Moineauworld

Tag: workforce training

Why U.S. Men Are Exiting the Workforce | Analysis by Brian Moineau

Posted on by Brian Moineau

When fewer men are in the workforce: what's really going on

The share of American men working or searching for a job recently hit the lowest level since 1948, aside from the pandemic — and that sentence makes you pause. It suggests a structural shift, not just a quarterly wobble. Over the last few years, men at both ends of the age spectrum — younger and older — have been stepping out of the labor market in numbers that economists and journalists find striking. This post unpacks the why, the how, and the what-next in a conversational, evidence-minded way.

Fast snapshot

  • Fewer men are counted as "in the labor force" (employed or actively looking) than at almost any point since the U.S. Bureau of Labor Statistics began tracking this in 1948.
  • The declines are concentrated among younger men (teens to 30s) and older men (late 50s and up).
  • The causes are multiple: health and disability, shifting family roles, skills and job mismatch, incarceration and legal barriers, retirement choices, and long-run changes in demand for certain kinds of labor.

Why the headline matters

This isn’t just an accounting curiosity. Labor force participation affects wages, tax revenue, social stability, and how we think about opportunity. When men drop out of work, families lose income; employers scramble to find labor; and policymakers face hard choices about training, benefits, and social supports.

Transitioning to the evidence: the data show clear long-term trends and recent accelerations. Federal series from the BLS and compilations on FRED and other data sites document the decline in the male participation rate that the Washington Post reported. Complementary analyses from think tanks and labor economists help explain what’s behind the numbers. (Sources at the end.)

The pieces of the puzzle

  • Health, disability, and mental health

    • Disability rates among working-age men have risen in some groups, and opioid- and mental-health-related problems discourage or prevent steady work. Long-term health shocks can push men out of the labor force permanently.

  • Education and skills mismatch

    • The modern economy increasingly rewards higher education and cognitive/technical skills. Men without those credentials see fewer good opportunities in manufacturing and routine middle-skill jobs that have been automated or offshored.

  • Criminal records and re-entry barriers

    • A significant share of prime-age men who are not working have criminal records. Legal barriers and employer screening can shut large numbers out of the formal labor market.

  • Family, caregiving, and social norms

    • Younger men sometimes opt out temporarily to pursue education, caregiving, or nontraditional work paths. For some, the calculation of costs (childcare, housing, transportation) versus wages makes work less attractive.

  • Retirement and delayed retirement patterns among older men

    • Some older men who might previously have retired later are now leaving the workforce earlier for health or family reasons — while others stay longer, creating a complicated age mix.

  • Labor demand and macro conditions

    • Softer job openings, shifting industry composition, and technology that replaces routine tasks all reduce opportunities for certain male-dominated occupations.

These factors interact. A factory closure combines with an injury, a criminal record, or low local opportunity and the outcome is often permanent detachment from work.

The numbers that sting

Look at the long-run series: male labor force participation has been trending down for decades. The broad participation rate for men today is at a level not seen since the late 1940s, except during the pandemic slump. That’s not just a blip; it’s the result of cumulative changes in sectors, policy, and demographics. (See sources below for the BLS/FRED historical series and recent analyses.)

Who’s most affected

  • Young men without college credentials: they face the steepest odds of non-participation, particularly in areas hit by industrial decline or with limited service-sector alternatives.
  • Older men with health problems or marginal attachment to the labor market: a health shock or caregiving need can push them out for good.
  • Men with criminal justice involvement: barriers to employment after incarceration remain a major structural problem.

Why policy debates are hard

There’s no single fix. Policies that help one group can miss another. Consider these trade-offs:

  • Expand training and credentialing programs: helpful for many, but slow and expensive.
  • Improve healthcare and disability support: necessary for humane outcomes, but can reduce incentives to return to work unless paired with re-entry supports.
  • Remove legal barriers for hiring people with records: promising, but politically contentious.
  • Boost demand via fiscal policy or job guarantees: effective but costly and often politically divisive.

A smart approach mixes prevention (education, addiction services, mental health), removal of unnecessary barriers (licensing reform, reentry supports), and demand-side measures where needed.

A few surprising nuances

  • The decline is not uniform across places. States and metro areas with strong service economies or tech hubs often show different patterns than rural, manufacturing-dependent areas.
  • Women’s participation trends have their own story, and gendered labor shifts interact. In some households, the woman’s work status influences the man’s decision to participate.
  • Some “drops” represent voluntary choices (education, entrepreneurship, caregiving), not just failure to find work. Distinguishing between voluntary and involuntary nonparticipation matters for policy.

What employers and communities can do

  • Invest in local hiring pipelines and on-the-job training that don’t require lengthy credentials.
  • Partner with reentry programs and reduce unnecessary licensing that bars hiring.
  • Offer flexible schedules and support services (childcare, mental-health access) that help keep or bring people back into work.

A reality check

These trends reflect deep structural changes. We shouldn’t expect quick reversals. But targeted policy and local action can blunt the harm and help reattach many men to stable employment.

My take

This moment is an invitation to re-think how we value and structure work. If the economy is leaving some men behind because jobs have changed, then our social and policy responses must change too — not with quick fixes, but with a realistic combination of health supports, fair hiring practices, training tied to real opportunities, and community-based solutions. That’s how we rebuild durable pathways back into the labor market.

Sources

Apple Engineers Teach Factories AI Quality | Analysis by Brian Moineau

Posted on by Brian Moineau

Why Apple engineers are checking bacon labels — and why that matters for U.S. manufacturing

The image is deliciously odd: senior Apple engineers hunkered down beside a label press in Vermont, teaching a 54-person label maker how to use cameras and open-source AI to spot slightly off-color bacon packaging before it ships. It’s the kind of moment that makes headlines because it’s unexpected — but the story behind it reveals something more consequential about tech, supply chains, and how large companies can influence manufacturing on the ground.

What happened (the quick version)

  • Apple launched the Apple Manufacturing Academy in Detroit this year in partnership with Michigan State University as part of a broader U.S. manufacturing investment program.
  • Through the Academy and follow-up consultations, Apple engineers have been working with smaller manufacturers — not just Apple suppliers — on practical problems: sensor deployments, predictive maintenance, and computer vision for quality control.
  • A notable example: ImageTek, a small label printer in Vermont, received help creating a computer-vision tool that flagged bacon labels with a wrong tint before they reached a customer. That catch likely saved contracts and revenue. (Reported by WIRED on December 17, 2025.)

A few things that make this worth watching

  • It’s hands-on, real work. This isn’t a glossy PR class where executives talk about strategy; Apple staff are helping with shop-floor problems: cameras, algorithms, Little’s Law to find bottlenecks, and low-cost sensor networks. For many small manufacturers, that level of applied engineering is prohibitively expensive or simply unavailable.
  • The help is practical and tactical, not just theoretical. Small manufacturers described the Apple teams as candid, experienced, and willing to hand off code and guidance rather than locking up IP. That lowers friction for adoption.
  • The timing is strategic. Apple’s program ties into a much larger U.S. investment push (Apple increased its U.S. commitment and opened a server factory in Houston, among other moves). Helping suppliers and adjacent manufacturers strengthens the domestic ecosystem that supports high-tech production.
  • It’s a PR win — and potentially a policy lever. Demonstrating concrete investments in U.S. manufacturing can influence political conversations about tariffs, incentives, and reshoring.

Lessons for small manufacturers

  • Define a clear problem statement. Apple’s Academy reportedly prioritizes companies that can articulate a concrete challenge. That turns vague interest into feasible pilots.
  • Start with affordable pilots. ImageTek’s camera-and-vision setup sits beside the press for now — a low-risk way to prove value before full integration. Polygon expects to spend around $50k for fixes that might otherwise cost ten times as much through traditional consultancies.
  • Data-based decisions beat “muddle through” approaches. Sensors and simple analytics can quickly surface root causes — humidity, worn rollers, timing issues — that manual inspection can miss.

What this means for bigger debates

  • Reshoring isn’t just about moving final assembly. Building resilient supply chains requires investment across tiers — tooling, sensors, software skills, testing culture, and quality processes. Apple’s effort suggests that the “soft infrastructure” of expertise and training matters as much as factory square footage.
  • Large firms can raise the tide, but they won’t (and likely won’t want to) carry every ship. Apple’s engineers can seed capability and show paths; scaling will require equipment vendors, local consultants, community colleges, and public programs.
  • There are potential tensions. Even if Apple hands off code and claims no ownership now, tighter relationships between platform companies and small manufacturers raise questions about dependency, standards, and who benefits from later upgrades or downstream sales.

Examples from the Academy that illuminate the approach

  • ImageTek (Vermont): AI-enabled color-checking on labels prevented a costly quality slip for a food customer.
  • Amtech Electrocircuits (Detroit area): Sensors and analytics to reduce downtime on electronics lines used in agriculture and medicine.
  • Polygon (Indiana): Industrial engineering advice using Little’s Law to map bottlenecks and inexpensive sensor-driven diagnostics to double throughput ambitions.

These are small, specific wins — but they’re the kinds of wins that add up to stronger local competitiveness.

Practical takeaways for manufacturers and policymakers

  • Manufacturers: invest in problem definition, partner with programs that provide both training and hands-on follow-through, and pilot low-cost solutions first.
  • Industry groups and community colleges: scale hands-on curricula that teach applied machine vision, sensors, and basic industrial engineering so more firms don’t have to rely on a single large corporate partner for expertise.
  • Policymakers: incentive programs that combine capital grants with training and technical assistance amplify impact. The “last mile” of deployment is often where public funding can make a difference.

My take

It would be easy to write this off as a cute PR vignette — Apple folks inspecting bacon labels — but that misses the point. The striking detail is not the bacon; it’s the mode of intervention: experienced engineers applying practical, low-cost fixes and coaching teams how to adopt them. That’s the kind of catalytic help small manufacturers often lack. If Apple’s effort scales — through the Academy’s virtual programs, MSU partnership, and other ecosystem players — it could help lower the barriers for many businesses to adopt modern manufacturing methods. That’s not just good for those companies’ bottom lines; it’s how a sustainable, competitive domestic manufacturing base gets rebuilt: one practical fix at a time.

Final thoughts

Technology giants stepping into the training and transformation space changes the game from “let’s talk about reshoring” to “let’s make factories measurably better.” The story of bacon labels is an entertaining hook, but the enduring value will be measured in throughput, contract wins, and a generation of smaller manufacturers who can compete because they were taught how to instrument and measure their own operations. If more big firms follow suit — and if public institutions and local trainers scale these methods — U.S. manufacturing may indeed get a meaningful productivity boost.

Sources