Standard errors for tract-level school enrollment ratios from ACS tables

My organization often needs to estimate the net school enrollment ratio of children and youth ages 3-24 (inclusive) for various levels of geography including census tracts. For larger geographies, we estimate this directly from the PUMS and calculate standard errors using replicate weights. For tracts though we’re stuck with what we can get from Table B14003 SEX BY SCHOOL ENROLLMENT BY TYPE OF SCHOOL BY AGE FOR THE POPULATION 3 YEARS AND OVER. Obtaining the numerator, the total number of students enrolled in public or private school between the ages of 3 and 24, requires summing 24 individual estimates in the table, which breaks enrollment down by age, gender, and control of school. I’ve used the methods outlined by Census in their “Accuracy of the Data” documents to obtain the approximated SE for this sum but I have serious doubts that these approximations tell us much of anything given that so many individual MoE estimates have to be aggregated. Does anyone have a suggestion for a better way to calculate or even indirectly estimate the SE for school enrollment of this age range at the census tract level? Thank you!
Parents
  • Hi Patrick,

    In the first session of the ACS Data User Group Conference, a number of us wrestled with this problem. The session was titled, Aggregating ACS Estimates and Calculating Margins of Error. I am afraid that there is no good answer to calculating the SE after combining geographies or collapsing categories. The problem lies with the unknown covariance term. Check the presentations to see if you pick up any useful suggestions.

    Best,
    Warren
Reply
  • Hi Patrick,

    In the first session of the ACS Data User Group Conference, a number of us wrestled with this problem. The session was titled, Aggregating ACS Estimates and Calculating Margins of Error. I am afraid that there is no good answer to calculating the SE after combining geographies or collapsing categories. The problem lies with the unknown covariance term. Check the presentations to see if you pick up any useful suggestions.

    Best,
    Warren
Children
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