I saw that the response rate for the CPS nationally had fallen recently from ~82% to ~67%, which may raise some issues about greater uncertainty when using the survey to estimate population characteristics. I was wondering if it was necessary to incorporate this change in the response rate when analyzing Basic Monthly Survey microdata from IPUMS–especially for smaller, subnational samples?
Specifically, I’m interested in calculating population estimates for a few mountain-plains states. I want to look at how employment and hours have changed over the past few months (and compared to that period in 2019 and 2018) along a few dimensions: race, education level, gender, wage, and industry (aggregating the IND variable to 2-digit NAICS level). Ideally, I would be able to have a cross-tab (e.g. compare how employment rates have changed between women of different races) of two or three of these variables. I was also thinking about comparing how ‘job losers’ who were included in the CPS rotation for multiple months of 2020 differed on one or more of these dimensions.
However, I’m worried about having a sufficiently large sample at the state level to estimate these values–especially because of the lower response rate. Is there anything I can do to ensure my estimates are okay, other than use the standard errors that I can calculate for population estimates (eg. using R)? Or would this lower response rate already be incorporated into standard error estimates, for example because it just translates to a smaller sample size?