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Will Algorithmic Tools Help or Harm the Homeless? [psmag.com]

 

On any given night, more than half a million people in the United States are homeless. Many who lose their housing do so only briefly, but 40 percent become chronically homeless.

Currently, many city's social service programs operate within a "progressive engagement" model that prioritizes providing aide—things like housing subsidies, behavioral services, and job training—to the people who have been homeless the longest. There's an obvious logic to this distribution method: People who have struggled the longest may be the most in need of help. However, the longer people are homeless, the more difficult it becomes to help them. The persistently homeless sustain $10,000 more in costs—medical and otherwise—every year than those with housing. "After all of the wreckage of being on the sidewalk for a year or three, health problems double, substance abuse goes up, and police encounters go way up," says Daniel Flaming, the president of Economic Roundtable, a non-profit that does policy research on issues like homelessness.

But what if there was a way to identify the people most at risk for persistent homelessness and offer them aide, altering their course and preventing years of untold suffering and expenses? Economic Roundtable thinks they have developed a tool that can, according to a new report. The report introduces two statistical modeling tools that aim to pick out the adults most at risk of becoming chronically homeless after losing their jobs and the children receiving public assistance most at risk of entering long-term homelessness during their first three years as adults.

The tools were developed using Los Angeles' unusually extensive, linked, and publicly available pool of data for county residents who receive food stamps, Medicaid benefits, and other social services. In most cities, this kind of data is hard to acquire and difficult to synthesize across different social-service agencies, Flaming says. So Economic Roundtable's access to this much data gives their tools a unique ability to predict chronic homelessness risk. "It's being able to have your arms around an entire population," he says.

Homelessness policy experts believe the tool—or one like it—will be crucial in slowing the growth of homelessness. "If we don't do something to stop the flow, the dynamic processes that lead people into homelessness will be playing catch up forever," says Gary Blasi, a University of California–Los Angeles law professor and longtime advocate for the unhoused who sits on the Economic Roundtable board.

As the homelessness crisis becomes increasingly impossible for the public to ignore, some areas have begun to devote more money to homelessness services, but the Economic Roundtable believes the funding is not always being spent very efficiently. In Los Angeles, where the homeless population has spiked by half over the last decade and 75 percent of the city's 58,000 homeless people are unsheltered, a small sales tax increase passed in 2017 is projected to add $3.55 billion for homelessness services over the next decade. "That is a continuous flow of money that can be used either effectively or ineffectively," Blasi says. "And right now," because of progressive engagement, "it's being spent in ways that are not necessarily contributing very much to the reduction of homelessness."



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