Читать книгу In the Midst of Plenty - Marybeth Shinn - Страница 13

Before describing more about the characteristics of people experiencing homelessness, we review the data that inform our descriptions. These data are extensive but not infallible, especially when considered for a particular city.

The Department of HUD mandates communities and organizations that accept HUD funds to keep records in a Homelessness Management Information System (HMIS) and report both numbers and characteristics of people who used homeless shelters and other assistance programs to the federal government. Communities report numbers in categories, not individual records of people or households, for HUD's national accounting. When the data system was first created, there were major concerns about the privacy and safety of vulnerable households, and the decision was made not to create a single, national data system but instead to have communities report aggregate data to HUD and to share data with each other if they wanted to do that.

The best national data come from these local systems, aggregated into the Annual Homeless Assessment Reports to Congress (Henry, Bishop, de Sousa, Shivji, & Watt, 2018; Henry, Mahathey, et al., 2018). An advantage of a community‐wide system is that it allows calculation of the number of people who use some sort of facility over the course of a year and avoids double‐counting of people who use more than one program during that time. Even if all homeless‐serving organizations in a community cooperate in a single system, someone who moves from a shelter in one town to a shelter in another town could be double‐counted. Thirteen states⁴ have state‐wide homeless management information systems, either because they are small states with only one planning organization for homelessness or because they have succeeded in merging local systems into a statewide system. People who move across state lines are still missed, as are people who move across communities in many of the larger states.

Entities that do not receive federal funds do not have to report data to the HMIS on people who use their facility, leading to estimates in some communities that are based on weighting up the data that is reported based on the number of beds in these other facilities. In Nashville TN, where Beth lives, only 3% of the beds in emergency shelters were included in 2016, because the Rescue Mission and another large faith‐based provider did not participate. More recently, both providers agreed to cooperate, but because of incompatibility of computer systems, a city staffer had to reenter their data for 2017 by hand.

Gradually, around the country, systems are improving, as communities overcome the technical difficulties implementing information systems and as additional programs without federal funding agree to submit data to local systems. An important reason for the improvement is that many communities use the data for local planning and performance measurement and not just responding to a federal requirement. As of 2017, an estimated 70% of people who used emergency shelters and transitional housing programs⁵ were included in HMIS reporting (Henry, Bishop, et al., 2018).

In addition to the required reporting about people who use homeless assistance programs, HUD requires communities to do a point‐in‐time (PIT) count of all people experiencing homelessness, both sheltered and unsheltered, at least every other year, and many communities do one annually. The count happens on a specific night at the end of January, because in cold weather people experiencing homelessness are more likely to sleep indoors, where they are easier to count. But how to count people who are not sleeping in a shelter on that night but instead on the “street?” In most communities, teams of outreach workers and volunteers go to known locations, and ride around in cars in the middle of the night to try to spot and sometimes interview people who are out of doors. However, people experiencing homelessness often have good reasons to remain hidden, and counters are told not to put themselves at risk by searching for them, so such counts are inevitably incomplete.

Together with colleagues, Beth tried to judge just how incomplete the street count was in New York City in 2005 (Hopper, Shinn, Laska, Meisner, & Wanderling, 2008). The City has one of the most sophisticated counts in the country. It divides the entire city into small packets of a few blocks, a transportation hub, or a subway station. It uses the best information available –from police, outreach workers, and previous counts—to estimate whether a homeless person will be found there. It then sends teams of volunteers on foot to all the packets where it expects to find people, and a random sample of the others, in the middle of the night with instructions to interview everyone found there and ascertain whether they are homeless. (People who are sleeping are counted without waking them.) The street count is then the actual number counted in places where people were expected plus a statistical extrapolation from places where they were not.

We did two things to estimate the undercount. First, we planted people masquerading as homeless in locations where we knew counters were assigned to see whether they were counted. (If so the plant gave a sticker to the counting team for its tally sheet, to be sure that the plants were not confused with people actually experiencing homelessness.) By all accounts, the counters accepted finding the plants as a challenge that motivated them to be more thorough. Beth was one such plant: I shivered in a torn coat on the lower level of the Union Square subway station near New York University where I taught at the time, hoping that none of my students would come by, but also hoping the counters would find me so I could go somewhere warmer before my tour of duty ended at 4 a.m. The counters nabbed me and, overall, 71% of the plants. In most cases that were missed, the counters never showed up. Occasionally there was confusion over boundaries (did the counters' park zone include the bench between the park wall and the street?). Additionally, counters gave more or less plausible reasons (an apparent tryst or undercover stakeout) why they assumed someone was not homeless and did not ask.

Second, we visited soup kitchens, mobile food programs, drop‐in centers and the like over the next 2 days, and asked people where they had been on the night of the count. If they were homeless and not in shelter, we asked follow‐up questions to ascertain whether they could have been counted, if counters sent to their location had done exactly what they were instructed to do. For example, if people said they were on the subway, we asked whether they went to the end of the line, where counters moved onto cars to interview people who did not get off. Only 70% of people were in places where they could plausibly have been counted. Others—on a rooftop, in an abandoned building or a stairwell of an occupied one, in a parking structure, or on a porch behind shrubbery were not visible to teams walking the streets.

To be included in the street count, a person had both to be in a visible place (as all the plants were) and to be counted. Thus the proportions from the two stages of our study multiply—suggesting that about half of the people who were sleeping rough that night were missed, although that estimate is not precise. Further, people were more likely to be found if they were in Manhattan, where most buildings are flush with sidewalks and most alleys are walled off, than in outer boroughs, where the varied streetscapes provide more hidden places. The rest of the country looks more like the outer boroughs than like Manhattan, suggesting that street counts elsewhere probably miss more. For example, in the huge geography that constitutes the Los Angeles metropolitan area, many people who sleep under freeways and in other dispersed locations probably are missed.

Unsheltered people who are found during the PIT count are added to the numbers in shelters and transitional housing programs that night. New York's overall PIT count is also better than those of many other cities, because people have a legal right to shelter. So a much larger proportion of homeless people in New York stay in facilities where the count is essentially perfect. However, people who are not found, or who are unsheltered on a different night would not be included in the PIT count, although they may appear in annual numbers if they also use shelter during the course of the year.

Despite their shortcomings, the most detailed national information on the characteristics of people who experience literal homelessness come from these two sources—the administrative data in Homeless Management Information Systems and the one‐night, PIT counts. The latter are less detailed, because it is hard to ask people a lot of questions about themselves when counting them in unsheltered locations in the middle of the night. What do these data sources tell us?