Table 1 Variables used for analysis
From: Determinants and consequences of employer-provided training program resilience post-Covid-19
Variable | Used as | Type |
|---|---|---|
Disruption to training due to Covid-19 | Dependent variable | Binary |
Effect of Covid-19 on practical skills | Dependent variable | Likert (1–5) |
Motivation to train: ‒ To retain our employees ‒ To replace retiring skilled workers ‒ To save on recruitment costs ‒ To screen new hires ‒ To shift from degree- to skills-based hiring ‒ To build a diverse workforce ‒ To help hire/retain local talent ‒ Because it’s the best way to get the right workers ‒ Because graduates don’t meet needs | Independent variable | Likert (1–5) |
Program characteristics: ‒ Program duration ‒ Presence of trainers ‒ Program has a curriculum ‒ Program is accredited ‒ Program is paid | Independent variable | Binary (exception: program duration – linear, in three-month steps) |
Program recognition ‒ Company-specific credential ‒ Occupational license ‒ Postsecondary credit ‒ Other credit | Independent variable | Binary (exception: postsecondary credit—categorical no/yes towards a degree/yes but not towards a degree) |
Program type | Control | Categorical: ‒ Apprenticeship ‒ Professional development ‒ On-the-job training ‒ Internship |
Program includes youth | Control | Binary |
Program gender/race different from firm | Control | Binary |
Covid: stringency of measures | Control | Numeric (1–5) |
Covid: impact to date | Control | Numeric (1–5) |
Covid: Expected future impact | Control | Numeric (1–5) |
Industry | Control | Categorical: ‒ Mining, construction, manufacturing and utilities ‒ Health and education ‒ Other services |
Sector | Control | Categorical: ‒ Public ‒ Private ‒ Non-profit |
Firm size | Control | Categorical: ‒ Small (1–49) ‒ Medium (50–249) ‒ Large (250+) |