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A large sample of Indiana high school students shows a near-linear relationship between 8th grade math test scores and participation in varsity athletics. Academic success and participation in extracurriculars appear to be complementary.

We find large gaps in varsity sports participation by academic achievement, which we measure using students’ eighth-grade math test scores. Students who attended the smallest schools (first quintile) were 2.8 times as likely to participate in varsity sports as those who attended the largest schools (fifth quintile). We find that after adjusting for school size 35% of students in the top achievement quintile played varsity sports in 2025, compared with just 12% of students in the bottom quintile. In general, the relationship between achievement quintiles and varsity sports participation was remarkably linear. [The exception was football], the largest sport by participation [17% of all varsity athletes and overwhelmingly male]. Here the top quintile [in 8th grade math] had 20% lower odds of playing than students in the middle achievement quintile.

AI Summary. Firms' wage-setting norms are sticky, rising only from 2.7% to 3.5% even as inflation peaked near 7%, causing real wages for workers who stayed in their jobs to fall systematically. By the time inflation subsided, the median firm's wage rule had converged to ~3%, roughly matching inflation.

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ADP microdata from 2016–25 suggest firms set wages according to “wage norms” ~ invariant to inflation. The 2020–21 inflation surge mechanically reduced real wages. By the end of 2025, 34% of incumbent workers’ real wages were lower than in 2020.

Do sticky wage norms systematically reduce real wages during inflation spikes?

Core argument: Firm-level modal wage rules peaked at 3.5% in 2022–2023 against roughly 7% inflation, meaning nominal rigidity systematically eroded real wages for job stayers throughout the inflationary episode.

Roughly 42% of all nominal wage increases below 6% were within 0.01 percentage points of a whole or half number [Figure 6]. Figure 9 plots the employment-weighted average modal wage change across firms (solid line) alongside inflation rate (dashed line) from 2016 through 2025. In the pre-pandemic period, firm-level wage rules were relatively stable at a median of 2.7%, modestly above the rate of inflation. Beginning in 2021, inflation rose sharply, peaking at approximately seven percent in 2022. The average modal wage change also rose, reaching a peak of 3.5% in 2022 and 2023. By 2025, the median firm had a wage rule granting increases of three percent, roughly in line with inflation. The stickiness of firms’ wage rules in the face of inflationary pressure contributed to the systematic fall in real wages for job stayers. Evidence from Belgium [which has strong wage indexation], suggests that declining real wages, rather than inflation itself, helps explain the persistence of depressed consumer sentiment during the 2021–2024 period.

Takeaways by Macro Roundup® AI

  1. Firm-level modal wage rules peaked at 3.5% in 2022–2023 against roughly 7% inflation, meaning nominal rigidity systematically eroded real wages for job stayers throughout the inflationary episode.
  2. The median firm’s modal wage increase converged to 3% by 2025—matching inflation rather than exceeding it—marking a reversal from the pre-pandemic norm of 2.7% modestly above price growth.

AI Summary. Prime-age (25–54) and older (55–64) employment rates in Europe exceed those in the U.S., disproving the claim that European welfare systems suppress work. Higher-welfare northern European countries tend to have higher employment rates than lower-welfare southern ones.

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Despite Europe’s high social spending relative to the US, Chris Giles notes that prime-age adult (25–54) labor force participation in the Eurozone has overtaken that of the US, and there has been a dramatic convergence in the LFP of older workers.

Does European welfare actually discourage work?

Core argument: Prime-age adults (25–54) and older workers (55–64) both achieve higher employment rates in Europe than in the U.S., refuting the premise that generous welfare systems suppress labor force participation.

It does not matter whether you use EU or Eurozone data, prime-age adults (between 25 and 54) in Europe are more likely to be in work than those in the US. Older people (between 55 and 64) also have higher employment rates in Europe. Younger people (between 15 and 24) are more likely to have a job in the US, but that results from Europeans educating themselves for longer. The proportion of young people not in education, employment or training is higher in the US than in Europe. So welfare is not stopping work. More than that, the higher-welfare north of Europe tends to have higher employment rates than the south, although there is convergence within the Eurozone. Spain, in particular, has enjoyed rapid improvements.

Takeaways by Macro Roundup® AI

  1. Prime-age adults (25–54) and older workers (55–64) both achieve higher employment rates in Europe than in the U.S., refuting the premise that generous welfare systems suppress labor force participation.
  2. The U.S. records a higher share of young people (15–24) not in education, employment, or training than Europe, indicating that lower U.S. youth employment reflects weaker human capital investment, not stronger labor markets.
  3. Within Europe, higher-welfare northern economies consistently outperform lower-welfare southern ones on employment rates, though intra-Eurozone convergence is underway, led by rapid gains in Spain.

AI Summary. The Current Population Survey systematically understates income receipt, with nearly half of measured variables showing downward bias of 40% or more when benchmarked against administrative tax and program records. Bias is modest for Social Security programs (under 15%) but exceeds 40% for pensions, unemployment insurance, and food stamps.

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CPS reports of receipts in dollar terms are 40–60% below administrative benchmarks for income sources such as SNAP and pensions. Linked records show most of the gap comes from recipients reporting no receipt at all, not underestimating amounts.

Does survey data systematically undercount household income sources?

To directly measure bias in survey estimates, we calculate the difference between the weighted survey estimate and the survey target constructed from published totals [TSE or total survey error], using public-data adjustments for intentional coverage differences for average dollars received and the recipient share of the population across our income sources. Figures 1 and 2 summarize TSE in the CPS from 1984 to 2022, expressed as a share of the survey target for our measures of recipients (seven income sources) and dollars (eight income sources). In levels, TSE is almost always negative for both recipients and dollars (with SSI dollars an exception in recent years), indicating that survey means are systematically biased downward. For nearly half of the variables the bias is 40% or more. Yet, there is substantial heterogeneity across income sources. TSE tends to be more modest for SSA programs (specifically OASDI and OASI, for which the net bias is below 15% for recipients and dollars), while it is much larger for income sources such as pensions, UI, and SNAP (for which the net understatement exceeds 40%).

AI Summary. AI-driven data-center expansion and related professional hiring have added roughly 1.05m jobs above trend since 2022–2023, spanning electrical contracting, equipment manufacturing, software development, and data science. The job gains exceed what broader construction, manufacturing, and professional employment trends would predict.

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The Economist estimates that so far the AI boom has created ~1mm new jobs in the US, exceeding their estimate of ~200,000 layoffs attributed to AI since mid-2023.

Is artificial intelligence creating a genuine employment boom or temporary hiring surge?

Core argument: AI-linked demand has generated roughly 730,000 above-trend jobs in engineering, software development, and data science since 2022, substantially outpacing near-term displacement effects.

[We] tracked five industries at the heart of the data-centre build-out, from electrical contracting to equipment manufacturing. Since 2023 employment in them has risen by roughly 320,000 more than broader construction and manufacturing trends would suggest. Not all of those jobs owe their existence to AI—grid upgrades and other factory building matters too. [We also] tracked employment in professional occupations closest to the AI boom—engineers, software developers, mathematicians and data scientists—and compared their growth since 2022 with professional employment overall. These roles have added roughly 730,000 jobs above trend in recent years. AI will not have created every single one of them. But it has almost certainly created quite a few.

Takeaways by Macro Roundup® AI

  1. AI-linked demand has generated roughly 730,000 above-trend jobs in engineering, software development, and data science since 2022, substantially outpacing near-term displacement effects.
  2. Data-centre construction has added approximately 320,000 above-trend jobs across electrical contracting and equipment manufacturing since 2023, with grid upgrades and broader factory-building contributing alongside AI demand.

AI Summary. Non-college workers ages 22–34 are experiencing historically low unemployment relative to their own two-decade range, outperforming college-educated peers on that relative measure. College graduates still hold an absolute advantage, with a 2.7% unemployment rate versus 4.7% for high-school-only workers.

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In 2026, the 12-month moving-average unemployment for college-educated 22–34-year-olds is above its post-2003 mean, while the rate for non-college peers is historically low. Prime-age college grads still have lower unemployment than those with no degree.

Is the job market finally tightening for workers without degrees?

Core argument: Non-college workers ages 22–34 are experiencing one of their strongest job markets in two decades, with unemployment rates near historic lows relative to their own 2003–present range, outperforming their college-educated peers on that relative measure.

The unemployment rate for workers ages 22 to 34 who never graduated from college has rarely been lower in the past two decades. To gauge how the job market has shifted for each cohort, [Gad Levanon, Burning Glass’s chief economist] compared current unemployment rates for the different groups with their own range of unemployment rates since 2003. The analysis included data through July. By that measure, the job market looks much better for blue-collar workers, including those in construction and on manufacturing lines, and manual-service workers. It is [however] still easier to find a job with a college degree. The unemployment rate for degree-holders in their prime working years—ages 25 to 54—averaged 2.7% for the 12 months ending in July - well below the 3.6% rate for workers with just some college education, and 4.7% for people with a high-school diploma only.

Takeaways by Macro Roundup® AI

  1. Non-college workers ages 22–34 are experiencing one of their strongest job markets in two decades, with unemployment rates near historic lows relative to their own 2003–present range, outperforming their college-educated peers on that relative measure.
  2. On an absolute basis, a college degree still confers a significant labor-market advantage: prime-age degree-holders averaged 2.7% unemployment versus 3.6% for some-college workers and 4.7% for high-school-only workers over the 12 months ending July.

AI Summary. Global student performance in mathematics and reading has declined sharply since 2018, with reading scores falling 14 points and math scores falling 9 points between 2022 and 2025 alone.

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Mean OECD math and reading scores fell roughly 25 PISA points—about a year of schooling—between 2018 and 2025, alongside reduced motivation and engagement and increased carelessness, exemplified by a near-doubling of “hasty readers.”

Are global education systems failing to teach core skills?

Core argument: The share of “hasty readers” — students giving fast, incorrect responses — nearly doubled from 7% in 2018 to 11% in 2025, with math showing a parallel rise between 2022 and 2025, signaling deteriorating effort and engagement rather than pure skill loss.

In math, performance remained close to the 2003 level up to 2018, then dropped sharply btw 2018 and 2025. Mean performance dropped by 9 score points in mathematics and about 14 score points in reading btw 2022 and 2025. Over the 2018-2025 period, the largest drops [in reading] were among the most advantaged 25% of socio-economic status, and the socio-economic gap with the most disadvantaged students reduced somewhat, by 11 points, on average across 35 OECD countries. In mathematics, the share of disadvantaged students scoring below proficiency Level 2 increased in previous cycles and remained high in 2025; among advantaged students, the share scoring below proficiency Level 2 increased, reaching 18% in 2025, about 4pp. Students’ reports of their motivation for learning and engagement with school also declined between 2022 and 2025. The proportion of “hasty readers” – those who gave fast and incorrect responses – increased by about five percentage points, on average, from almost 7% in 2018 to 11% in 2025. Analyses for math, also show an increase in the proportion of hasty responses between 2022 and 2025.

Takeaways by Macro Roundup® AI

  1. The share of “hasty readers” — students giving fast, incorrect responses — nearly doubled from 7% in 2018 to 11% in 2025, with math showing a parallel rise between 2022 and 2025, signaling deteriorating effort and engagement rather than pure skill loss.
  2. Among the most advantaged quartile of students, the share scoring below math proficiency Level 2 reached 18% in 2025, up approximately 4 percentage points, indicating that academic decline is no longer concentrated among disadvantaged populations.

AI Summary. New York City's top 1% captured nearly two-thirds of real income growth between 2019 and 2024, versus under 40% nationally, driven by capital gains, dividends, and business income rather than wages.

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Btw 2019 and 2024, pre-tax, pre-transfer real median income in New York City fell 3.2%. The top .1% tax units, ~ households, (mean income ~$24mm) saw real growth of ~25%, whereas the bottom 90% (mean income ~$45,000) fell 0.8%.

Is capital income concentration widening faster in major cities than nationally?

Core argument: Nearly two-thirds of New York City’s real income growth from 2019–2024 accrued to the top 1%, versus under 40% nationally, driven by faster-rising capital gains, dividends, and business income rather than wage divergence.

Between 2019 and 2024, the New York City’s income shares at the top of the distribution rose faster than the nation's, and nearly two-thirds of the real income growth over the period accrued to the top 1%, compared with under 40% nationally. The result also holds when volatile capital gains are excluded. Real median income fell over the period, and real average income for the bottom 90% of tax units was essentially flat. Adjusted for local prices (but not for transfer programs), the purchasing power of income for the lower 90% of New Yorkers is close to one-fifth below that of the bottom 90% nationally. The divergence at the top is predominantly a story of non-wage income. Wage and salary income shows a much milder widening, and occupational wage data that exclude bonuses show base pay growing faster in lower-wage occupations than in higher-wage ones, and within many occupational groups wages are converging rather than growing more unequal.

Takeaways by Macro Roundup® AI

  1. Nearly two-thirds of New York City’s real income growth from 2019–2024 accrued to the top 1%, versus under 40% nationally, driven by faster-rising capital gains, dividends, and business income rather than wage divergence.
  2. The bottom 90% of New York City earners hold purchasing power roughly one-fifth below their national counterparts after adjusting for local prices, even before accounting for transfer programs.