Home Impact Hiring: How Data Will Transform Youth…

Report

Impact Hiring: How Data Will Transform Youth Employment

Youth unemployment in the United States is an intractable problem, especially for the vulnerable segment of ‘opportunity youth’—young people between the ages of 16 and 24 who are neither in school nor at work. Existing programs to address this challenge are expensive and difficult to scale, underscoring the need for a radically different approach to effect change at the ecosystem level.

Advances in predictive talent analytics form an important element of a scalable solution. These technologies enable employers to identify promising talent from a larger pool than they might traditionally consider and to make hiring decisions based on data rather than intuition. Because predictive talent analytics enable two-sided matching of youth with opportunities based on potential, youth seeking jobs can identify the opportunities that best match their talents, while employers can identify job-seekers who are best suited to perform successfully in their specific open roles. This creates a more liquid, better-functioning talent market, which breaks down a number of the barriers that hold youth back today.

To the best of our knowledge, the promise of these new talent analytics tools has not been tested in the context of youth employment. The Rockefeller Foundation and its grantee Incandescent, therefore, partnered with Knack—a startup that’s developed mobile video game technology to measure talent markers and predict job performance potential—to conduct a pilot of game-based talent analytics with youth enrolled in programs at a number of community organizations. Six hundred opportunity youth participated in the pilot, allowing us to compare the full data set of their aptitude revealed through gameplay with the corresponding data set for current jobholders at four companies.

The results of our study are compelling. Some key findings include:

  • 83 percent of youth who participated scored at or above the level of a company’s “average performers” for one or more of the jobs above.
  • 65 percent of the youth scored at least at the average of the company’s designated “high performer” for one or more of the four jobs.
  • When comparing the scores of the 155 youth with some college to those of the 430 youth with no college, there was no case where the percentage of youth scoring above the “average performers” or “high performers” was significantly different. For example, 34 percent of the no college youth scored at least as high as the average of the employees currently in the financial analyst role, versus 29 percent of the youth with some college.

 

This research breaks down prevalent (and costly) myths about opportunity youth, presenting evidence that they have a similar distribution of traits and aptitudes as the general population, that they have the high potential necessary to advance beyond entry-level jobs, and that the aptitude and skills demonstrated are not related to gender, ethnicity, or their level of educational attainment.

As so many fields have in recent years, entry-level hiring must also make the transition from relying on untested intuition, to leveraging the power of data and evidence. Employers now have access to talent analytics tools that can enable them to develop a deep understanding of what attributes drive good performance for their current employees, apply tools to objectively assess these attributes, and access broader talent pools to find individuals with the most valued attributes. The talent analytics tools that enable this vision for data-driven hiring already exist. The key obstacle to their implementation is institutional will.

Our study strongly indicates that data-driven tools hold great promise for changing the course of youth employment. Abandoning misconceptions about opportunity youth and transforming outdated hiring practices will not only enable employers to source talent widely and build a high-quality entry-level workforce, but will also move the needle on youth employment at the ecosystem level.