Labor force participation across those w/ and w/o children

I’m gathering disaggregated LFPR’s by quarter, using a couple of variables to filter for those with young kids ages 5 and under, and essentially doing three steps to gather LFPR stats:

First, I’m gathering the non-institutionalized civilian population count for those with kids 5 and under (sample code below),

GANICpopYOUNGCHILD2019_Q1 ← GAyear2019%>%
filter(month >= 1, month <= 3, popstat == 1, nchild >= 1,
yngch >= 1, yngch <= 5)%>%
group_by(new_race = haven::as_factor(new_race), sex = haven::as_factor(sex))%>%
summarize(GANICpopYOUNGCHILD2019Q1 = sum(wtfinl))

Then I’m gathering the LF count for those same type of parents (sample code below),

GALFcountYOUNGCHILD2019_Q1 ← GAyear2019%>%
filter(month >= 1, month <= 3, labforce == 2, nchild >= 1,
yngch >= 1, yngch <= 5)%>%
group_by(new_race = haven::as_factor(new_race), sex = haven::as_factor(sex))%>%
summarize(GALFcountYOUNGCHILD2019Q1 = sum(wtfinl))

And then I’m merging those two objects and using the mutate function to calculate the LFPR quotient for that group. Ultimately, some of my quarterly LFPR numbers for each demographic seem very high, including some quarters of 90% or more across each demographic of men, and at times 80% or more across each demographic of women. As a means of identifying mothers and fathers with young children, should I be using MOMLOC, POPLOC, AGE, and PERNUM variables instead? Or will my numbers smooth out if I’m narrowing the parental ages to prime working years of 16 to 54?

The YNGCH variable is calculated based on MOMLOC, POPLOC, etc., so using those will get you the same result. For what it’s worth, those figures don’t seem excessively high to me – the LFPR for parents tends to be higher than the general population. See for example this analysis from the BLS. Some states have much higher rates than others, as well.

I also noticed that you’re using YNGCH>=1, but that would exclude children under 1.