BY FRANK BRITT – 4 MINUTE READ
This summer, Amazon launched a new credit card designed specifically for consumers with bad credit. It’s got the same perks as a typical Amazon Store card—like 5% cash back on purchases. And it includes financial literacy tips to help shoppers learn more about building credit, with the goal of eventually “graduating” to a full-fledged credit card, according to CNBC.
But Amazon’s foray into financial products for the “unbanked” also reflects efforts a vexing (and growing) problem. The FDICnow estimates that there are 8.5 million households in the United States with no checking or savings account. Left with few, if any, paths toward developing a credit history (or reliably saving), unbanked adults turn to payday lenders and check cashers, whose exorbitant fees have long-term impacts on the ability to build wealth over time. It makes it “more expensive to be poor.” And it thwarts the odds that unbanked Americans will cross the growing chasm to the middle class.
THE DIGITAL DIVIDE IN THE LABOR MARKET
As it turns out, a similar trend may be playing out in the U.S. labor market. The digitization of recruiting has created opportunities for many but rendered millions of Americans invisible to the hiring managers who guard the gates to economic mobility.
The digitization of the labor market has been a boon for job seekers—who now have access to millions of job postings through sites like Indeed, Monster, and ZipRecruiter. Workers aren’t limited by geography, and they can spot job opportunities—and even emergent fields—well beyond the boundaries of their social networks and communities.
But as the application process has transitioned online, employers in search of better-fit candidates have also become reliant on reams of digital metadata. They expect to be able to filter and sort applicants by keywords. They want to seek out candidates with specific attributes and credentials and ignore job seekers who don’t fit the bill. And as turnover accelerates, many look to technology improve the signal-to-noise ratio in talent sourcing. There is software to help employers identify qualified candidates faster, reduce the cost of candidate acquisition, and place candidates in jobs depending on their personalities.
