Why 'Resting' Statistics Exclude Caregiving Youth

‘Disconnected’ Youth Are More Likely Than Peers to Do Caregiving and Housework

The Federal Reserve Bank of St. Louis posted a short piece last week. In U.S. statistics, young people aged 18 to 24 who are neither working nor in school are called “disconnected.” As of 2024, that’s 16% of the age group. So far, nothing new.

But this time, instead of just counting how many there are, the research team looked at how these young people actually spend their days. They pooled 22 years (2003–2024) of data from the American Time Use Survey¹. The category that stood out wasn’t gaming or YouTube. It was caring for household members. About 41% of disconnected youth had cared for a family member on the day they were surveyed. Among their non-disconnected peers, that figure was 16%.

Here’s a question worth sitting with, dear reader: we’ve been calling these people “disconnected,” so why has it taken this long to ask what they actually do all day?

What matters here isn’t just the daily life of American youth. It’s that the label we attach to a statistic can predetermine our diagnosis of its cause. In South Korea, the exclusion criteria for the “resting” (swieoteum, Korea’s official statistical category for economically inactive people who report doing nothing in particular) category are more finely subdivided than in the U.S. — which means that, by definition, caregiving youth like the 41% we just saw don’t even make it into that category to begin with.

A Day in the Life of the Young People America Calls “Disconnected”

Let me flag one thing first. What the research team showed isn’t “how many hours were spent” but “whether the activity happened at all.” That’s why the chart’s subtitle says “Probability.” 41% doesn’t mean 41% of the day — it means 41% of people did caregiving that day.

Measured that way, here’s what the results look like.

It’s not just caregiving. In household activities too, disconnected youth came in at 76%, higher than their peers at 64%. That’s time spent cooking, cleaning, doing housework — activities that don’t register anywhere in labor statistics.

Work and education, by contrast, were low. Of course — not working and not being in school is literally the definition of “disconnected.” But here’s what’s interesting: work-related activity isn’t 0%, it’s 10%. The research team pins down what that 10% actually is: job hunting, interviews, and informal income activities like selling goods or providing small services. People who are statistically “unemployed” are out there hustling to make money. Education shows the same pattern, at 22% — even though these aren’t enrolled students.

The most decisive finding is in the second chart. Socializing and leisure sit near 95% for both groups. Eating and drinking time is also near 95% for both. Phone calls: 18% versus 17.5%. Consumption: 43% versus 42%. There’s essentially no difference. In other words, they’re not disconnected because they’re out having fun. Their leisure time is identical.

The one category that stood out as notably lower was travel/mobility — 82% versus 94%, a 12%p gap. That means they go out less. Put alongside the high shares of caregiving and housework, this suggests reduced mobility comes from spending more time at home.

The research team’s conclusion was this: “Not disengagement, but constraints.”

The Bank of Korea reached the same conclusion

Korea has a similar statistic. It’s called “resting” (swieotseum, the official labor-status category for people who report simply “resting” rather than seeking work).

According to an issue note the Bank of Korea released in January 2026, the share of “resting” status among young people (defined here as ages 20-34) who are economically inactive² rose from 14.6% in 2019 to 22.3% in 2025 — up 7.7 percentage points in six years.

And among the patterns the Bank of Korea uncovered, one runs head-on into conventional wisdom.

Their expectations aren’t high. This is the finding that most sharply contradicts the popular narrative. The average reservation wage³ for “resting” young people was ₩31 million (~$22,300). That’s roughly on par with other unemployed youth. When asked about their preferred employer type, small and medium-sized enterprises (SMEs) ranked highest at 48.0%, followed by public institutions at 19.9% and large conglomerates at 17.6%. Their expectations were actually lower, not higher, than those of other unemployed young people, who preferred large conglomerates and public institutions most.

Many have prior work experience. This is where most of the increase came from. The number of “resting” young people with prior job experience grew from 360,000 in 2019 to 477,000. These are people who got in once, then came back out.

Education level leans toward the conventional story. This part needs precision. Among “resting” young people, those with two-year college degrees or less made up an average of 59.3% between 2019 and 2025 — 6 in 10. The “resting” rate among young people with a two-year degree or less was 8.6%, nearly double the 4.9% rate for those with a four-year degree or higher. Regression analysis also shows a 6.3-percentage-point gap favoring those with less education. That said, the Bank of Korea adds that “recently, the number of ‘resting’ young people with four-year college degrees or higher has also been rising.”

This third finding can lead to very different conclusions depending on how you read it. Read one way — “they can’t get in because their education level is low” — it becomes an individual failing all over again. But the Bank of Korea reads it the opposite way. If expectations are already low, and yet the less-educated still can’t get in, that’s not a matter of will — it’s a matter of the barrier itself. That’s why the report’s policy recommendation is to “focus on drawing young people with two-year degrees or less into the labor market.”

The Bank of Korea points to two causes: AI-driven technological change, and companies’ preference for experienced hires. In other words, entry-level positions themselves are disappearing.

Two countries’ central banks, in the same year, using different data, arrived at the same sentence: the problem isn’t attitude — it’s structure.

But the two statistics don’t count the same people

You can’t just set these two statistics side by side and compare them. There are two reasons.

First, the age ranges differ. The Fed looks at 18–24. The Bank of Korea’s analysis covers 20–34 — ten years wider, extending ten years further up. A 33-year-old who’s not working and a 19-year-old who’s not working are completely different stories, yet one statistic captures both while the other captures only one.

Second — and this matters more — the two categories are built in opposite directions.

The American “disconnected” definition is simple. If you’re not employed and not in school, you’re in. Just those two conditions. So people raising kids, people caring for sick parents, people who are themselves ill — all of them fall inside this category.

Korea’s “resting” (swieoteum) works the other way: it’s built by exclusion. Among the economically inactive population, anyone who cites a clear reason — child-rearing, housework, being in school or taking classes, illness or disability, job-prep, exam-prep, awaiting military conscription — gets subtracted out first, leaving only a residual category. What’s left is people who couldn’t give any reason and simply answered “I was just resting.”

Put these two differences together and the problem comes into focus.

The very people the Fed found once it opened up the data — young people doing care work and housework — are, by definition, excluded from the start in Korea’s statistic. If you’re doing care work, you’re filed under “child-rearing.” If you’re doing housework, you’re filed under “housework.” Neither can ever land in the “resting” box.

So the conversation is bound to drift toward “why aren’t they working?” — because the category, by construction, keeps only those who answered “I was just resting” after everyone who cited a reason has already been removed. The statistical definition itself smuggles in a judgment before the analysis even begins.

America’s looser definition swept care-and-housework youth into a single category, and when researchers went back and dug into the data, the pattern of care work and housework emerged. Korea filters out anyone who cites such a reason before the fact, so no matter how closely you analyze the “resting” category, that pattern can never surface.

One more thing worth flagging. There’s a number the media loves to cite: “450,000 resting young people.” This is not the entire resting-youth population. It’s a subgroup — those who answered that they “don’t want a job at all.” That figure rose from 287,000 in 2019 to 450,000, a 56.8% increase over six years. For reference, per the 2025 employment trend data, the total “resting” population in their 20s and 30s is 717,000. Headlines often run with the number alone, without conveying who, exactly, is being counted.

We count the size every month, but we survey daily time use only once every five years

It’s not that Korea lacks tools to measure time. There’s the Time Use Survey run by the National Data Agency (formerly Statistics Korea). The 2024 results came out in July 2025, surveying roughly 25,000 people aged 10 and older across 12,750 households.

The problem is the survey cycle and sample size.

The Time Use Survey runs once every five years. It started in 1999, and 2024 marks its sixth round. The US ATUS, by contrast, runs annually — and the analysis I’ve been drawing on stacked 22 years of that data to isolate a small subgroup. With a five-year cycle and a sample of 25,000, it’s statistically difficult to isolate “resting youth” as a distinct group in any meaningful way. And on top of that, five years is enough time for the youth labor market to become an entirely different market.

The result is where we stand now: the size gets updated every month, while how these people actually spend their day remains, in effect, unknown.

This matters because size determines the budget, while content determines the design. Trillions of won in youth support budgets get allocated every year — with the size known and the design unknown.

Think it through, and the prescriptions diverge completely.

• If the barrier is entry thresholds → the answer is loosening hiring practices and the preference for experienced hires.
• If it’s career interruption → the answer is re-entry ladders.
• If it’s a sense of helplessness → the answer is access to psychological and medical care.

These are three policies with different budget line items, different responsible ministries, different performance metrics. Yet right now they’re all lumped together under a single question: “what should we do for resting youth?” That’s because we’ve never actually measured their day.

The rested and disconnected are repeating the same mistake, just at the scale of national statistics.

Wait, let me output the full corrected fragment properly.

Oswarld’s Lens

I teach data management at Sejong University, and I give my students the same assignment every first class: before you interpret a metric, take apart its definition first. It’s the part they find most boring — they want to get to the numbers fast. But almost every misreading starts right there.

I’ve watched the same scene play out over and over when building GTM strategy. A dashboard shows “conversion is low,” and the team splits. Team A concludes “users just aren’t interested” and pours more money into ads. Team B pulls up session replays to see exactly where users stall. Team B is usually right. Look only at the aggregate number and it looks like a problem of attitude — but where people actually get stuck never shows up in that number. “Rested” and “disconnected” are repeating the same mistake, just at the scale of national statistics.

Let me be honest about three things, though.

First, don’t over-read the U.S. data. Since “disconnected” is defined to include caregivers, the high share of caregiving is partly an artifact of the definition itself. Reading it as “see, they’re all just caregiving” makes the same mistake in the opposite direction. The real finding in this data isn’t the caregiving share — it’s that the rate of social and leisure activity was nearly identical to peers. The assumption that “they’re just goofing off” doesn’t hold up in the data.

Second, the constraint theory doesn’t explain everything either. According to a Bank of Korea analysis, every additional year of unemployment raises the probability of being “rested” by 4.0 percentage points and lowers the probability of choosing to job-search by 3.1 percentage points. In other words, prolonged unemployment does turn into real helplessness. Miss that, and if you chalk everything up to “structural causes,” you can’t design policy. Sequence matters here: constraint is the cause, helplessness is the effect. See it in that order and you get a workable prescription; flip it, and you’re back to diagnosing “a lack of will.”

Third, I got something wrong while writing this piece. In my first draft, I wrote that highly educated people made up most of the “rested” youth — because that fit neatly with rebutting the “overqualification” theory. When I went back to the original Bank of Korea report, it was the opposite: 59.3% had a college degree or less. I picked the fact that suited my argument first and checked it later. I’m the person who teaches “take apart the definition first” — and I did exactly the opposite. Since that habit is the very thing this piece is criticizing, I’m leaving the mistake in rather than erasing it.

Closing

Here’s the summary.

1. When the U.S. Fed re-analyzed 22 years of time-use survey data, “disconnected” youth showed higher rates of caregiving (41%) and housework (76%) than their peers — but the rate of socializing and leisure activity was nearly identical to their peers.
2. The Bank of Korea also concluded that the problem with “resting” youth is structural, not a matter of expectations being too high. Reservation wage: ₩31 million (~$22,300). Their top choice of employer: small and medium-sized enterprises. But the method differed. America measured behavior. Korea asked about intentions.
3. Korea’s “resting” category is a residual — what’s left after everyone who cited reasons like childcare or housework has been subtracted out. When the category itself is defined as “people who rested for no stated reason,” the discussion naturally drifts toward “why aren’t they working?”

So here’s my proposal for youth policy debates: don’t just ask “how many are there?” — also ask “what do these people actually spend their day doing?” The Fed simply re-sliced this youth group out of data it already had. No new budget, no new survey was needed. It just changed the angle.

And this isn’t just about policy. When a team talks about “people with low participation rates,” the same choice applies. Do you just count the number, or do you check what they’re actually doing, and where things stall out?

If you or someone you know has been through a period that might get classified as “resting,” tell me in the comments where most of your days actually went during that time. What I’m most curious about is the activity that the statistics failed to capture. If enough responses come in, I’ll put together a follow-up in the next issue.

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💬 Tell me in the comments where your days actually went during that period · 📨 Share this piece with anyone who’s ever felt uneasy about being labeled “resting”

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References & Further Reading

Primary sources

• William M. Rodgers III, Alice L. Kassens, “How Are ‘Disconnected’ Young Adults Spending Their Time?”, St. Louis Fed On the Economy, 2026. 7. 14. Link ··· This is where today’s piece started. Just the second chart in the article (the social/leisure category) is enough to shake conventional wisdom. Every number I cite in this issue comes from reading the two charts in this piece — there’s no accompanying numerical text.

• Bank of Korea, “[Issue Note No. 2026-3] Characteristics and Assessment of ‘Rested’ Youth: A Comparative Analysis by Unemployment Type,” BOK Issue Note, 2026. 1. 20. Link ··· The section on reservation wages and desired employer type is the key part. It’s the cleanest rebuttal to the “kids these days have unrealistic standards” theory I’ve seen.

• National Data Office (formerly Statistics Korea), “2024 Time Use Survey Results,” 2025. 7. 28. Link ··· Proof that Korea has the same tool available. It also shows the limitation — the survey only runs every 5 years.

Background

• Korea Labor Institute, “An Analysis of the Recent Increase in the Youth ‘Resting’ Population,” Labor Review No. 218, 2023. 5. Link ··· This has the cleanest definition of what “rested” excludes — which categories get subtracted to leave this residual group. It’s the basis for Chapter 4 of today’s piece.

• St. Louis Fed, “Disconnected Young Adults: A Look at the Eighth Federal Reserve District,” 2024. 10. Link ··· The prequel, covering the regional, racial, and income distribution of America’s “disconnected” youth. Read alongside the time-use analysis, the full picture comes together.

• St. Louis Fed, “How Shifts in Labor Supply and Demand Shape Outcomes for Young Workers,” 2026. 6. Link ··· The second installment in the three-part series today’s piece belongs to. It covers why young people are pushed out first.

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The author is Oswarld (Kwangseob Ahn). Current roles: Adjunct Professor at Sejong University, Strategy Consultant at INLEVEL9. Career, research, books, and recent work are kept current on the About page. Latest · July 2026: HEMA-2: A Consolidation-Aware Tri-Memory Architecture with Multi-Channel Scheduling for Lifelong Conversational AI.

📝 Glossary

Footnotes

1. ATUS (American Time Use Survey): A survey sponsored by the U.S. Bureau of Labor Statistics and conducted annually by the Census Bureau. It has respondents record, hour by hour, “how you spent yesterday.” Because it measures behavior rather than asking about intentions, it’s relatively less distorted by self-reporting bias. ↩

2. Economically inactive population: People aged 15 and older who are neither employed nor unemployed. The unemployed are counted in official statistics because they’re “willing to work and actively job-seeking,” but once someone stops job-hunting, they drop out of the unemployment rate and shift into this category. This is why youth conditions can be bad even when the unemployment rate looks fine. ↩

3. Reservation wage: The minimum wage someone would need to be offered before deciding to work. It’s the threshold below which a person chooses not to work, making it the go-to metric for quantifying “standards” or expectations. ↩