Weights for disaggregated gross flows

Hello,

I am trying to construct gross flows by occupation (i.e., gross flows for each occupation group for individuals linked across two consecutive months in the basic monthly CPS) and I would be grateful for any guidance on which weights I should use for this application.

In particular, while I have used PANLWT to successfully replicate gross flows for the entire (civilian) population, my question that I would like to discuss is whether PANLWT is appropriate for constructing gross flows for subpopulations (e.g., gross flows for occupational groups) or if these weights are meant only for aggregating survey data to construct gross flows estimates for the entire population. If not, do you have any insights into how I should go about constructing the ideal weights for this application?

To quote a question asked by August in this previous thread:

Can I use [PANLWT] to create [gross flows] estimates for certain groups of the population?

Thank you in advance for your time and help! :slight_smile:

As discussed in the previous thread, PANLWT is appropriate for gross flows analyses in general. I would encourage you to read pages 85-86 of Technical Paper 66, which discusses longitudinal weights in the CPS. It specifically states:

These longitudinal weights reflect the technique that had been used prior to 1994 to inflate the gross flow esti-mates to appropriate population levels. That technique inflates all estimates or final weights by the ratio of thecurrent month’s population controls to the sum of thesecond-stage weights for the current month in the matched cases by sex.

There is no mention of adjustment by occupation or any other characteristic. That being said, PANLWT is derived from WTFINL, which is adjusted for demographic characteristics. My best suggestion is contact the Bureau of Labor Statistics directly to see if they have any guidance on this issue; I would also recommend reviewing the literature in your field to identify how other researchers have handled this. Finally, if you are interested in creating your own longitudinal weights, you may be interested in the weighting resources for linking IPUMS CPS data.

1 Like