I have a table from iPUMS for 2021 5 year ACS. I want to sum the person weight in the PUMS data to create a single county total. The PUMS data distinguishes between different years in the data product - 2017, 2018, etc. When I compare the results to a standard ACS table with the same variables, the PUMS data is 2x to 5x higher across all variables. This seems more than just a difference in methods.
The different years stands out to me. Should I be summing up all 5 years or doing something else like summing each year and getting the average? Or something else entirely?
EDIT: SOLVED. I was comparing the wrong thing.
Here is some information that you might find useful.
You can check your calculation by creating variables in the PUMS dataset that correspond to the categories in the detailed ACS table. You can then create a table that corresponds to an ACS detailed (or subject or data profile) table. Look at the ACS table on data.census.gov selecting the PUMA (public use microdata )geography that uses the same PUMA as the PUMS data. If you define everything correctly you should get a result that is close to the result for the other method. To get a "margin of error" for the PUMS data use the replicate weights. The MoE is in the data.census.gov table. In general PUMAs and counties do not correspond. A PUMA may include several counties or a county can have several PUMAs. I general there is no direct correspondence between PUMAs and counties. If you want data that gives you the correspondence use GEOCORR.
This is a useful way to check you PUMS data calculations/computer code.
Make sure to use the same "vintage" and period (1 year or 5 year) for both the PUMS and data.census.gov tables.