I have two poverty dashboards based on ACS5 data, one has a state filter, one just a full listing of counties. No prior year because of COVID etc, so I'll wait till 2022 comes out and put 2022 vs 2021 data in. I might put in a little more effort, but I also want a housing dashboard, gov programs (SSI, UI,SNAP etc.), Race, Grandparents and maybe a few more. Not looking for perfect, but just some good recaps.What I really hoped to find when I first look for Census data. They both look about the same so I'm only posting one.
It might be helpful to know who your prospective audience and use case for this is. It's helpful that you provided baseline US numbers for context (state might be useful as well). I'd consider including…
The issue of "county based" poverty numbers or even any numbers from all of the counties is that there are very many small counties in the United States. In fact, only about 1,300 are large than…
The issue might be clearer if you refer to each of them with their full range, 2018-2022 and 2017-2021. If you compare the two, you're not comparing 2022 with 2021. Sometimes we'll use the final year as…
The issue of "county based" poverty numbers or even any numbers from all of the counties is that there are very many small counties in the United States. In fact, only about 1,300 are large than 20,000. So when you look at the Poverty population, which is say 20% in some counties, you are looking a universe size of 260, and a sample size of possibly as small as 20 to 40, SAIPE since it is model based is more likely to be more accurate for this estimate, and Other estimates may be better for other purposes rather than the ACs Sample. Which is around 10% at best for the five year.
Yes, you are correct, and I might add a population filter to help. SAIPE is better in a number of ways and might be something I do after bfrss and cdc wonder mortality. There is just something my mind likes about data for every single country from ACS. Everything is an estimate and I want to hit a home run in ACS data (maybe 10 dashboards) before I fight the next windmill.
I'm working on a R package that will give the Supplemental Poverty Measure (SPM) at the tract level. Eventually I hope to post the package on GItHub. The method uses PUMS data including the SPM PUMS data https://www.census.gov/data/datasets/time-series/demo/supplemental-poverty-measure/acs-research-files.html. The SPM is a research measure that, unlike the Census poverty threshold measure, includes income/support from government programs such as SNAP as well as expenses based on variables that vary across geographies (read "local cost of living.") https://crsreports.congress.gov/product/pdf/R/R45031 The statistical method is small area estimation using IPF to adjust a PUMA level cross tabulation (13 variables) to ACS 5-year marginal tract level tables. I've done all the census tracts in Virginia (about 2000 tracts), which takes several days on my PC (20gb memory). You can then aggregate the tract level synthetic data to higher level geographies. All 13 covariates (including SPM) are carried along. It is possible to add replicate weights to the synthetic (output) data file. Any comments welcome.