Enumerating the Hidden Homeless: Strategies to Estimate the Homeless Gone Missing From a Point-in-Time Count

Agans Robert P. (), Jefferson Malcolm T., Bowling James M., Zeng Donglin, Yang Jenny and Silverbush Mark
Additional contact information
Agans Robert P.: University of North Carolina, Carolina Survey Research Laboratory, 730 Martin Luther King Jr. Blvd., Bolin Creek Center, Chapel Hill, NC, U.S.A.
Jefferson Malcolm T.: University of North Carolina, Carolina Survey Research Laboratory, 730 Martin Luther King Jr. Blvd., Bolin Creek Center, Chapel Hill, NC, U.S.A.
Bowling James M.: University of North Carolina, Carolina Survey Research Laboratory, 730 Martin Luther King Jr. Blvd., Bolin Creek Center, Chapel Hill, NC, U.S.A.
Zeng Donglin: University of North Carolina, Carolina Survey Research Laboratory, 730 Martin Luther King Jr. Blvd., Bolin Creek Center, Chapel Hill, NC, U.S.A.
Yang Jenny: University of North Carolina, Carolina Survey Research Laboratory, 730 Martin Luther King Jr. Blvd., Bolin Creek Center, Chapel Hill, NC, U.S.A.
Silverbush Mark: Los Angeles Homeless Services Authority, 811 Wilshare Blvd., Los Angeles, CA 90017, U.S.A

Journal of Official Statistics, 2014, vol. 30, issue 2, 215-229

Abstract: To receive federal homeless funds, communities are required to produce statistically reliable, unduplicated counts or estimates of homeless persons in sheltered and unsheltered locations during a one-night period (within the last ten days of January) called a point-in-time (PIT) count. In Los Angeles, a general population telephone survey was implemented to estimate the number of unsheltered homeless adults who are hidden from view during the PIT count. Two estimation approaches were investigated: i) the number of homeless persons identified as living on private property, which employed a conventional household weight for the estimated total (Horvitz-Thompson approach); and ii) the number of homeless persons identified as living on a neighbor’s property, which employed an additional adjustment derived from the size of the neighborhood network to estimate the total (multiplicity-based approach). This article compares the results of these two methods and discusses the implications therein.

Keywords: Homeless count; hidden homeless; unsheltered homeless population; Horvitz-Thompson estimator; multiplicity-based estimator (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:30:y:2014:i:2:p:15:n:4

DOI: 10.2478/jos-2014-0014

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