I’m trying to compute simple % shares of each occupation (at as detailed a level as possible) by five educational attainment categories for the entire nation, for each year going back several decades. E.g. I’d like to know that 94% of Financial managers had a BA or more in 1989, 3% had an associate’s degree, 1% had a high school diploma, etc. (I made up these numbers.) Then repeat for 1990, 1991, up till 2013.
I’ve seen this analysis done before using the March Supplement at a broad occupational classification level (22 occupations), but I am wondering if I could do this for more detailed occupations (100+ occupations). My question is how I might be able to increase the sample size to make these calculations. Would it be methodologically sound to use the monthly CPS data and pool cases in each month into their respective years?
If this is plausible, general approach, there are a few considerations I’d like to ask about

Will I need to account for rotation groups, e.g. do I need to run the analysis on households where MISH = 4 and/or MISH = 8? This would reduce the case count, so if it’s not necessary for this purpose it would be great to use all rotation groups, but I’m unsure whether this would lead to large distortions.

Can I still use the WTFINL weighting variable to calculate these % shares if I pool monthly data (and if I select only one or two rotation groups)?

I understand the records in each month of the monthly CPS data sum to the entire population of the US (after weighting). If I were to pool the monthly cases, I’d then get 12 times the population of the US in each year. But if my aim is simply to calculate the share of workers in a particular occupation that has, say, a Bachelor’s degree, do I need to be concerned about the underlying population count in this case? (i.e. 50 out of 100 gives the same proportion as 100 out of 200, or are there methodological issues that I’m missing?)
Thanks very much for your help!