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
  • At the ACS conference a number of commenters referenced a Census Bureau "guideline" that no more than 4 ACS categories can be combined before the effect of covariance undermines the utility of the result. It was not entirely clear where that figure came from, but I do recall seeing it myself.

    You might be able to limit the number of categories you use to 12, rather than 24, by subtracting rather than adding. For example, take the total number of Males Enrolled in a Public School and subtract the 25 to 34 and 35 and Over subcategories. The method for calculating the SE would be the same as for the addition of multiple values, but now you use three terms to get the result instead of six. My recollection is that this an acceptable procedure and might reduce the scale of your covariance issue.
Reply
  • At the ACS conference a number of commenters referenced a Census Bureau "guideline" that no more than 4 ACS categories can be combined before the effect of covariance undermines the utility of the result. It was not entirely clear where that figure came from, but I do recall seeing it myself.

    You might be able to limit the number of categories you use to 12, rather than 24, by subtracting rather than adding. For example, take the total number of Males Enrolled in a Public School and subtract the 25 to 34 and 35 and Over subcategories. The method for calculating the SE would be the same as for the addition of multiple values, but now you use three terms to get the result instead of six. My recollection is that this an acceptable procedure and might reduce the scale of your covariance issue.
Children
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