Margin of error for a new geography

Hi!

To calculate margins of error for derrived estimates (such as when you group across geographies) see Chapter 8 of the Handbook

https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018_ch08.pdf

It provides formulas and examples.

Hope that is helpful.

It seems like Chapter 8 of the handbook deals with aggregating whole geographies (unless I'm reading it wrong) and it seems like OP is wanting to aggregate whole geographies and some 'clipped' or partial geographies. Am I correct in thinking that if it's not included in the handbook, then it's not recommended to work with partial tracts / geographies?

I don't think the handbook--by this omission--is intentionally trying to comment on the appropriateness of working with partial geographies. More to the point, the statistical principles guiding how to adjust MOEs when aggregating whole units are straightforward and easy to specify. The principles guiding MOEs for partial units are entirely unsettled. I took a stab at this issue, indirectly, in this 2007 paper. I don't know of much other research on this specific subject. In short, it depends on exactly how you're disaggregating from the whole to the parts, and the simplest approaches tend to be highly error-prone, so the MOE will tend to grow much larger than if you stick to whole units. Using partial units may still be adequately accurate for many applications, but to my knowledge, no existing literature or formulae will tell you exactly how to determine that.

Ah! Gotcha. Somehow I missed the "clipped" part and assumed they were doing point-in-polygon (whole block). The issues with using partial blocks are FAR greater than those of aggregating MOE. Population and housing are rarely distributed evenly within a block.

Beth Jarosz and Jonathan Schroeder,

I'm facing a very similar issue to OP and am following up here to inquire whether you're aware of any research on the subject of estimating MOE for geographies derived from polygon intersections (i.e., in Beth Jarosz's example above using partial blocks). In my case, these intersections are derived from ACS block groups, city council districts, and neighborhood boundaries. I understand this will result in large MOE, but these are the data I'm dealing with.

Feel free to advise this should be a new post all-together if you feel that's more appropriate (it just seemed in line with OP's inquiry and this line of conversation).

Thank you for any and all guidance!

I still know of no simple general guidance for estimating MOEs for disaggregated (i.e. split) summary data.

Through IPUMS NHGIS, we distribute geographically standardized time series tables, which provide 1990-2020 census statistics for 2010 geographies (census tracts, block groups, etc.). We do this by aggregating block data from each census year. In many cases, the source blocks are split among multiple 2010 units, so we apply interpolation to estimate how the source data are distributed among target units. We don't compute MOEs, but we do report lower and upper bounds, determined by assuming that either all of the split blocks' population and housing are in a given target unit (the upper bound) or none of the split blocks' characteristics are in the target unit (the lower bound). That's a simpler concept and easier to compute, so you may want to use that. The downside is that these bounds can be very large, and typically much larger than the actual statistical "90%" MOE.

I still know of no simple general guidance for estimating MOEs for disaggregated (i.e. split) summary data.

Through IPUMS NHGIS, we distribute geographically standardized time series tables, which provide 1990-2020 census statistics for 2010 geographies (census tracts, block groups, etc.). We do this by aggregating block data from each census year. In many cases, the source blocks are split among multiple 2010 units, so we apply interpolation to estimate how the source data are distributed among target units. We don't compute MOEs, but we do report lower and upper bounds, determined by assuming that either all of the split blocks' population and housing are in a given target unit (the upper bound) or none of the split blocks' characteristics are in the target unit (the lower bound). That's a simpler concept and easier to compute, so you may want to use that. The downside is that these bounds can be very large, and typically much larger than the actual statistical "90%" MOE.