Statewide LEA Identification Rates of Youth Experiencing Homelessness
Because of a variety of challenges that we discuss throughout the report, LEAs most likely have not identified all youth experiencing homelessness. As a result, these unidentified youth may not have received the necessary services to obtain equal access to the same free, appropriate public education as other students. Homeless education experts use approximately 5 to 10 percent of economically disadvantaged students as a benchmark to determine whether an LEA has identified all youth that experience some form of homelessness during a school year. Using this best practice, we determined the identification rate of each LEA in California to explore whether they sufficiently identified youth experiencing homelessness in academic year 2017–18. The interactive graphic at the bottom of this page allows users to examine the identification rates for youth experiencing homelessness at each LEA, county and statewide. As shown in the graphic below, the statewide identification rate was 6 percent for school year 2017–18. However, in an example shown in the following animation, Merced County had an identification rate of just 3 percent. Further, Los Banos Unified School District reported nearly 10,000 economically disadvantaged students but identified only 38 students as experiencing homelessness for an identification rate near zero percent.
Use the interactive graphic below to explore information submitted by each LEA on youth experiencing homelessness statewide and by county.
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Source: Unpublished California Longitudinal Pupil Achievement Data System data from the California Department of Education’s cumulative end-of-academic year 2017–18 data.
Notes: To maintain the confidentiality of small groups of students that include youth experiencing homelessness, we redacted from the above graphic nine LEAs with less than 30 total enrollment and at least one homeless youth.
We based our calculation of the statewide identification rate on the cumulative counts of youth at the school level, which is not equivalent to the cumulative counts of youth at the state level because the data is not additive. For example, if a student transferred between districts during the academic year, the school and district level data would include that student twice—once in each school, while the state level data would count that student once. For our purposes, we conducted our assessment of identification rates using the school and district level cumulative data to determine if LEAs identified these youth in each LEA in which they enrolled.
We display the student demographic information as zero for LEAs which did not certify their data for academic year 2017–18.