In Simple Words
Employer demand alignment acts like a custom supply chain between job training programs and hiring managers. Program staff act as intermediaries who consult local employers to discover exactly which roles are open and what technical skills are missing, then tailor training curricula directly to those hiring specifications so graduates move smoothly into unfilled openings.
Job seekers at Per Scholas in New York
- Without WorkAdvance
- 20% worked in IT in year two
- With WorkAdvance
- 61% worked in IT in year two
At Per Scholas, the year-two IT employment rate was 61% for WorkAdvance trainees and 20% in the control group. The review suggests that provider maturity and employer relationships could help explain variation between sites; it does not randomize those features.
See source →Research question
Publicly funded job training programs often suffer from misalignment with real-time labor market demand, spending public resources to train candidates for outdated or declining job roles. This misalignment results in low placement rates and minimal wage expansion for graduates.
Participants
Human job seekers and regional business employers evaluated across four WorkAdvance program sites (Towards Employment, Madison Strategies, St. Nick's Alliance, and Per Scholas).
Study Design
Multi-site Randomized Controlled Trial (RCT) evaluated by Hendra et al. (2016) and Katz et al. (forthcoming) analyzing variations in provider maturity and employer network depth.
Results
Mature providers with deep sector employer connections generated far higher outcomes; Per Scholas achieved a 41 percentage point gain in targeted IT employment (61% vs. 20%, p < 0.01) and a 19.6% long-term earnings increase in Year 6 (p < 0.01), whereas newer providers lacking established employer relationships generated smaller or non-statistically significant initial earnings gains.
Takeaway
Establish formal employer advisory committees to audit skill demand and directly co-design technical training coursework prior to launching workforce cohorts.
Source and citation
See source →Read alongside the research
Plain-English glossary
Original explanations, informed by the references below. Examples are illustrations, not data from the studies above. The research text stays unchanged.
- Percentage points
The difference between two percentages.
ExampleA rise from 40% to 50% is 10 percentage points. Relative to the starting value, that is a 25% increase.
Office for National Statistics · Percentages ↗Read more about percentage points →- Randomized controlled trial
A study that uses chance, like drawing lots, to decide who receives each option being compared. Researchers compare the groups’ outcomes to estimate the effect of those options.
ExampleA lottery assigns volunteers to a training programme or a comparison group. Researchers then compare their outcomes.
GET-IT glossary · Randomized study ↗Read more about randomized controlled trial →- Statistically significant
A label used when a result passes a chosen statistical threshold, often p < 0.05. It does not show how large or useful an effect is. A result that is not statistically significant does not prove there is no effect.
ExampleA study reports p = 0.03 against a 0.05 threshold. It passes that test, but the effect could still be too small to matter.
American Statistical Association · Statement on p-values ↗Read more about statistically significant →