I’m trying to calculate the number of occupation transitions in the US that happen in a month and in a year. Using OCC2010 and PANLWT, I calculate that between Feb and March 2019 ~11 million workers switched occupation (~144 million remained in their occupation). I do this for all consecutive months and find that ~142 million people switch occupation every year. This seems like an unreasonable amount of occupation transitions, it would suggest almost all the labor force switches occupation in a year. I don’t know what I am doing wrong.
To give more details, I am using CPS data for 2019, variables OCC2010, YEAR, MONTH, PANLWT. I first drop all entries with “nan” PANLWT value, not in the labor force, or with no occupation. I use PANLWT and calculate a labor force of 158 million for February. When I do the yearly calculation I get a total count of ~1599 million monthly occupation remains and ~ 142 million occupation switches.
I am using PANLWT as suggested in this answer (Weights for linking CPS basic monthly data). I have also tried using WTFINL and results are similar.
In this post (Calculating Quarterly Unemployment Rates by Race for CA) they suggest to divide by number of pooling samples. Is this something I should consider? E.g. dividing by 12 month (or 11 possible monthly switches)? However, if I do this, I don’t know how I would interpret monthly job transitions, why would I divide those by 12?
Alternatively, I could perhaps divide by the number of months an individual is sample?
Any help would be much appreciated, thanks!