Which demographic groups of graduates faced the biggest setbacks in licensure or employment?

1. Early‑career hires: the universal squeeze on recent grads

Multiple summaries and surveys describe a structural hiring slowdown that hits virtually all recent graduates: unemployment/underemployment for recent grads rose (one report cited 7.1% for ages 20–29 as of Oct. 2024; another set shows 5.8% as of March 2025), and employers increasingly avoid hiring recent grads because they lack experience ^{[1] [3] [2]}. The practical consequence is widespread underemployment and longer job searches rather than impacts confined to a single demographic group ^{[6] [7]}.

2. Students without internships — a clear, measurable vulnerability

Inside Higher Ed’s compilation flags internship participation as a concrete divider: about 12% of students had not done and did not expect to do an internship before graduating, and only roughly half of first‑generation students had completed internships versus 66% of their peers — a measurable gap tied to later earnings and placement ^[2]. Those without internships report lower average starting earnings and face employers’ new preference for candidates with prior experience ^{[2] [1]}.

3. First‑generation and less‑resourced students face outsized risk

While no single source supplies a full demographic ranking, the Inside Higher Ed data show first‑generation students are less likely to have internships (50% vs 66%), a proximate mechanism for poorer placement and earnings ^[2]. Reporting on hiring trends also notes employers avoid new grads for entry roles because of perceived lack of “real‑world experience,” which compounds disadvantage for students who could not access internships or unpaid work ^{[2] [8]}.

4. Majors and skills: field‑specific setbacks mimic demographic effects

Analyses emphasize differences by field: STEM and certain tech/health fields still show stronger demand and higher starting pay, while other majors face steeper hiring declines and automation risk ^{[1] [2]}. These field effects can correlate with demographic patterns (e.g., representation by major), but the provided sources do not quantify demographic subgroups’ outcomes within majors — they only document that majors matter for job prospects ^{[1] [2]}.

5. Licensure disruptions: program rules and timing create disparate impacts

Federal guidance on Gainful Employment and program length restrictions can make short programs ineligible for Title IV support or affect licensure timelines; the Department of Education guidance highlights examples (nail technician, barbering, massage therapy) where program hour requirements change eligibility and funding, potentially harming students in those specific vocational tracks ^[4]. The sources do not report demographic breakdowns of who is most affected, so “which demographic groups” by race/ethnicity/gender is not specified in current reporting ^[4].

6. International and country‑specific cases: teacher‑trainee backlog example

In Ghana, teacher‑trainees who passed the national licensure exam reported waiting nearly two years for posting into government service, prompting threats of protest — showing a clear demographic cohort (education licensure graduates) facing extended employment delay ^[9]. This is a national example of a licensure‑to‑employment bottleneck; again, sources do not break that group down by other demographics ^[9].

7. Employer behavior and technology: hidden agenda and shifting criteria

Surveys of executives and C‑suite views indicate employers are redesigning roles and eliminating many traditional entry‑level positions — one survey reported AI eliminated entry‑level roles at 52% of firms — creating structural barriers that disproportionately affect early‑career candidates lacking specialized experience ^[5]. These employer incentives reflect corporate priorities (efficiency, automation, skills consolidation) that may not align with higher‑education assumptions about degree→job pathways ^[5].

8. What the sources don’t tell us — limits and missing demographic detail

No provided source gives a comprehensive, comparable ranking of demographic groups (race, gender, disability status, socioeconomic quintiles) by licensure or employment setback size. The coverage documents mechanisms (lack of internships, program eligibility rules, employer redesign) and examples (first‑generation gaps, vocational program hour changes, Ghanaian teacher trainees) but not a unified demographic analysis; researchers and policymakers need disaggregated datasets to answer the original question fully ^{[2] [4] [9]}.

9. Bottom line for readers and policymakers

Current reporting points to concrete vulnerable cohorts — students without internships (including many first‑generation and less‑resourced students), graduates from majors with weak demand, students in short vocational programs affected by federal rules, and sector‑specific licensure cohorts like teacher trainees — but available sources do not provide a ranked, demographic breakdown across race, gender, or income ^{[2] [1] [4] [9]}. Policymakers should prioritize disaggregated outcome data collection and targeted supports (paid internships, transition programs, clear licensure timelines) to identify and mitigate the largest setbacks.