Hi all - I'm trying to determine countries of origin for international migrants to my state in 2022. Is there a pre-made ACS table that I could find in the data explorer that would show this? I haven't been able to find one, but thought I'd post here in case I'm missing something.
I turned to the PUMS data, analyzing the MIGSP variable, but the margins of error are too high even when I aggregate by continent. I think even if I pooled two years of PUMS data the MOE would still be too high for any valid analysis and I can't use the 5 year data because it's not recent enough.
Thought if I could find a pre made ACS table at least the MOEs would be a bit better since it's a larger sample.
Thanks for any thoughts you may have.
You can get the total population who moved from outside of the U.S. from table B07204 but I can't think of any pre-tabulated disaggregation by origin--PUMS is the best option. Would pooling 3 years of PUMS…
There is a "special product" on migration moves; it's not a base "B" table; it's always a few years older than the most recent ACS.
Documentation is here www.census.gov/...…
You can get the total population who moved from outside of the U.S. from table B07204 but I can't think of any pre-tabulated disaggregation by origin--PUMS is the best option. Would pooling 3 years of PUMS data work?
Also, if you're willing to share, I'm curious what state you're working in that has large MOEs for estimates by region/continent...
Thanks, I'm in DC, hence the large MOEs.
I'm investigating a recent surge in international migration that we saw in the 2022 and 2023 population estimates so farthest I could go back would be 2021, but I think even then MOEs would be too high. With the one year's worth of data I've got a Z of .6 when testing if the number of migrants from one continent is statistically different from the number of migrants from another continent. I did a quick calculation to simulate adding another year's worth of data and got Z=0.9
I'm no expert in statistics, so if anyone has any other ideas for hacks, I'm all ears.