglas Campbell.
Discrimination on #EconTwitter
This paper documents discrimination in the formation of professional networks among academic economists. We created 80 bot accounts that claim to be PhD students differing in three characteristics: gender (male or female), race (Black or White), and university affiliation (top- or lower-ranked). The bots randomly followed 6,920 users in the #EconTwitter community. Follow-back rates were 12 percent higher for White students compared to Black students, 21 percent higher for students from top-ranked universities compared to those from lower-ranked institutions, and 25 percent higher for female compared to male students. Notably, the racial gap persists even among students from top-ranked institutions.
That is from a new AERInsights paper by Nicolás Ajzenman, Bruno Ferman, and Pedro C. Sant’Anna. Here is a useful picture from the paper. Being at a top school, or at least pretending to be, is what really matters?
Do not forget
Estimating real-world vaccine effectiveness is vital to assessing the coronavirus disease 2019 (COVID-19) vaccination program and informing the ongoing policy response. However, estimating vaccine effectiveness using observational data is inherently challenging because of the nonrandomized design and potential for unmeasured confounding. We used a regression discontinuity design to estimate vaccine effectiveness against COVID-19 mortality in England using the fact that people aged 80 years or older were prioritized for the vaccine rollout. The prioritization led to a large discrepancy in vaccination rates among people aged 80–84 years compared with those aged 75–79 at the beginning of the vaccination campaign. We found a corresponding difference in COVID-19 mortality but not in non-COVID-19 mortality, suggesting that our approach appropriately addressed the issue of unmeasured confounding factors. Our results suggest that the first vaccine dose reduced the risk of COVID-19 death by 52.6% (95% confidence limits: 15.7, 73.4) in those aged 80 years, supporting existing evidence that a first dose of a COVID-19 vaccine had a strong protective effect against COVID-19 mortality in older adults. The regression discontinuity model’s estimate of vaccine effectiveness is only slightly lower than those of previously published studies using different methods, suggesting that these estimates are unlikely to be substantially affected by unmeasured confounding factors.
From Charlotte Bermingham, et.al. There is plenty of other research yielding broadly similar conclusions. The Covid vaccines saved millions of lives, well over two million lives even from a conservative estimate.
For the pointer I thank Alex T.
David Splinter on how much tax billionaires pay
Here is his comment on the paper presented here:
Summary: The U.S. tax system is highly progressive. Effective tax rates increase from 2% for the bottom quintile of income to 45% for the top hundredth of one percent. But rates may be lower among those with the highest wealth. This comment starts with the “top 400” tax rate estimates by wealth in Balkir, Saez, Yagan, and Zucman (2025, BSYZ), and adjusts these to account for Forbes family wealth being spread across multiple tax returns, to avoid double-counting capital income, to include missing taxes, and to apply standard tax and income definitions. This results in “top 400” effective tax rates exceeding overall tax rates by 13 percentage points. Still, the “top 400” tax rate is lower than for the top hundredth of one percent, suggesting a modest decline in effective tax rates at the very top when ranking by wealth. However, this is an unsurprising deviation from progressive rates because the tax system targets income, not wealth. Compared to the annual estimates in BSYZ, longer-run estimates are more appropriate for top wealth groups, which have volatile wealth and concentrate charitable giving into end-of-life bequests. End-of-life giving suggests long-run top 400 effective tax-and-giving rates could exceed 75%.
How Much Tax Do US Billionaires Pay?
We estimate income and taxes for the wealthiest group of US households by matching Forbes 400 data to the individual, business, estate, and gift tax returns of the corresponding group in 2010–2020. In our benchmark estimate, the total effective tax rate—all taxes paid relative to economic income—of the top 0.0002% (approximately the “top 400”) averaged 24% in 2018–2020 compared with 30% for the full population and 45% for top labor income earners. This lower total effective tax rate on the wealthiest is substantially driven by low taxable individual income relative to economic income. First, the C-corporations owned by the wealthiest distributed relatively little in dividends, limiting their individual income tax unless they sell their stocks. Second, top-owned passthrough businesses reported negative taxable income on average in spite of positive book income, further limiting their individual income tax. The top-400 effective tax rate fell from 30% in 2010–2017 to 24% in 2018–2020, explained both by a smaller share of business income being taxed and by that income being subject to lower tax rates. Estate and gift taxes contributed relatively little to their effective tax rate. Top-400 decedents paid 0.8% of their wealth in estate tax when married and 7% when single. Annual charitable contributions equalled 0.6% of wealth and 11% of economic income in 2018–20.
That is from a new NBER working paper by
Addendum: Here is a comment from David Splinter.
How Retrainable are AI-Exposed Workers?
We document the extent to which workers in AI-exposed occupations can successfully retrain for AI-intensive work. We assemble a new workforce development dataset spanning over 1.6 million job training participation spells from all US Workforce Investment and Opportunity Act programs from 2012–2023 linked with occupational measures of AI exposure. Using earnings records observed before and after training, we compare high AI exposure trainees to a matched sample of similar workers who only received job search assistance. We find that AI-exposed workers have high earnings returns from training that are only 25% lower than the returns for low AI exposure workers. However, training participants who target AI-intensive occupations face a penalty for doing so, with 29% lower returns than AI-exposed workers pursuing more general training. We estimate that between 25% to 40% of occupations are “AI retrainable” as measured by its workers receiving higher pay for moving to more AI-intensive occupations—a large magnitude given the relatively low-income sample of displaced workers. Positive earnings returns in all groups are driven by the most recent years when labor markets were tightest, suggesting training programs may have stronger signal value when firms reach deeper into the skill market.
That is from a new NBER working paper by