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- Skills Mismatch
Washington, D.C. — National Skills Coalition today released a new poll of 500 likely Iowa Democratic caucus-goers, which shows the overwhelming majority of caucus-goers want to see an increase in government funding for skills and job training and are more likely to caucus for a Presidential candidate who vocally advocates for increased funding.
With 12 percent of voters still undecided – a potentially decisive share of the electorate – the poll shows that more than half of undecided voters (55 percent) are more likely to support a candidate who vocally supports “increasing government funding for skills training in the U.S.”
Skills training refers to education and training at community colleges and community organizations that prepare workers for jobs and careers that fall between a high school degree and a bachelor’s degree.
The poll, which was conducted by ALG Research, also shows that Iowa caucus-goers prioritize “more investment in skills and job training” (64 percent) over “free college for all” (27 percent).
They would rather see the country deal with the future economy by “creating guaranteed retraining and support programs for anyone who loses their job” (73 percent) compared to “providing a universal basic income of $1,000 per month for each American” (19 percent).
Likely Democratic caucus-goers also see skills training as an indispensable solution to addressing the future of work. 92 percent agree that greater investment in skills training would help ensure workers can upgrade their skills to keep pace with new technologies; 93 percent support job retraining for new energy technologies like wind power to fight climate change and move the country to clean, renewable energy; and 91 percent support providing skills retraining at no cost to any worker who loses their job because of automation.
Methodology: On behalf of National Skills Coalition, ALG Research conducted 500 live telephone interviews with likely 2020 Democratic caucus-goers in Iowa. Interviews were conducted between January 13-16, 2020. Expected margin of sampling error is ±4.4% with a 95% confidence level.