- Technology is progressing rapidly. Today’s widely used tools may be obsolete six months from now, yet it is impossible to predict which specific technical skills will be required for future jobs.
- Training students solely on technical skills does not guarantee they will possess the behavioral competencies necessary to perform in real-world workplaces.
- Investing scarce public resources into training for skills with an uncertain shelf life and fluctuating value from employer to employer is poor public policy.
WHICH “SKILLS” DO EMPLOYERS REALLY WANT?
Developing intelligent policies to combat workforce inequality requires acknowledging that employer demand for “skills” actually refers to a constellation of content knowledge, technical abilities, and applied intelligence.
Per the National Association of Colleges and Employers’ 2018 Job Outlook survey, eight out of 10 employers reported that applicants’ problem-solving and teamwork abilities influenced hiring decisions; only six out of 10 employers reported the same for technical skills. An earlier Economist Intelligence Unit survey revealed similar findings: problem-solving and the ability to work as part of a team were the top workforce skills CEOs considered when making hiring decisions.
This set of priorities has emerged in many other employer attitudinal surveys, a trend that prompts several questions:
- Are problem-solving and teamwork actually “skills,” or are they behavioral competencies?
- If problem-solving and teamwork are discrete skills, then how do we train for them?
- If they are competencies, do these survey findings suggest that employers are conflating skills with behaviors?
Problem-solving and the ability to work in team settings are more accurately defined as cognitive skills or, better yet, behavioral competencies related to yet distinct from mechanical, task-oriented skills. These competencies describe functional behavioral patterns. Effective problem-solving involves integrating factual knowledge with experiential contexts to create a solution. We marvel at the successes of machine learning algorithms, but problem recognition and the freedom to learn from failure are what make these algorithms so effective in solving domain-specific problems. From the success of machines, we can infer (with increasing confidence) that the realization of optimal solutions requires exposure to diverse experiences. Humans need the same exposure that we give machines.
Employer demands for problem-solving and teamwork abilities encompass a desire for dynamic workers who can combine content knowledge and experiences in real-world contexts. Despite a bevy of behavioral research indicating “little transfer of training from one type of problem to another or even across different versions of the same problem,” we cling to the idea that mechanically training for problem-solving and collaborative teamwork is possible, measurable, generalizable, and transferable to unpredictable real-life environments.
Humans need the same exposure that we give machines
Training for technical skills without behavioral competency and context adaptations ensures that only individuals whose résumés reflect an appropriate mix of specific skills and behavioral competencies will have access to employment opportunities. This pattern erects labor market barriers and compounds disparities. Weinberger (2014) confirmed the existence of a wage premium for two cohorts (1972 and 1992) of young white men whose high school tenure included participation in sports and other leadership activities, which she argues helped cultivate their social skills prior to entering the labor market. Even after controlling for a host of socioeconomic factors, including college attendance, math scores, and family background, her results remained robust: “the labor market increasingly favors workers with strong endowments of both cognitive and social skills.”
Chosen excerpts by Job Market Monitor. Read the whole story at Why we should train workers like we train machine learning algorithms